# Intelliarts

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### Posts

#### “Economics of LLMs” White Paper
URL: https://intelliarts.com/blog/llm-costs-roi-enterprise-adoption-guide/

#### “eMobility Regulations” White Paper
URL: https://intelliarts.com/blog/ev-charging-regulations-compliance-guide/

#### “Leveraging RAG Systems” White Paper
URL: https://intelliarts.com/blog/rag-systems-for-llms-white-paper/

#### “Machine Learning for Insurance Business” White Paper
URL: https://intelliarts.com/blog/ml-in-insurance-white-paper/

#### “Predictive Lead Scoring For Insurance” White Paper
URL: https://intelliarts.com/blog/predictive-lead-scoring-in-insurance-white-paper/

#### “Predictive Maintenance in Manufacturing 2023” White Paper
URL: https://intelliarts.com/blog/predictive-maintenance-manufacturing-white-paper/

#### “Tech Trends in E-Mobility” White Paper
URL: https://intelliarts.com/blog/e-mobility-technology-trends/

#### 12 NLP Business Applications: Benefits & Examples
Natural Language Processing (NLP) technology is regarded as one of the key ways of digital transformation. Among the benefits of NLP for business are obtaining valuable insights, automating routine tasks, etc., which brings higher operational efficiency and better financial outcomes.
Back in 2022, NLP market revenue worldwide amounted to $24 billion and is predicted to surge to over $43 billion by the end of 2025. The trend reflects the rising infiltration of businesses in various industries with NLP-based technologies.
From this post, you’ll discover applications of NLP, the categories of the technology, its benefits, and potential pitfalls with its implementation to be aware of. 
Why is NLP important for businesses?
NLP is the branch of AI that encompasses machine tasks related to understanding human language. It powers up an entire set of software solutions, which are provided in the below sections. The versatility of NLP in handling human language makes NLP essential across diverse business domains, including:

Manufacturing. Here, NLP aids in production by interpreting machine communication for predictive maintenance (predictive maintenance domain overview), synthesizing workers’ insights, and analyzing technical documents 
Insurance. NLP in insurance automates claims processing, analyzes customer feedback, and identifies fraudulent activities by extracting key patterns and information.
Digital marketing. In this field, NLP enables customer targeting through social media analysis, optimizes content for SEO, and personalizes engagement through automated responses.
Retail. Businesses in this domain leverage NLP to analyze contexts of customer reviews, drive virtual assistants, spot trends in social media, and more. 
Healthcare. Here NLP for business can assist by analyzing patient records and physician’s notes and extracting vital information from them.
Legal. In the legal field, NLP enables cross-referencing, document analysis, and information extraction, and ensures compliance with relevant laws and regulations.

Probably, there is always one or another way in which NLP can come of great usefulness to any business.
Which are the major categories of NLP technology?
Since natural language processing in artificial intelligence is a rather complex matter, it can be differentiated into several major categories, which are the following:
Natural Language Understanding (NLU)
NLU involves the comprehension and interpretation of human language by machines, which allows for:

Interpretation of human language
Context understanding
Sentiment analysis
Intent recognition

Basically, it’s the ability of systems to understand context, sentiment, and intent of written and spoken language. 
Natural Language Generation (NLG)
NLG is the process of transforming structured data into natural language, which can be used for:

Creation of human-like text
Automated report writing
Creation of chatbots and virtual assistants
Data-to-text conversion

NLG enables machines to produce human-like content for various needs and applications.
Optical Character Recognition (OCR)
OCR technology works by scanning and analyzing printed or handwritten characters in an image and converting them into machine-encoded text that can be edited and searched. It allows for:

Conversion of scanned documents
Text recognition from images
Editable text creation
Searchable automated data extraction solutions

OCR is widely used to convert scanned paper documents or PDFs into data in easily scannable digital format.
The benefits of NLP in business
Now, let’s review the advantages of Natural Language Processing usage. We’ll see what value an NLP solution can bring for the investment needed:

Improved customer satisfaction. Through timely and relevant interactions, businesses can enhance the customer experience, fostering brand loyalty.
Cost savings. Automation of processes like customer support and data analysis with the help of NLP can significantly reduce operational expenses.
Competitive advantage. Leveraging NLP gives businesses an edge by tapping into deeper insights and more efficient processes comparing to competitors that are not using the technology.
Enhanced productivity. Employees can focus on higher-value tasks by offloading repetitive or data-heavy tasks to NLP-driven solutions, boosting overall organizational efficiency.
Risk mitigation. By monitoring communications and transactions, NLP can identify anomalies that may indicate fraud or other risks, allowing businesses to act proactively.
Predictive lead scoring. As one more advantage of NLP, utilizing NLP for predictive lead scoring solutions allows businesses to more accurately prioritize and engage potential customers, significantly boosting marketing efficiency.

The benefits of implementing natural language processing for business are numerous. The big idea here is to identify what pressing business needs NLP can address and utilize its capabilities accordingly, so you can maximize the value of your investment.
You may additionally discover a related to NLP matter — machine learning-based intelligent underwriting in another article based on the experience of our ML team. 
What are business applications of Natural Language Processing?

As of now, NLP technology is used extensively in multiple industries and ways with the prospect of optimizing business performance and driving customer satisfaction. Possible business uses of natural language processing include the following:
1. Email filtering. One of the key Natural Language Processing examples is email filtering. NLP algorithms categorize and sort emails, reduce spam, and highlight important messages, thus improving productivity. 
2. Language translation. As compared to other NLP business applications, this capability offers real-time language translation services for seamless cross-border communication.
3. Patient voice analysis. It enables healthcare providers to interpret patients’ voice-based symptom descriptions, fostering more accurate and efficient diagnoses.
4. Electronic Health Records (EHRs) processing. This application of NLP in business enables us to extract meaningful insights from EHRs to enhance patient care and fuel medical research.
For example, IBM Watson — an NLP-based cognitive computer system by IBM, the technology corporation, is used to analyze clinical information, research material, medical evidence from cancer centers, and the Personal Health Information (PHI) of patients. The technology is extensively used by Humana, a leading US provider of Medicare supplements and health insurance. Watsons’ conversational AI exploit by Humana recieve more than 7,000 call from 120 providers per business day. 
5. Document conversion. Through OCR, different types of documents can be converted into editable and searchable data, improving information accessibility. Image To Text Converter simplifies this process by transforming scanned images into machine-readable text.
6. Document analyzing. Understanding and categorizing large volumes of documents makes information management more efficient.
As another example of natural language processing business application, ROSS Intelligence — an NLP-powered legal research platform, is used worldwide to recognize and understand the context, syntax, and meaning of case law. It largely simplifies the document analysis process. 
7. Smart assistants. With the help of NLP, assistants like Siri or Alexa understand and respond to user commands, enhancing customer service and interaction.
8. Online searching. NLP-driven search engines interpret user queries to provide relevant search results, optimizing the user experience.
It’s known that Google uses NLP to provide better search results, personalize advertisements, and more.
9. Duplication detection. An NLP-powered plagiarism checker can detect duplicate content or plagiarism to ensure content originality and legal compliance.
10. Social media monitoring. Social media chatter about brands can be monitored, allowing for effective reputation management and customer engagement.
11. Automatic insights provision. Using NLP-based algorithms, it’s possible to generate automatic insights from vast data sets, driving decision-making.
12. Sentiment analysis. It enables companies to analyze customer reviews and feedback to gauge public sentiment about their products or services and guide strategic decision-making. 
The number of applications of NLP in business is high and is gradually increasing as more ways to use smart technology in business operations arise. 
Need NLP Experts for Your Project? Intelliarts’ trusted AI engineers can bring your solution to lifeGet in touch
Difficulties with implementing NLP for business transformation purposes
As with any other technology, the implementation of NLP may have some pitfalls. Among the most critical you need to be aware of:

Data privacy and security and regulation compliance. Implementing NLP may be involved with handling sensitive data such as patient medical history and physicians’ notes. Sensitive data must be protected to comply with privacy regulations including GDPR and HIPAA. Consulting with a HIPAA-compliant software consultancy during the development phase helps ensure that these AI models do not inadvertently leak protected health information, as mismanagement can lead to legal issues and loss of trust.
Resource insensitivity. Training and operating NLP models require substantial computational resources. The needed investment and potentially high energy consumption involved can be an entry barrier.
Data collection and annotation. Gathering and annotating enough quality data for training and testing may be difficult and time-consuming. Using inadequate or biased data obtained in the process of business operations or from open sources can lead to inaccurate or unfair model performance.
Language complexity and ambiguity. NLP must navigate the intricacies of human language, such as idioms and ambiguities. Understanding subtle meanings across contexts is complex, posing significant challenges for implementation.
Evaluation and metrics in NLP. Assessing NLP involves dealing with multiple factors like task specificity, intricacies of human language, human judgment alignment, and trade-offs between metrics. Issues with the availability of ground truth data, robustness, generalization of metrics, interpretability, explainability, and scalability add layers of complexity to evaluation. 

Additionally, sensitivity to changes, ethical considerations, and alignment with business goals further contribute to the non-trivial nature of selecting and applying the appropriate evaluation metrics in NLP.
Despite the complex nature of NLP developments, partnering with a trusted vendor of ML services will bring your project to success with a high chance.
Intelliarts experience
Here at Intelliarts, we specialize in AI and ML services and related technologies including NLP. Let’s take a look at two of the Natural Language Processing use cases our team had:
B2B job sourcing platform development
A challenge was to ensure high-quality job description parsing and matching candidate profiles with job requirements to streamline candidate sourcing on the platform. Additionally, the solution should incorporate Diversity, Equity, and Inclusion (DEI) principles.
The Intelliarts team built an ML solution that can compare candidate profiles from job boards or social media sites like LinkedIn with the descriptions of positions that companies intend to fill. The solution that includes a semantic search engine that supports multiple search filters, such as age, gender, racial origin, etc., was built with the aid of NLP for ease of searching and matching based on key phrases in textual descriptions. The model shows over 90% accuracy for gender and ethnicity detection. 
Case study in federal criminal matters 
A challenge was to create a solution capable of assessing how likely people will commit a crime. The assessment is based on the questionnaires examinees fill out in order to complete a security designation form intended to assess mental health stability. 
The solution was a specialized ChatGPT prompt. Users can utilize the prompt through a developed application, by uploading filled questionnaires and assessment rules for questionnaires. This way businesses operating in legal fields or lawyers can run a quick and effective assessment of the probability of a particular person committing a crime.   
Final take
NLP use cases and NLP applications in business are vast. From email filtering and virtual assistants to social media monitoring, smart technology can aid businesses from multiple domains in numerous ways bringing improved customer experience, cost-efficiency, and increased productivity. However, the complexities of handling NLP development make such projects demanding to qualifications of specialists working on it. 
Trusting your NLP project to expert developers is always the right call. With substantial experience in NLP, AI, and ML, the Intelliarts team can assist you with AI development of any complexity or with technical consulting.

#### 6 Causes of Agricultural IoT Sensor Data Issues & How to Fix Each One
TL;DR

Most agricultural sensor data problems start upstream, while their cost appears downstream. Small field-level errors can distort analytics or ML without looking obviously invalid.
Missing data should be handled based on its cause. Interpolation can help in suitable gaps, but it can also hide uncertainty.
Data normalization does not guarantee trustworthy data. Provenance must survive transformation so each value remains traceable.
Reliable IoT pipelines should expect connectivity and processing failures. Buffering, replay, and observability help keep those failures recoverable.
ML systems need visibility into data quality. Raw, corrected, and imputed values should remain distinguishable throughout the pipeline.

European Commission JRC study found that 79% of surveyed EU farmers already use at least one crop-specific digital technology. The study covered 1,444 farms across nine EU countries, showing how deeply data-driven systems have already entered day-to-day crop production.
As more agricultural decisions depend on sensor-fed systems, collecting data is only part of the challenge. The harder question is whether that data remains trustworthy once it enters the pipeline. 
This article examines six major causes of agricultural IoT sensor data issues and shows how to prevent them from affecting downstream decisions.
What causes sensor data quality issues in the field?
Field sensor data becomes unreliable when measurements are distorted or lose the context required for downstream use. The visible anomaly may appear far from the point where the data was compromised.
Most agricultural IoT data quality issues originate in one of six areas:

Sensor calibration drift
Missing or gapped readings
Heterogeneous sensor formats
Connectivity and transmission failures
Environmental and installation problems
Timestamp, duplication, and processing errors

However, these problems often overlap, so teams need to trace an unreliable reading from the device to its final use in analytics or ML.
Business Insider’s reporting shows that rural connectivity remains a practical constraint. Missing data, outliers, bias, and drift were identified as common faults.
Intelliarts saw these problems firsthand while centralizing almost 2,000 sensors across more than 500 fields for our agritech customer. As such, QA/QC checks covered deployment errors, inactive devices, unreliable records, and failed uploads. 
Read our complete sensor inventory management system success story.
From what we observed, the visible symptom rarely reveals the source. That’s why there’s a need to help engineering teams narrow down the source before correcting or inputting the data. 
The table below maps each common agricultural IoT data issue to its typical symptom, operational impact, and first diagnostic step:One pattern we have consistently observed is that teams can damage useful evidence when they start with IoT data cleaning. Interpolation may conceal a failing device, while an incorrect data normalization rule may make valid precision agriculture sensor data appear abnormal.
[table id=33 /]

A safer diagnostic process follows the data path in order:

Validate the source. Check whether the value is physically plausible and consistent with reference or neighboring sensors.
Inspect transmission. Look for sequence gaps, delayed messages, retries, and duplicate deliveries.
Review processing. Confirm unit conversions, schema mappings, timestamps, and aggregation logic.

Raw readings should remain unchanged, while corrected and imputed values should include a quality flag, rule version, and processing timestamp. That’s the traceability to aim for. 
The following sections will delve deeper into the six causes mentioned above.
Building an IoT-driven agriculture solution? Let’s make sure your data pipeline preserves the evidence you need for reliable analytics and decision-making.Contact us
Cause 1. Sensor calibration drift
Sensor calibration drift is a gradual loss of measurement accuracy after deployment. It is particularly dangerous for agricultural IoT data quality because readings can remain plausible while accumulating enough bias to affect irrigation decisions and ML features.
The main contributors include:

Soil characteristics. Soil texture, composition, and bulk density can change how dielectric sensors convert electrical response into volumetric water content.
Salinity. Higher salinity increased overestimation across most tested devices; sensor-specific calibration brought seven sensors to about ±0.02 cm³/cm³ accuracy.
Temperature and moisture conditions. Temperature extremes can increase monitoring error; long-term field research identified soil temperature as the dominant environmental factor affecting sensor accuracy.
Ageing, contamination, and hardware degradation. Long field deployments can gradually change sensor response as components deteriorate or contact surfaces accumulate contaminants.

At this point, you are probably questioning: what does calibration drift look like in the data?
Teams should look for patterns that develop over time, especially:

Growing offset from manual reference measurements;
Persistent divergence from comparable nearby sensors;
Seasonal bias that cannot be explained by weather or field operations;
Increasing correction magnitude during repeated calibrations.

These signals matter because range checks alone may classify a biased reading as valid. For precision agriculture sensor data, historical consistency is often as useful as physical plausibility when detecting drift. 
For extra information on best practices for data processing and transformation in ML, read the corresponding blog post by Intelliarts. 
How to handle sensor calibration drift without degrading yield models
The first step is to establish a field baseline soon after installation. Then compare later readings against reference sensors or manual samples on a defined schedule. If several nearby devices move together, the field condition may have changed. If one sensor separates progressively, calibration becomes the stronger suspect.
Once drift is confirmed, calibration should be approached as a versioned data operation. Here’s how you can handle sensor calibration drift without degrading yield models based on Intelliarts’ experience with precision farming using IoT.

Keep the original sensor reading unchanged so engineers can audit later corrections and compare them against the raw historical series.
Store the corrected value separately and link it directly to the raw observation that triggered the calibration adjustment.
Record the calibration coefficient and version so downstream systems know which correction logic produced each adjusted measurement.
Attach the correction timestamp and a data-quality flag to distinguish verified measurements from uncertain or retrospectively adjusted values.
Validate the corrected series against trusted references before allowing it into model retraining, feature generation, or production scoring.

This approach is also consistent with Intelliarts’ data-preparation practices, which emphasize data validation checks, continuous data profiling, and metadata management for traceability. 
Cause 2. Missing or gapped sensor data
Missing sensor data appears when expected readings never reach the dataset or arrive too late for their intended use. In agriculture, these gaps can distort time-series trends, alerts, and ML features even when the sensors themselves remain operational.
Common causes include:

Network outages. Weak or unstable connectivity can interrupt sensor uploads, leaving otherwise valid readings stranded at the edge.
Battery or hardware failure. Power loss, resets, and damaged devices can stop sampling without generating an explicit missing-data event.
Gateway downtime. A working sensor may disappear from the dataset when its gateway loses power, storage, or network access.
Pipeline failures. Records can be lost after transmission because of failed ingestion jobs, processing bottlenecks, or downstream crashes.
Damaged devices. Physical damage, moisture, corrosion, or machinery can interrupt measurements and create persistent gaps in sensor data.
Limited rural connectivity. Weak cellular or LPWAN coverage can delay transmissions, increase packet loss, and create recurring gaps across remote fields.
Failed API calls. Timeouts, authentication errors, or unavailable endpoints can prevent collected readings from reaching downstream applications or storage.

The scale of the problem is measurable. One LoRaWAN precision-irrigation deployment reported an average data loss rate of 5.51% across six data loggers, with gateway placement and network disconnections contributing to signal loss. 
How to manage missing or gapped sensor data
First, detect gaps against the expected sampling interval, like 4 samples/hour.  Second, use measures to restore smooth sensor operation, if possible. Finally, handle gaps using the recommended treatment method based on the gap type. 
A regular suite of measures includes the following: 

Detecting gaps with expected sampling intervals
Buffering readings locally at the edge
Retrying failed transmissions
Backfilling data when connectivity returns
Marking missing values explicitly
Choosing an imputation method based on the variable
Adding data-quality flags
Preventing imputed data from being treated as observed data

For confirmed gaps, the treatment should depend on the corresponding variable as specified in the table below:
[table id=34 /]
A USGS study across six sites found that soil-water imputation performance depended on site, depth, precipitation, temperature, and gap duration. Its dynamic model achieved R² values of 0.75–0.91, supporting the case against one universal imputation rule.
“An interesting aspect to note is that when an agricultural IoT product starts scaling, sensor maintenance can become surprisingly expensive. At some point, you need to understand which data problems actually affect business decisions and which ones can be tolerated.” — Alexander Barinov, a Managing Partner at Intelliarts
Cause 3. Heterogeneous sensor brands and data formats
Agricultural IoT platforms often combine sensors from several vendors, each with its own schema, units, interfaces, and reporting conventions. Because of that, without normalization, equivalent measurements can enter the agriculture data pipeline as technically different records.
Typical inconsistencies include:

Units. Celsius, Fahrenheit, pressure scales, moisture conventions.
Field names. Different labels for identical measurements.
Sampling frequencies. Different intervals between sensor readings.
APIs. Different endpoints, authentication, limits, payloads.
Communication protocols. MQTT, HTTP, LoRaWAN, proprietary gateways.
Precision levels. Different resolution, decimals, and uncertainty.
Metadata standards. Inconsistent device and calibration context.
Time zones. Local time, UTC offsets, DST.

You can also explore how software connects these data sources in our overview of top precision agriculture tools. 
This interoperability problem is established well beyond agriculture. The OGC SensorThings API specifically defines a standardized way to manage observations and metadata from heterogeneous IoT sensor systems, using a common model for sensors, observations, properties, locations, and data streams.
How to normalize data from heterogeneous IoT sensor brands
Normalize heterogeneous sensor data by translating every vendor payload into one canonical schema before it reaches analytics, automation, or ML workloads. Vendor-specific details should remain traceable, but downstream systems should consume one consistent representation.
A practical normalization layer should:

Define a canonical data model for timestamp, measurement type, value, unit, device identity, location, and quality metadata.
Map vendor-specific fields into that model, so fields such as temp, temperature_c, and soilTemp resolve to one internal attribute.
Standardize measurement units during ingestion and retain the original unit so conversions remain auditable and reversible.
Convert timestamps to UTC while preserving the source timestamp and offset when they are needed for debugging or compliance.
Preserve original device identifiers alongside internal IDs, preventing normalization from breaking traceability back to the physical sensor.
Attach sensor metadata such as type, model, firmware version, field location, and calibration state to each relevant datastream.
Validate acceptable ranges after conversion because unit or mapping errors can otherwise produce syntactically valid but physically impossible measurements.
Version transformation rules so historical records can always be tied to the mapping and conversion logic used at ingestion time.
Retest vendor integrations after firmware or API updates to catch schema and payload changes before they affect normalized data. 

From our work with data-heavy agricultural systems, the same principle applies even when teams use a custom schema. 
Intelliarts’ agriculture interoperability guidance recommends standardizing units and timestamps while retaining source tagging when data arrives through IoT devices, APIs, and other operational systems.
Want to see how Intelliarts addressed sensor quality in a production agritech platform? Read our agritech case study.
Cause 4. Connectivity and data transmission failures
Agricultural sensors often operate far from stable network infrastructure. Weak coverage, poor gateway placement, limited bandwidth, interference, and power constraints can interrupt transmission even when devices continue collecting valid data.
Common effects of connectivity and data transmission include:

Delayed readings as well as stale or late data.
Packet loss, which usually looks like incomplete time-series data.
Out-of-order records, which can appear as a broken event sequence.
Duplicate transmissions resulting in inflated event counts. 
Complete data loss or partially unrecoverable readings.

Rural connectivity remains a practical constraint for precision agriculture. From what Intelliarts observed in our agriculture projects, a lot of farmers still face weak cellular and other connectivity in remote areas. 
Deployment conditions matter as well. LoRaWAN study showed that gateway placement on natural elevations improved rural coverage. 
To make transmission more resilient:

Buffer readings locally during outages
Retry failed transmissions with controlled backoff
Acknowledge delivery of critical messages
Use idempotent ingestion and sequence numbers
Preserve original event timestamps
Compress payloads on constrained networks
Monitor signal, queues, gateways, and batteries
Match protocols to range and power needs

AWS also recommends store-and-forward architecture, retry logic, and reconnect monitoring for intermittently connected IoT devices. Although it’s a concern, rather for your trusted software engineering partner that will be working on your agricultural IoT project. 
Learn about Intelliarts' experience with agriculture and agritech projects.Our team has worked with large farming and agritech businesses to boost cost-effectiveness.Explore our AgTech expertise
Cause 5. Environmental conditions and incorrect sensor installation
Even correctly calibrated sensors can produce unreliable readings when field conditions or installation distort what they measure. Placement errors are especially problematic because they may create consistent, believable bias instead of obvious failures.
Typical causes include:

Poor sensor placement outside representative field zones
Incorrect installation depth for the crop root zone
Air gaps or weak soil-to-sensor contact
Water ingress and physical damage
Direct sunlight affecting exposed sensors
Obstruction by vegetation or debris
Damage from animals or farm machinery
Local microclimates unrepresentative of the wider field

For soil-moisture sensors, installation quality directly affects accuracy. Field guidance from the University of Arizona also identifies soil variability, extreme temperatures, physical damage, and installation quality as recurring reliability issues.
Before deployment, field controls should verify that each sensor is installed where its readings remain representative and physically reliable. This is especially important for soil sensors, where placement and soil contact directly affect measurement accuracy. 
A practical field-control process should:

Standardize installation procedures for depth, orientation, spacing, and soil contact.
Record location and installation depth as part of sensor metadata.
Validate readings after deployment before using them for automation or analytics.
Compare nearby sensors to detect isolated spikes, flatlines, or persistent divergence.
Schedule physical inspections for exposed, damaged, shifted, or obstructed devices.
Use redundancy for critical measurements where a single faulty sensor could trigger costly actions.
Link maintenance records to sensor history so anomalies can be traced back to field interventions

Installation metadata matters downstream as well. As the experience of Intelliarts engineers confirms, reading from the wrong depth may be technically valid yet represent a different soil layer than the model expects. 
That’s why we make an extra effort to provide the context needed to trace anomalies and protect downstream analytics. 
Cause 6. Timestamp, duplication, and data processing errors
Pipeline-level errors can affect otherwise valid sensor readings after they are transmitted from field devices. The main risk is semantic corruption: valid telemetry can be assigned to the wrong event-time window, counted more than once, or processed under outdated schema logic.
Here are the adverse effects of common failure types:

Timezone mismatches and clock drift can place readings in the wrong analytical window.
Duplicate delivery after retries can inflate counts, averages, or event-based metrics.
Late and out-of-order events may arrive after downstream aggregates have already been calculated.
Schema and transformation changes can silently alter field meanings, types, or derived values.
Failed batch jobs can leave partial datasets or stale downstream tables.

Time handling deserves particular attention. Apache Beam distinguishes event time from processing time because records are not guaranteed to be processed in event-time order. 
Its guidance on watermarks and late data shows why pipelines need explicit rules for records that arrive after a window has closed.
A reliable processing layer should:

Normalize timestamps to UTC before cross-device aggregation.
Keep event and ingestion time separately to expose transmission and processing delays.
Assign unique event IDs and apply deterministic deduplication rules.
Handle late records explicitly through watermarks, correction windows, or controlled recomputation.
Version and validate schemas before new payload structures reach production transformations.
Preserve raw data so failed transformations can be reproduced and corrected.
Monitor pipeline health through freshness, failure, duplicate, and late-arrival metrics.

From Intelliarts’ data engineering work, observability is equally important once the pipeline scales. 
Read our blog post on best practices to scale and optimize data pipelines for more information on how to detect defects before they impact analytics or ML outputs.
How to build a reliable IoT sensor data pipeline for agriculture
For teams building an IoT sensor data pipeline, agriculture introduces additional reliability requirements because telemetry originates in distributed field environments. It’s typically enforced through the following pipeline layers:  

Sensor and device layer 
Gateway or edge layer 
Secure ingestion layer 
Raw storage layer 
Data validation layer 
Data normalization layer 
Analytics and ML pipelines 
Monitoring layer
Application layer

For smart farming data management, this architecture also makes data-quality failures visible before they affect downstream decisions. 
See the structure of a reliable IoT sensor data pipeline in the infographics below:

Architecture defines where reliability is enforced. But it’s equally important to see whether those controls are actually holding. The table below maps each data-quality dimension to its enforcement point and a metric that can be tracked in production.
[table id=35 /]
Based on Intelliarts’ data engineering work, pipeline reliability also depends on observability at each processing stage. In our blog post on how we built a big data pipeline, we explain data transformation, DevOps, and security aspects, as well as how to get more business value out of processing data. 
How sensor data quality affects agricultural ML models
Sensor quality affects agricultural ML at two levels: the prediction produced today and the relationships the model learns for future decisions. Errors that look minor in telemetry can become systematic once they enter training data or feature pipelines.
These data-quality requirements also underpin broader AI in agriculture use cases.
The immediate impact varies by workload:

Yield prediction: Calibration bias can distort moisture and weather features used to estimate yield.
Irrigation recommendations: Stale or imputed readings can represent field conditions that have already changed.
Crop stress detection: Spikes and sensor noise can resemble genuine physiological stress.
Equipment failure prediction: Missing or reordered telemetry can break temporal degradation patterns.
Pest and disease alerts: Incorrect timestamps or locations can associate environmental conditions with the wrong event.

These downstream effects are only the visible symptoms. At the ML-engineering level, the larger concern is how sensor errors propagate into training distributions, feature pipelines, and production model behavior. 
“For some ML projects, especially those that are just slowly getting up and running, bad sensor data is a major money-wasting factor. Basically, even a poorly trained model has never been such a huge issue compared to unusable real-life data.” — Mariana Franchuk, Agritech Solution Expert at Intelliarts
In production MLOps, this means monitoring the data feeding the model as closely as the model itself. 

 Historical bias and false correlations. Repeated sensor error can become embedded in the training dataset and appear statistically related to yield, stress, or disease outcomes.
 Training-serving skew and regional accuracy. When production feature distributions diverge from training data, performance can deteriorate across new fields, regions, or seasons. Google approaches training-serving skew as a distinct production monitoring problem.
 Model drift. Sensor replacement, aging, or changing field conditions can alter production feature distributions over time, even when the deployed model remains unchanged.
 Data-quality features and confidence scores. Quality flags, imputation status, and confidence estimates give models additional context about input reliability.

Based on Intelliarts’ experience, raw, corrected, and imputed values should also remain distinguishable before feature generation. Otherwise, preprocessing can hide uncertainty that the model and monitoring layer still need.
Want to turn agricultural sensor data into reliable ML predictions?We can help you build the data pipelines, quality controls, and MLOps infrastructure.Let's talk
Agricultural sensor data quality checklist
Picture this: by the time poor sensor data appears as training-serving skew or model drift, the failure may have already propagated through your storage, transformations, feature generation, and production inference. 
At that point, teams need to determine whether the degradation comes from the model, the data pipeline, or the physical sensing layer.
A useful data-quality review, therefore, has to follow each observation end to end. To help with this, the Intelliarts team prepared an agricultural sensor data quality checklist:

Simply print it, or download it and open it in a graphical editor of your choice. Conduct the assessment, record the relevant metrics, and check off each completed item. This will give you a clear, practical view of your sensor data quality across the pipeline:
How Intelliarts helps improve agricultural IoT data pipelines
Intelliarts has 27 years of software engineering experience and 90+ large-scale projects, with expertise in agritech, data engineering, and machine learning. 
Our agriculture portfolio covers sensor management, field-data automation, land sampling, analytics, and ML-powered products. Our expertise also covers custom AI development for products that depend on production-grade ML. 
Before defining architecture, we assess the factors that determine whether the product can scale in production: field-data quality, hardware constraints, legacy integrations, connectivity, seasonal workflows, and expected operating scale. 
High-risk assumptions can be validated through discovery or a focused PoC before full development.
You can see how this approach works in practice across several Intelliarts agritech projects:

Sensor management platform: The solution centralized nearly 2,000 sensors across 500 fields and added QA/QC and lifecycle management, supporting 260% operational scaling.
Indigo Ag land-sampling automation: Intelliarts modernized and automated land-sampling workflows, reducing manual intervention and process delays.
Field-data automation: The platform reduced data collection from weeks to minutes and helped expand coverage to 2.6M+ U.S. acres.

See more projects via the Intelliarts portfolio page.
If the next stage of your agritech product depends on better data, automation, or scalability, we can help define it, so don’t hesitate to reach out. 
Final take
Reliable agricultural IoT starts with confidence in the data itself. Sensor readings must remain trustworthy as they move through the pipeline and into analytics or ML. Strong quality controls make failures visible before they distort downstream decisions. 
They also preserve the context needed to trace anomalies back to their source and correct them without compromising the wider dataset. 
Intelliarts brings 27 years of engineering experience and a track record of 90+ large-scale projects. In agritech, our software has supported 260% operational scaling and helped expand field-data coverage to 2.6M+ U.S. acres. Our average customer relationship lasts more than four years. 
If you need an experienced partner for your next agritech initiative, contact us, and our experts will get back to you shortly. 
Specialized software engineering assistanceIf your agricultural IoT data pipeline needs better scalability or greater ML readiness, then you might use a specialized software engineering serviceTalk to the Intelliarts experts

#### 7 Best Practices for Effective Lead Scoring
Does it sound familiar to you: your marketing team produces plenty of leads on a regular basis, but the number of closed deals isn’t growing? Ineffective lead scoring could be the reason here.
Earlier, the Intelliarts team helped out the partner in the insurance industry to improve lead scoring by using technology. This allowed the company to cut off 6% of ineffective leads and increase the profit up to 2.5% in a few months. By using this example and other hands-on experience, we decided to cover 7 lead scoring best practices so you could stop wasting time on the leads with low conversion potential.
Understanding lead scoring
Lead scoring aims to filter out and identify leads who are most likely to make the purchase. This way, your sales team gets a chance to focus the effort on those most likely to convert.
Usually, lead scoring intends to assign a value to leads based on their behavior and/or interest in the product or service. This could be a numerical value (from 1 to 100) or simply categories like “hot” / “warm” / “cold”. As soon as the lead reaches a certain score defined by the team, a sales representative contacts them to close the deal.

Look at the simplistic example above just to catch the idea. And remember that different companies use different attributes to calculate the score — these parameters completely depend on what matters to you.
In modern sales and marketing strategies, lead scoring is a valuable tool to:

Increase sales efficiency by timely responding to the most promising leads and ignoring those that are less likely to convert
Improve company profitability as the result of more efficient sales — statistics say that companies that employ lead scoring achieve up to 70% increase in ROI as compared to those that don’t prioritize leads as part of their business model
Understand your customers’ buying journey better to use these insights further in marketing and customer success

Top 7 lead scoring best practices
Now when we briefly explained what is lead scoring, we can move to B2B lead scoring best practices to get you covered:
1. Start small, scale up later
A popular mistake is to strive for the perfect lead scoring system, especially if it’s the first time for the company to set up lead scoring or you’re new to the process. This way, you risk getting caught up in the details, multiplying the criteria, only to find out that your lead scoring model doesn’t work. In the worst case scenario, it’s a few months after the hard work.
Instead, make a modest start and scale up gradually. You can choose from 5 to 10 most important attributes, e.g., number of website visits, downloads of white papers, registrations for webinars, etc. The idea is to keep your score as simple as possible to be able to adjust it easily.

2. Specify criteria for lead scoring
For more objectivity, you need to define lead scoring parameters based on which you will score the leads. Traditionally, we distinguish between explicit and implicit lead scoring:

Explicit criteria deal with your external lead characteristics, usually demographic and firmographic data, e.g., company size, industry, geographic location, age, marital status, etc. These are easy to score — just compare the lead’s attributes to your ICP (Ideal Customer Profile).
Implicit criteria evaluate the lead’s behaviors and interest in your product or service. Lead scoring examples in this case could include content downloads, website visits, social media engagement, purchase intents, etc. For sure, these are more difficult to track, yet they also show the leads that are more motivated to buy. Consider using lead scoring software that we mention below to help you monitor the data.

At the same time, when assessing leads’ behaviors, don’t limit yourself to positive scores only. Leads could stop interacting with your content or show unwanted behaviors like canceling appointments or visiting the career page. And it’s a good idea to mirror this in negative scoring.

Also, consider using repetitive scoring among other best practices with lead scoring to strengthen your parameters. Most businesses focus their score on one specific action like a person visiting the pricing page and getting 10 points. The truth is that repetitive actions could prove the lead’s bigger interest in your products or services, and it’s worth adding 10 points per each visit to the pricing page.
3. Establish thresholds and workflows to go
Having scores for each of your leads isn’t enough to make it work — you need thresholds to know when it’s time to act as well as workflows, i.e., clear instructions on what actions to take. (By the way, a modern AI SDR can help here by automatically triggering a call, SMS, or email sequence once a lead hits a certain threshold.)
Thresholds are helpful to identify when leads require immediate reaction and when you still have time for nurturing. It’s also useful to sift through the leads and choose the most qualified ones to process first and ignore or leave for later those that are less likely to convert.

For example, you might determine that as soon as your lead achieves an 85 score, it’s qualified. So the sales team should contact the lead to close the deal. However, if the sales representative sees the lead with a 90 score, it reaches out to them first.
When assigning your threshold values, make sure you don’t make them too low. This way, your salespeople will spend too much time on the leads that aren’t sales-ready, causing high customer acquisition costs. Making thresholds too high is also bad since your company risks losing quality leads to competitors. As the Flow State Sales expert recommended, a trained sales team can reduce costs by using the MEDDIC sales process to close sales effectively.

For better efficiency, we also recommend combining thresholds with workflows. For each threshold, prescribe an action plan for the sales and/or marketing team to know what to do. Check the example of a lead scoring workflow below.

4. Bring automation with predictive lead scoring
Manually setting up your lead scoring system is a good starting point, but it’s too time-consuming and troublesome to go. As soon as you’re ready to scale up, think about automation — and predictive lead scoring is the best way to achieve this.
Based on the user’s historical data, predictive lead scoring helps to calculate the score by matching the current leads with the ICP. It also uses machine learning algorithms to help the company predict the most motivated buyers. As a result, businesses benefit from:

Automation and faster response to leads
Reduced human error in lead processing
Less time spent on lead scoring
Real-time updates to lead profiles and, thus, more accurate results
Ability to customize the model and include unique lead scoring criteria
Better conversion rates
The learning nature of predictive models — over time, the predictive lead scoring model can notice important patterns that push your leads to make the purchase

Of course, predictive lead scoring implementation has its nuances to consider. Here are 3 best practices shared by the
Intelliarts data experts after we completed lead scoring projects:

Pay attention to data collection: the best types of data to collect are demographic and firmographic data, customer behavior data, and purchase history.
Diversify data sources: consider using Google Analytics, CRMs, social media, website forms, industry reports and market research studies, data aggregators, third-party providers, web scraping and online data.
Make it easier with personnel training: talk to agents about implementation plans, join the effort with ML engineers to organize employee workshops, and leave time for adaptation.

We’ve also just published a 20-page research paper on best practices for predictive lead scoring in insurance, where we summarized our experience in the industry. Get your copy to read about each of the mentioned best practices and discover 7 more.
  Research PaperSupercharge Your Insurance Sales: Top best practices for predictive lead scoring Get our predictive lead scoring white paper
5. Align your sales with marketing
Marketing and sales departments complement each other. While marketing teams usually know where to look for leads and how to attract them with different marketing materials, salespeople work with your leads directly and can provide unique insights into their behaviors and preferences.
So what could your sales reps shed light on? Different trends and patterns that work (and don’t work) like the main pain points of your prospects and how they attempt to solve these. Also, the information about demographics, decision-making process, and industry insights that can turn into opportunities for the pipeline. Consolidating these insights in a shared sales dashboard template helps both teams review the same data during discussions without relying on assumptions.
So no wonder that companies where sales and marketing teams work in sync show 67% better efficiency in closing deals. If you’re wondering how to bring this idea to life, here’s hands-on advice for sales marketing alignment: start with bi-weekly pipeline reviews performed by the sales and marketing teams. Later, you can carry these out once a month and involve the heads of the departments only.
6. Get more insights from customers
Another way to improve your lead scoring process is to talk to your existing customers and discuss why and how they decided to partner with you. Create a survey, send them an email, or, much better, schedule a call with them to collect more information on:

Their demographic and firmographic data to contrast with your ICP
Their behaviors, preferences, and business needs
What incited customers to convert and what repelled them from doing this
What specific activities and/or content assisted them in their decision-making process

Needless to say, customers may describe their sales experience in a different way as compared to your salespeople. It’s a good idea to listen to both sides to discover more about decision making. Also, make sure to interview both types of customers — those engaged in the shorter and longer sales cycles to get a fuller picture.
7. Optimize the model regularly

Your lead scoring model shouldn’t be set in stone. Instead, it’s better to continuously monitor, update, and refine your lead scoring. As your business grows and changes over time, so should lead scoring. For example, you can introduce a marketing channel to your campaign and start converting more leads from there. Or shifts could happen in your audience segments, customer journey, or market fluctuations. Remember how B2B businesses had to move most of their marketing activities online because of the rise of the COVID-19 pandemic?
All of this is to be reflected in your lead scoring strategy for better results. Make your model as flexible and dynamic as possible. And again, the best way to do this is to involve emerging technologies. With predictive lead scoring, you can set up monitoring dashboards and track the results efficiently. Revising predictive lead scoring models is easier too as you don’t have to recalculate everything and make changes manually.
Looking to improve your lead scoring process? Partner with a software engineering company like Intelliarts to build a solution that fits your sales goals.
How to calculate a lead score?
Regardless of whether you choose to calculate a lead score manually or use some software, here is how to do it right:

This formula will help you know the percentage of leads that have converted and, thus, evaluate the performance of your marketing campaigns. Let’s say you had 50 leads and 5 of them became your customers. Your lead score is 10%:

6 software solutions for lead scoring
To speed up the process, you can also involve ready-made lead scoring software available in the market. Here are some of the most popular ones to consider:
Salesforce
Source: help.salesforce.com
Salesforce is a great tool that offers both manual and automated lead scoring. For manual lead scoring, users need to create a new score field and then set up an actual scoring mechanism by using the existing Salesforce properties, such as industry, emails opened, etc.
Another option is to use the so-called Einstein Lead Scoring, the tool for predictive lead scoring in Salesforce. Using machine learning algorithms, this feature:

analyzes past leads to see what they have in common with the current leads for more conversions
has internal categories for specific lead text fields that have different values but share similar meanings like different job titles for the same job
reanalyzes lead data every 10 days and refreshes the score

Freshsales
Source: https://www.freshworks.com/crm/sales/
If you want something simpler (and cheaper), Freshsales is another solution to prioritize your leads. The tool gives 4 options for lead scoring: by properties, email activity, web activity, and application activity.
Besides, users can assign points to leads that align with their buyer persona to ensure they focus on the most relevant prospects. Respectively, deducting points is also possible if the lead doesn’t meet the qualification criteria.
LeadSquared Sales
Source: help.leadsquared.com
One more alternative includes LeadSquared Sales. The tool provides a number of default criteria based on which users can rate their leads and which are the most common across industries. Some examples are website visits, phone calls, emails opened, etc.
Also, you can add custom criteria or activities, as it’s called in LeadSquared. This feature enables users to set up and assign scores to new activities that are unique to your industry.
Hubspot
Source: hubspot.com/products/lead-scoring
It’s unlikely that you haven’t heard of Hubspot. So if you’re looking for quality, it’s a good option in lead scoring. Hubspot also offers both manual and automated lead scoring that help users prioritize leads based on thousands of data points. With Hubspot’s automated lead scoring, you:

Leverage data points gathered through the entire customer journey to assess and assign lead scores
Create an intelligent system that learns over time and self-optimizes lead scoring
Scale your lead scoring system along with your business growth — in Hubspot, users can build up to 25 different lead scoring systems
Combine traditional and predictive lead scoring

ActiveCampaign
Source: activecampaign.com/sales-crm/lead-scoring
In case you’re looking for a more straightforward tool, with a shorter learning curve, ActiveCampaign seems more suitable here. The tool sends email notifications when leads “get hotter” and allows assigning tasks based on lead score changes, for example, to send follow-up emails timely.
An unusual feature is an opportunity to assign scores both to individual contacts and overall deals, so the sales team could get insights into the likelihood of the sales efficiency — even if the primary contact has changed.
Ready-to-use software can save you time, but it’s inefficient and expensive in the long term. Think about custom solutions for predictive lead scoring, which will grant you with:

Much better performance as these are designed specifically for your company and industry. For instance, custom lead scoring models are trained using your customer data and can include or exclude unique criteria that are important to your business
Higher accuracy of results since custom lead scoring models are optimized for one specific task as compared to off-the-shelf solutions, which are usually designed to perform different sales and marketing functions at once
Lack of limitations, such as in types of data or input formats. Tailored to your business needs, custom-trained models won’t struggle to grasp the nuances of industry-unique jargon or slang, which is a common problem for generic lead scoring software
Flexibility — in case your business grows and changes or new data appears, custom lead scoring models can be updated and/or retrained easily

Creatio

Creatio provides a flexible lead scoring solution that blends manual configuration with AI-driven predictive models, allowing sales teams to identify high-potential leads more effectively. Recognized among the best AI CRM, Creatio’s approach helps businesses adapt scoring criteria to fit unique workflows and industries. With Creatio’s lead scoring, you can:

Configure custom scoring rules based on your specific sales processes and industry requirements.
Use AI-powered predictive scoring to automatically identify and prioritize leads with the highest likelihood to convert.
Continuously update lead scores as new data becomes available, ensuring accuracy over time.
Integrate scoring seamlessly into automated workflows to trigger timely follow-ups and personalized engagement.

Salesmate

Salesmate is a powerful lead generation CRM software and lead scoring tool that helps businesses streamline their sales and marketing processes. Its robust lead scoring feature enables sales teams to prioritize leads based on behavior, engagement, and other criteria. 
With customizable scoring models and automated lead scoring, Salesmate ensures that your team focuses on high-potential leads, boosting sales efficiency. With this tool, a user can:

Customize lead scoring models tailored to your business needs. 
Automate scoring based on historical data and behavioral insights. 
Seamlessly integrate lead management tools for small businesses like this one with various marketing automation platforms to create a cohesive, data-driven system for more efficient lead management.

How Intelliarts can help you with lead scoring?
In today’s dynamic business environment, lead scoring stands out as a crucial tool for organizations to prioritize and convert potential leads into customers. As a provider of custom machine learning solutions, Intelliarts can help you strengthen lead scoring by adding data-driven insights, behavioral analytics, and automation for more accurate predictions.
We build end-to-end predictive lead scoring that helps businesses sift through the leads and provide a steady stream of qualified leads to your sales team. If you’re planning to introduce predictive lead scoring (or any other ML-related project) to your value chain, feel free to book a consultation with our expert team. We’ll be glad to translate your business idea into action.
Get the ML expert consultationContact us

 

#### 8 Ways How Technology Consulting Can Improve Business Efficiency
The aspiration toward better outcomes is absolutely natural for businesses across all industries. It’s a frequent practice for top managers to initiate the integration of innovative technologies or modernization of some processes. For this, they may require external expertise. That’s why tech consulting can improve business efficiency of any company.
As a business owner or a C-level executive, you may find it beneficial to get a grasp of ways to enhance business efficiency through IT consulting. You’ll also get an insight into technologies that can be implemented, what businesses reap the most benefit from using this service, and how to find the right consulting partner.
How tech consulting can improve business efficiency
Technology consulting is intended to help businesses and organizations achieve strategic objectives through leveraging tech. Here are the advantages of technology consulting for businesses:
#1 Achieve business goals
Running a business requires excellent expertise in technology, industry, and market, as well as a substantial experience in analytics. Technology consulting firms can assist business owners and their teams by providing expert advice on how to turn a business idea into a properly implemented tech project. They may also:

Align IT assets with projected goals
Prioritize and guide digital transformation
Coordinate activities within IT projects
Outline a high-level IT strategy and execute it
Design and develop digital systems
Prepare the client’s infrastructure
Integrate the technology into business processes

As an example, the Intelliarts team delivered tech consulting services to Dorel Juvenile Group — a manufacturer of juvenile products. We identified ways to make the application more stable and scalable and detected the features that the app lacked. We also created a detailed roadmap for modernizing application infrastructure and provided custom software development services.

#2 Maximize operational efficiency
Technology consulting transforms businesses by maximizing their operational potential, not in the last place. Digital consulting firms can assist a company by identifying areas for improvement and suggesting new solutions. Some of the possible interventions here are:

Propose automation tools and techniques
Help migrate to cloud-based systems
Increase the flexibility and scalability of business processes
Recommend group scheduling tools such as Doodle or WhenAvailable to streamline team coordination and optimize meeting times

The collaboration between Coca-Cola European Partners and IBM — the multinational technology company, is a vivid tech consulting case. IBM is responsible for the marketing distribution of Coca-Cola products and improving the company’s operational efficiency through digital systems integration, data analytics, and IoT and AI technologies.
CCEP’s CIO Peter Brickley notes: “Our successful collaboration with IBM over the last few years has given us the confidence to take the next step in our strategic cloud-first digital transformation.”
#3 Blend technology into business processes
Digital transformation consulting can help companies utilize the industry’s most relevant technological advancements. Consulting agencies assist with researching and evaluating various technology options and considering factors such as cost, ease of implementation, and scalability. Amongst services that advisory firms may provide, there are:

Development of an implementation plan with the steps required to integrate the technology
Execution of the plan in cooperation with an in-house team
Monitoring and evaluation of the results
Identification of necessary adjustments or refinements
Possible steps for carrying out a software implementation plan are shown in the image below.

Optimizing the efficiency of a company through tech advancements requires substantial expertise. If you are exploring ways how to improve business with tech consulting, reach out to our team of well-seasoned tech experts.
#4 Improve business resilience
Resilience is an organization’s ability to respond and adapt to interruptions or unanticipated changes that could threaten its operations, people, assets, brand, or reputation. Digital consulting firms can advise on and help to implement measures to raise the company’s overall responsiveness in times of crisis. Examples include the following:

Adoption of Agile methodologies such as Scrum and Kanban
Integration of DevOps practices into operational processes, including the use of DevOps dashboards to monitor system health and facilitate timely responses
Development of business continuity plans

#5 Enhance sustainability
Business or corporate sustainability is the strategy for the management of environmental, social, and economic demands. Tech consulting firms can help to evaluate the possibility of integrating sustainability practices, identify the most cost-effective technologies, and execute implementation plans. Amongst possible interventions here are:

Development and integration of AI-based renewable energy solutions
Optimization of energy consumption
Help with the adoption of circular economy principles
Implementation of sustainable supply chain practices
Assistance with bringing a business closer to green IT

See our Renewable Energy Software Development Solutions
#6 Provide training and support
As a rule, implementation of new technologies, as well as building a new product or automating processes, come with the need to provide training to employees. Agile certification courses can be particularly beneficial in equipping teams with the right skills to adapt to evolving project requirements efficiently.
Technology consulting company can advise on what training processes to introduce and assist with their successful execution. Amongst the services that advisory firms may provide, there are:

Development and customization of training plans
Monitoring and evaluation of the effectiveness of training
Ongoing support to the IT infrastructure, including helpdesk or support hotline and on-site maintenance and troubleshooting

#7 Optimize costs and reduce risks
Another benefit of IT consulting services lies in optimizing business expenses and reducing associated risks. Expert consultants may perform a thorough analysis of a company’s technology infrastructure. This way, they identify areas where cost savings and risk reductions can be achieved. Possible interventions here are:

Performing cybersecurity, operational, and other risk audits
Suggestion of best practices for risk management
Proposal of measures for consolidating systems
Help with the migration to more efficient platforms

Consulting on and helping with predictive maintenance in manufacturing is an additional example of how tech consulting can optimize operational costs.
#8 Help to stay ahead of the competition
Depending on the niche, a business may be present in a more or less competitive environment. Either way, making steps towards higher marketability is key to remaining in demand regardless of the situation on the market. A trusted IT consulting partner can:

Conduct an analysis of business operations and identify areas where technology can provide a competitive advantage
Provide advice on what technology to leverage
Help to establish and encourage a culture of innovation within the organization

Let’s take a look at another example of how to improve business with tech consulting. GE Healthcare, a global medical technology leader, announced a collaboration with Accenture — a consulting company. GE-Accenture alliance implemented a new data management system that will help them streamline their supply chain and reduce inventory levels. They aim to use their combined consulting businesses and analytics expertise to help healthcare providers save 25% to 50% of their current costs to re-process denied claims.
Keep reading the post to discover what technology implementations can be discussed and worked on within the tech consulting service scope.
Innovative solutions to adopt with the help of technology consulting
One of the factors of success is the wise integration of innovative technologies in operational and business processes. Let’s find out what implementations tech consulting firms can offer:
AI & ML-based solutions
Branches of AI & ML are Natural Language Processing (NLP), predictive analytics, computer vision in insurance, Robotic Process Automation (RPA), virtual assistants, etc. As an example, engineers from Intelliarts developed an integrated ML-based solution that minimizes false alarms in the automated optical inspection system.
Cloud Computing solutions
A digital consulting firm can assist a business in taking advantage of Infrastructure as a Service, Software as a Service (SaaS), Platform as a Service (PaaS), hybrid cloud, cloud migrations, and other technology implementations. This scope falls within cloud strategy and advisory services.
IoT/IIoT solutions
IoT and Industrial Internet of Things (IIoT) innovative technology fields encompass a vast array of usable solutions. These include sensors and devices, data analytics and visualization solutions, connective and networking solutions, security systems, and edge computing models.
Read also: IoT in supply chain
Business Intelligence solutions
A wise inclusion of business intelligence solutions can greatly impact the way companies derive insights from their business data to support decision-making. Possible technology implementations here that consulting firms can advise on are data warehousing, self-service business intelligence, data governance, predictive analytics, etc.
Process Automation solutions
This field encompasses a range of technology implementations that focuses on streamlining decision-making and other business processes within the organization. Examples of what tech consulting firms can help with include Business Process Management (BPM), workflow automation, and data integration.
Now, let’s find out in what niches technology consulting is in high demand and how this service can help different businesses.
Businesses that get the most profit from technology consulting
The operation of most companies is dependent on tech implementations to a certain extent. Among niches, businesses that reap the most benefits of hiring an IT consulting firm are manufacturing, insurance, energy, and digital marketing. However, financial, healthcare, retail, and logistics industries are also saturated with innovative technologies. That makes consulting a demanded service among related businesses as well.

Manufacturing
Tech consulting can help streamline operations through improved predictive maintenance and better quality assurance. It can also optimize supply chain management.
Insurance
Technological implementations of machine learning in the insurance industry can improve customer acquisition and enhance fraud detection capabilities. They can accelerate underwriting and help to achieve higher operational efficiency.
Energy
IT consulting can advance companies toward optimizing energy production, distribution, and consumption. Besides, it can help with the smart integration of renewable energy technology.
Digital Marketing
Technology consulting can help leverage data analytics to provide informed market insights. AI-driven technologies also offer additional content creation and SEO capabilities, and support effective link building strategies to enhance online visibility.
Financial Services
IT system modifications here are mostly focused on improving cybersecurity and compliance. Besides, they can improve customer experience through digital channels.
Healthcare
IT business consulting can reduce medical care costs and increase medical personnel’s productivity and patient care quality. It also ensures HIPAA, HITECH, and FDA compliance.
Retail
Retailers can achieve high-level customer service and provide personalized communication. This will increase efficiency, reduce costs, and improve customer loyalty.
Logistics
Digital consulting may offer the improvement of logistics networks and a higher automation level. It can be achieved through advising on location design, intralogistics, etc.
Law
Law firms heavily rely on technologies to remain compliant with local regulations as well as to help them manage their clients. In many cases, law firms benefit from digital consulting to help their websites become more effective extensions of the legal advice they provide, such as through the publishing of meaningful legal insights regarding the areas of law customers need support in. This in turn improves the customer experience.

Tech consulting benefits for companies are numerous. There is always something to enhance at an operational or administrative level. That’s why collaborating with consulting experts to identify areas of improvement and execute tech implementations is always the right call.
Check also: Custom Marketing Software Solutions
Success stories of businesses that have benefited from technology consulting
It’s hard to imagine a well-established business that has never required technology consulting assistance. Here are some of the success stories of famous companies that benefited from such a service:
DHL
DHL, being one of the leading global logistics companies, was keen on maintaining its edge in a highly competitive and rapidly changing environment. For this, DHL needed to innovate its operations. 
To address these needs, DHL partnered with IBM, among other tech firms, for technology consulting and solutions. One of the significant outcomes of this collaboration was the adoption of IoT (Internet of Things) technologies.
DHL’s embrace of IoT and other technological advancements significantly enhanced their service offerings. They were able to provide better traceability, faster shipment times, and more accurate delivery predictions.
Kodak
As digital photography began to emerge in the 1990s and 2000s, Kodak found itself struggling to adapt, even though they had early access to digital camera technology.
Kodak sought assistance from consulting firms, including Accenture, to help in its transition from film to digital. The consultancy aimed to provide technological strategies and implementation assistance to make Kodak competitive in the digital imaging space.
While Kodak had significant internal challenges, the digital initiatives backed by consulting insights helped it establish a foothold in the digital space.
Let’s proceed with learning how to select a trusted consulting company among many on the market.
How to find a tech consulting partner for your business?
Finding a great tech partner is already half the battle. Here are some things to consider when hiring an IT consulting firm:
 
Expertise and experience
Check portfolio and case studies to find if a particular vendor of consulting services has experience in your industry or with similar challenges. The number of successfully completed projects should exceed, preferably, several dozen. You also should check or ask whether they specialize in the technologies you need advice on.
Reputation and reviews
Look for testimonials or reviews on their website, social media platforms, or third-party review websites. This way, you’ll get an idea of the quality of service, responsiveness, and reliability of a chosen provider of consulting services.
Flexibility and customization
Give preference to a company that offers customized solutions tailored to your unique business needs and goals. They should be able to adapt to your requirements and offer scalable solutions that can grow with your business.
Communication
Desirably, you should partner with a company that values clear communication and productive collaboration. They should be responsive to your queries, provide regular updates, and be transparent about their progress.
People value and customer retention
The way companies treat their employees can tell a lot about the potential partnership. After all, when employees feel valued and supported, they are more likely to be engaged, productive, and motivated. As for customer retention, the prevalence of recurring customers indicates satisfaction and, likely, excellent project outcomes.
Drive to innovate
It’s good if your tech consulting partner is familiar with industry-latest technology, but it’s even better if they have the drive to innovate and can come up with some unique, state-of-the-art solutions. For one, Intelliarts has the R&D department responsible for idea exploration and looking for potential for significant breakthroughs.
Cultural proximity
Another good idea is to look for similarities in history, ethnicity, religion, language, and geography between you and a tech consulting firm. Having a lot in common considerably simplifies communication, benefits long-term cooperation, and typically leads to better project results.
Cost and value
To opt for a cheaper or more expensive service is not a valid concern. Instead, estimate the value that your business will receive from one or another consulting option and stick to the optimal one.
If you are in search of a trusted tech consulting company, consider Intelliarts as an excellent option. We specialize in big data and AI technologies and can provide qualified big data consulting as well as execute any technological developments. We assisted customers from across multiple industries, with most attention paid to manufacturing, insurance, fintech, healthcare, and retail niches.
Final take
Companies increasingly use digital technologies to keep up with the pace in the competitive environment and improve business outcomes. Many organizations rely on external expertise and opt for tech consulting services. This way, they get assistance with the identification of areas for improvement and the execution of a digital transformation plan. An information technology audit can further ensure that digital transformations are aligned with business goals, enhancing transparency and security. The outcomes that businesses strive to achieve are higher customer satisfaction, increased revenue, better business efficiency, and higher quality of products and services offered.
You should be confident in the high expertise of a firm that provides IT consulting services to you. If you aim at getting the most value from digital transformation, then let a team of expert AI consultants and ML engineers from Interlliarts contribute.
Need a tech consultation?Get expert guidance from Intelliarts to solve your software challenges with confidence.Contact Us

#### 9 Best Agritech AI Development Firms in 2026
TL;DR

MarketsandMarkets puts AI in agriculture on track to reach $4.7 billion by 2028, growing 23.1% a year.
Vendor directories grew at a similar pace, and most rank firms by team size, rate, and review count. None of those show whether a firm has carried an agricultural model into production.
Agritech founders, CTOs, and heads of data are choosing AI software partners with expertise to cover their agritech needs.
This guide ranks the best agritech AI development firms based on named clients, production evidence, engagement model, and the terms that apply after launch. 
We explain how technical co-building differs from fixed-scope delivery, why only two firms here document systems running across repeated seasons, and where Intelliarts fits.

AI in agriculture was worth $1.7 billion in 2023 and is forecast to reach $4.7 billion by 2028, at 23.1% annual growth. The supplier market has widened accordingly, and public directories rank firms by headcount, hourly rate, and review volume. Those measures say little about whether a vendor has taken an agricultural model into production.
The best agritech AI development firms combine machine learning and data engineering with direct experience in agricultural systems: field data collection, remote sensing, IoT, and production deployment.
The comparison below scores nine of them against published project evidence, and sets out the selection work that sits on the buyer’s side. 
How we selected the best agritech AI development firms
The Intelliarts experts compiled this selection of the best agritech AI development companies based on published project evidence. To stay objective, we used publicly verifiable facts and conclusions derived from them alone, regardless of whether any of Intelliarts’ experts had previously worked with any of the providers.
We reviewed case studies, technical documentation, and service pages from each vendor, scored eight criteria, and required at least one named agricultural client or a documented agricultural deployment.
[table id=23 /]

Important note. Production deployment and evidence quality carried the most weight in our final ranking. A firm can have serious custom AI development capability and still not have much running on a farm. That’s why evidence-backed cases are prioritized.

From our work in the industry, data engineering criteria should sit high on this list. How much of a project goes into preparing data depends on what a company already holds, so no vendor can quote it upfront without having full information about infrastructure and the project. 
What tells you something is whether they ask. Firms with agritech delivery behind them run a data readiness review before scoping, and they price the build once they have seen your sources. 
The weighting above reflects where agritech projects actually spend time. Here’s one carbon farming case study by Intelliarts to demonstrate it plainly:

Challenge: The program required each grower to supply three to five years of historical field records. Manual entry ran up to eight hours per grower, submissions arrived incomplete, and enrollment drop-off was heavy.
Solution: Intelliarts rebuilt the intake around remote sensing and the client’s inference API, so crop type, planting dates, and field boundaries arrived pre-filled, and growers only reviewed them.
Results: Entry time fell from hours to minutes. Enrolled acreage reached 2.6 million, retention in one program held at 80%, and more than one million salable carbon credits were generated.
Model development was the smaller half of that engagement, which is the pattern worth checking in any vendor’s case studies. Read the full success story here.
Best agritech AI development firms at a glance
The best agritech AI development firms below are split into three groups: specialists with named farm clients, engineering firms carrying one strong agricultural project, and satellite analytics vendors that also take custom builds. 
Read the evidence column first, since it separates delivery history from positioning.
[table id=24 /]
Five of these firms publish agricultural client names: Intelliarts, Folio3 AgTech, EOS Data Analytics, Geniusee, and Lemberg Solutions. The other four work under NDA in agriculture, which means their outcome figures cannot be checked with a reference call before you sign. 
That distinction matters more than portfolio size when you shortlist AI development companies for agriculture, and the sections below cover each firm in detail.

Important note. These nine are not complete substitutes for one another. EOS Data Analytics sells a subscription monitoring platform with custom geospatial work attached, and Folio3 AgTech leads with its own products, such as AgriERP and Cattlytics. 

Both hold a place on this list because they take custom engagements around those products, and buyers put them on the same shortlist as pure development vendors. 
Firms like Intelliarts, Intellias, and Lemberg Solutions build to specification and hand over the code. Many programs need both: you license the imagery layer, then bring in a development partner such as Intelliarts to connect it to ERP and machine data and build models on top. 
Maybe you would even need the implementation of precision farming using IoT alongside AI software. Decide which part of the stack you are buying before you shortlist vendors.
Need an AI development partner with experience in real-world agricultural systems? Let’s discuss your agritech use case and find the right path to production.Contact us
1. Intelliarts: Best for technical co-building and production AI
Intelliarts fits agritech companies that need machine learning running in production and want an engineering partner working next to their in-house team. Operating since 1999, the company builds agricultural data platforms, ML models, and field-facing software, and 90% of its clients return with new projects.
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What we build for agriculture projects:

Custom agritech software development and multi-season product co-development
ML and predictive analytics, including forecasting on sparse field data
Remote sensing and satellite imagery processing
Agricultural data engineering, ETL and ELT pipelines, third-party data integration
IoT and sensor integration across field and lab workflows
Cloud infrastructure on AWS and Azure

Best fit. Companies with a product in market that needs data infrastructure, models, and monitoring running reliably, plus teams planning roadmaps across several seasons.
“An interesting thing is that sometimes we tend to continuously look at how often an agronomist overrides the model’s prediction. That frequency number is then correlated with our predictions for future renewal and retraining measures needed to be taken.” — Alexander Barinov, a Managing Partner at Intelliarts
Project example: land sampling automation for Indigo Ag

Challenge. Sampling ran on spreadsheets and manual handoffs. Samples went missing between field and lab, and the data quality feeding the carbon program suffered as a result.
Solution. Intelliarts redesigned the process around tablets, QR-coded sample tracking, and field sensors, with lab results syncing back into the analytics stack.
Results.

Sensor losses reduced to fewer than 4 per 1,000
Lab results available to data scientists without manual consolidation
Five Intelliarts teams running the program in parallel at peak

Read the full success story here
From our engineering team. The demanding part of that project was not the analytics. Field crews work in dust, with gloves on, and often without signal, so sample tracking had to survive offline for hours and reconcile cleanly once a tablet reconnected. 
We spent more time on sync logic and conflict handling than on the reporting layer. That ratio holds across most agritech builds we take on, and it is the part vendors underestimate at proposal stage.
Want to See How We Build Custom Agritech Systems?Explore Intelliarts' agriculture engineering expertise, from field data collection to ML models in production. Explore our services
2. Intellias: Best for large-scale digital agriculture platforms
Intellias suits enterprise agriculture platforms that need multi-country rollouts and sustained platform engineering. The firm has delivered agricultural projects since 2018 and employs around 3,000 engineers, which supports parallel workstreams that smaller agritech software development companies cannot staff.
Capabilities that matter for agriculture:

GIS and geospatial mapping, including NDVI analysis of satellite imagery
IoT sensing for greenhouses and vertical farms
Risk scoring and heat maps for chemical runoff and leaching
Farm management platform engineering and architecture work

Project example. A 12-person team has worked since 2018 on crop monitoring inside a farm management platform combining GIS, satellite imagery, and weather data. A separate vertical farming system built with image recognition and IoT climate control reports 60% energy savings.
Best fit. Large agribusinesses and platform owners that need several teams at once and multi-year platform ownership.
3. Folio3 AgTech: Best for crop and livestock solutions
Folio3 AgTech carries the deepest named-client roster here, built on two decades of agriculture work and 600+ delivered projects. The unit runs its own products alongside custom builds, with an advisory board drawn from pork production, animal health, and seed genetics.
Capabilities that matter for agriculture:

Computer vision for livestock counting and monitoring
ERP implementation on NetSuite and Dynamics 365, plus traceability software
BI and self-service analytics on Power BI and Tableau
Herd, feedlot, and seed certification workflows

Project example. Weaver Popcorn Hybrids went live on a grower ERP in five months and kept the project under $50,000, with manual Excel reporting reduced by 80% and daily tool adoption up 50%. Separately, drone-footage cattle counting for Australia’s second-largest beef producer is published at 99.9% accuracy.
Best fit. Livestock, animal health, and food supply businesses that need operational systems first, with AI solutions for agriculture applied to herd monitoring. 
4. SEP: Best for agricultural equipment and embedded systems
SEP has built software for agricultural equipment manufacturers since 2011, working from Indiana as a 180-person employee-owned team. Founded in 1988 with 100% US delivery, it sits at the highest rate band among the agriculture software development firms here.
Capabilities that matter for agriculture:

Embedded in-cab display and guidance software
Machine-to-machine telemetry across large equipment fleets
Fleet data platforms and dealer-facing systems
Cloud migration for OEM data estates

Project example. An 8+ year partnership with a heavy farming machinery manufacturer covers embedded display software across a tractor generation. A separate web application reduced the time to search, analyze, and display data across tens of thousands of machines by 99%, and a fleet management build delivered 10x faster data calculations with dashboards localized in four languages.
What to check. SEP publishes no agricultural AI or ML project, and its machine learning case studies sit in aerospace, energy, and finance. Agricultural clients stay unnamed under OEM agreements, and engagements start at $100,000 with US-only rates.
Best fit. Equipment manufacturers and telematics providers that need embedded engineering and US-based delivery.
5. Itransition: Best for enterprise AI integration
Itransition brings 28 years of enterprise software work, 3,000+ professionals, and a strong industrial computer vision practice. Agriculture sits outside its listed verticals, so weigh it for integration depth against agriculture software development firms with domain history. 
Capabilities that matter for agriculture:

Image classification and defect detection with CNN architectures
Data engineering, BI, and enterprise system integration
Legacy modernization across ERP and CRM estates
MLOps practice, evidenced outside agriculture

Project example. An image recognition proof of concept for plant pathology used ResNet-50 classifiers, with synthetic training data generated from 2,000 provided photos, reaching 80% correct pathology identification. The client demonstrated it to investors eight months after kickoff.
What to check. That proof of concept is the only agricultural delivery in the public portfolio, and the client is unnamed. Itransition’s agriculture pages describe work by third parties such as Blue River Technology and OneSoil, so read them as market commentary.
Best fit. Enterprises folding agricultural data into existing ERP and BI systems, where integration weighs more than agronomic modeling.
6. EOS Data Analytics: Best for satellite crop monitoring and yield prediction 
EOS Data Analytics operates its own satellite analytics platform with 160 employees, including 55 in-house data scientists and GIS specialists. Founded in 2015 and headquartered in Menlo Park, it is the remote sensing specialist among these AI development companies for agriculture.
Capabilities that matter for agriculture:

Vegetation index processing, including NDVI, NDRE, NDWI, and MSAVI
Crop classification and field boundary detection
Yield prediction using regression methods, Random Forest, XGBoost, and an adapted WOFOST crop model
Radar and optical fusion for soil carbon measurement

Project example. AgriProve oversees more than 700 projects covering over 175,000 hectares and manages over 70% of all soil carbon projects registered in Australia’s ACCU Scheme. Working with EOSDA reduced its project onboarding from months to weeks.
What to check. Most wins are platform subscriptions or white-label deployments, and the published methodology list describes what EOSDA can apply, not what runs on every job. Their own cotton yield proof of concept used Random Forest alone, with WOFOST excluded.
Best fit. Teams that want imagery analytics running quickly and can build around a licensed platform.
Planning an AI project in agriculture? Intelliarts can help you move from prototypes to production-ready solutions.Let’s discuss
7. Geniusee: Best for agritech startups scaling IoT platforms 
Among the agritech software development companies here, Geniusee is the one built around startup delivery, with 250+ specialists across Austin, Warsaw, Stockholm, and Kyiv. 
Founded in 2017, the firm sells dedicated teams and outstaffing on time-and-materials terms.
Capabilities that matter for agriculture:

Agricultural IoT data pipelines on AWS for soil moisture, rainfall, and tank sensors 
Satellite monitoring and weather modeling
Mobile applications for field use
Credit scoring models for smallholder financing

Project example. RealmFive rebuilt its data architecture with Geniusee and reports 40% lower latency, new farms onboarded 30% faster than originally projected, and four times the growth in device connections handled without performance loss. Its mobile app passed 50,000 downloads at a 4.5 rating.
What to check. The agritech portfolio holds two references. The 50% farmer income figure on the Ricult page describes Ricult’s own program results in Thailand before the engagement, and computer vision is not evidenced in either project.
Best fit. Funded agritech products scaling sensor networks and grower-facing apps. 
8. Lemberg Solutions: Best for livestock computer vision and embedded devices
Lemberg Solutions builds agricultural hardware and edge AI with 200+ experts, working in agriculture since 2007. Five agriculture clients are named publicly, and the firm covers the device layer most agritech development partners leave to specialists.
Capabilities that matter for agriculture:

CNN training for weight estimation and crop quality assessment
Edge inference on NVIDIA Jetson with Intel RealSense depth sensing
Embedded firmware, including GNSS correction modems
Device interfaces on Raspberry Pi and Qt

Project example. Barkom’s portable weighing device measures pigs in motion using a model tested on almost 17TB of real farm data, returning weight in 8 to 10 seconds at 98% accuracy and cutting weighing time by a factor of 24. Inarix and AgriData Innovations followed with grain quality and germination analysis tools.
What to check. Evidenced AI is computer vision and edge inference, with no satellite, yield forecasting, or MLOps platform work. The Syngenta Group engagement on their site is Drupal website development and carries no AI component.
Best fit. Hardware-adjacent agritech, livestock analytics, and inference that runs on the device.
9. Innowise: Best for field robotics and weed detection
Innowise runs 3,500+ IT professionals from Warsaw and delivered one of the more demanding agricultural systems in this comparison. Robotics perception is the reason it earns a place.
Capabilities that matter for agriculture:

Plant segmentation and stem detection networks
Laser targeting control with millimeter-level range detection
Computer vision pipelines on PyTorch, OpenCV, and MMSegmentation
Robotics software and control integration

Project example. An autonomous weeding system built by six specialists over 14 months kills over 100k weeds per hour, with laser parameters tuned to determine range to 2 mm, and reports 64% fertilizer savings. 
What to check. The client sits under NDA as a European producer of autonomous agricultural robots. Innowise’s agriculture industry page lists case studies drawn from HR, retail, healthcare, and travel, which makes its agricultural depth harder to assess than the robotics case alone suggests.
Best fit. Robotics and autonomous equipment builders that need perception models and control software inside a custom agritech software development program. 
Which agritech AI development firms are best positioned as technical co-builders?
Among the best agritech AI development firms compared here, Intelliarts, Intellias, and SEP hold the strongest co-building records, each with multi-year engagements where their engineers worked inside the client’s own delivery structure. 
The remaining providers in the list above offer mainly fixed-scope builds or platform subscriptions, which suit a defined deliverable and work less well for a product that keeps evolving.
A technical co-builder such as Intelliarts takes part in:

Product discovery and data readiness assessment
Architecture decisions and technology selection
ML and data pipeline construction
Integration with farm management systems, ERP, and machine data
Deployment, monitoring, and retraining
Roadmap planning across seasons
Selection and negotiation of third-party data providers, e.g., imagery and weather APIs
Data governance, residency, and grower data ownership terms
Test automation and QA under field conditions, including hardware-in-the-loop
Other engineering, software, and analytics-related services

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Intelliarts, as a long-term agritech development partner, offers agritech AI development, Intelligent forecasting, and precision agriculture among our other services. We have dedicated teams as well as engineering leads available for reference calls before you commit.
Have an agritech AI idea but need the right technical partner to bring it into production?Get in touch with the Intelliarts team
Which firms have built production-grade ML pipelines for precision agriculture?
Intelliarts and EOS Data Analytics show the clearest documented evidence of machine learning pipelines running in production on farms, followed by Innowise and Lemberg Solutions, whose models run at the edge on robots and field devices. 
The remaining firms publish deployed software without models, models without deployment, or agriculture without either.
Production-grade ML pipelines are those that ingest, process, deploy, monitor, and retrain on their own schedule with field conditions taken into consideration. The respective scope of service is shown in Intelliarts’ data engineering page. 
What a vendor should be able to provide across the full cycle in case of a production-grade ML pipeline:

Automated data ingestion from imagery, sensors, and third-party providers
Data processing, validation, and reconciliation
Model deployment into the systems people use daily
Monitoring for drift and data quality
Retraining on a defined schedule or trigger
Integration with farm management platforms, ERP, and machine data
Scalability across acreage, devices, and seasons
Error handling when a feed, sensor, or API stops responding

Programs tied to carbon credits or sustainability reporting add even more requirements. Here’s what an Intelliarts expert has to say about that:
“Carbon and compliance put reproducibility into the spotlight. You might ask: why? Well, just imagine you have to rebuild a 2023 prediction with legacy code and outdated imagery, years later.” — Marta Kufalska, an Agritech Solution Expert at Intelliarts
And here’s our list of evidence on documented agricultural ML in production per agritech provider in the blog:
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Across the best agritech AI development firms reviewed here, the stage least often evidenced is retraining and drift monitoring, which is the stage that decides whether a yield forecasting model holds up in season two as well as it did in season one. 
Intelliarts and EOS Data Analytics are the only two with documented systems running through repeated seasons. 
Interested in exploring how ML engineers at Intelliarts handle data pipeline projects? Read how we built a big data pipeline on an actual case in another blog post. 
What should you compare beyond team size and price?
Compare production evidence, the seniority mix behind the rate, and the terms covering monitoring and retraining. Agricultural technology consulting firms will advise on the roadmap, and these checks apply to whoever writes the code afterwards. 
Team size and rate say what a month costs. They say nothing about the second season, when accuracy drifts, and the original team has rotated off. 
The checklist below sets out what to compare additionally, with indications of what exactly to look for and practical examples based on Intelliarts:

You check an agritech development firm’s past project references by getting the engineer who ran the delivery onto the call, then matching what they describe against the published case study. NDA coverage is standard in agriculture, so the real question is whether any client will speak to the work at all. 
From the work of Intelliarts experts in the industry, an engineer who has shipped a field data system already knows harvest, laboratory assessment, seasonality, and other farming-related aspects. Intelliarts can staff agritech programs with engineers who have shipped field data and remote sensing systems and therefore can offer the best expertise for your project. 
Having a proper tech stack can also be crucial for an agriculture project. Take a look at top precision agriculture tools that you or your trusted vendor might be using in another one of our blog posts. 
How to choose the right agritech AI development firm
Start with the decision the system should improve, confirm your data can support it, and compare the best agritech AI development firms only after those two are settled. 
Projects that stall usually run the sequence backwards, with an agriculture AI development company selected before anyone has inventoried the field records the models would need. 
The steps below run from problem definition to a signed discovery phase, with the artifact each one should leave behind:

Discovery is the cheapest place to find out that three seasons of records are missing or that a satellite feed cannot cover your region in June. 
Intelliarts usually opens agritech engagements with a data readiness review and an architecture outline for that reason, so the estimate reflects the sources you hold today. 
For companies that need an assessment beforehand or struggle to evaluate their project viability, we offer both advice during a free discovery call as well as paid technical consultation for more in-depth matters. 
Find more information on how to choose an agritech company in this article.
Which agritech AI development firm should you choose?
Choosing among the best agritech AI development firms comes down to evidence you can verify: named clients, models that survived more than one season, and written terms covering the period after launch. 
Platform vendors, ERP specialists, and engineering partners all appear on this list, and they cater to varying project needs. 
Intelliarts fits companies looking for a long-term technical co-builder across agricultural data, ML, and software development. We have more than 27 years of experience delivering software services for international customers from different domains, including agriculture. 90% customer return rate and in-house expertise, allowing us to deliver development of any complexity. 
Looking to explore more ways of utilizing AI in your business? Read 10 AI in agriculture use cases covering crop monitoring, forecasting, and computer vision.
Entrust us with your agritech AI projectWe will evaluate your project, conduct a strategy session, and provide an accurate quote. End-to-end, business-focused assistance.Contact us

#### AI Impact on Renewable Energy: 50+ Key Statistics
Over the past few years, renewable energy has experienced a significant transformation through the integration of artificial intelligence (AI) technology. AI has revolutionized resource utilization, operational efficiency, energy management, and power system reliability in the renewable energy market. Moreover, AI’s capability to optimize energy distribution and storage systems, as well as its advanced forecasting models, enables more efficient grid management and better integration of renewable energy into existing power systems.
In this blog post, we’ll dive deep into the latest AI in renewable energy statistics and trends and explore how this technology has paved the way for a more sustainable and resilient energy future.
Artificial intelligence in renewable energy market report highlights
 
Allied Market Research has recently published a report on the latest statistics on AI in renewable energy, highlighting key market forecasts from 2023 to 2032. Here are the main takeaways:

Market growth: The market size of AI in renewable energy reached $0.6 billion in 2022 and is projected to surge to $4.6 billion by 2032, with a compound annual growth rate (CAGR) of 23.2% in the period from 2023 to 2032.
Market fragmentation: The market share of artificial intelligence in the energy market is characterized by significant fragmentation, with key players, including Alpiq, AppOrchid Inc., ATOS SE, Enel Green Power, Enphase Energy, Flex Ltd., General Electric, Hazama Ando Corporation, Origami Energy Ltd., Siemens AG, SmartCloud Inc., Vestas, and ZenRobotics Ltd.

Development type

Cloud segment: The cloud segment was the leading revenue contributor in 2022, accounting for more than half of the market share. Its dominance is attributed to the efficient management of energy storage solutions, such as batteries, through accurate prediction of optimal charging and discharging times. Furthermore, cloud-based platforms facilitate the deployment of IoT for solar energy projects, enabling real-time data analysis and remote system management.
On-premises segment: This segment is projected to have the highest CAGR of 24% from 2023 to 2032, driven by AI’s role in integrating on-premises renewable energy systems with the electrical grid, enhancing the management of excess energy and demand response programs.

Component type

Solution segment: Leading the market with nearly two-thirds of the share and showcasing a 32.0% CAGR in 2022, the solution segment benefits from AI’s ability to facilitate peer-to-peer energy trading among consumers generating excess renewable energy.
Service segment: Expected to grow at a CAGR of 24.0% by 2023, this segment includes applications like grid management, which adjusts energy use based on real-time supply fluctuations and price changes.

End-use industries type

Key revenue contributors: Utilities, as well as energy distribution, segments emerged as the primary revenue contributors as compared to energy generation and energy transmission. The utilities segment is expected to reach a 23% CAGR, while energy distribution is projected to reach a 24% CAGR. 
Utilities: AI helps utilities schedule field maintenance crews efficiently, for example, by keeping in mind travel time and employee skills. This lowers costs and improves outage response times.
Energy distribution: AI analyzes large data volumes from sensors, smart sensors, etc. to track the grid’s state in real time.

Geographical insights

Asia Pacific: This region was the top revenue contributor in 2022, accounting for two-thirds of the global market. With the highest projected CAGR of 24%, growth is driven by substantial wind energy potential in China and India. AI and machine learning (ML) optimize wind farm operations by predicting wind patterns and adjusting turbine orientation to increase productivity.

Key artificial intelligence in energy market trends
To maximize the benefits of AI in renewable energy, businesses should stay informed about key AI in renewable energy market trends:
1. 50 use cases of AI: Recent estimates by the Indigo Advisory Group indicate that AI already has more than 50 possible applications in the energy sector in the age of renewables, from grid maintenance to load forecasting. What’s more, the market for AI technology in this sector could reach a value of up to $13 billion.
2. Predictive analytics: AI and ML algorithms analyze extensive datasets, including weather forecasts and historical energy production data, to provide precise predictions for renewable energy generation. This helps operators optimize energy production schedules and integrate renewable energy into the grid efficiently. (International Journal of Informatics Visualization)
3. Grid management: AI dynamically adjusts energy flows, monitors energy storage systems, and predicts potential grid disturbances to minimize system disruptions and ensure seamless renewable energy integration. (International Journal of Informatics Visualization)
4. Predictive maintenance: AI analyzes data from sensors to predict equipment failures before they occur. This approach can reduce grid outages by up to 30% compared to traditional methods. (E.ON)
 
5. Grid resilience: $188 billion extra will cost to replace power grid equipment and transformers needed from 2020 to 2050 due to its reduced lifespan because of climate change. AI can help to avoid these extra expenses by keeping transformers within optimal operational ranges. (BNEF)
6. AI-powered EV charging management: AI solutions for electric vehicle (EV) charging management can significantly reduce carbon emissions. Flexible EV charging can lower emissions by 96% by 2040. (BNEF)
7. Optimizing renewable energy systems: AI enhances the design and operation of solar farms, wind turbines, and other energy facilities. For example, ML algorithms maximize solar panel efficiency by analyzing sunshine patterns and topography and recommending optimal deployment locations and angles. (IETE Journal of Research)
Read our article to discover how to use machine learning for solar panel fault detection to reduce downtime and lower maintenance costs.
 
8. Advanced forecasting models: AI-driven solar power forecasting models can reduce errors by 30% compared to traditional methods. (Advances in Solar Photovoltaic Energy Systems) Another example includes Google’s network model developed with DeepMind, which predicts wind power output up to 36 hours in advance, increasing its financial value by 20%. (Google)

9. Popular AI algorithms in renewable energy: Scholars identify several AI and ML algorithms used in renewable energy systems, including Support Vector Machines (SVM), Feed-forward Backpropagation Neural Network (BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Naïve Bayes (NB), Genetic Algorithm (GA), Artificial Neural Network (ANN), Radial basis function neural networks (RBFNN), and others. Among these, ANN is recognized as the best modeling technique, with accuracy reaching 94-99%. (IEEE)
Stay tuned for more insights as we delve deeper into how AI continues to transform the renewable energy landscape.
AI in renewable energy market share by region

To gain more meaningful insights, let’s review the analysis of AI in renewable energy market share by region and compare the numbers:
Asia Pacific
The Asia Pacific region leads in AI in renewable energy market growth. According to Precedence Research, the market share is projected to grow from $4.8 billion in 2023 to $44 billion by 2032, with a CAGR of 28%. This is even more promising compared to the earlier results from Allied Market Research.
Key drivers for this growth include significant electricity shortages and rising demand in countries like India. For instance, India’s electricity generation from solar and wind energy, currently at 75 watts, is expected to increase to 175 watts by 2022 and potentially reach 500 watts by 2030 with the help of AI. Additionally, the presence of key market players specializing in AI in this region fuels growth.

North America
North America had the highest AI in renewable energy market share up until 2022, driven by technological advancements. Now it’s expected to show the second-fastest growth rate, as per the Precedent Research artificial intelligence in renewable energy market forecast. This growth is attributed to the continued adoption of AI technologies and solutions, as well as the digital transformation of the energy sector. For example, AI adoption for smart home solutions is particularly popular in North America.
Europe
Europe also plays a significant role due to its strong focus on renewable energy and technological innovation, as per the Allied Market Research. The European Union recognizes AI’s potential to positively impact the renewable energy sector, especially in terms of load forecasts, demand predictions, and improved grid flexibility.
That’s why we observe a strong emphasis on sustainable and responsible AI solutions, as well as substantial governmental support and regulatory frameworks. Countries like Germany, France, the UK, and Italy lead in deploying AI technologies in renewable energy. Initiatives such as I-NERGY, which plans to implement 15 pilot AI use cases in renewable energy, are gaining traction and funding.

However, the high cost of implementing AI technologies remains a significant barrier, necessitating investment in research, expertise, and high-performance computing infrastructure.
Latin America
The prognosis for Latin America is less promising. Despite the Latin American Energy Organization (OLADE) setting a renewable energy goal of 70% by 2030, AI in renewable energy market size remains small due to a shortage of skilled professionals and high technology costs. The rapid advancements in AI also complicate understanding its full impact and potential, leading to incomplete strategies by businesses.
Middle East
In the Middle East, renewable energy, particularly solar and wind, is growing substantially. Solar power capacity increased from 1 GW in 2015 to 8.4 GW​ in 2021. The UAE is a key player, with projects like the Mohammed bin Rashid Solar Park aiming for 50% clean energy by 2050. AI optimizes energy management, enhances grid stability, and improves efficiency in these projects. However, GlobalData Plc forecasts that thermal power plants will still constitute 70% of total capacity by 2035. Political instability, infrastructure gaps, lack of finances, and low social acceptance are major obstacles.
Africa
As everywhere else in the world, the renewable energy market in Africa is growing, focusing on sources such as hydropower, wind, solar, bioenergy, and geothermal. AI improves forecasting, maintenance, and overall management of renewable energy systems. Countries like South Africa, Egypt, Ethiopia, and Kenya lead in renewable energy installations and innovations. However, Africa lags behind other regions in adopting AI in renewable energy due to a lack of open data ecosystems, quality data to support AI models, low connectivity, and limited digital skills.
AI in energy market growth factors

The growing share of AI in the renewable energy market is driven by several factors:

Increasing electricity demand: Both emerging and established regions see a rising electricity demand, together with the growing need for smart energy grids and the overall digitalization of the energy sector. (Precedence Research)
Efficiency and optimization: AI identifies bottlenecks in the energy sector, optimizing processes and contributing to market growth. It enhances the efficiency of systems like wind turbines and solar panels, making them more cost-effective and accelerating their adoption. (Precedence Research)
Sustainability and environmental concerns: The global shift towards sustainability and concerns about carbon and greenhouse gas emissions drive the development of clean energy technologies. AI improves these systems’ efficiency, further promoting their adoption. (Precedence Research)
Government initiatives: Governments worldwide support AI in renewable energy through policies and funding. For example, Great Britain recently backed eight AI projects in renewable energy with £1.7 million ($2.1 million).
Smart grids as a national policy: The US Department of Energy chose smart grids as a national policy goal, with AI serving as the system’s brain. Model-based predictive control can improve energy efficiency by 10.2% to 40%. (SAP Smart Grid Report)
COVID-19: The COVID-19 pandemic also added to the growth of the global AI renewable energy market. Energy manufacturing units were halted during the outbreak, hindering the market’s development in 2020. (Precedence Research)
Smart home solutions: AI-driven smart home energy management systems reduce household energy usage by up to 10%, providing real-time data and insights for informed decision-making and energy-saving actions. (WEF)

Empower your renewable energy business with AI Learn more
Final thoughts
AI is not just enhancing renewable energy systems — it’s transforming them into highly efficient, dynamic entities. From demand predictions to power generation and yield forecasting to smart grids, AI optimizes every stage of energy generation, consumption, and distribution, ensuring maximum efficiency. Respectively, the artificial intelligence in renewable energy market size continues to grow as AI advances, highlighting its critical role in this sector. This synergy between AI and renewable energy represents a pivotal step toward a sustainable and environmentally conscious future.
Want to take a step forward to bring your renewable energy project to maximum efficiency? With our domain knowledge of AI in the renewable energy market, Intelliarts can partner with you to enhance your energy solutions through automation and valuable data insights. Our company builds renewable energy software and end-to-end AI-driven solutions and can help you with:

Predictive maintenance for your renewable energy assets
Energy demand forecasting
Smart grid solutions
Power management systems
EV charging management solutions
EV fleet management
Electric vehicle data analytics

Looking for a tech partner?Let's talk

#### AI in Marketing Statistics & Insights
Why isn’t marketing working like it used to? Most marketing teams have the data. The tools are smarter than ever. But conversions? Stalling. CAC? Creeping up.
It’s not just for the lack of effort – marketers work harder than ever. What’s missing is clarity. And that’s exactly where AI in marketing statistics is starting to shift the equation. In 2024, 3 out of 4 marketers used at least one or more AI tools, according to a Hubspot survey.
At Intelliarts, we have experienced and witnessed how the appropriate AI tools can enable teams to transition out of general campaigns and increasing expenses to specific, high-value, data-driven marketing. But AI isn’t magic after all. It’s methodical. And it works when you know where to apply it.
In this post, we’ll show what that looks like in practice — with stats, results, and the unexpected blockers most teams usually don’t talk about.
If you’re trying to figure out how to actually make AI part of your marketing strategy — this is your starting point.
Automate Your Marketing Processes with AI ToolsIntelliarts’ custom marketing software development boosts efficiency and keeps you ahead of the competition.Explore our service
How marketers are using AI in 2025
AI isn’t a shiny new tool anymore — it’s become part of the everyday toolkit for marketing teams. AI in marketing and market size of AI tools is expected to grow significantly in the next few years (make sure to check the statistics at the end of the article). According to a recent HubSpot report, nearly 3 out of 4 marketers used at least one AI tool in 2024 — more than double the number from the year before.
From what we’ve seen across agencies, in-house marketers, and startups, the majority aren’t using AI to write full blog posts or launch entire campaigns. Instead, they’re folding it into their automated workflows in small, practical ways that save time and unlock capacity.

But here’s what’s most interesting: it’s not just about experimenting with new tech. In fact, most marketers are turning to AI to plug real gaps in their workflows. We’re talking about things like:

Replying to customer queries faster without ballooning support costs
Creating content that’s a better fit for specific audiences (without starting from scratch every time)
Analyzing data on several campaigns in real time with the help of AI, which allows marketers to identify trends and make adjustments in a short period of time
   Segmenting customers based on behavior and preferences using AI-driven segmentation and AI marketing automation statistics capabilities that are way beyond the manual approach of segmentation

Speeding up the boring stuff, such as editing drafts, summarizing reports, or generating content ideas

 
Bottom line? AI acts as a co-pilot, not the whole crew. It’s great at helping you move faster but as long as you’re behind the steering wheel.
Want to learn how to do data extraction automation with ChatGPT? Feel free to read our article.
AI in digital advertising and marketing
We usually read about the use of AI in marketing research, but its influence is much broader. AI is transforming the modern marketing strategy, whether it is smarter customer segmentation, content creation, or campaign optimization. So, what are the most important areas of AI  marketing applications that bring tangible benefits? Let us take a closer look at them along with figures and success stories.

Getting segmentation right
Figuring out who should get what message has always been a headache. Traditional lists are broad, and blasting the same email to everyone rarely hits the mark. 
Figuring out who should get what message has always been a headache. Traditional lists are broad, and blasting the same email to everyone rarely hits the mark. AI changes that by digging through all kinds of customer info, from what they’ve bought to how they interact with your emails, and sorting people into groups that make sense. Specifically, AI-powered segmentation helps teams tailor email marketing campaigns to specific behaviors and preferences without manual list-building.
Stat: Latest AI marketing stats show 20%–30% higher engagement metrics from personalization efforts (Hubspot, 2025).
In other words: people pay more attention when it feels like the message was made for them.
According to a recent study featured in Forbes, some models do exceptionally well. And the outcome? Emails that actually feel like they’re made for the reader. It’s no surprise this kind of targeted messaging can boost open rates by about a third.

Content creation
A poor strategy may put a strain on marketing, but campaigns tend to stall even when they are well-planned. It’s because teams are unable to keep up with the demand for new, relevant assets. AI can assist teams to test more variations within a shorter period of time: headlines, visuals, calls-to-action, all of which are customized to different audience slices. To speed things up even further, many marketers combine AI tools with design resources like free templates, reducing production time without sacrificing brand consistency.
Marketers can concentrate on what works in messaging, and offload the mundane and routine work to automation, instead of rewriting the same copy ten different ways. This shift reflects growing seo automation trends that enable teams to streamline workflows and focus more on strategy. Let’s check some artificial intelligence in marketing data below.
Stat: The content created with the help of AI, such as an AI Avatar Generator, can be generated five times faster than the one created manually. It doesn’t necessarily mean it’s better but this allows marketing teams to develop multiple versions of the content to target various audience groups and have a competitive advantage (Jasper, State of AI in Marketing 2025).
AI in marketing statistics show 70% of companies that use AI for their departments use AI for at least 1 function, while 62% of marketers who use AI also say it improves their job enjoyment.

Personalization
AI marketing trends show that the majority of marketing teams are aware that personalization is important, but scaling it often implies a compromise between speed and precision. And most departments don’t want to compromise precision.
Marketers can create content variants that directly address various behaviors, interests, or prior actions with AI, but they don’t have to rewrite everything each time. That makes campaigns tailored, not “templated”.
Stat: It might sound simple, but these smarter subject lines can lift click rates by around 10%, which adds up fast. For marketers fine‑tuning messaging and timing, an analytics tool for social media can offer helpful indicators about audience reactions and engagement shifts.
Guaranteed LLM apps: Our team offers custom chatGPT development service.
Stretching your ad budget further
When you’re putting money into digital ads, you want every cent to count. AI-powered bidding systems keep adjusting on the fly, learning which spots and audiences give you the best return. 

Stat: Predictive analytics can forecast customer behavior and trends, allowing businesses to anticipate needs and tailor their strategies and budgets accordingly. More advanced teams also measure incrementality to understand which AI-driven optimizations genuinely move the needle. This kind of real-time tweaking can cut your cost to acquire customers by nearly a third — down to 37%, according to marketing AI statistics from SuperAGI, making your ad spend much more efficient.
Ads that speak to people
AI doesn’t stop at segmenting and bidding — it also predicts which ads will resonate with different groups. That means your marketing dollars go toward campaigns with a better chance of connecting and converting.
Stat: 71% of marketers believe generative AI, such as Pika Labs, will allow them to focus more on strategic work, including personalized messaging (Salesforce).
Marketers see personalized ad relevance jump by at least 25%, driving stronger results without wasting budget (Microsoft).
Intelliarts can elevate your marketing and operations with AIGet consultation
AI in action: Driving ROI and marketing success
When every dollar spent on marketing is important, AI helps by making a real difference and showing clear results everywhere.

Customer acquisition costs slashed nearly in half

It’s no longer necessary to guess which leads will convert. Lead generation with AI and lead scoring studies data in detail, identifying patterns and signs that show which prospects are most promising. Because of this, companies can reduce their customer acquisition costs by 30% and attract contacts who are truly interested and more likely to become customers.

Conversions moving up

AI doesn’t only help find the right audience; it can also change messages and personalized offers depending on customer actions and preferences. When personalization is applied, users have a better time and they are more likely to become paying customers. The result? Conversion rates can jump by around 20%, helping businesses grow without having to pump more money into acquiring traffic.

Getting more from every ad dollar

AI can help you make the most of your digital advertising budget, even if it’s not huge. AI helps marketers find out where their ads are most effective and then move their budgets to those channels and audiences. With this strategy, businesses can see a 5% to 10% rise on average in the return they get from their ad spending.
Make sure to check our article on AI poc development examples if you want to learn about how to validate ideas and minimize risks with adopting new AI marketing tools.
Intelliarts on AI’s real impact across marketing
When we talk about marketing, our clients see firsthand how AI moves beyond hype. It’s about unlocking growth through smarter decisions. No fluff, just results.
Nowsite (Network Marketing Platform)
Intelliarts improved the stability of Nowsite’s CRM through code refactoring, architecture redesign, and quality-control systems. As ChatGPT and LLMs grew up, we naturally enhanced AI throughout the platform together with the Nowsite’s team: personalized AI bots, expert-trained chatbots, social media, post generation tools, auto website and email campaign builders, as well as scalable prospect search.

Key results of CRM integration: 

100,000 users in 140 countries
112% year-on-year user growth
130% revenue growth
New strategic partnerships (Herbalife)
95% user satisfaction
Support for more than 50 languages

Optimizely (CDP & Marketing Platform)
The real-time Customer Data Platform for Optimizely was built from the ground up and combines more than 60 integrations into single user profiles. Now, it has instant AI-based segmentation, predictive behavior insights, and multi-channel activation.
The platform supports millions of messages per month with real-time orchestration, which allows quicker campaign execution, consolidated data, more personalized messages, and more revenue-generating marketing.
What was the metric for success:

Real-time execution of millions of messages
Faster launches
Improved campaign performance

Need a Tailored Marketing Solution?We build an AI-driven custom marketing software.Learn more
What’s holding businesses back?
Many businesses want to tap into AI’s full marketing potential but keep running into familiar roadblocks. Here are the biggest ones — and how to overcome them.
Budget constraints
According to IMH, 34% of marketers say limited budgets stop them from experimenting with AI. In fact, AI’s benefits don’t always show up overnight. For companies watching every dollar, investing without quick returns feels risky. Start small, run tests, and grow gradually — no need for all-in bets from day one.
AI tool adoption rate
Introducing AI tools into the current marketing systems is hardly a smooth process. Integration requires time, technical expertise and, in some cases, workflow changes. If you have difficulties, it is possible to hire AI experts or consultants to make the process easier and prevent delays and frustration.
 

Nearly a third of marketers cite the slow adoption rate of the current tools as the main reason why they can’t use AI at its full potential, according to a 2024 Statista survey.
In another survey conducted by ChainStoreAge, nearly a half of marketing departments simply don’t have the technical expertise to utilize AI just yet.

At present, many marketers are underutilizing AI, but the impact of AI in marketing is already transforming the business landscape, helping leaders make data-informed decisions with greater efficiency and accuracy. From predictive insights to immersive experiences, AI is redefining how marketers handle repetitive tasks, make decisions, and connect with customers. 
– Christina Inge, author of “Marketing Analytics: A Comprehensive Guide and Marketing Metrics”
Concerns about data privacy
Privacy and fairness of data are crucial, particularly in regulated sectors. Nowadays, AI systems are being fitted with in-built safeguards, which enable businesses to maintain trust as they utilize customer data in a responsible manner. This is what the statistics say:

52% of marketers list data privacy as the top challenge for AI adoption (ChainStoreAge).
According to Ortto, new legal frameworks, such as the EU AI Act and US CCPA, are prompting more companies to deploy privacy-preserving AI features (e.g., anonymization, secure storage).

Job loss concerns
One of the biggest fears is that AI will replace marketers overnight. The reality is that the goal of AI is to augment human abilities, allow marketers to do their work better, more efficiently and more creatively. It is not substitution but cooperation.

Predictions for 2025–2030
Looking ahead, AI in marketing is set to become the backbone of most strategies, forecasted to power up to 95% of digital marketing strategies by 2030 according to Gartner research. Adoption jumped sharply from 29% in 2021 to 88% in 2025, with expectations that AI  will reach over 95% adoption by 2030 (IBM).
This shift is driven by the ability of AI platforms to analyze vast amounts of marketing data in real time, enabling smarter decisions, precise segmentation, and superior campaign optimization. Here are a few more important observations:

AI turns vast, messy marketing data into clear insights that fuel targeted segmentation and smarter campaign optimization — no guesswork needed.
In paid media, expect AI-driven real-time changes to become the rule. With machine learning, campaigns adjust instantly to what consumers do and how they interact — lifting conversion rates up to 20% (Statista) and getting more out of the marketing spend.
This means budgets are no longer static; they flex and shift alongside live data.  Under the increasing pressure of CFOs (63% of marketing leaders report increased scrutiny according to Single Grain), teams are abandoning fixed plans in favor of dynamic frameworks. These are test-and-learn models, quarterly reallocations, and setting aside up to 15 percent of budgets to in-flight optimization. This is not a trend, it is a matter of survival in the results-oriented environment.
On the privacy front, ethical AI is setting new standards for personalization. Consumers want clarity and control over how their personal data is being used (85% of consumers, according to consumer behavior PwC data), and regulators are demanding it too. AI solutions that put privacy first build trust, a crucial factor in boosting customer retention, improving customer experience, brand awareness, and brand loyalty, according to Amazon.
Looking at AI marketing statistics and current MarTech trends, marketers who marry innovation with respect for privacy will lead the pack in growth and efficiency. For example, 67% of americans are concerned with how companies use their data and this number will continue to grow. Adapting your marketing AI efforts accordingly is paramount for long-term success.

All in all, the global generative AI investment is expected to increase by 24.4% each year as businesses invest in AI-based creative, personalization, and optimization solutions (Semrush, Statista). While AI in marketing improves, market share is expected to grow too, from $28,67 billion in early 2025 to almost $100 billion by 2031.

Summing up
AI has now officially passed the hype line in marketing. No more fancy demos, it is all about results: reduced lead costs, increased engagement, and more intelligent use of current budgets. Based on what we have observed, the greatest victories are achieved by teams that do not use AI to replace people, but to refine decision-making where it counts.
At Intelliarts, we have assisted businesses in transforming their slow tools and bulky processes to lean and AI-enabled systems. In one case, a client reduced lead costs by 30%. Others experienced 100% increase in users following the implementation of AI personalization into their platform.
But all is not rosy. Budget, integration friction, and data privacy remain the impediments to teams. Firms that pilot the tools, gauge their effect early, and develop around the workflow of their teams, not the manner in which vendors sell it.
Want to know about what AI unlocks for your marketing?Talk to our team

#### AI Is Transforming Farming: 10 Real-World Use Cases
By 2034, the revenue of AI in the agriculture market is expected to reach $12.47 billion, with each AI segment showing increased revenues. Agriculture technology-as-a-service is already an entirely new, prosperous market, and the adoption of AI in this domain is growing overall. In that regard, what applications of AI should agriculture companies prioritize, and how should they approach high-tech development? 
In this post, you’ll explore the value-adding benefits of using AI in agriculture, examples of real-life AI applications in agriculture. Moreover, you’ll explore providers of corresponding software solutions in the industry. Besides, you’ll explore artificial intelligence in agriculture challenges and find out about three high-tech implementations of this technology that await humanity in the near future.
Benefits of AI in agriculture: The top advantages you should consider

Before we proceed with exploring applications of AI in farming and adjacent sectors, let’s take a look at the benefits that the usage of AI have already shown:

Increased crop yield through optimized resource allocation: AI can help analyze soil fertility, weather patterns, and crop requirements. This allows farmers to use resources like water, fertilizers, and nutrients more efficiently. One of the key benefits of AI in farming is boosting yield without increasing land use.
Reduced water wastage with precision irrigation: Monitoring soil moisture and weather forecasts with AI enables farmers to use just the right amount of water. Respectively, businesses do not only conserve water but improve soil health, which proves the key benefits of using AI for soil health analysis in sustainable agriculture.
Enhanced pest and disease control for healthier crops: Computer vision paired with machine learning are able to detect early signs of crop stress, pests, or disease. As a result, farmers can provide timely treatment.
Improved livestock health and productivity tracking: With AI systems, farmers and businesses monitor animal behavior, feeding patterns, etc. in real time. They detect health issues early if any and can then improve feed plans and routine across the herd.
Efficient use of autonomous machinery in farming operations: A good solution to reduce labor costs is to use self-driving tractors, drones, and harvesters powered by AI. These can work around the clock, provide real-time data collection, bring more precision. Thus, this automation represents the most noticeable benefits of AI in agribusiness, especially for large-scale farms.
Better decision-making with actionable insights from big data: From satellite imagery to sensor data, AI processes large datasets to be able to deliver real-time recommendations for planting, harvesting, and crop rotation. In this long run, companies benefit from smarter, more strategic decisions at every stage of the farming cycle.
Minimized operational costs through automation and AI efficiency: Like in other fields, AI brings automation to agriculture, for example, by speeding up weeding, spraying, or crop monitoring. The result? Lower labor costs and higher productivity.
Higher profitability with accurate market trend predictions: AI in agriculture also contributes to smarter financial outcomes. It helps to predict commodity prices, demand fluctuations, and buyer behavior so farmers can choose better timing for sales and supply planning. 
Sustainability improvements with eco-friendly farming practices: From reducing chemical use and carbon footprint to promoting regenerative practices, AI supports sustainable agriculture. For example, the use of AI for soil monitoring and crop planning helps farmers build healthier ecosystems.
Enhanced food security through predictive supply chain management: AI also supports global food resilience by predicting disruptions, logistic optimization, and reduced food waste.

You may explore how agriculture software development by Intelliarts reshapes your business operations and assists in other ways from our corresponding service page. 
How is AI used in the agriculture sector?

The usage of AI revolves around capabilities such as image processing, data processing, and text and imagery generation. Altogether, it derives into a range of specialized applications of AI for farming. The list includes: 
#1 Predictive analytics for smarter farming
AI analyzes historical and real-time data, such as weather patterns, soil conditions, and wheat growth, to provide farmers with actionable insights. Researchers on predictive models show the high potential impact of optimizing planting schedules, forecasting yields, and anticipating challenges like pest infestations or extreme weather. As such, forecasting of planting dates and fertilizer application rates can achieve a 15% increase in average yields over three growing seasons.
Examples:

Yield forecasting to predict harvest quantities.
Soil nutrient mapping for precise fertilization.
Predicting pest outbreaks based on weather and crop conditions.
Identifying optimal planting and harvesting windows.
Anticipating drought or flood risks for mitigation planning.

Providers:
Intelliarts, John Deere, IBM Watson Agriculture, Corteva Agriscience, BASF Xarvio, Syngenta Cropwise
There’s a range of AI models available on the market. Most of them can analyze data. Explore whether Claude AI or ChatGPT for your business can be proven to be a perfect fit for smarter farming, among other applications. 
#2 Automated AI-powered crop monitoring
AI-powered sensors and drones use computer vision to monitor crop health, detect growth anomalies, and identify issues such as nutrient deficiencies or pests. This helps farmers make timely decisions and reduce losses.
Examples:

Drone-based imaging for spotting pest infestations early.
Monitoring growth patterns and detecting underperforming areas.
Identifying nutrient deficiencies in specific crop sections.
Mapping fields to analyze irrigation effectiveness.
Spotting weed growth for targeted removal.

Providers:
Intelliarts, PrecisionHawk, Climate FieldView, Sentera, AgEagle, SlantRange
Automated crop monitoring largely depends on computer vision technology. Explore AI and computer vision in the corresponding post by Intelliarts. 
#3 Smart irrigation systems
AI-enabled systems analyze weather data, soil moisture levels, and plant requirements to automate water distribution. These systems are particularly useful in drought-prone areas and play a key role in conservation of water resources in modern farming.
Examples:

Automated drip irrigation systems based on soil moisture levels.
Smart sprinklers adjusting to real-time weather updates.
Water usage optimization to prevent over-irrigation.
Tailored irrigation schedules for crop-specific needs.
Detecting and addressing water leaks in irrigation networks.

Providers:
Netafim, CropX, Valmont Industries, Lindsay Corporation
#4 AI-powered pest management
AI models analyze pest behavior and environmental conditions to predict outbreaks and recommend mitigation strategies. Drones and cameras identify infestations early, allowing targeted pesticide applications.
Examples:

Using drones to map pest hotspots for targeted spraying.
Predicting pest migration patterns based on weather data.
Early identification of pest larvae for intervention.
Reducing pesticide overuse by applying it only in affected zones.
Tracking and analyzing pest resistance trends over time.

Providers:
Taranis, AgriSense, Pessl Instruments, Bayer Digital Pest Management
#5 Livestock health and productivity tracking
AI monitors livestock health and behavior using sensors, cameras, and wearables. It detects early signs of illness, monitors activity levels, and optimizes feeding schedules to positively impact productivity and animal welfare.
Examples:

Monitoring cattle temperature to detect fever early.
Tracking activity levels to identify breeding windows.
Detecting irregular eating patterns indicative of illness.
Optimizing feed distribution for weight gain and milk production.
Analyzing herd movement to detect stress or overcrowding.

Providers:
Intelliarts, Cargill, Connecterra, Allflex Livestock Intelligence, HerdDogg, SCR Dairy
Streamline Data Tracking in AgricultureDiscover how Intelliarts automates data processing for smarter farming.Read Case Study
#6 Autonomous farm machinery
AI-powered autonomous tractors, harvesters, and sprayers handle tasks like plowing, planting, and harvesting with minimal human intervention. These machines use GPS and sensor data to optimize operations and reduce waste.
Examples:

Autonomous tractors for precision plowing and planting.
GPS-guided harvesters for minimal crop damage.
AI-powered drones for crop spraying.
Weed-removal robots for targeted herbicide application.
Self-driving machinery for transporting produce.

Providers:
John Deere, CNH Industrial, Kubota Smart Agriculture, Raven Autonomy
#7 Optimizing supply chains
AI improves efficiency in agricultural supply chains by predicting demand, optimizing logistics, and ensuring timely delivery of produce. It minimizes food waste and reduces operational costs.
Examples:

Real-time inventory management for perishable goods.
Predicting market demand for crop planning.
Route optimization for faster produce delivery.
Detecting and mitigating supply chain bottlenecks.
Automating quality checks during transport and storage.

Providers:
Intelliarts. IBM Food Trust, FBN, SAP Agriculture, Tive, Trimble Agriculture
#8 AI market price predictions
AI analyzes market trends, weather data, and global trade patterns to predict crop prices. This allows to make informed selling decisions which offers to mitigate risks and enhance profitability.
Examples:

Predicting crop price fluctuations based on weather forecasts.
Analyzing market demand trends for optimal selling times.
Assessing the impact of global trade policies on pricing.
Identifying high-demand markets for specific produce.
Evaluating the profitability of crop diversification.

Providers:
Intelliarts, Gro Intelligence, FarmLogs, Agricultural Market Information System (AMIS), Geora, Kynetec
#9 Disease detection in crops
AI leverages image recognition, drones, and sensors to identify diseases in crops at early stages. By analyzing patterns and symptoms, it helps farmers take timely action to minimize yield loss and prevent spread. It also works in the case of automated land sampling, as revealed in a corresponding success story by Intelliarts. 
Examples:

Detecting fungal infections on leaves using drone imagery.
Identifying bacterial diseases based on crop discoloration patterns.
Monitoring soil-borne pathogens with in-field sensors.
Analyzing weather and humidity to predict disease outbreaks.
Recognizing pest-related damage in crops.

Providers:
Plantix, Taranis, Scouting AI by Xarvio, Skycision
Explore AI and big data case studies by Intelliarts to find out whether we should become your trusted agritech partner for an AI in agriculture project.
#10 Custom AI farming solutions
Custom AI solutions for agriculture are tailored to specific farm needs, integrating tools like sensors, software, and analytics to address unique challenges, enhance productivity, and maximize efficiency across operations.
Examples:

AI-driven soil testing tailored to crop requirements.
Personalized irrigation schedules based on farm size and soil type.
Custom algorithms for organic farming pest control.
Specific AI tools for hydroponic or vertical farming optimization.
Bespoke farm management platforms and dashboards for small-scale farmers.

Providers:
Intelliarts, Prospera, AgriWebb,FarmLogs, OneSoil, FarmWise
AI for agriculture: Development challenges & considerations 
Let’s proceed with revealing common AI for farming and agriculture development challenges: 

Integration with existing systems. Implementing AI often requires compatibility with legacy infrastructure, such as outdated machinery or siloed software systems. Ensuring seamless integration can demand significant customization and technical expertise.
Data quality and accessibility issues. AI relies on accurate and abundant data. Many farms face challenges in collecting reliable data due to outdated equipment, lack of IoT devices, or inconsistencies in data entry.
Upfront cost considerations. The high initial investment for AI tools, infrastructure, and training can be a deterrent, particularly for small and medium-sized farms with limited budgets.
Connectivity challenges in rural areas. Many agricultural regions and countries lack reliable internet or cellular coverage, which limits the deployment of AI solutions for agriculture dependent on cloud computing or real-time data transmission.
Workforce readiness and skill gaps. Farmers and workers may lack the technical skills to operate or maintain AI systems, requiring training programs and ongoing support.

Still, even the listed challenges should not prevent you from getting started with your AI in agriculture project. Some difficulties are imminent, regardless of the project. However, they can totally be addressed through solid planning and flawless technical execution before they can actually hurt productivity or investment returns. 
Get a Custom AI Quote for AgricultureIntelliarts delivers end-to-end AI solutions for your project.Contact Us
Here are several tips on starting with AI in agriculture from Intelliarts experts: 

Partner with experienced AI developers for smooth adoption. Collaborate with experts to customize solutions that align with your farm’s specific needs and existing infrastructure.
Start small with modular AI tools to scale progressively. Test AI on one aspect of your operations (e.g., irrigation or pest management) before rolling out full-scale implementation.
Invest in IoT devices and sensors to improve data quality. Reliable hardware ensures accurate and actionable insights for your AI systems.
Provide training programs for farmers and staff. Equip workers with the skills to understand, operate, and benefit from AI-driven technologies.

Addressing technical challenges in innovative tech development requires substantial experience and expertise in software engineering.
The future of artificial intelligence in agriculture today
Finally, we’ve reached the review of AI future trends in agriculture. Although the applications listed above may sound quite futuristic already, there’s a range of even more innovative, experiment-level directions in which AI is predicted to develop. They are the following:

Generative AI for crop improvement: Generative AI is being used to identify ideal genetic traits for breeding crops with improved yield, disease resistance, and adaptability to climate change. By analyzing large genomic datasets, AI can speed up the development of resilient crop varieties tailored to specific regions.
AI-driven regenerative agriculture. AI technologies can help farmers transition to regenerative practices by providing real-time soil analysis and crop rotation recommendations. Besides, AI systems may monitor environmental health and offer strategies to reduce inputs like synthetic fertilizers.
Vertical farming automation with AI. AI sensors can optimize water usage, nutrient delivery, and light exposure to maximize crop yields in limited spaces. This allows indoor vertical farms to operate more sustainably while reducing food miles in urban areas.
Generative AI. Large Language Models (LLMs) such as GPT or Claude can help analyze agronomic reports, weather data, and sensor inputs to deliver quick insights and recommendations to farmers or agronomists. For instance, through GPT development, agribusinesses can build an advisory system for identification of optimal sowing dates and suggestion of crop protection measures.

Discover more about using AI in vertical farming, AI-driven nutrient management, and monitoring crops in the video below:

The future of AI can significantly impact businesses, individual consumers, and broader society. Explore custom ML solutions additionally to obtain a more complete picture of how innovative technologies can boost your company. 
Read also about AI-driven vehicle damage assessment.
Final take
AI for agriculture shows strong potential to reinforce traditional processes and practices with innovative technologies. Predictive analytics, automated AI-powered crop monitoring, smart irrigation systems, AI for farm management, and livestock productivity tracking are only a small portion of what AI can offer agriculture and farming businesses. However, it’s important to be aware of technical challenges and upfront costs of implementing AI in agriculture. 
Should you need assistance with AI in farming and agriculture development, don’t hesitate to reach out to Intelliarts — a custom ML solutions provider. With more than 24 years of experience on the market, delivering on-demand solutions to businesses from across many industries, including agriculture, we can help you reinforce your business capabilities through innovative technology.

#### AI Models for 4G Anomaly Detection & Optimization
Have you known that traffic demand prediction can be achieved with the help of detecting anomalies for 4G networks? Advancements in machine learning and artificial intelligence today allow to optimize 4G networks and, thus, prevent congestion, reduce latency, and ensure balanced and efficient use of network resources.
 In this article, software engineering company Intelliarts talks about one particular use case of traffic prediction and describes our research on how to detect anomalies for 4G network optimization with the use of machine learning technology.

#### Automated Data Extraction Using ChatGPT AI
Since the release of ChatGPT by OpenAI in 2022, most people in nearly all industries have tried a generative AI tool at least once. The market size for Generative AI is expected to show a CAGR of 24.40%, resulting in a market volume of US $207 billion by 2030. The technology can come of usefulness in multiple ways. One such is extracting data from documents with OpenAI.
Read this post to discover applications and use cases of ChatGPT-based AI to extract data from documents, the challenges and limitations of the technology, and its prospects.
How can OpenAI GPT help extract data from documents?
 
ChatGPT by OpenAI is a Large Language Model (LLM) designed to understand and generate human-like text based on the inputs it gets. The technology leverages large-scale ML and Natural Language Processing (NLP), enabling applications such as generative AI in marketing, and allowing it to provide an answer to a data extraction question based on a specific query.
If you’re exploring how to automatize reading documents with GPT, you can apply this technology across a variety of document extraction tasks, such as summarizing, processing, and interpreting key data points automatically. In enterprise RAG (Retrieval-Augmented Generation) systems, integrating ChatGPT can enhance the accuracy and efficiency of document data extraction processes.
Among the top large language models, ChatGPT stands out for its advanced capabilities in document data extraction. Let’s get started with reviewing applications of OpenAI GPT in this field. This list of possible ways to use the technology includes but is not limited to:

Contextual understanding. Grasping the context in which words or phrases are used. This capability is crucial for tasks like sentiment analysis, machine translation, and dialogue systems.
Automated responses. Extracting and interpreting customer queries from emails or text-based support channels to provide automated but accurate responses. It’s also useful in knowledge management, where automated FAQs can be generated or updated.
Text summarization. Generating concise summaries of long documents, reports, or articles which aids in quick decision-making and information dissemination.
Named Entity Recognition (NER). Identifying and classifying named entities like names of persons, organizations, locations, expressions of time, quantities, and more. This is important for information retrieval, data mining, and customer service bots.
Question answering. Receiving a question and then providing an accurate and concise answer. This can be applied in domains like customer service or academic research.
Invoice processing. Extracting relevant financial data from invoices for automated entry into accounting systems.
Medical records management. Extracting and summarizing critical information from health records for easier access and interpretation by healthcare professionals.
Market research. Analyzing news articles, reports, and other documents and extracting data points like market trends, customer preferences, or competitive intelligence.
Resume screening. Sifting through resumes to extract educational background, skills, experience, and other relevant information for automated initial screening.

Automating AI chatbot data extraction, especially with OpenAI GPT, can be helpful in many ways, depending on the particular needs of businesses across various sectors.
If you wonder how to extract data from documents using ChatGPT from a technical perspective and require professional assistance, don’t hesitate to contact ML engineers from Intelliarts.
Examples of successful use of OpenAI GPT in a data extraction task
Despite generative AI technology becoming openly available not so long ago, it’s already being utilized extensively. Here are some of the real-world open AI-based document data extraction examples along with other generative AI use examples that showcase the growing popularity of the technology in the business landscape:
Viable generative analysis platform
The Viable platform allows companies to handle customer support tickets better and retrieve actionable insights from customer interactions to improve their Net Promoter Score (NPS).
They started exploiting the capabilities of fine-tuned OpenAI’s LLMs to analyze qualitative data on a scale that exceeds conventional techniques. This way they are able to help their customers to make sense of the vast amounts of data they generate through communicating to customers. The Viable’s customers claim that the generative analysis feature saves them nearly 1,000 hours per year.
Yabble feedback analysis platform

The Yabble platform allows companies to extract data from customer feedback to inform their business strategies and save time on processing data manually.
The Yabble Count, an AI tool powered by OpenAI ChatGPT, can analyze thousands of comments and other unstructured data sets, categorize them by sentiment, and organize data into themes and subthemes. Ben Roe, Head of Product at Yabble, says: “Users were loving how easy it was to finally understand mountains of data and feedback forms and have that information presented in a digestible way.”
It’s worth noting that generative AI is not the only technology capable of performing data extraction tasks. You may also utilize document extraction, non-generative AI designed to pull out specific information from documents, or rule-based document extraction software.
The detailed use cases are only a few of the numerous examples of adopted data extract with ChatGPT since companies tend not to disclose information about such matters. The scope of industries, businesses operating within which utilize ChatGPT data extraction broadly is shown in the infographic below.
You may also be interested in learning more about ML-based data extraction in insurance in another blog post of ours.Simple 
AI data extraction from PDF file

First, let’s get to know the simple process of information extraction using ChatGPT, although similar Generative AI models can also be utilized for this purpose:
#1 Upload an image to ChatGPT
Convert your PDF files to images using any available converter software for this purpose. Then, simply drag the image into the ChatGPT window, or alternatively, upload it using the provided interface. This process may take a brief moment to complete. Verify that you’ve upgraded to GPT-4 if an image button is not visible.
#2 Enter a ChatGPT prompt
Enter a prompt that will detail the model of your task. When providing a prompt to ChatGPT-4, it’s important to adhere to the best practices for interacting with Large Language Models (LLMs). This will help ensure that the model understands your request and provides an accurate and helpful response.
Prompt example: Extract the data from this image as plain text.
You may modify a prompt for complex extraction tasks or when there is a need for certain standardization:
Advanced prompt example: Extract the data from the image and format it as plain text, ensuring that all dates are standardized to DD/MM/YYYY. Additionally, identify any missing data points and provide a brief summary of any notable trends or patterns in the data.
Upon extraction, ChatGPT will provide text in the development environment as code.

#3 Verify the correctness of extracted data
Inspect the data before passing it any further and utilizing it. It’s crucial to quality-check the information extracted by LLMs, especially those designed for public use, as they may contain slight errors with significant consequences.
Here are several ways for data verification you may consider exploring:

Spreadsheet software: Use programs like Microsoft Excel or Google Sheets to paste the extracted data and visually inspect it. These tools can also be used to perform basic data checks and to compare extracted data against source data if available.
Text comparison tools: Tools like diff in Unix-based systems, WinMerge for Windows, or online services like Diffchecker allow you to compare the extracted text against the original document side by side to spot differences.
Regular expressions: Regex can be utilized to search for patterns that would indicate common OCR mistakes or to validate formats (like dates and numbers).
Machine learning quality checks: For more advanced needs, machine learning models can be trained to detect anomalies or outliers in OCR output data, flagging potential errors.

ChatGPT’s responses can be unpredictable, with varying levels of reasoning and accuracy. The system’s responses are dependent on the specific prompt and context of the user’s interaction, which means that identical prompts may yield different answers. Currently, there is no standardized approach to ensure consistent quality in its responses.
LLM-based automated data extraction with the opportunity to retrieve data through queries: RAG approach
Let’s get to know how to establish a system that is capable of extracting data from PDF and other files containing textual layers automatically and how to establish document nodes, i.e., non-relational (or NoSQL) databases, for the extracted data with the opportunity to retrieve information from it by using queries.
#1 Reading the file
First, you need to read the file from which you intend to extract information. If you’re dealing with PDF files, the PyPDF library comes in handy. This tool allows you to extract text from PDFs that contain text layers.
In the absence of text layers, Optical Character Recognition (OCR) libraries or layout Transformer models are recommended. These alternatives help in recognizing and converting images of text into actual text data.
An example of what the result of reading the file and extracting the text data is shown below:

#2 Vector index creation and splitting text into chunks
After obtaining the raw text from your file, the next step involves creating document nodes using the llama-index framework. This process is crucial for embedding the text using models like OpenAI’s embedding model.
Large files may require splitting into text chunks, which can be done using various chunking strategies or sizes to manage the data more efficiently. Besides OpenAI, other embedding models like Cohere are also viable options. There are leaderboards available to review and choose from open-source models based on performance and suitability.
An example of creating document nodes is shown in the image below:

#3 QueryEngine interface
In this stage, you need to utilize the QueryEngine interface, which facilitates Retrieval Augmented Generation. The process includes two phases:
Phase 1: Retrieving chunks relevant to an input query or task.
Phase 2: Utilizing an LLM to generate answers based on these relevant chunks.
This approach allows for more precise and relevant data extraction with ChatGPT and response generation.
#4 Experimenting and results
At this stage, you can begin experimenting with the setup and recording the results. Adjustments and optimizations can be made based on the performance and accuracy of the data extraction and response generation. Example of what data retrieval based on query looks like is shown in the image below:
The system is also capable of providing structured outputs, like JSON, which can be particularly useful for integrating the extracted data into other applications or workflows seamlessly.

Challenges & limitations of GPT AI-based data extraction
As with any other technology, using AI to extract data from documents is not deprived of complexities you should be aware of. Here is a list of the major challenges of document data extraction via ChatGPT:
Ambiguity and contextual errors. While GPT is good at general language tasks, it can misinterpret ambiguous terms, resulting in GPT not always discerning the correct meaning based on context.
Difficulty with numerical data and visual elements. GPT models are primarily text-based. So, trying to extract statistical or mathematical data as well as analyzing complex document structures like tables, spreadsheets, or forms may not be error-free. It’s also true in the cases of dealing with PDFs that include images, diagrams, or graphs. For those, you’ll need additional tools that support OCR (Optical Character Recognition) and image recognition.
Legal and ethical concerns. If you’re extracting sensitive or personal information, GPT doesn’t provide any built-in privacy safeguards. This poses risks in terms of data security, and you may face non-compliance with regulations like HIPAA or GDPR.
Read also: Ethics of Data Collection
Lack of accuracy & consistency. GPT can be inconsistent in its responses, even to the same questions about the same documents. So, it requires validation steps to ensure data reliability.
Lack of domain-specific knowledge. This mostly concerns general-purpose GPT LLM since specialized models are typically well-trained on domain-specific data. So, it’s worth understanding that the general model may not understand jargon or complex terminology.
Token limitation. Each GPT model has a maximum token limit, typically ranging from a few hundred to a couple of thousand tokens. This constrains the amount of text you can process in a single go, complicating the extraction from longer documents.
Document text extraction with ChatGPT can be recommended to utilize. However, it’s worth considering that the technology wasn’t specifically designed for this task. So, such solutions need customization and probably the use of additional instruments to become high-performance.
There are ways in which the listed challenges can be addressed through custom AI development. For example, a provider of such services can utilize a multi-modal approach, combining the benefits of different AI algorithms. Another opportunity is to add validation layers that check the accuracy and quality of ChatGPT model responses.
Partnering with the right ML service provider will ensure that technology limitations won’t be an issue during a project. Drop specialists from Intelliarts a line and let’s discuss the opportunities.
Future & prospects of document data extraction via OpenAI GPT
It’s possible to predict a growing utilization of data extraction using AI ChatGPT technology. The reason is that potentially, it can develop in the following ways:
Improved structure recognition. Future iterations could be fine-tuned to better understand structured data like tables, forms, or even coded languages, thereby making GPT models more versatile in document extraction tasks.
Ethical and legal safeguards. As AI ethics and regulations mature, built-in features for data privacy and compliance checks could become standard, mitigating legal and ethical concerns.
Integrated multi-modal capabilities. Next-generation versions could potentially integrate with OCR and image recognition technologies to handle documents with mixed media, making them more comprehensive in their extraction capabilities.
Error correction and validation. Advanced validation algorithms could be built in, either as part of GPT or as a complementary system, to automatically verify the accuracy of the extracted data.
Real-time updating and learning. If future versions can be updated in real-time or even adapted on the fly, they could offer more current and context-sensitive data extraction, addressing the knowledge cutoff issue.
Improved scalability. Advances in hardware and optimization algorithms could potentially address the token limitations, allowing for efficient processing of longer documents in one go.
Collaborative AI systems. GPT models could work in tandem with other specialized AI systems for even more effective and nuanced data extraction tasks.
When it comes to data extraction using AI, despite the technology’s limitations as of 2023, it can be significantly improved over the next decade. So, adopting generative AI today is the first step to utilizing the advanced technology to its fullest extent in the near future.
If you want to learn more about the role of Natural Language Processing in the business landscape, read another post in our blog.
Intelliarts experience with OpenAI GPT
Here at Intelliarts, we have substantial experience delivering projects involved with NLP, generative AI, and associated technologies. Our related cases include the following:
Questionnaire assessment solution for an expert network firm

A challenge was to create a solution that would help in assessing how likely people are to commit a crime. The documents for data extraction in this case were the questionnaires filled out by the examinees. The initial purpose of the questionnaires is to complete a security designation form intended to assess mental health stability.
The solution was a specialized ChatGPT prompt, created and extensively tested by the Intelliarts team. We also developed an application that can help users utilize a prompt effectively by simply uploading filled-out questionnaires and assessment rules for them. With the help of this document data extraction via OpenAI GPT solution, businesses operating in legal fields or lawyers can run a quick and effective assessment of the probability of a particular person committing a crime.
Learn more about Intelliarts custom LLM development services.
B2B job sourcing platform development

A challenge was to ensure high-quality job description parsing and matching candidate profiles with job requirements. This would help the client to streamline candidate sourcing on the platform. As an additional requirement, the solution should comply with Diversity, Equity, and Inclusion (DEI) principles.
The solution was an NLP technology-driven ML model created by the Intelliarts team. It can compare candidate profiles from job boards or social media sites like LinkedIn with the positions that companies intend to fill. It’s done by analyzing textual descriptions and extracting and matching key phrases. The solution includes a semantic search engine that supports multiple search filters, such as age, gender, racial origin, etc., and shows over 90% accuracy for gender and ethnicity detection.
Looking for a trusted software development partner? Intelliarts experts are here to discuss your needs.Contact us
Final take
Using ChatGPT AI to extract data from documents has been proven useful to a variety of businesses and is becoming increasingly widespread. The technology can help to generate short summaries, extract key information, and more. However, it’s worth keeping in mind the challenges and limitations of the technology like lack of consistency, difficulty with numerical data, etc. Anyway, the future of document analysis with ChatGPT seems promising.
Getting your project up and running is easier when you partner with the right team. With substantial experience in Generative AI, NLP, and ML, the Intelliarts team can assist you with the development of any complexity or with technical consulting.
 

#### Best Practices for Enterprise RAG System Implementation
To show the growing importance of RAG solutions, Databricks, a global data and AI analytics company, reports that 60% of their LLMs use RAG. The high potential of this technology, given the high popularity of AI in general, is what makes it worthy of good consideration on a business level. 
Currently, AI-driven solutions are of huge popularity across businesses and industries. Retrieval Augmented Generation (RAG) is exactly the technology that offers better AI performance, whether it’s a common chatbot or an enterprise-level solution for data analytics.
In this post, you’ll discover the common enterprise RAG system implementation strategy, its architecture pattern, key concepts, and related considerations. Besides, you’ll obtain an overview of the 10 best practices for utilizing and integrating RAG systems in enterprises. 
  White PaperDriving Business Efficiency with RAG Systems and LLM Integration Get your RAG white paper
Common architecture pattern of RAG systems for enterprise
RAG systems are AI models that combine information retrieval and text generation to provide more accurate and contextually relevant responses. 
RAG systems are often employed in Large Language Model (LLM) solutions, like ones based on ChatGPT or Claude AI where they aid in retrieving relevant information from large datasets or documents needed for handling complex queries. In tasks requiring up-to-date or domain-specific knowledge, such as customer support, research, and enterprise applications, RAG systems offer unmatched response accuracy improvement. 
The RAG technique is well-suited for Natural Language Processing (NLP) applications, as it combines retrieval-based methods with generation-based models. It allows for effectively harnessing the strengths of both approaches.
Different embedding models can yield varying results. Some models might capture the overall style or intent of a question, while others may focus on individual words rather than the actual meaning. Experimenting with multiple embedding models is beneficial to identify which best aligns with your intended results.
— Volodymyr Mudryi, AI, ML, and data science expert at Intelliarts. 
The infographics depicting a common pattern for enterprise architecture for RAG is shown below:

Basically, what the demonstrated architecture of the advanced Retrieval Augmented Generation model reveals is that RAG handles user queries with enhanced security, retrieval, and response generation processes. Starting from user input, it follows steps including authentication, input guardrails, and query rewriting. It utilizes components like encoders, retrieval systems, and ranking to refine information from stored data (embedding, document, history, and feedback storage). 
This enterprise RAG architecture pattern can be broken down into these RAG workflow steps:

User input and authentication
Input validation with guardrails
Query rewriting and encoding
Information retrieval from storage
Ranking and response generation
Output validation with guardrails
Final response delivery to the user

You can explore additionally what RAG models are and how they are used in another blog post by Intelliarts. 
The list of benefits of RAG system in large-scale organizations SLAs (service-level agreements) in enterprises is shown in the image below:

10 best practices for using Retrieval Augmented Generation (RAG)
Let’s have an in-depth insight into the best practices for RAG system implementation, based on real practical experience of engineers from Intelliarts:
#1 Provide context on how the output was generated
In enterprise RAG systems, providing context around generated outputs is essential for building user trust and transparency. By offering users insight into the sources of information, they can verify data accuracy, which is especially valuable in sensitive areas like compliance or customer support. Intelliarts often integrates source attribution in RAG solutions, allowing users to track data origins in real time, enhancing credibility and confidence in the system.
#2 Feed the LLM product integration data
Integrating data directly from enterprise tools like CRM or ERP systems is crucial for RAG systems to provide contextually relevant and accurate responses. This integration allows the RAG system to deliver responses tailored to the business’s unique needs. Intelliarts regularly incorporates data from internal systems, ensuring real-time insights and improved response relevancy, which is especially beneficial in customer support and operations management.
#3 Continuously evaluate the outputs to spot issues and areas of improvement
To maintain optimal performance, RAG systems should undergo regular evaluations, focusing on consistency, load handling, and edge cases. This approach ensures that potential issues are quickly identified and addressed. At Intelliarts, we apply rigorous testing methods in all custom RAG implementations, allowing us to refine systems continuously and uphold performance standards, even under varying operational conditions.
#4 Utilize dynamic data loading/updating
Dynamic data loading ensures that RAG systems operate with the latest information, preventing outdated data from affecting response accuracy. This capability is crucial in fast-paced sectors where information constantly changes. Intelliarts integrates real-time data loading into RAG systems, allowing users to interact with the most up-to-date insights, which enhances response quality in areas like finance and customer support.
#5 Maintain hierarchical document structure
Keeping a hierarchical structure in documents enhances RAG systems’ ability to retrieve relevant information accurately. Organizing content with clear headings and subheadings ensures that retrieval focuses on contextually appropriate sections. This structure not only boosts response accuracy but also enables more meaningful, layered responses, especially important for industries dealing with complex data.
#6 Implement a thoughtful chunking strategy
Chunking documents into smaller parts improves retrieval efficiency but can create challenges if relevant information is split across chunks. To optimize chunking, use document structures like sections or paragraphs and experiment with an embedding-based strategy, like chunk size or overlapping, to balance information granularity and processing efficiency, thus enhancing RAG performance.
#7 Optimize retrieval quality with relevance scoring
Relevance scoring prioritizes the most applicable data during retrieval, improving the precision of responses. By aligning retrieved data closely with user queries, RAG systems deliver more contextually accurate answers. Intelliarts employs relevance scoring in enterprise RAG solutions like retrieval-based chatbots, especially for applications requiring precise data interpretation, such as regulatory compliance and financial analysis.
#8 Establish feedback loops for continuous learning
RAG system integration with feedback loops adapts over time, learning from user interactions to enhance response quality and accuracy. This continuous learning approach is key to meeting evolving business needs. Intelliarts integrates user feedback into RAG systems, refining data retrieval accuracy based on real-world usage, which is particularly beneficial in applications with dynamic requirements like customer service.
#9 Use version control for data sources and models
Version control is critical in RAG systems to manage changes to data sources and model configurations, especially in industries requiring strict audit trails. Intelliarts incorporates version control into enterprise RAG solutions, allowing for consistent tracking and updates, which supports data integrity and compliance in sectors like finance and legal.
#10 Use ensemble techniques to reduce bias
Combining outputs from multiple models through ensemble techniques helps reduce variance, making the system more robust. Intelliarts employs this approach in enterprise RAG systems, ensuring balanced insights that are especially valuable in decision-making contexts. This technique supports unbiased results, crucial for sectors like human resources and customer support, where fair responses are essential.
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RAG strategy for enterprises and an overview of main concepts and considerations
As mentioned previously, RAG is an extension of an AI-driven system. So, should you ask yourself how to how to implement RAG in enterprise, then here’s a best practice for enterprise RAG system implementation step-by-step:

Select the AI model that’s suitable for an intended purpose
Integrate RAG mechanism 
Define data sources and structure
Establish query-response flow
Implement feedback and monitoring mechanisms
Ensure data security and compliance
Scale and maintain the system

Since the steps to implement RAG systems in enterprises are now understood and AI insights obtained, we may proceed with reviewing key concepts and considerations related to this process. They are provided in the table below:

The step-by-step process of installing RAG in your business and a consideration of enterprise RAG system integration key concepts should give you valuable insights into RAG and help you form your RAG development needs. (To ensure your RAG deployment is secure and compliant, enterprises can also leverage AI pentesting tools to automatically detect vulnerabilities, misconfigurations, and exposed data, providing an added layer of protection.)
Intelliarts experience with RAG 
Here are several case studies by Intelliarts revealing RAG business development cases:
Analysis report generation with RAG-based system success story
The Intelliarts team is working on an ongoing project for a customer who handles massive amounts of data to provide actionable insights into China’s political economy. They have an extremely large data pool in PDF format. Currently, analysis is provided by domain experts, and a single request may take as long as an hour. So, there’s a crucial need for automation and process improvement. Here’s how this case unfolds: 
Challenge: To create a RAG-enhanced AI solution that will be able to execute all sorts of summarization and data retrieval procedures. A development difficulty, in this case, is an inappropriate structuring and storage of the original data pool and lots of unfitted data. 
Solution: A custom RAG system that contains guardrails and a query refiner, retrieval functionality, and a vector database. It can generate responses to users’ queries, return sources, and tailor responses based on images contained in PDF files from the storage.
Result: Currently, the time it takes to respond to user’s request is shortened from hours to minutes. Still, since the project is ongoing, we look forward to refining the RAG system and bringing even more value.
Looking for a trusted tech partner to provide you with AI development services? Let Intelliarts address your business needs. 
RAG-enhanced AI recommendation system development  success story

The Intelliarts team partnered on a project to develop an AI-powered recommendation system, the “step suggester,” to assist students in an AI learning platform for cybersecurity coursework. This tool offers real-time guidance to students facing challenges, improving course completion rates.
Challenge: Create a RAG-enhanced AI system that analyzes student actions in a virtual environment, delivering tailored recommendations to reduce dropout rates. The main challenge was structuring and analyzing large volumes of unorganized data while incorporating cybersecurity insights.
Solution: Intelliarts built a PoC for an AI assistant combining machine learning, LLMs, and programmatic algorithms. The system identifies points where students get stuck and provides actionable steps, leveraging custom algorithms and LLM responses for each task.
Result: The AI agent reduced support time and improved course completion rates. The PoC confirmed the solution’s feasibility, setting the stage for full-scale deployment across 700 course tasks.
RAG-enhanced ChatGPT chatbot creation success story

The Intelliarts team developed an AI chatbot data extraction solution using ChatGPT to help an NGO quickly retrieve information on gun safety from their extensive knowledge base.
Challenge: The NGO’s growing data made it challenging for employees to find relevant information efficiently. They needed a solution to extract data quickly from their knowledge base.
Solution: Intelliarts created a ChatGPT-powered chatbot that performs data extraction and analysis on user queries, leveraging RAG methods. The chatbot was trained to focus on gun safety topics and provide references to support its responses. This system integrates both textual and tabular data handling, using advanced ML features for accurate and contextually relevant answers.
Results: The chatbot reduced information retrieval time from hours to minutes, saving significant operational time. It also aligned with the NGO’s tone, allowing employees to streamline their workflow, improve productivity, and improve decision-making with the RAG system. The AI assistant is seamlessly integrated into the NGO’s system, providing a user-friendly, personalized solution.
Final Take
RAG systems offer enterprises advanced capabilities in data retrieval and context-aware response generation, making them invaluable across applications like customer service, content generation, and compliance. By following best practices, businesses can unlock the full potential of RAG systems. Understanding what is the best available RAG implementation for your specific needs ensures that you harness the technology’s full potential. 
For enterprises looking to leverage RAG, partnering with experienced AI developers can streamline implementation and drive impactful results aligned with business goals. Here at Intelliarts, we have substantial in-house AI development expertise. Over the span of 24 years on the market, we have developed a number of fully-fledged AI solutions, including ones reinforced with RAG systems. 
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#### Big Data Strategy in 2026: The Intelliarts Framework for AI-Ready Data Architectures
Modern-day businesses generate vast amounts of data in their operational processes. In 2027, the global big data analytics market size will exceed $100 billion, depending on the sector. Statistics show that the upward trend is quite solid, and more businesses are likely to adopt strategic big data in their digital environment.
As evident, most companies today do not have any problems with data volume. They struggle with making it usable for AI. At this stage, a successful big data strategy is about building an AI-ready data foundation that can continuously generate insights and predictions.
At Intelliarts, we approach this challenge through a structured methodology, designed to align business goals, data architecture, and AI capabilities into a single, scalable system. This playbook helps enterprises move from fragmented data initiatives to production-ready AI ecosystems.
What is a big data strategy?
A successful data strategy is what links business goals with technology solutions. You can get an insight into how the data is approached across different levels of data strategy from the infographics below.

In simple terms, what is a data strategy? This is a clear plan for how an organization will collect, manage, and analyze data and derive value from it to meet specific business objectives. Based on our years of experience in machine learning and data science, Intelliarts specialists distinguish the next components of a data strategy:
#1 Business goals and objectives
Specific, measurable objectives provide a framework for what it’s intended to achieve with the data. Examples may include:

Reducing operational costs
Improving customer satisfaction
Identifying new revenue streams
Optimizing supply chain processes
Discovering business opportunities

Setting clear goals will guide data collection, analysis, and utilization efforts.
#2 Prioritization of use cases and applications
A big data business strategy can cover an extensive range of use cases and applications, such as:

Predictive maintenance
Sales forecasting
Customer segmentation
Social media monitoring
Workforce analytics

From our clients’ experience, we know that it’s crucial to prioritize the listed to maximize ROI. Focus first on projects that yield quick wins or solve pressing issues. Subsequent phases can tackle more complex or long-term projects.
#3 Data governance policy
A robust data governance policy ensures that data is consistent, trustworthy, and used responsibly. It outlines who has ownership of data, establishes data quality benchmarks, and details the procedures for data access and sharing.
A strong governance policy can minimize the risks of data silos, inconsistencies, and compliance issues, which can impede the successful execution of a big data strategy.
#4 Data privacy and security needs
Data privacy and security are not optional but integral to a big data strategy. Complying with regulations like HIPAA, GDPR, or CCPA is just the starting point.
A comprehensive strategy will go beyond compliance to include data encryption, regular technical audits, and incident response plans.
Failing to adequately address privacy and security concerns can result in financial penalties and reputational damage. For example, understanding the CSPM definition helps organizations strengthen their cloud environments and proactively address compliance requirements as part of a holistic data security strategy.
#5 Data architecture
A well-designed data architecture is the backbone that supports data storage, integration, and retrieval. This involves choosing between data lakes, data warehouses, or hybrid solutions based on your specific needs.
Data architecture also concerns itself with data flow, scalability, and ensuring that the technology stack aligns with both current and future business requirements.
Here’s the sum up of all 5 components:

Since the big data strategy composition is clear, let’s find out why your data strategy might fail.
Why traditional data strategies fail in 2026
Forbes informs that 95% of enterprise AI initiatives deliver zero measurable return. Most failures of AI projects owe to poor data quality, lack of data governance, and disconnected data ecosystems. At the same time, research says that only 24% of companies describe themselves as data-driven, and only 2% name data literacy among their priorities.
The root problem is architectural. Traditional data strategies were built for reporting and dashboards, and they don’t consider real-time decisioning, ML pipelines, or scalable AI systems. This results in:

Fragmented data silos that block model training
Inconsistent data quality that erodes trust in AI outputs
Rigid architectures that cannot support real-time or adaptive systems
Governance models that lag behind regulatory and security demands

In 2026, these limitations become a significant barrier to AI adoption and competitive advantage. And to move forward, organizations should shift from simple data management to AI-ready data architecture and replace static strategies with adaptive, architecture-first methodologies.
If you’re unsure where to start, think about data strategy consulting that can help you identify gaps and define a clear path forward.
The Intelliarts big data strategy framework
At Intelliarts, we treat big data strategy as an AI-readiness journey. Based on our experience delivering data platforms across industries, we’ve developed a structured data strategy framework that outlines the essential big data strategy steps for building AI-ready systems and allows companies to effectively handle complex data sets and analytics at scale.
Now when you have an understanding of what essentials are good to have from the beginning, let’s proceed with learning the composition of a big data analytics strategy.

Step 1: Assess data maturity
From our experience, most organizations overestimate their data readiness. The first step is to evaluate your current data landscape, including data quality, infrastructure, accessibility, and data governance. This involves analyzing existing systems, identifying gaps, and understanding whether your data can realistically support advanced analytics or AI use cases.
Step 2: Align data with business goals
Integrating data strategy with your business strategy ensures that data initiatives contribute to the achievement of business goals. A well-defined strategy to deploy big data technology in a firm should involve reviewing business objectives, aligning data metrics with business KPIs, prioritizing data projects, and ensuring cross-departmental alignment. This integration also involves defining strategies for utilizing big data in corporate growth.
When the two strategies are well-aligned it allows for effective decision-making and a higher likelihood of success.
Step 3: Design scalable architecture
Traditional architectures are one of the biggest blockers to AI.
At Intelliarts, we design architectures that support scalability and real-time processing, as part of our data architecture strategy services. Depending on the use case, this may include data lakes, data warehouses, or hybrid models. But the key of your big data architecture is that it grants seamless data flow and future-proof infrastructure.
A well-designed architecture becomes the foundation for integrating analytics and machine learning into everyday operations further.
Step 4: Select the right data stack
From our project experience, selecting the right tools depends on your specific data needs, scalability requirements, and team capabilities. This includes data processing frameworks, storage solutions, and analytics platforms.
Consider making an effort to:

Select appropriate analytical tools. The Intelliarts team recommends AWS SageMaker, Azure Machine Learning, H2O Driverless AI, IBM Watson Studio, DataBricks, Minitab, and Qlik AutoML.
Leverage predictive analytics and other ML technologies like descriptive analytics, data mining, business intelligence, etc.
Select appropriate visualization tools. Here our team usually chooses Power BI, Tableau, Grafana, and Amazon QuickSight.
Define your data storage strategy. Decide between data lakes, warehouses, or lakehouse architectures (e.g., Databricks Lakehouse) based on data variety, latency requirements, and AI use cases.
Standardize data processing frameworks. Use distributed processing engines like Apache Spark or Flink to handle large-scale data transformations and enable real-time pipelines.
Plan for real-time vs batch processing. Clearly separate use cases that require streaming (e.g., fraud detection, IoT) from those suitable for batch processing (e.g., reporting, historical analysis).
Ensure interoperability and integration. Your stack should support integration across APIs, ETL / ELT pipelines, and third-party systems to avoid data silos.
Incorporate MLOps capabilities. We recommend using tools like MLflow, Kubeflow, or native cloud MLOps services to help manage model lifecycle, versioning, and deployment at scale.

Whether it’s cloud-based ecosystems or specialized tools, the general idea is to build a cohesive modern data stack that supports both current workloads and future AI initiatives.
You may be interested in learning more about big data value, data analytics, and machine learning in another of our blog posts.
Step 5: Enable AI & LLM capabilities
At this stage, we move to AI in production, as a key focus area in AI data strategy consulting. Without it, even the most advanced data platforms remain cost centers that produce reports instead of driving decisions and automation.
At Intelliarts, we help companies integrate machine learning pipelines, predictive analytics, and LLMs into their data ecosystems at this step. This includes setting up environments for model training, deployment, and continuous improvement.
If we sum up all our experience, data strategy best practices here include:

Establish model success criteria upfront. From our experience, it works great to define your metric earlier, whether it’s revenue increase, cost reduction, or process time savings.

Design for production latency requirements. Consider whether your models need real-time inference, near-real-time responses, or offline scoring. This directly impacts your system design.
Control data and model drift. Set up monitoring to detect changes in data distribution or model performance before they impact business outcomes.
Ensure explainability and transparency. Especially in regulated industries, models should provide interpretable outputs to support compliance and stakeholder trust.
Design for LLM and AI integration. Include vector databases (e.g., Pinecone, Weaviate), embedding pipelines, and orchestration frameworks (like LangChain) if generative AI is part of your roadmap.
Plan for reuse and scaling of AI assets. Treat models, pipelines, and components as reusable building blocks rather than one-off implementations.

Step 6: Governance & scaling
As systems grow, governance becomes a critical success factor.
A strong governance framework is a prerequisite for data quality, security, and compliance while also enabling controlled access across teams. This includes defining ownership, establishing data standards, and implementing monitoring processes.
Equally important is thinking about scalability. To strengthen your enterprise data strategy, make sure that your cloud data platform strategy can evolve with growing data volumes along with new use cases and changing regulatory requirements.
How to implement a big data strategy
While the Intelliarts framework explains how to build a big data strategy, the next question is how to execute it in practice. Below is a practical implementation approach we typically follow in real projects.
Timeline
A big data strategy is not a one-time initiative. Take an iterative approach for better results:

As for project time estimations, it’s worth noting that small-scale projects take about three months from start to finish. Average to large-scale projects take six months or more to complete.
Some factors affecting the duration of data strategy development and execution project are:

Team size
Volume and complexity of data
Potential data issues and technical debt
Infrastructure specificities
The need to comply with regulatory or other specific requirements

It can be recommended to consult with a specialized agency to get accurate estimations of the potential project’s time and costs.
Team structure
Implementing a big data strategy requires the strong expertise and experience of a diverse team. The list of irreplaceable specialists in this project includes: data engineers, data scientists, data analysts, big data and AI architects. For professionals working in these roles, a clear tech career growth plan can help them expand their technical depth, build cross-functional skills, and progress toward senior or leadership positions.
Additionally, the project may also require the involvement of cloud specialists, business analysts, data governance and compliance officers, domain experts, DevOps engineers, and project managers. Big data consultancy with a potential services provider can help with obtaining more accurate information.
In smaller teams, roles may overlap. In enterprise environments, clear ownership is critical to avoid bottlenecks.
Modern data architectures explained
From our experience, choosing the wrong architecture is one of the most expensive mistakes companies make in data strategy. This is actually why many companies rely on data platform consulting. Many of our clients have invested heavily in tools, but without the right architecture in place, they struggled to scale AI initiatives or deliver real-time insights.
Below is how we approach modern data architecture 2026 in practice:
Data mesh vs data lakehouse vs data warehouse
In our projects, architecture choice always depends on data complexity, scale, and organizational structure. And it’s never trends.

Data Warehouse works best for structured data and BI reporting, often powered by platforms like Snowflake in modern cloud environments. However, according to Deloitte, traditional warehouses struggle to support advanced analytics and AI workloads due to limited flexibility.
Data Lakehouse is what we most commonly recommend today. It combines the flexibility of data lakes with the performance of warehouses, which makes it suitable for both analytics and machine learning. Reports show that Databricks (a well-known lakehouse architecture) grows over 55% year over year, as organizations seek unified platforms for BI and AI.
Data Mesh is more an organizational approach than technology. It works well for enterprise data strategy where teams need ownership of their data domains. However, be aware that without strong governance, mesh adoption can lead to fragmentation, as we’ve experienced firsthand.

Pro tip: If you’re building for AI, start with a lakehouse approach and introduce mesh principles only when your organization is ready for distributed ownership.

Real-time vs batch data processing
Over 60% of organizations today are increasing investment in real-time data, yet many struggle to justify ROI due to unnecessary complexity. Based on our experience, Intelliarts ML team recommends not to overengineer your real-time systems where it’s not needed:

Batch processing remains more cost-effective and sufficient for use cases like reporting, trend analysis, and periodic model training.
Real-time processing works better for time-sensitive scenarios such as fraud detection, dynamic pricing, IoT, or personalized user experiences.

Somehow, companies often default to real-time because it sounds more advanced. However, the truth is that you don’t always need it, but this approach does add to complexity and cost. So better start with batch-first architecture. And introduce real-time pipelines only where speed directly drives revenue or user experience.
Streaming pipelines
Confusion also appears when it comes to streaming. At Intelliarts, we choose streaming pipelines to ensure continuous data flow and immediate insight generation. This can be especially useful in high-velocity data environments.
Typical components we implement include:

Event streaming platforms (e.g., Apache Kafka)
Stream processing engines (e.g., Flink, Spark Streaming)
Real-time data ingestion and transformation layers

When we implement streaming pipelines, our ML team always does best to:

Build resilient pipelines with retry, replay, and fault-tolerance mechanisms
Maintain strict schema governance across streams
Continuously monitor latency and throughput
Avoid blending batch and streaming without clear boundaries

How big data strategy powers AI & LLM systems
As partly mentioned, your big data strategy also impacts the efficiency of AI and LLM initiatives. In short, there are 4 critical ways of how big data implementation can affect your AI and LLM systems:
1. Data availability and quality
As you know, AI systems are only as good as the data they rely on. A strong strategy ensures consistent, clean, and well-governed data pipelines. This is especially critical for data strategy for LLMs, where poor data quality leads to hallucinations and irrelevant outputs.
In practice, this includes:

Standardized data ingestion and validation
Data lineage and quality monitoring
Centralized, reusable datasets

2. Scalable infrastructure for AI
AI depends on architectures that support continuous training, real-time inference, and high-load processing. All this is possible only with a solid data foundation.
3. Context for LLMs through internal data
LLMs deliver real business value only when connected to proprietary data. This requires structured pipelines for retrieval, embedding, and secure access.
Pro tip: We observed that some companies fail when they adopt LLMs, but don’t connect them to the data that actually matters.
4. AI-ready data components
Modern AI systems rely on specialized data infrastructure that must be designed upfront:

Feature stores to standardize and reuse features across ML models
Vector databases to enable semantic search and LLM retrieval
RAG (retrieval-augmented generation) pipelines to combine LLMs with internal knowledge sources
Production data pipelines to continuously feed and update AI systems

Gartner predicts that in 2026, over 80% of enterprises will have used generative AI APIs or deployed production-ready LLM applications. So this makes AI readiness a critical competitive factor, and companies need to treat data strategy as the foundation for AI.
Are you looking for a trusted provider of big data consulting services? Reach out to Intelliarts and let’s discuss cooperation opportunities!
How to choose the right data stack
When you choose your data stack, make sure you match it with data maturity and use cases. Judging from our projects, the wrong stack can bring risks like unnecessary complexity or rising costs. Vice versa, the right one leads to scalability, interoperability, and faster time to value.
To help you out, here is a data stack comparison table where you can get started. But better to take advantage of big data consulting services with professionals:

As big data experts, we can say that the real differentiator lies in how these decisions translate into business outcomes. This is actually where theory meets execution so let’s look at a few case studies to explore how all these principles work in practice.
Real-world big data strategy examples
As mentioned, we at Intelliarts have substantial experience helping our customers create a big data strategy and bring it to life. Our related big data strategy example includes the following:
DDMR data pipeline case study

Challenge: The challenge was to help collect data on Firefox and optimize the efficiency of the big data collection process. Since the company was selling clickstream data, the success of its sales department largely depended on how the business handled big data.
Solution: The solution was a browser application and an end-to-end data pipeline which included data collection, storage, processing, and delivery. The architecture was designed for high scalability and performance, enabling efficient handling of massive clickstream datasets.
Outcomes: We grew from zero to operating clusters with 2000 cores and 3700 GB of RAM. During the cooperation, our customer multiplied its annual revenue and won a base of loyal customers.
Real estate case study

Challenge: The challenge was to predict homeowners who are going to sell a house before the property is put up for sale. The company was tracking more than 1400 property and demographic data attributes and had a huge dataset with more than 60 million data records, and it was necessary to ensure the raw data was processed effectively.
Solution: The solution was the re-uploading of data to Amazon S3 data storage, completing data transformation, and establishing data processing with the help of AWS Glue. This created a reliable foundation for machine learning model development.
Outcomes: The model that can operate on the processed data can predict at least 70% of homeowners who are going to sell their houses. This result is a lot above the average in the industry.
Looking for a trusted provider of technology consulting?Reach out to Intelliarts experts.Let's talk
Big data strategy readiness checklist (Downloadable)
Before investing in big data architecture or tools, or AI initiatives, it’s critical to understand one thing: are you actually ready? This checklist will help you decide.

How to use this checklist? If you answer “no” to more than 30% of these questions, your organization is not yet ready for full-scale AI adoption. Focus on closing the gaps first.
Also, don’t forget to save this checklist for later – grab it as a ready-to-use PDF for your team.
Common challenges and how to overcome them
Building a big data strategy is a challenging task by itself. So there is a lot of stumbling blocks that you’ll face on your way:
1. Poor data quality
Challenge: Inconsistent, incomplete, or unreliable data.
How to overcome: Implement data quality checks at the pipeline level. Assign data ownership and enforce standards continuously.
2. Fragmented data ecosystems
Challenge: Data silos across systems.
How to overcome: Invest in integration-first architecture (lakehouse, unified pipelines). Prioritize interoperability over adding new tools.
3. Overengineering infrastructure
Challenge: Teams build overly complex real-time or distributed systems.
How to overcome: Adopt a “start simple, scale when needed” approach. Use batch processing by default and introduce complexity only when justified by business value.
4. Governance, security, and compliance
Challenge: Regulatory requirements (GDPR, HIPAA, etc.) and security concerns slow down adoption.
How to overcome: Embed governance and security into architecture from day one, including access control, data lineage, and auditability.
5. Difficulty moving AI to Production
Challenge: Many organizations get stuck at experimentation and fail to operationalize models.
How to overcome: Implement MLOps practices early, standardize deployment processes, and integrate AI outputs into real business workflows.
Also, we advise not to try to solve everything at once. From Intelliarts experience, it works better to take an iterative approach: start with high-impact use cases and scale systematically.
Final take
A big data strategy is a comprehensive plan for a business to approach its data and build a foundation for AI.
From our experience, success doesn’t come from isolated tools or one-time initiatives. It comes from a structured approach that aligns business goals, modern architecture, and AI capabilities into a single, scalable system. This is exactly what the Intelliarts framework is all about.
Whether you’re starting from scratch or evolving an existing data ecosystem, the key is to build with AI in mind from day one. At Intelliarts, we combine over 24 years of experience with deep expertise in data engineering, analytics, and AI to bring your data project to success.
Planning to take on a big data project?Talk to our experts

#### Big Data: Exploring the 5 V’s with Analytics and Machine Learning
In today’s data-driven world, businesses and organizations are constantly looking for ways to leverage the vast amounts of data at their disposal to make informed decisions and gain a competitive advantage. This is where big data analytics comes in.
In this article, we’ll explore the how’s and why’s of big data analytics, looking at the 5 V’s of big data and the benefits and challenges of using big data in business.

#### Business Guide to Smart Grid Technologies
Global electricity demand is expected to rise by 62% by 2050, and up to $3.4 trillion is projected to be invested in grid modernization by 2040. That’s exactly why businesses should no longer hesitate to invest in smart grids, since not reaping the strategic and financial advantages of smart grid investments means lagging behind. 
In this post, you’ll explore beneficial opportunities that arise from investing in smart grid management systems. Besides, you’ll delve into technology implementations in detail. After that, you’ll discover use cases of using smart grid infrastructure in business and familiarize yourself with related challenges and solutions. 
Smart Grids in a Regulated EV LandscapeLearn how EV charging regulations across the EU, US, and UK are shaping grid-ready infrastructure and smart charging strategies.Read the white paper
Why businesses are increasingly investing in smart grid technologies
Above everything, businesses need to understand the rationale behind energy projects. Here’s a list of positive and negative drivers that make companies invest time and effort into smart grid technology: 
Positive drivers or opportunities:

Competitive differentiation
Integration of renewables and EV infrastructure
Operational resilience and business continuity
Data-driven decision-making
Demand response participation
Scalability of energy infrastructure
Enhanced customer and stakeholder trust
Access to new revenue streams

Negative drivers or risks: 

Rising energy costs and demand for real-time efficiency
Regulation, Environmental, Social, and Governance (ESG), and decarbonization pressures
Infrastructure aging and blackout risk
Grid instability due to decentralization
Cybersecurity vulnerabilities
Penalties and fines for inefficiencies or emissions
Rising stakeholder expectations

The examples of businesses most interested in the smart grid integration are provided below: 

Discover more about smart grids from our blog post on balanced energy grid as well as the opportunities and challenges related to its implementation. 
Core smart grid technologies
The smart grid not only carries electricity but also information, enabling intelligent and efficient power use, integrating energy sources like solar panels and electric vehicles. — Robin Chase, co-founder of Zipcar
Smart grid infrastructure doesn’t exist separately from other domains and technology clusters. Here are tech solutions that compose smart grids or work with them in close connection: 
#1 IoT for real-time monitoring and control
In smart grids, the Internet of Things (IoT) connects physical grid components, such as smart meters, transformers, and circuit breakers, to digital systems. These devices continuously collect, transmit, and sometimes act on data (like voltage, current, or temperature), enabling grid operators to monitor conditions and remotely control infrastructure in real time.
Examples of technology implementation:

Smart meters installed in homes to provide two-way communication of usage data.
Sensors on transmission lines that detect fault locations and trigger automated rerouting.
IoT-enabled thermostats and EV chargers supporting demand response programs.

Value-adding benefits of technology implementation:

Faster fault detection and restoration
Greater visibility across transmission and distribution networks
Improved load balancing and real-time grid optimization

Need a Custom IT Solution?The Intelliarts team is here to help.Explore our service
#2 AI/ML for predictive analytics and optimization
AI and ML in smart grids analyze large datasets to predict trends, identify patterns, and automate grid decisions. These technologies help anticipate equipment failures, optimize power flows, and improve forecasting for energy demand and renewable generation. Along with other advantages, it adds a lot to effective energy management in smart grids.
Examples of technology implementation:

Predictive maintenance systems that detect anomalies in transformer operation.
ML models forecasting energy demand based on weather, time-of-day, and user behavior.
AI-driven energy management platforms that adjust grid parameters in real time.

Value-adding benefits of technology implementation:

Reduced downtime and maintenance costs
Increased grid reliability and efficiency
More accurate planning for renewable integration and peak loads

To communicate these insights to non-technical stakeholders, teams often auto-generate executive visuals and slide narratives using Powerpoint AI, turning forecasts and KPIs into decision-ready decks in minutes.
#3 Edge and cloud computing
Edge computing processes data locally at the point of collection (e.g., at substations or smart devices), enabling real-time decisions without waiting for cloud-based analysis. Cloud computing, in its turn, handles complex analytics and large-scale data storage, offering centralized control and long-term insights. To get a detailed look at how these technologies are applied in real-world scenarios, the AWS reInvent guide highlights both edge and cloud computing approaches.
Examples of technology implementation:

Edge devices that automatically isolate a fault and reroute power without central input.
Cloud-based energy analytics platforms that analyze fleet-wide data from distributed energy resources (DERs).
Hybrid systems combining edge-based protection with cloud-based forecasting.

Value-adding benefits of technology implementation:

Lower latency and faster response to local events
Scalable infrastructure for grid-wide coordination
Continuous service even during network disruptions

Looking for custom renewable energy software development services? Don’t hesitate to reach out to Intelliarts. 
#4 Secure data communication & interoperability
This technology ensures that devices across the grid can securely exchange data and function together, even if built by different vendors. Secure communication prevents tampering, while interoperability ensures compatibility through standardized protocols.
Examples of technology implementation:

Use of IEC 61850 protocol for substation automation.
Encrypted VPN tunnels for smart meter data transmission.
Interoperable platforms integrating EV chargers, solar inverters, and utility back-end systems.

Value-adding benefits of technology implementation:

Strong protection against cyber threats and data breaches
Flexibility in adding new grid technologies
Cost savings through reduced integration effort and vendor independence

#5 Advanced Metering Infrastructure (AMI)
AMI solutions refer to systems that measure, collect, and analyze energy usage data through smart meters and enable two-way communication between utilities and customers. AMI supports real-time billing, outage detection, and dynamic energy pricing, helping to modernize energy consumption management.
Examples of technology implementation:

Smart electricity meters that transmit real-time usage data to the utility every 15 minutes.
Remote meter disconnection/reconnection and firmware updates.
Customer portals showing real-time consumption and pricing for load shifting.

Value-adding benefits of technology implementation:

Time-of-use pricing and customer energy awareness
Reduced operational costs through remote services
Outage management and load forecasting accuracy

Explore smart grid software development services by Intelliarts — a trusted technology partner. 

Use cases of smart grids in business
Smart grid technology is becoming more well-known due to increased publicity. But while enthusiasts delve into technicalities, businesses should look more at the actual application of smart grids in real life, and ways to monetize it. Let’s explore use cases of smart grids in business, then.
#1 Smart charging optimization use case by Intelliarts

Company background:
A software engineering firm that delivers custom cloud and IoT solutions, helping businesses in energy, mobility, and tech optimize operations and reduce costs.
Use case details:
Built a smart power management system for EV Connect. This enabled scheduling power limits and managing energy usage via APIs and dashboards, which helped avoid peak-time loads. The system included dynamic load balancing, real-time monitoring, and compliance with the UK smart charging regulations.
Outcomes:

Reduced electricity costs through off-peak charging
Avoided infrastructure expansion by balancing load across chargers
Improved grid stability and regulatory compliance
Enabled data-driven decisions with analytics and reporting

Explore the smart power management system success story by Intelliarts. 
#2 Self-healing grid use case by Electric Power Board of Chattanooga (EPB)
Company background:
A municipal utility in Tennessee, known for pioneering the U.S.’s first gigabit internet city and one of the most advanced smart grids.
Use case details:
Deployed a 9,000-mile fiber-optic network with 1,400+ automated switches and smart meters across 600 mi². The system enables real-time fault isolation and self-healing of the grid, paired with energy usage monitoring via the myEPB platform.
Outcomes:

55% reduction in outage minutes
19 million customer outage minutes avoided annually
4.7 million lbs of CO₂ emissions avoided per year

Explore EPB grid use case
Read about the usage of smart grid in IoT projects from another of our blog posts. 
#3 Community microgrid use case by ComEd

Company background:
A major electric utility serving Illinois and a subsidiary of Exelon, known for its smart grid and microgrid innovation.
Use case details:
Implemented the Bronzeville Community Microgrid, integrating solar, battery storage, and smart controls to serve over 1,000 customers. It connects with the IIT microgrid for a resilient cluster system.
Outcomes:

Full grid-independent operation during outages (islanding success in May 2024)
~75% energy cost reduction for local housing authority
Enhanced energy resilience in a critical urban area

Explore community microgrid use case
There will be embedded energy storage [at every layer of the grid]. It will be a collection of microgrids. — Sumit Bose, a senior engineer at  GE Global Research
Key challenges when integrating smart grid solutions
 
Finally, let’s get through the entire range of possible challenges of integrating smart grid technologies in business:
#1 Legacy systems and interoperability gaps
Older infrastructure was not designed for smart grid energy efficiency and the newer standards in the industry. That’s why it often lacks compatibility with modern smart grid technologies, causing integration delays and high upgrade costs. Another part of the issue is that many of the legacy systems can’t communicate with IoT sensors or real-time control software.
Solution: The issue is typically tackled by using a combination of infrastructure reconstruction and software improvement measures. For example, developers use middleware, APIs, and protocol converters to bridge old and new systems. Modular upgrades help avoid full replacements while enabling partial smart grid integration.
For an additional insight, explore our success story on OpenADR protocol implementation.
#2 Data overload and analytics readiness
Smart grid infrastructure generates vast amounts of data from sensors, meters, and controllers. Without proper analytics tools, this leads to information bottlenecks and poor decision-making.
Solution: Among all the measures that can be utilized in that regard, dev companies usually utilize cloud platform-based storage, real-time data pipelines, and edge computing. An extra advice for businesses here is to properly train teams in big data tools like Spark or Kafka to ensure that they can handle smart grid-scale analytics correctly.
Read our blog post on best practices for using data pipelines to obtain more information. 
#3 Regulatory and standardization inconsistencies
Smart grid regulations may differ across regions. As such, interoperability standards for the U.S., which are IEEE 2030 and global standards, i.e., IEC 61850, data privacy standards,  Distributed Energy Resources standards, and others, may not be interchangeable. Standard misalignment causes project delays and additional compliance costs. That’s what makes it hard to deploy uniform smart grid management solutions. 
Solution: Among possible solutions, it’s recommended to use smart grid systems with a modular architecture that supports multiple regional standards and protocols. Additionally, it’s always useful to use abstraction layers or adapters to decouple business logic from local compliance rules. Also, don’t hesitate to involve IoT regulatory experts during the planning phase to identify mandatory certifications.
#4 Talent and technical expertise shortages
Businesses often face difficulty finding professionals with experience in grid automation, IoT integration, and energy analytics. This shortage leads to delays, suboptimal system performance, and over-reliance on external vendors.
Solution: Develop a strategic workforce plan that combines short-term vendor partnerships with long-term internal capability building. It’s absolutely crucial to consult with external experts for implementation and knowledge transfer. You may combine the latter with launching in-house training programs focused on smart grid technologies, if the size of your business and the magnitude of the project allow. Always strive to collaborate with only a few, or better, only one trustworthy vendor of smart grid management and integration services. 
Note. The listed challenges do not serve as a warning against using the smart grid infrastructure. Despite the fact that any technology implementation comes with risks, the smart grid benefits make the project worth the investment in most scenarios. If you feel unsure about smart grid integration, don’t hesitate to reach out to Intelliarts for a technology consulting session. 
Final take
Smart grid technologies offer a strategic path toward efficiency, resilience, and innovation in energy management. From IoT to AI, cloud, and interoperability solutions, each component plays a role in enabling intelligent, scalable power systems. Businesses investing in smart grid infrastructure not only future-proof operations but also unlock new revenue models, meet regulatory demands, and stay competitive in a dynamic energy landscape. Now is the time to act on smart grid potential.
Here at Intelliarts, we have more than 25 years of experience creating tailored AI, ML, smart grid, and software solutions. With substantial expertise, a senior team, and a 90% customer return rate, we are ready, willing, and able to roll out your best smart grid infrastructure project.
Looking to Boost Your Energy Infrastructure?Let’s power up your next project together.Reach out to us

#### Choosing the Right Emission Management Software Solution
Emissions management in agriculture is both an operational necessity and a regulatory requirement today. FAO reports that agrifood systems emitted about 16.5 gigatons of CO2, which is roughly 32% of global anthropogenic emissions. Agricultural companies that track CO2 emissions can’t afford to have their data scattered across spreadsheets. This will quickly break their scale and auditability.
In this post, you’ll review the emissions management software options your business has. Besides, you’ll find out how to choose the best solution, what tech features to look out for, and what the industry-best emissions software practices are. 
Common approaches to emission management in agriculture
Emissions management in agriculture is the structured process of measuring, managing, and reducing greenhouse gas emissions across farm operations and supply chains.
While the process itself may be clear, what software to use to manage emission management efforts is not that easy to figure out. 
For starters, let’s take a look at three approaches to utilize emissions management solutions that are specifically designed to tackle this green energy challenge:
#1 Off-the-shelf emissions management platforms
These are vendor products that provide a complete, ready experience. They typically include standard methodologies, factor libraries, dashboards, and reporting outputs. They aim to help teams move from spreadsheets to consistent calculations with minimal configuration. 
Pros

Fast rollout, often in weeks
Lower upfront spend compared with a build
Standardized outputs that support consistency across sites
Vendor maintenance, updates, and security practices
Easier onboarding for non-technical users
Common integrations are often available out of the box

Cons

Limited flexibility for unique farm practices or niche commodities
Data model constraints can force awkward workarounds
Custom analytics can be expensive or impossible
Integration depth may stop at file uploads or basic APIs
Methodology changes depend on the vendor roadmap
Risk of paying for unused modules as needs expand

#2 Custom-built emissions management solutions
This approach delivers a purpose-built platform designed around your operations, data sources, and governance rules. It gives full control over data structures, factor management, audit trails, and decision logic. It fits complex agribusinesses and sustainability teams that need high data confidence, advanced analytics, or differentiation in Monitoring, Reporting, and Verification (MRV). 
Pros

Full control over data and calculation logic
Strong fit for unique workflows and internal KPIs
Better support for advanced analytics and scenario planning
Clearer ownership of data quality rules and approvals
Easier to evolve with program changes and new regulations
Can become a strategic asset across the value chain

Cons

Higher initial investment and longer lead time
Needs strong product ownership and stakeholder alignment
Requires ongoing maintenance and platform operations
Data integration work can exceed expectations

Explore precision ag tools in the industry in another blog post by Intelliarts. 
#3 Hybrid or modular approach
This combines a core platform or analytics layer with custom integrations and tailored data pipelines. Teams keep vendor strengths for reporting and governance, while they build the agriculture-specific pieces that matter most. This approach fits organizations that need speed but cannot accept shallow integration or rigid workflows.
Pros

Balanced speed and flexibility
Less build scope than compared to a fully custom
Better integration quality than pure off-the-shelf
Phased rollout across regions and business units
Reduces risk by reusing proven components

Cons

Shared accountability across vendor and custom layers
Integration complexity still requires strong engineering expertise
Vendor constraints remain in the core modules
The architecture discipline is essential to avoid fragmentation

Each approach reflects a different level of control over data, methods, and workflows. See additional comparison factors in the table below and an in-depth insight:

How to choose the right approach for your agri-business
Choosing between software options is never simple. To assist you, the Intelliarts team has prepared a decision framework that can guide you through the nuances of selecting software, while simultaneously helping with a software implementation scope: 
Step 1: Define your sustainability goal and the outputs you must produce
Input: Primary objective (compliance, certification, carbon credits, supply chain transparency), buyer or regulator requirements, deadlines.
What to do:

Pick the single primary goal for the next 12 months.
Write down required outputs (reports, certificates, verification files, supplier disclosures).
Mark non-negotiables (audit trail, evidence handling, factor transparency, multi-entity reporting).

 Outcomes:

A clear “definition of done” for the software.
A shortlist of must-have capabilities.

 Decision rule:

Standard, template-based outputs → off-the-shelf may fit.
Verification workflows or program-specific rules → lean hybrid or custom.

Need assistance with your best emission management project? See how Intelliarts helped our customer with a data automation solution in a success story from our portfolio. 
Step 2: Assess data maturity and system interoperability
Input: List of data sources, e.g., Facility Management Systems (FMS), Enterprise Resource Planning (ERP)/procurement, logistics, energy, suppliers, livestock systems, spreadsheets, sample exports, API access notes.
What to do:

Inventory the sources that feed calculations needed for evaluating emissions management.
Rate each source as reliable, usable with cleanup, or unreliable.
Document how data moves today (manual copy, CSV export, API).

Outcomes:

A data maturity snapshot and gap list.
A realistic view of phase-one automation potential.

Decision rule:

Mature data and simple interoperability → off-the-shelf is viable.
Mixed quality that needs normalization → hybrid is usually best.
Highly fragmented data plus strict governance needs → custom or hybrid.

Need help evaluating your tech capabilities for an emission management dev project?Let Intelliarts experts assist.Contact Us
Step 3: List your must-integrate systems and required integration depth
Input: Systems that hold emissions-relevant data (FMS, ERP/procurement, logistics, energy, IoT/sensors, supplier tools) and what you need from each.
What to do:

Mark systems that are critical for the first reporting cycle.
Count them and note API vs export-only.
Label each integration as upload/export, one-way API, or bi-directional sync.

Outcomes:

An integration count and complexity signal.
Clarity on whether you need simple connectivity or real automation.

Decision rule:

0–2 systems, mostly exports → off-the-shelf is often enough.
3–5 systems, mixed APIs/exports → hybrid or custom are usually best.
5+ systems or bi-directional sync required → custom.

Step 4: Map workflow complexity and audit-readiness requirements
Input: Operating scenarios across regions, commodities, and programs; assurance needs; evidence and approval expectations.
What to do:

List distinct workflows you must support (by region, commodity, program).
Count different rule-sets (methodologies, eligibility, evidence requirements).
Confirm audit-readiness needs: traceability, factor governance, change logs, evidence queues.

Outcomes:

A “workflow uniqueness” and governance requirement set.
Early clarity on whether standard tools will force workarounds.

Decision rule:

One standard workflow and basic governance → off-the-shelf can work.
Some variations and moderate controls → hybrid is safer.
Exception-heavy workflows or audit-grade control → custom or hybrid.

Step 5: Set your time-to-value window
Input: Deadline for first outputs (reporting cycle, program launch, buyer request) and delivery capacity.
What to do:

Set the go-live date for a usable baseline.
Define the phase-one scope versus the phase-two scope.
Align timeline with realistic delivery constraints.

Outcomes:

A phased scope that prevents overbuilding.
A feasibility filter for the approach.

Decision rule:

Weeks → off-the-shelf.
3–6+ months with strategic upside → hybrid or custom.

Step 6: Assess ROI and make the final choice
Input: Current manual effort (hours per cycle), staffing costs, error/rework rate, audit risk, growth assumptions, and cost estimates for each option.
What to do:

Quantify today’s costs: collection, reconciliation, and reporting cycle time.
Estimate benefits: efficiency gains, risk reduction, growth enablement.
Compare 2–3 year TCO across options (subscription, integration/build, maintenance, internal time).
Choose the approach using the triggers below.

Outcomes:

A defensible decision and rationale you can communicate.
A phased investment plan tied to measurable gains.

Decision rule:

Off-the-shelf if goals are standard, integrations are light, workflows are simple, and speed drives ROI.
Hybrid if you need vendor speed but must add custom pipelines, validation, or agriculture-specific workflows to scale.
Custom if deep integrations, unique workflows, audit-grade controls, or proprietary analytics drive ROI.

Important note! If you choose an off-the-shelf approach, you still need to undergo the process of selecting emissions management software. Alternatively, with hybrid and custom options, you need a trusted technology vendor to assist you throughout the project. 
See the entire decision framework visualized below:
 
Key features to look for in emissions management software
Let’s return to the challenge of choosing between off-the-shelf, custom, or hybrid software. As mentioned, off-the-shelf software already has lots of features available. This may sound like a positive trait. It sometimes is. However, having lots of functionalities you don’t use results in either overload or excessive payments for subscriptions. After all, you are oftentimes charged for features you don’t use, as they are included in one or another subscription tier anyway. 
In most agriculture projects we work on, emissions reporting becomes difficult long before calculations start. The main challenge is collecting data from different systems and making sure it is consistent enough to trust. — Yurii Bondarenko, Software Engineer at Intelliarts
In the case of custom and hybrid approaches, you are more flexible in choosing what you want to include in your configurable air emissions software. But only if you are partnering with a trusted provider of software engineering services that can tailor your app exactly to your needs, of course. 
So, here’s a suite of key features of emissions inventory software you are most likely looking to have included in your software:

Automated data capture (IoT, APIs, satellite imagery). Pulls activity data from devices, enterprise systems, and external sources to reduce manual collection errors. Includes validation rules, timestamps, and source tagging to keep inputs audit-ready.
Centralized data management and visualization dashboards. Consolidates all emissions-related data into one governed layer with consistent units, site structures, and permissions. Dashboards provide drill-down views by scope, facility, category, and time to support decision-making.
Greenhouse Gas (GHG) calculation engines compliant with GHG Protocol. Calculates Scope 1, 2, and 3 using transparent, repeatable methodologies aligned to the GHG Protocol. Supports emissions factors governance and standardized outputs for carbon accounting.
Reporting automation (Environmental, Social, Governance (ESG) solutions, customer data platform, carbon credit readiness). Automates the reporting process with reusable templates, period snapshots, and evidence packs for audits. Produces structured exports aligned with corporate sustainability disclosures and common questionnaires.
AI-driven insights for emission reduction strategies. Identifies hotspots, anomalies, and key drivers, then recommends actions based on patterns in usage and operations. Helps prioritize initiatives by estimated reduction impact, cost, and feasibility.
Audit trail, data quality controls, and compliance governance. Tracks who changed what and why, with approvals and versioning to make reviews defensible. Enforces compliance through role-based access, validation checks, and documented methodologies across the organization.

Aside from the provided list of key functionalities, you may opt for some additional features and capabilities. Alternatively, you may need to know what to exclude from a subscription of the configuration list. Either way, here’s a list of features that are usually considered optional even in the best emissions management software options:

Built-in carbon credit marketplace access
Blockchain-based traceability modules
Native sustainability training/e-learning hub
Integrated project management suite (tasks, Kanban, OKRs)
Real-time digital twin of facilities
Mobile-first field data capture app
Automated assurance-provider workspace (audit collaboration room)

Finally, let’s move on to some important highlights of custom emissions management software. After all, a tailored app is, oftentimes, a much better solution to your mission management needs, even with any team size or budget constraints respected.
But before you find out why custom solutions often win in agriculture, take a look at the example of a software solution by Intelliarts. 

Challenge: An agrotech company had to submit 3–5 years of field records, which took weeks, caused inconsistent data, and limited enrollment scale for a carbon-farming program.
Solution: Intelliarts co-developed a “Lightning” workflow that auto-prefills boundaries, crops, and key events using remote sensing plus the client’s Best Guess API, then guides users through validation. 
Results: Data entry dropped to minutes, filed acres rose nearly 3× to 2.6M+, and a program hit 80% retention.
Explore grower data collection success story.
Why custom software development often wins in agriculture
Agriculture rarely fits into a neat, standardized data model. Emissions drivers sit across soil and field operations, yield outcomes, livestock performance, energy use, transport routes, supplier inputs, and weather variability. Custom software development addresses this reality directly. It gives you a flexible foundation that matches how your farms and partners actually operate. Another best thing about custom options is that they are as advanced as any other top AI tools in agriculture. 
Audit requirements usually arrive earlier than advanced analytics. Teams first need confidence in their data before they can use it for optimization.— Alexander Barinov, a managing partner at Intelliarts
Here’s an overview of custom development value-adding benefits mapped to the capabilities needed for running emissions programs at scale:

In practice, Intelliarts helps agtech teams operationalize this approach without overbuilding. We design resilient data pipelines that normalize noisy farm inputs and implement quality checks that increase confidence in emission tracking. When clients are ready, we extend this foundation with AI models for forecasting and optimization, plus ESG insight dashboards that connect operational signals to sustainability targets.
Don’t hesitate to explore agriculture software development services by Intelliarts, a trusted software development agency. 
Final take
To move from reporting to actual emission reductions, match your software approach to your goals, data reality, and timeline. Off-the-shelf tools suit standardized outputs and light integrations. Hybrid works when you need vendor speed plus custom pipelines and validations. Custom wins when audit-grade governance, deep interoperability, and analytics drive ROI. 
Here at Intelliarts, we have more than 25 years of experience providing top-tier software solutions, including automation and management apps for green, agriculture, and EV companies. With a 90% customer return rate and 92% of senior staff engineers in-house, we are ready, willing, and able to contribute to your best emissions management software.
Build your best emissions management softwareOur team has expertise in AgriTech software development.Explore more

#### Claude vs ChatGPT: Which AI Model is Best for Your Business?
Over the past few years, businesses have been rapidly adopting AI to automate and improve their processes. About 64% of businesses expect AI to increase productivity, so the hopes put into AI are huge. Claude AI vs ChatGPT are some of the most commonly utilized options on the market for such purposes. But which one should you use for your particular business objectives? 
In this post, you’ll discover key differences between Claude and ChatGPT, explore their use cases, and find out in what scenarios the Intelliarts team, as software dev experts, would use one or another model. 
Overview of Claude and ChatGPT

Claude AI, developed by Anthropic, is an advanced large language model designed for natural language understanding and interaction. 
Claude AI is named after Claude Shannon, a pioneering figure in information theory. The platform is tailored for business use, particularly in customer service automation, data analysis, and content generation, providing real-time responses. It uses reinforcement learning from human feedback (RLHF) to improve over time, making it a flexible and adaptable tool for a wide range of industries.
At the same time:

ChatGPT, developed by OpenAI, is a large language model designed for natural language processing and understanding.
ChatGPT is based on the GPT (Generative Pre-trained Transformer) architecture and is capable of generating human-like text based on input prompts. ChatGPT can engage in conversations, provide detailed explanations, assist with content creation, and automate tasks like customer support and data analysis. It continuously improves through reinforcement learning and feedback.
Claude vs ChatGPT: Key differences & similarities
Let’s proceed with Claude vs ChatGPT comparison across eight factors below:

So what’s different?
While both Anthropic’s Claude vs OpenAI’s ChatGPT are powerful LLMs, they differ in many areas. Let’s discuss the top 5 differences between these most popular LLMs:

Multimodality and ecosystem features: ChatGPT offers broader, “all-round” support for features like image generation, voice input/output, plugins, and browsing. For instance, a user can upload an image and generate a caption for it or modify the image using ChatGPT. Claude has a more limited functionality for now and fewer integrations. For example, you can upload an image there for analysis, but it still doesn’t have the full image generation.
Long-form reasoning strength: Claude works with long-form prompts much better than ChatGPT. It’s often praised for handling deep, long-context inputs and producing focused outputs. ChatGPT will need more guidance in this case.
Use cases: A specific AI agent can perform better depending on a use case. In brief, Claude is believed to be most useful in creative writing, coding, and logic-intensive tasks. ChatGPT is more generalized across different types of tasks, mostly because of its diverse integrations.
Integrations: ChatGPT has more benefits in this case, with the ability to integrate with many third-party tools, such as Slack, CRMS, and Google apps. Claude is suitable for businesses with deeper contextual work and stricter data handling. Respectively, it has fewer integration options (though still more than enough!).
Data privacy and enterprise control: Claude puts stronger emphasis on data privacy and safety, offering options that limit data retention and model training on user inputs. In contrast, ChatGPT, especially the free version, may use conversations for training unless users opt out or use enterprise plans.

What’s similar?
We’ve talked about the core differences between Chat GPT vs Claude. Now let’s move to top 5 similarities:

Both are advanced LLMs: Both are AI agents built on powerful LLM architectures that can handle tasks like summarisation, translation, information retrieval, etc.
Text generation and conversations: ChatGPT and Claude are designed to generate natural-language responses in the most efficient way, including engaging in dialogues, answering questions, brainstorming content, etc.
Technical tasks: Both models perform well when it comes to supporting programming- and logic-oriented tasks such as writing code, debugging, or generating tech documentation. They still might differ in strength and style.
Multi-purpose: ChatGPT and Claude are general-purpose AI assistants rather than narrowly specialized tools, although they perform better in particular use cases.
Need for human oversight: Both platforms work as assistants and cannot replace humans. This means that despite their potential, ChatGPT and Claude share limitations, such as AI hallucinations, factual errors, and context limits. Thus, users must verify their outputs.

You may be interested in exploring AI development solutions to automate processes.
Model lineup and access
Before diving into pricing and performance, it’s worth understanding what model families each platform offers.
Claude family (chat & API)
The Claude model family by Anthropic offers a tiered approach to capability, enabling users to choose models that suit their business workloads These could be as lightweight interactive chats as heavy agentic workflows via API. The current lineup includes models:

Haiku — fastest and most cost-efficient
Sonnet — balanced speed and capability
Opus — highest capability

Developers can access these models through the Claude Developer Platform and via cloud partners, such as Amazon Bedrock or Google Vertex AI with supported regions outlined in Anthropic’s documentation.

ChatGPT family (chat & API)
The ChatGPT ecosystem now features the GPT-5 family of models, offering different performance levels and pricing for both chat and API users. Through the ChatGPT interface and API, users can access models optimized for diverse workloads, from enterprise-grade reasoning and coding to lightweight automation and summarization. Each model tier differs based on cost, speed, and context-size requirements.
OpenAI’s dev documentation and model-selection guide help choose the appropriate model for their workload, whether it’s research, coding, or interactive chat.

Availability by region, free vs paid tiers
Note that access to both Claude AI vs ChatGPT varies by region and tier. For example, ChatGPT’s Free tier still offers strong features, for example, in terms of model access or image uploads, but with stricter usage limits.
ChatGPT has also introduced region-specific plans, such as “ChatGPT Go” in India. These offer expanded access at lower cost but are geographically restricted.
On the Claude side, model availability via cloud partners shows regional coverage across multiple AWS/Google regions for models like Claude 3 Opus, Claude 3 Sonnet, etc. If a business operates in a regulated region like the EU, it’s better to check whether the model family supports the region.
Pricing and value
With both model families now outlined, the next logical step is to look at what it actually costs to use them. Below is an overview of how Claude and ChatGPT are priced in 2025, from monthly chat plans to API token rates and enterprise-grade total cost of ownership (TCO).
Chat tiers: Monthly plans and limits
Both AI agents offer subscription-based chat access tiers that scale from free usage to enterprise-level plans.
For ChatGPT:

The Free tier ($0/month) gives access to GPT-5 for everyday tasks, with limited messages, uploads, image generation, and context size
The Plus plan ($20/month) offers faster responses, higher usage limits, and expanded GPT-5 access, including agent mode, deep research, and Codex. It also grants image and limited video generation through Sora 1
The Pro plan ($200/month) provides unlimited GPT-5 access with the fastest response time and expanded reasoning. It also has full Sora 1 integration and priority access to new experimental features
The Business and Enterprise plans ($25-30/user/month) offer secure collaborative workspace, larger context windows, and more advanced security features

For Claude users:

The Free tier ($0/month) offers access to Claude 3 Haiku with standard response speed and limited daily usage
The Pro plan ($20/month) unlocks more capable Claude 3 Sonnet, which grants faster performance, higher usage limits, and priority access during peak hours
The Max plan ($100/month) expands on Pro with X5 or X20 more usage, higher output limits, memory across chats, and early access to new Claude features
The Team and Enterprise ($25–$30 per user/month for standard seats and $150 for premium seats) provide access to the full Claude 3 family, including the high-end Opus model, along with increased rate limits, collaboration features, admin controls, and dedicated support

API pricing
Because of its impact on scalability and cost efficiency, API pricing is more relevant for developers and product teams that build custom integrations. Both Claude vs ChatGPT-4/GPT-5 use a token-based billing model, where input (prompt) and output (response) tokens are charged separately together with optional image or tool-use fees.
Review the chart to compare Claude vs Chat GPT pricing in more detail:

Total cost of ownership: Teams & enterprise
When evaluating enterprise-scale adoption of Anthropic Claude vs ChatGPT, it’s also important to look beyond per-seat subscriptions and token rates and focus on TCO:

For ChatGPT, the Team plan starts at $30 per user/month (or $25 with annual billing) for small-to-mid sized teams. For enterprise businesses, it’s around $60 per user/month with a minimum of 150 seats. 
For Claude, the Team tier costs $30/user/month. Meanwhile, enterprise pricing is negotiated and not widely published.

For both platforms, TCO includes user seats, API consumption, premium models, larger context windows, integrations, governance and compliance features.
Performance by use case
Now, let’s review particular use cases of Claude AI vs GPT-4/GPT-5 in business:
Writing & editing
When to choose ChatGPT
ChatGPT is a go-to solution for businesses that need to generate creative ideas and content. Whether it’s coming up with marketing strategies, creating social media posts, or drafting compelling advertising copy, ChatGPT’s creativity and language capabilities make it a powerful tool for agencies and companies in marketing and advertising. 
Example: BuzzFeed famously employs ChatGPT to produce quizzes, articles, and personalized content, streamlining content creation while maintaining engagement.
Since ChatGPT employs Natural Language Processing (NLP) technologies, you may be interested in exploring additional NLP use cases benefits and examples.
When to choose Claude
Claude is particularly effective for summarizing long texts, refining complex reports, or generating documentation. Its balanced tone and contextual understanding make it suitable for professional writing, such as legal summaries, technical reports, and compliance materials.
Coding & dev workflows
When to choose ChatGPT
If you need help with code generation or debugging, ChatGPT is well-suited for the tech industry. It can generate code snippets, explain programming concepts, and assist in debugging code, making it a valuable tool for developers looking to save time or work through complex coding problems. So, if you’re wondering what is best for coding, ChatGPT or Claude, ChatGPT is the preferred choice in this case.
In more advanced discussions, developers may also touch on topics like anti debugging techniques when analyzing how software behaves under different inspection or testing scenarios, which can deepen overall technical understanding.
Example: GitHub Copilot, powered by OpenAI’s models, assists developers by suggesting code and even debugging issues in real time.
When to choose Claude
Claude’s Sonnet and Opus models are designed for complex reasoning and multi-step logic. This could be helpful for backend development, automation scripts, and code review. Moreover, through Claude Code (available in Pro and Team plans), users can create, execute, and test scripts directly.
Research & synthesis
When to choose Claude
Claude truly excels in research-heavy contexts. Its large context window enables it to handle long, complex documents, and this is ideal for legal reviews, policy analysis, and academic or financial reports. It’s particularly effective in synthesizing multiple data sources and extracting insights from unstructured information.
So, here’s where Claude AI is the best at:

Large-scale document processing: Handles long, complex documents efficiently with its high token limit, ideal for contracts or compliance reviews
Summarization of extensive content: Provides concise summaries from large bodies of text
Data-intensive research and analysis: It’s well-suited for synthesizing large, complex datasets or scientific reports

Example: JP Morgan’s COIN (Contract Intelligence) system uses AI to analyze thousands of legal contracts.
When to choose ChatGPT
While Claude specializes in document comprehension, ChatGPT works best for idea generation and exploratory research. Use it for brainstorming topics, generating structured outlines, or simplifying technical information for broader audiences.
Multimodal (images, audio, video)
When to choose ChatGPT
OpenAI’s GPT-5 and GPT-4o families include strong multimodal support, allowing users to upload and interpret images, generate visuals, and even create video. This makes ChatGPT a natural choice for marketing, design, and education industries that rely on visual or audio inputs.
When to choose Claude
Claude supports image and text analysis but does not yet offer full multimodal generation. It can interpret diagrams or visual data within documents, making it suitable for analytical rather than creative visual tasks.
Data analysis & agents
When to choose Claude
For sectors that rely on data-intensive research and analyze large datasets, such as financial services and banking, Claude AI shines. It can help with processing market reports, financial statements, and regulatory guidelines, making it easier for analysts and compliance teams to interpret vast datasets and gain actionable insights. This is particularly valuable for financial marketing agencies aiming to craft data-driven campaigns that align with market realities. So, if you’re asking, is Claude AI better than ChatGPT to analyze financials? The answer is likely yes.
Example: BlackRock uses AI tools, including Claude AI, to analyze financial data and optimize investment strategies.
When to choose ChatGPT
For businesses that handle large volumes of customer interactions, ChatGPT’s conversational capabilities are unmatched. It’s an excellent choice for developing chatbots or virtual assistants to automate customer support, answer inquiries, and provide personalized service in real time. E-commerce, in particular, benefits from this as it aims to improve customer experiences with scalable, efficient service.
Example: Instacart uses the ChatGPT plugin to power a personal grocery shopping assistant. 

When it comes to the listed applications, Claude vs GPT has specific context when to be used. Here’s what our expert has to say about Claude AI vs ChatGPT comparison: 
Both models are strongly capable, and it’s hard to tell for sure whether Claude AI or ChatGPT should be used for NLP, development, automation, or other tasks. I find it exciting to apply both models to discover which one is a better fit.  — Andry Shutka, ML and big data expert at Intelliarts
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Benchmarks & real-world tests
While user experience often defines which model “feels” better, quantitative benchmarks help reveal where Claude and ChatGPT prove best technically. Let’s review the results of each.
Reasoning and math
Claude 3 Opus 4.1 leads in structured reasoning and complex mathematical logic, especially when multiple intermediate steps or long-context calculations are needed.
GPT-5 still shows better results when it comes to symbolic reasoning and improved chain-of-thought planning. In enterprise settings, this means that GPT-5 provides more precise results for forecasting, optimization modeling, and financial simulation.
Coding benchmarks
When measured on programming datasets such as HumanEval+, MBPP, and LeetCode-Hard, GPT-5 remains the strongest coding model. Plus, it’s architecturally aligned with Codex and has integrations with developer tools like GitHub Copilot and VS Code, which makes it more practical for everyday coding tasks.
GPT-5 is the smartest coding model we’ve used. Our team has found GPT-5 to be remarkably intelligent, easy to steer, and even to have a personality we haven’t seen in any other model. — Michael Truell, Co-Founder & CEO at Cursor
Claude Sonnet 4.5, meanwhile, performs well on algorithmic reasoning and code explanation tasks, often producing cleaner and more readable code.
Long-context retrieval
Claude has a clear advantage here. Its architecture supports context windows up to 200K–500K tokens, enabling it to process hundreds of pages of material in a single pass. This makes it more reliable for enterprise document analysis, compliance auditing, and legal discovery.
GPT-5 narrows the gap with 256K tokens on Pro and Enterprise. However, for uninterrupted reasoning over large documents, Claude Opus remains the top performer.
Creative writing prompts
Both models produce high-quality creative text, but their tone differs. Go for ChatGPT when you need storytelling, marketing copy, and dialogue generation. The model leans toward expressive, story-driven outputs with natural voice and emotional range.
Claude Sonnet 4.5 tends to deliver structured and balanced creative outputs. It’s ideal for thoughtful essays, professional scripts, or long-form narratives. In blind user tests, Claude ranked top 1 for narrative consistency, while GPT-5 ranked top 1 for originality and humor.
Context window and memory
The ability to remember, reference, and reason over large amounts of text determines how effectively a model can perform long-running or multi-document tasks. This refers to memory and context capabilities of the two models.
Max context size and practical limits
Claude’s context window remains one of the largest in the industry. This is 200K tokens across all models and paid plans, with Claude Sonnet 4 supporting up to 500K tokens for users on Enterprise plans. This allows users to process the equivalent of several hundred pages of content in a single query.
In contrast, OpenAI’s GPT-5 supports context windows of up to 256 k – 400K tokens, depending on the plan and interface. While its raw context limit is slightly smaller than Claude’s, GPT-5 introduces improved context management and retrieval efficiency. This allows the model to focus on the most relevant information within long inputs and makes it more practical for multi-document reasoning or exploring codebase.

Document-grounded chat workflows
Claude’s long-context strength translates directly into document-based workflows. Users can upload contracts, policies, research papers, or entire codebases and receive consistent, context-aware analysis.
In enterprise environments, Claude’s focus on grounding responses within uploaded materials helps reduce AI hallucinations and improve factual accuracy. Anthropic reports that introducing the “Citations” feature in Claude reduced source hallucinations and formatting issues from 10% to 0% and increased references per response by around 20%.
ChatGPT offers a different approach. It relies on retrieval-augmented memory, referencing documents stored within a workspace rather than keeping them all in active context. Its “projects” and “data analysis” tools make document-driven chats more modular. In other words, users can switch files, generate summaries, or visualize data interactively without re-uploading.
Session continuity and workspace memory
Session continuity refers to how long a model can “remember” within a single active chat. If to compare Claude vs GPT:

For Claude, session continuity is handled entirely within the context window. The model retains all previous turns in a session (up to 200K–500K tokens). Once the chat is closed, memory resets unless you’re using Anthropic’s enterprise connectors.
For ChatGPT, continuity was initially limited to a single browser session or chat thread. But when the “projects” and “memory” features were introduced, it’s now extended to several days or weeks. “Projects” can act as persistent workspaces where the model can “remember” documents, former outputs, and instructions.

In contrast, workspace memory extends beyond one session. In 2025, both ecosystems moved from static session memory toward persistent workspaces, but they differ philosophically:

ChatGPT introduced Workspace Memory, where users can view, edit, or delete saved context. This gives users precise control over what the model remembers. This memory is opt-in by default and now powers “projects” and “tasks”, benefiting users with ongoing collaboration or multi-day workflows.
Claude takes a different, privacy-first approach. It doesn’t store user data between chats unless integrated via enterprise APIs. Within a single session, its memory still remains highly consistent due to extended context.

Safety, controls, and governance
As AI systems become more deeply embedded in business workflows, their reliability and security measures matter as much as performance. Both platforms invest heavily in security and compliance, though their approaches differ again.
Guardrails, style guides, and red-teaming
Both Anthropic and OpenAI have established multi-layered safety pipelines to grant responsible model behavior. Anthropic employs a Constitutional AI framework, training Claude models to follow predefined ethical principles rather than relying solely on reinforcement learning. This approach improves transparency and reduces biased or unsafe responses. Recent reports show that Claude models reached AI Safety Level 3, reflecting Anthropic’s focus on measurable safety thresholds.
OpenAI maintains internal red-teaming networks to probe for bias, misuse, and emergent risks. This means that a group of experts deliberately stress-test models to uncover risks before each major release. Besides, the platform relies on Preparedness Framework, which defines risk tiers and capability thresholds for model deployment. It’s also considered one of the industry’s most formal safety governance systems.
Prompt injection and data-leak protections
GPT-5 introduces stricter sandboxing and Model Spec security layers to prevent prompt leaks through connectors, GPTs, or browser tools.
Similarly, Claude models are trained with an explicit “refusal criterion” that detects and resists indirect prompt injections. Enterprise users can apply additional moderation or AWS Bedrock Guardrails for policy enforcement.

This way, Claude’s refusal training tends to make it more conservative in edge cases. Meanwhile, ChatGPT provides more granular control for business connectors and multi-tool use thanks to model-specs.
Content policy differences
The two companies diverge slightly in policy scope and data handling. OpenAI applies a broad content policy restricting adult, violent, or political content. It also automatically moderates outputs through the OpenAI API moderation endpoint.
To compare, Anthropic emphasizes privacy and autonomy. Claude’s API does not train on user prompts by default, and Anthropic guarantees no use of business data for future model training. That’s why enterprises with strict security policies often prefer Claude’s approach.
Integrations & ecosystems
Both AI agents become more powerful when they act inside your systems, automating repetitive tasks and serving as a layer between human and data. That’s why integrations matter.
ChatGPT integrates deeply across business applications, offering one of the most extensive automation ecosystems on the market. The AI assistant:

Allows users to build and deploy custom “GPTs”, i.e., specialized assistants, which can call APIs and access company tools
Can be connected via platforms like Zapier or Make to hundreds of workplace apps. So users will benefit from automated workflows in Docs, Sheets, CRM platforms, ticketing systems, etc.
Has a large plugin ecosystem for web search, browsing, analytics, workflows
Supports strong developer ecosystem, including GitHub Copilot and VS Code assistants

Example workflows

Auto-generate email & CRM updates from inbound leads
Automatically categorize and summarize support tickets
Convert spreadsheet data into insights and push notifications to team

How is Claude different from ChatGPT? Claude focuses on more secure, document-driven workflows inside enterprise systems. This AI tool:

Supports Tool Use for controlled API interactions and safe agent actions
Integrates directly with Google Workspace, Slack, and via AWS Bedrock so Claude can interact with your enterprise content without compromising data privacy
Supports Claude Code for technical workflows, including code generation and execution
Performs well in long-document analysis, knowledge extraction, internal policy navigation, and compliance automation

Example workflows

Scan contracts to extract risks and upload findings to internal systems
Read policy docs to generate compliance summaries
Classify documents and add them to secure repositories automatically

Enterprise and compliance
One more key difference between Claude and ChatGPT at the enterprise level lies in how each platform approaches compliance and data governance. These factors are especially critical in highly regulated industries, such as legal, banking, energy and utilities, or public sector.
Consider the core areas in detail:

Data privacy and retention: For personal use (ChatGPT Plus, Pro, or Free), there is default data sharing. On Business and Enterprise, ChatGPT states that it doesn’t train models on user data by default. Claude’s policy is no different. The policy has changed since September 2025, as earlier Anthropic was against training on user data by default. 
Security certifications and external audits: OpenAI claims alignment with ISO 27001, 27017, 27018, and 27701, and SOC 2 Type 2 compliance for enterprise customers. Anthropic aligns with CSA STAR, ISO 27001/27017/27018, and SOC 2 Type 2 certificates. At the same time, it’s better to request the actual audit reports or certificates for the specific model version you plan to use.
Identity, access, and activity controls: Business plans in ChatGPT support SAML SSO, domain‐bound access, role‐based user permissions, audit logs, and usage analytics for teams. Similarly, Claude offers SSO (SCIM), role‐based access controls, domain capture, audit logs, and compliance API for high‐security deployments.
Deployment options: ChatGPT is available as SaaS (OpenAI cloud). Also, its business accounts support dedicated infrastructure, custom deployments, and region-specific availability. As for Claude, it’s available via public API but, as mentioned, also integrated into AWS Bedrock and Google Vertex AI. The latter offer VPC, private endpoints, and region‐specific deployments.

UX and day-to-day experience
In everyday use, the difference between a model that simply answers and one that truly assists often comes down to three UX factors: speed and latency, UI ergonomics and prompt tooling, and voice/chat personality.
Speed and latency
Fast response time is vital for user satisfaction. Claude Haiku 4.5 was specifically built for customer service bots and low latency applications, where response time matters the most. As for ChatGPT, it offers documentation on latency optimization, such as time to first token or streaming. Along with helping to optimize speed, it also explains how it depends on prompt size, model choice, and infrastructure.
UI ergonomics and prompt tooling
User day-to-day experience also largely relates to how easy it is to use the OpenAI agent and how well the interface supports efficient workflows. ChatGPT continues to invest in prompt-tooling ergonomics by developing the “projects” feature, custom GPTs, memory control, and the visual prompt editor.
In contrast, Claude is more focused on long-context sessions, document uploads and tool integrations, and interface guidance on latency tuning.
Voice/chat personalities
The “feel” of the interaction also influences adoption and satisfaction. ChatGPT users mostly refer to its multimodal capabilities and conversational tone. The latter is especially helpful in marketing and support settings.
Claude takes a calmer, more professional tone suited for enterprise contexts. It’s usually less prone to informal or “creative” chat modes.
Claude vs ChatGPT: Which should you choose?
So… is Claude actually better than ChatGPT? Or is ChatGPT better than Claude? Neither. Choosing between Claude vs ChatGPT ultimately comes down to which strengths matter most for your business, whether it’s safety, multimodality, or specific use cases.
If you prioritize coding velocity
Choose ChatGPT as it speeds up development loops and supports developers where they already work. The tool:

Integrates well into developer workflows (GitHub Copilot, VS Code, CLI agents)
Shows strong performance on practical coding tasks and debugging
Helps with faster prototyping for engineering teams

If you need brand-safe writing
Select Claude, which is preferred for legal, compliance, and policy-aligned editorial tasks. The AI agent:

Remains a more consistent tone for regulated and professional contexts
Proves strong contextual grounding from long compliant materials
Shows low hallucination rates in official document generation

At the same time, ChatGPT can be useful for more creative writing tasks.
If you care about multimodal/image generation
Go for ChatGPT, which offers richer formats and media-driven UX. ChatGPT:

Has full multimodal capabilities (text, image, audio, video via Sora)
Offers more expressive conversational and voice modes

If you’re an enterprise with strict compliance
Obviously, Claude. The AI agent aligns closely with internal data governance and audit requirements. Plus, it:

Has strong access governance and private deployment via AWS/Google Vertex
Is more suitable for risk-sensitive sectors

If you want the largest ecosystem/extensions
ChatGPT might be a better choice as an automation hub across business systems. The AI tool:

Has a vast plugin and automation ecosystem
Offers custom GPTs that extend capabilities to any internal workflow
Is more flexible for operational scaling

How to choose between Claude and ChatGPT for business: Our team’s insights
Disclaimer: At Intelliarts, we don’t utilize Gen AI in our customers’ projects unless otherwise discussed. Even on such occasions, Gen AI is utilized purely for some minor process automation purposes and never for actual coding. 
However, our experience using Gen AI models on research and development projects, intended to validate certain assumptions and test particular technologies, allows us to share some insights. For example, we explored the approaches and benefits of using ChatGPT for data extraction tasks in one of our research projects. 
And if you’re looking for ways to connect AI technology with your actual business objectives, read how to use AI proof of concept in another of our blog posts. 
From a technical perspective, it’s possible to compare different models of Claude vs ChatGPT 4 and ChatGPT 3.5 by specific benchmarks. The results of such a comparison are shown in the table below: 

Basically, based on the benchmarks, Claude outperforms ChatGPT on any code and logic tests. However, for coding and programming, which is better, ChatGPT or Claude? ChatGPT generally shows great results in tasks like code generation, debugging, and handling specific programming queries. 
In practice, the difference in performance between the highest-performing Claude and GPT models is less notable in coding-related benchmarks, but ChatGPT tends to be more adaptable for programming tasks due to its broader and more user-friendly support in development environments. Moreover, both Gen AI are being improved rapidly, so in the near future, the situation may change.
Our experience shows that you can rely on Claude AI on the following matters:

Data-intensive tasks. Since ChatGPT is much more limited in its maximum token size, there is no other choice but Claude AI for processing intensive data. 
Processing specialized niche reports. Claude AI is less prone to errors and inaccuracies. Combined with, once again, a larger token limit, it makes Claude AI a far better choice for any report summarization or generation-related tasks.

Should we need to use AI for working processes, ChatGPT would be our choice in the following scenarios:

All forms of assistance to customers. Claude AI is not a user-friendly AI. It’s suited more to industry professionals rather than for assisting regular users. It’s because of the way it processes and outputs information. At the same time, ChatGPT excels here.
Code assistance. Frankly enough, ChatGPT happens to find solutions to some minor development-related issues more effectively and with less prompt engineering effort needed. It’s also easier to use when it’s needed to modify some data, as it’s less likely to misunderstand such a request. 
Creative task assistance. For tasks like generating marketing ideas, creating social media content, or drafting blog posts, ChatGPT proves to be more effective. Its ability to generate creative text and engage in brainstorming sessions makes it a more suitable tool for industries focusing on content creation and innovation.

Additionally, both models work well with extracting info from documents or finding discrepancies between them. Besides, Claude AI features vs ChatGPT shows that simple automation tasks, like transferring documents to different storages based on their titles, are flawlessly executed by both AI on most occasions. 
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Setup guides
Below are straightforward steps and best-practices to get started with both platforms.
Getting started with Claude

Getting started with ChatGPT

Prompting patterns and reusable templates
To make it easier for you, here are some prompt ideas to get you started:

Role-based prompts: “You are a trained analyst, summarize the data.”
Action-oriented templates: “List the top five risks, rank them, and provide mitigation steps.”
Chunk-and-synthesize: For long documents: upload in chunks → ask for summaries → combine into final output.
Interactive chains: “Ask follow-up questions based on the previous answer unless you’re ready to produce a final summary.”
Safety reminders: “Ensure your output does not include private or sensitive user data. Cite your sources.”

Alternatives to consider
While ChatGPT and Claude covers the majority of enterprise needs, there are other players and approaches worth mentioning:

Gemini by Google DeepMind — Strong in multimodal reasoning and large-scale generative workflows; fully integrated with Google Cloud services, what makes it a great alternative for teams deeply embedded in the Google ecosystem
Perplexity by Perplexity AI, Inc — An AI platform that blends retrieval-augmented results with conversational interaction; ideal for enterprises focused on knowledge-worker search and research assistance
Open-source options — Models such as Llama 3, Falcon, and platforms like Open‑Assistant provide full control, on-premises deployment, and customizability. They’re ideal if you have internal ML expertise, strong governance needs, or require full transparency and cost control

Final take
The key considerations for choosing between Claude and ChatGPT for projects bring us to final insights. Claude AI is more suited for data-heavy, document-based tasks as well as analytics. At the same time, ChatGPT is more commonly employed for creating problem-solving and in all sorts of solutions that interact with customers. Keeping that in mind, choosing between these two models for your particular process or project should be more straightforward and should easily address your questions like is Claude AI better than ChatGPT or vice versa.
Here at Intelliarts, with more than 24 years of experience and dozens of successful case studies under our belt, our AI/ML and big data experts can help you with an AI-based solution of any complexity. From business and high-tech advisory to end-to-end development execution, we can offer it all and cover any of your business needs.

#### Computer Vision for Automated Damage Detection
Computer vision technology brings many opportunities, including the replacement of manual inspection to a certain extent. That’s why the Intelliarts team found it promising to start working on an automated car damage assessment project.
Let’s have an in-depth look at how to solve AI damage detection tasks using computer vision from the experience of the Intelliarts team of ML engineers, what particular algorithms can be used, what the process of training and evaluating a model for detecting visual damage on a car is, and what popular algorithm may produce better outcomes in a computer vision project.
Streamline your inspections!Automate car damage detection with AIExplore custom AI solutions
AI car damage assessment vs. car damage detection
Let’s start with the difference between car damage detection and car damage assessment when powered by computer vision.
Car damage detection focuses on recognizing the presence of damage and identifying damaged areas. In contrast, car damage assessment goes a step further — it evaluates the extent and cost of vehicle damage based on identified damage with the prospect of making repair decisions. 
Basically, car damage detection is a core part of the broader car damage assessment.
In traditional workflows, car damage assessment is often closely connected to a large load of manual document processing and property damage assessment. Insurance companies, for example, may experience a high volume of vehicle damage claims, especially during peak seasons. That’s exactly when AI-powered vehicle damage inspection app solutions come into play, offering automated support to speed up the process.
Computer vision algorithms process post-accident vehicle imagery. Smart systems can recognize a car validating whether it’s the same one as in document evidence. They can also identify damaged car elements and estimate preliminary damage and repair costs.
While the conventional workflow is complicated and time-consuming, AI damage detection includes only 4 steps:

Building a well-performing AI car damage recognition system may be complex. Don’t hesitate to reach out to reach out to our team of well-seasoned AI engineers should you need assistance.
Computer vision-assisted vs. manual car damage assessment
While manual vehicle inspection is prevalent, computer vision-assisted one gradually becomes complementary to it. In some cases, it even completely replaces assessment by a human worker. Let’s have a look at the most significant pros and cons of both types of car damage detection.
Main pros and cons of computer vision-powered car damage detection
AI damage assessment has the following strengths to offer:

Workforce use and safety. With AI technologies, human involvement in the vehicle inspection cycle can be minimized. It results in better worker safety as inspectors are not vulnerable to risks associated with assessing car damage manually, such as electrical or chemical exposure hazards, moving parts, slip and fall.
Cost savings. Automating simple insurance claim processing can lower operating costs. After all, everyday cases like scratches or glass cracks are easy to handle for the AI system, yet time-consuming for human inspectors.
High efficiency. Typically, it takes a computer vision system only a few seconds to process a single image. It makes working with batches of images incredibly time-efficient, allowing for more inspections to be completed in less time.
Accuracy. In contrast to manual assessments, which rely on human judgment and are prone to inconsistencies and errors, AI systems offer greater accuracy. Moreover, some types of damage, such as hidden flood damage, may not be easily detected through conventional means. AI-powered car scanning and inspection for a flooded car helps bridge this gap by using specialized algorithms to identify water-related damage that could be hidden beneath the vehicle’s surface.

Here are two limitations of computer vision systems for car inspection:

Accuracy depends on the image quality. Low image sharpness or inappropriate lighting may largely deteriorate image processing. Besides, there are still risks of vehicle owners intentionally making fake images or taking photos in the way it alters the view of the severity of the damage.
Some types of damage cannot be detected. In the case of deteriorations that don’t categorize as glass or metal ones or are hidden damages, image processing may not be enough for an accurate estimation. AI damage detection heightens  risk of fraud, but engineers may mitigate the issue by applying additional fraud-detection algorithms.

Main pros and cons of manual car damage detection
The strengths manual vehicle damage assessment is appreciated for are the following:

High reliability in specific cases. Trained inspectors can quite accurately estimate the severity of deterioration and the potential repair costs. They also can spot hidden damages, making it better for cases of inspection of flooded or extensively used vehicles.
Widely used approach. Since manual vehicle inspection is recognized as an industry-standard approach, finding specialists and establishing the optimal workflow should not present a difficulty.

Here are two limitations of the conventional approach to vehicle damage detection:

High rate of labor resource usage. In the conventional inspection cycle, manual labor is involved in nearly every step. It increases the probability of human error as well as brings difficulties related to human resource management.
Time-extensive procedures. Both document approval and manual car damage inspection take considerable time. This way, a single claim completion may take days or weeks.

Let’s summarize the key points of both AI damage detection techniques and compare them against each other.
 
AI damage detection will unlikely completely replace the manual one in a short while. Yet, its capabilities are enough to take on large batches of assessment cases. It’s also interesting to know how machine learning optimizes insurance claims processing, as one of the 5 machine learning applications.
Types of car damage to be detected with computer vision for vehicle defect detection
During the AI car damage inspection, algorithms use digital vehicle images to detect the following types of damage:
Metal damage
Deteriorations of the bumper, hood, doors, dickey and other metal car parts are classified as forms of metal damage:

Dents and deformations. Concavity on the metal surface caused by pressing the metal body inside is known as a dent. It’s often caused during car crashes when there is destructive pressure from external objects.
Scratch. When a hard or sharp object is moved against a metallic surface, it causes scratches. It’s the most prevalent type of car damage, and it may largely vary in its severity.
Tears. If a vehicle sustains extreme forces, the metal parts may be split into pieces. This damage is called a tear. It often affects the external surface of a car part as well as results in extra destruction to the inside of the car.

Metal damages are quite easy to detect, as they are well-visible. Scratches, being the most common type of surface defect, are a major focus of computer vision models. Advanced techniques in computer vision for scratch detection enable AI to assess the size, depth, and severity of scratches with high precision.
At the same time, the tricky part is the assessment of the destruction’s magnitude. Using deep learning techniques in computer vision allows the system to solve even such complex tasks.
Glass damage
Deteriorations of the windshield, back glass, car windows, headlight, and taillight are classified as forms of glass damage:

Crack. This surface damage may be caused by physical forces, extreme temperatures, weather conditions, extreme pressure, or combinations of these factors. It typically looks like a net of circles and lines that indicate surface areas where the glass integrity was compromised.
Chip. When a small chunk of glass comes out of the surface, it’s called a chip. Professionals refer to it as a “stone break” or “pit.” This damage doesn’t penetrate the glass of the way, and consequently, it doesn’t cause longer cracks.
Spider crack. If there was a heavy physical impact caused to glass by an object, like a rock, a spider crack is formed. It often looks like a small hole in the center, where the force was applied — an impact point. Multiple cracks are formed around this area, giving the glass surface the appearance of a spider web.

Usually, the severity of glass destruction can be both spotted and assessed quite accurately.
Other damage
Damages that are not categorized as either metal or glass damage or are not forms of impact damage fall within this definition. Examples include dislocation or replacement of car parts, gaps between car parts, missing parts, cosmetic damage done, for example, to car paint, and more. 
The capabilities of computer vision algorithms to spot non-destructive damage are limited, but they still may have something to offer.
What algorithms can solve car damage detection tasks?
For AI damage detection, engineers utilize the image segmentation algorithm. Its work is to attribute one or another class to a particular pixel in the image based on certain visual characteristics, such as color, texture, intensity, or shape. In the case of vehicle inspection, the class is either with damage or without damage. In AI vehicle damage inspection, the goal of image segmentation is to simplify or change the representation of an image into a more meaningful form and separate objects from the background to analyze it easily.
So what’s the difference between instance and semantic segmentation? The two major approaches are the following:

Instance segmentation
With this computer vision technique, each individual object is identified and labeled with a unique identifier. The first step of instance segmentation is object detection. In this phase, a computer vision algorithm attempts to detect all objects in the image and provide a bounding box, i.e., rectangular or square-shaped figure surrounding an object, to each of them. During the car damage detection and classification done over areas inside bounding boxes, the algorithm calculates the confidence or the likelihood of the particular object of interest with a specific class, e.g., car, tree, human, etc., being inside the bounding box.
In the second step of the technique, the algorithm performs segmentation in each of the bounding boxes and labels each pixel, indicating whether it belongs to an object or not.
Another requirement of instance segmentation is the usage of pixel-wise masks. These are binary images that are used to identify the location of objects or regions of interest within an image. Each pixel in the mask is assigned a value of either 0 or 1, indicating whether that pixel belongs to the object or region of interest. Pixel-wise masks can be generated manually by annotating images.
The way this algorithm works ensures that multiple instances of the same object are differentiated from one another, even if they overlap or are partially obscured by other objects in the image.
Semantic segmentation
The semantic segmentation technique involves dividing an image into multiple segments, each of which corresponds to a particular object or region of interest within the image, and classifying them separately. Unlike traditional image segmentation methods that simply partition an image into arbitrary regions based on pixel similarity, semantic segmentation aims to associate each segment with a meaningful semantic label, such as a person, car, building, tree, etc.
Semantic segmentation treats multiple objects belonging to the same class as a single entity. It can indicate boundaries of, for one, all people, all cars, or all buildings in the image, if necessary. It’s important to note that semantic segmentation allows for detecting damages only without distinguishing them. In contrast, instance segmentation can distinguish multiple distinct damages from each other.
Once trained, the semantic segmentation model can be used to segment new images by propagating them through the network and generating a pixel-level segmentation mask. The latter works similarly to pixel-wise masks in instance segmentation yet assigns a label to each pixel in an image instead of generating multiple masks, one for each instance of an object.
Instance segmentation vs. semantic segmentation based on real-life observation
While both approaches play important roles in AI damage detection, the choice of method depends heavily on the project’s goals and requirements. At Intelliarts, we have hands-on experience evaluating different AI models for vehicle damage detection tasks.
Let’s explore some real-life AI architectures that have proven effective in practice.
Mask R-CNN

Mask R-CNN (Region-based Convolutional Neural Network with masks) is a deep learning architecture for object detection and instance segmentation. It’s built upon the Faster R-CNN object detection model and has a segmentation part, i.e., a subset of layers operating on the input data.
Mask R-CNN works in two stages. In the first stage, it generates region proposals using a Region Proposal Network (RPN), which suggests regions of the image that are likely to contain objects. In the second stage, it performs object detection and segmentation by simultaneously predicting class labels, bounding boxes, and masks for each proposal.

U-Net

U-Net is a convolutional neural network architecture designed for image segmentation tasks. It’s incredibly popular for solving medical image segmentation tasks such as brain tumor segmentation, cell segmentation, and lung segmentation. It has also been adapted for other image segmentation applications, such as road segmentation in autonomous driving.
The U-Net architecture has a distinctive “U” shape, which is formed by the downsampling and upsampling operations. The network has a contracting path, which captures context and downsamples the input image, and an expansive path, which enables precise localization and upsamples the feature maps. In essence, the network can retrieve detailed information about the objects being segmented while also capturing the context and global structure of the image.

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In our recent research, the Intelliarts team tested the two popular neural network architectures used for image segmentation tasks — Mask R-CNN (for instance segmentation) and U-net (for semantic segmentation). Both computer vision algorithms were trained and then tested using the same car damage image datasets. We used precleaned and prepacked data from the publicly available Coco car damage detection and the Segme image datasets.
Despite the fact that Mask R-CNN has a more complex architecture and processes the region proposals rather than the entire image, testing revealed that actually, U-net, as a semantic segmentation-based algorithm, performs better.
U-net showed optimal outcomes in the first part of the test when the AI algorithms were used for identifying car damages and evaluating their magnitude. Besides, in the second part, when Intelliarts engineers made the computer vision models identify damaged car parts and recognize them, Mask R-CNN performed better as well. It brought our team to the conclusion that the semantic segmentation model, particularly the tested U-net, is currently a better choice when it comes to a vehicle damage inspection.
You may give a try to an online demo that presents an interactive playground for the trained AI damage detection model. The demo shows the capabilities of a computer vision-enabled model to detect car damage based on an input image or video frame.
The two-model AI solution by Intelliarts
Intelliarts engineers built a software solution composed of two AI models, one of which is used for AI car damage detection and the other one for car part detection. So, when a user inputs a damaged car’s image in the resulting solution, it indicates the damage and identifies the affected car part separately. As a result, the report generated by the system can specify outcomes like “left door — dent” or “front bumper — scratch.” The outcomes are then compared against similar cases in a prepared image database with repair cost estimations.

The success of this approach also relies on how to fine-tune the model for car damage detection, ensuring both models work together to offer accurate damage classification and assessment of repair costs. In this context, insights from a totaled car value calculator can naturally complement the analysis by helping frame discussions around whether repair costs outweigh the vehicle’s overall worth.
The finished car damage software solution is capable of recognizing particular car parts, detecting and categorizing multiple types of damage, such as metal or glass damage and dislocation or replacement of car parts. The solution helps to capture damaged parts of the vehicle and evaluate the value of repairs costs needed, as well as to assess the severity of the damage. The functionality of a trained AI model should be enough to solve most of the simple car damage insurance claims with little human supervision.

For product owners, the end goal of computer vision projects is to get business benefits that include the following:

Cost optimization. Automated detection can be done at a much lower cost compared with manual inspection, resulting in significant savings for insurance companies.
Reduction of labor-intensive tasks. Automating the lion’s share of car inspection cases can reduce the workload for insurance company staff and allow them to focus on other critical tasks.
Improved accuracy. Automated systems can analyze images of the car with greater precision, identifying even small damages that may have been overlooked by human inspectors.
Faster claim processing. Automated damage detection can speed up the claim-processing time, enabling insurance companies to settle claims faster. This may result in improved customer satisfaction and retention rates.

Partnering with a trusted ML service provider is half the success when it comes to implementing a complex computer vision project.
Final thoughts
Vehicle inspection is one of the resource-intensive tasks in traditional workflows. Businesses may use automated car damage inspection powered by AI to streamline claim processing when car damage detection is involved. The image segmentation algorithm is what can perform such tasks. Yet, it’s necessary to carefully choose an optimal neural network, which may be Mask R-CNN, U-net, or any other, to build a model on, train a model properly using data annotation techniques, and then estimate its performance.
Solving motor damage assessment through AI and other computer vision tasks requires strong expertise and substantial experience in the AI field. Here at Intelliarts, we have already assisted a number of customers by delivering well-performing AI systems. Should you need assistance with computer vision implementation or other areas of machine learning — feel free to reach out, our insurance software developers are ready to assist you.
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#### Computer Vision in Insurance: Benefits, Examples and Use Cases
Digital transformation is inevitably approaching businesses within the insurance industry, raising new challenges and bringing opportunities simultaneously. The use of modern technologies can improve the way insurers interact with their customers, streamline operations, enhance risk assessment and management, and a lot more.
The global AI market is projected to reach $184 billion by the end of 2024, showing a Compound Annual Growth Rate (CAGR) of 28.46%. The tendency seems to be solid. And, since the growth tempo is high, the market is still far from being saturated. 
As of now, the usage of computer vision for insurance purposes is rapidly increasing. Let’s find out how car insurance companies use AI, how this transforms the sector and what benefits and opportunities it brings. You’ll also review use cases of computer vision in insurance and explore a practical case study by Intelliarts on developing a two-model computer vision solution for car damage detection.

#### Custom CPMS Development for Charge Point Operations: Build vs Adapt vs Integrate
If your charging network is growing and your CPMS is slowing you down, you’re likely weighing three options: configure what you have, build a layer on top, or develop from scratch. 
The EV charging market is scaling rapidly, with global public charging infrastructure surpassing 4 million chargers over the past few years and continuing strong double-digit growth into 2025, according to the IEA Global EV Outlook 2025. At the same time, reliability remains a concern, with 20% of users reporting failed charging attempts in the J.D. Power 2025 EV Charging Study. As operations become more complex, standard CPMS platforms often fall short, and additional developments or even fully custom creation of a CPMS solution becomes more effective long-term. 
This article indicates when standard CPMS solutions stop supporting operational needs at scale. It also outlines the key signals indicating growing complexity and explains how custom CPMS development addresses these gaps, as well as compares practical implementation approaches.
What is custom CPMS development for charge point operations?
Custom CPMS development is the process of designing and building a tailored charge point management system that aligns with a specific operator’s charge point infrastructure and business logic.
Here’s a brief scope of what a charge point management system supports in the daily operations of an EV charging network:

Monitoring charger status, availability, and performance in real time
Starting, stopping, and validating charging sessions across locations
Balancing load and optimizing energy use based on grid conditions
Applying pricing models and handling payments and subscriptions
Detecting faults, triggering alerts, and supporting issue resolution
Managing drivers, fleets, partners, and access permissions

For many operators, a standard CPMS engine like AMPECO or Noodoe provides these capabilities out of the box. However, it often does so within predefined workflows, limited configuration options, and fixed integration models.
The difference between standard CPMS usage and building a custom platform layer instead comes down to control and flexibility.
With a standard CPMS, operators use predefined features and workflows. With custom CPMS development, workflows, integrations, and data flows are built around the real needs of an operator. The platform adapts to the business, not the other way around.
Custom CPMS development becomes preferred when an off-the-shelf EV charging software solution no longer supports efficient scaling, partner ecosystems, or differentiated service models.
In practice, going for custom CPMS development often does not mean replacing everything. Many operators keep a CPMS engine and build a custom layer on top of it. This layer handles integrations, orchestration, and operational logic that standard tools cannot support.
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When does a standard CPMS stop being enough?
A standard CPMS stops being enough when it limits operational control, slows down scaling, or forces workarounds in charge point operations. 
Taken directly from our customers’ and leads’ complaints, here are the exact operational bottlenecks you can spot in daily workflows that indicate CPMS limitations:

Workflows are rigid and cannot be adapted without manual intervention
Visibility is fragmented and does not provide complete real-time insight across chargers, locations, or partners
Platform logic is restrictive and forces teams to adjust processes to fit the system
Integrations are brittle and require constant fixes or manual synchronization
Response times increase and slow down incident detection and resolution across the network

If you are looking for specific reasons why it’s the case, to a certain extent, for every CPMS, they are as follows:

Partner ecosystems are difficult to expand, and newly emerging roaming and B2B integrations require custom workarounds
Hardware diversity, such as different charger models and protocols, creates inconsistency
Pricing models are constrained, and even a mix of dynamic tariffs, subscriptions, or partner-specific pricing may not be sufficient long-term and at enterprise scale

But how do you know it’s no longer a temporary issue? From Intelliarts experience, the tipping point is usually when operational overhead increases while control decreases, and that appears to be a strong tendency. Are you looking to expand while your off-the-shelf CPMS provider still adjusts tariffs on a monthly basis and releases workflow-breaking updates? Then the time to switch to a custom solution is now.
How does custom CPMS development typically work?
While the development procedure itself is common knowledge, CPMS has its own intricacies that EV businesses should know. Let’s delve into how the charge point operations software works based on several specialized solutions and extra dev steps they include:

 Discovery and operational requirements mapping

This measure serves to analyze real charge point operations and translate them into software logic. For example, it highlights where sessions fail, where billing diverges, and where manual intervention occurs. 

 Charger estate, protocol, and integration assessment

This step focuses on evaluating the infrastructure. In practice, charger behavior often differs from protocol specifications, with inconsistencies such as delayed events, missing statuses, or vendor-specific quirks. These factors define how much normalization and fault tolerance the system must include to remain stable.

 CPMS architecture and module planning

A CPMS is typically built around session lifecycle and event processing rather than static data models. Proper separation between device communication, operational logic, and billing ensures that failures in one layer do not cascade across the system.

 Core workflow design for monitoring, control, alerts, billing, and support operations

Workflows reflect non-ideal conditions such as interrupted sessions and incorrect data. That’s why procedures should include correction and recovery paths alongside the main flow, making the system more fail-proof. 

 Third-party integrations and data flows

Almost any modern-day public solution is built to interact with external services. Aside from security concerns, there are also data consistency and integrity issues. Cleansing methods must be implemented, and data flows in place should be able to handle retries, duplication, and asynchronous updates.

 Iterative optimization

As a general development practice, software should be improved based on actual usage data over time. In the case of CPMS, it means that failed sessions and reconciliation issues should inform workflow refinement in order to benefit reliability and ROI over time.
The following infographics show a simplified workflow from business needs to development, testing, rollout, and continuous improvement, including the steps described above and other dev phases:

Important note: Development of a specialized CPMS or even adding functionalities on top of the existing one requires substantial EV niche-related expertise and experience. Should you need advice and assistance, don’t hesitate to reach out to the Intelliarts team. 
What business scenarios justify a custom CPMS?
A custom CPMS becomes justified when standard platforms limit control over operations, integrations, or monetization, or fail to meet EV charging standards and protocols. 
Looking for a reliable CPMS development partner? From architecture design to full-scale implementation, Intelliarts offers to build scalable and production-ready charge point management solutions.
#1 Multi-site or multi-country operations
Key signals:

Fragmented visibility across regions
Local pricing or regulatory constraints
Growing number of partners and locations

Typical cases:

CPOs expanding across EU markets with different tax and tariff rules
Networks operating in urban and highway environments with different usage patterns
Operators managing roaming partnerships in multiple countries

Real-world signal (2025):
Updates tied to AFIR Regulation EU require consistent pricing transparency and roaming accessibility across EU member states. Industry coverage notes that operators face challenges aligning tariffs, VAT handling, and session data.
#2 Mixed hardware ecosystems and protocol complexity
Key signals:

Multiple charger vendors with inconsistent behavior
Missing or delayed session data
Increasing need for error handling and normalization

Typical cases:

Operators growing through acquisitions with legacy hardware
Fleets using a mix of depot and public chargers from different vendors
Networks integrating newer chargers alongside outdated firmware models

Real-world signal (2025):
Industry reporting from the International Energy Agency (IEA) highlights that interoperability issues and uneven protocol implementation remain a core barrier in EV infrastructure scaling. 
It can be noted in many growing EV businesses that hardware diversity introduces variability that only a well-designed CPMS can normalize. —  Alexander Barinov, a Managing Partner at Intelliarts, with over 10 years of experience in EV software solutions
#3 Custom pricing, billing, or partner logic
Key signals:

Complex pricing models beyond flat tariffs
Manual billing adjustments and corrections
Partner-specific settlement requirements

Typical cases:

Fleet operators with negotiated pricing agreements
Charging providers offering subscriptions or bundled services
Businesses working with multiple partners requiring revenue split logic

Real-world signal (2025):
Market data from the European Commission and energy regulators show continued volatility in electricity pricing across Europe. This drives adoption of dynamic pricing and time-based tariffs, increasing backend billing complexity beyond what standard CPMS tools typically support.
#4 Unique support, maintenance, or operational workflows
Key signals:

Manual intervention in daily operations
Custom support or escalation processes
Disconnected tools for monitoring and maintenance

Typical cases:

Operators with dedicated support teams handling high-volume incidents
Networks requiring strict SLAs for uptime and response times
Businesses managing maintenance across distributed charger fleets

Real-world signal (2025):
Recent EV infrastructure reliability studies indicate that a significant share of public charging attempts still fail or are interrupted. These findings continue to highlight gaps in automated incident handling. 
Find out more about EV charging complianceThe white paper presents five global frameworks changing the industry, built on Intelliarts’ 10+ years of experience delivering emobility software.Read the white paper
Build vs adapt vs integrate: Which route makes the most sense?
It’s safe to claim that the right approach depends on how much control, flexibility, and scalability your charge point operations require. Options solve different levels of complexity, so in order to make a good choice, you need to find what can cover your business needs effectively.
When adapting an existing CPMS is enough
Adapting works when your operations fit within standard system logic and only require configuration or light extensions.
Indications:

Single-country or limited network scale
Standard pricing and billing models
Minimal partner or roaming complexity
Stable hardware setup with a few vendors

What it means in practice: You configure existing features, use built-in workflows, and add minor integrations without changing core system logic. Suitable for small-scale or new EV businesses. 
When integration-heavy architecture is a better choice than a full rebuild
This approach keeps a core CPMS but builds a custom layer around it to handle integrations and operational logic. 
Indications:

Multiple external systems (payments, CRM, roaming)
Increasing need for custom workflows
Data inconsistencies between systems
Growing operational complexity without full system limitations

What it means in practice: The CPMS acts as a base, while custom services manage orchestration, data normalization, and business-specific logic. Suitable for the majority of businesses.
Important note: Intelliarts experience shows that sometimes it’s hard to clearly distinguish when pure off-the-shelf software adjustment ends and integration-based developments on top of it start. Usually, the charge point operator software usage is a mix of both until actual, long-term development is triggered. 
When full custom CPMS development creates strategic value
Full development is justified when standard platforms limit core operations and cannot support business requirements.
Indications:

Multi-country operations with regulatory complexity
Mixed hardware ecosystems with unstable behavior
Advanced pricing, billing, or partner logic
Heavy reliance on manual operations or workarounds

What it means in practice: You design and build the system around your operations, controlling workflows, integrations, and data handling end to end. Suitable for enterprise-level businesses. 
See the comparison table in the image below:

Summary:

Adapt when operations are simple and stable
Integrate when complexity grows around the system
Build when the system itself becomes the limitation

Which capabilities matter most in charge point operations?
As charge point operations scale, complexity shifts from infrastructure to control. The following capabilities define whether a CPMS can operate reliably under real-world conditions or require constant manual intervention.
Real-time monitoring and control
This capability provides continuous visibility into charger status and allows operators to act on it. It is built on event streams from chargers, often with delays or inconsistencies, which must be normalized before use.
From a development perspective, it requires stable event ingestion, state management, and command execution across unreliable devices. The complexity comes from handling out-of-order events and maintaining accurate charger state.
Benefits:

Faster issue detection and response
Reduced downtime across the network
Centralized operational control

Find out more about smart charging platform features that a high-performing solution should have. 
Alerting and incident management
This capability defines how the system reacts to failures. It is not just about alerts, but about linking events to actionable workflows.
It is built by defining trigger conditions, escalation logic, and resolution paths. Complexity lies in filtering noise, avoiding false positives, and ensuring alerts lead to action, not overload.
Benefits:

Reduced manual monitoring effort
Faster incident resolution
Structured and repeatable support workflows

Tariffs, billing, and monetization support
This capability determines how sessions are translated into revenue. It must handle incomplete or inconsistent session data while still producing accurate billing.
Development involves building pricing engines, validation logic, and reconciliation mechanisms. Complexity comes from edge cases such as interrupted sessions, delayed meter values, and pricing rules that vary by context.
Benefits:

Accurate and flexible billing
Support for complex pricing models
Reduced need for manual corrections

User, asset, and partner management
This capability manages access, ownership, and relationships across the system. It includes drivers, fleets, partners, and physical assets.
It is built through identity management, access control, and hierarchical data structures. Complexity arises from multi-tenant setups and overlapping permissions.
Benefits:

Clear control over access and roles
Scalable partner and fleet management
Consistent data across entities

Scaling a charging network exposes the limits of predefined workflows and rigid integrations. It also makes the cost and effort inefficiency of inconsistent asset ownership clear. Sometimes there’s no other good choice but to resort to extra development. — Yurii Aulikh, Software Engineer at Intelliarts
Analytics, reporting, and operational visibility
This capability turns operational data into insight. It aggregates events, sessions, and system metrics into usable reporting.
It requires data pipelines, storage, and aggregation logic. Complexity comes from ensuring data consistency across sources and handling incomplete datasets.
Benefits:

Better decision-making based on real data
Visibility into performance and failures
Continuous optimization of operations

See short technical details and benefits for these capabilities outlined in the infographic below:

What increases complexity and delivery effort?
Complexity in a CPMS engine is driven by real-world variability. The more unpredictability the system must handle, the harder it becomes to design, build, and maintain.

 Charger variety and interoperability

Chargers from different suppliers and driven by distinct software may not behave consistently, even when operating under the same protocol. This results in the following:

Different vendors send events in different formats or sequences
Some chargers delay, duplicate, or skip critical data
Firmware differences introduce unpredictable behavior

Why it matters: The system must normalize unreliable inputs and still maintain accurate session and billing logic.

 Legacy systems and third-party dependencies

External systems such as payments, roaming platforms, and internal tools operate with their own logic and timing. In practice, this leads to:

Data mismatches between sessions, billing, and payments
Asynchronous updates that arrive late or in the wrong order
Dependency on systems outside your control

Why it matters: Integration logic must handle inconsistencies without breaking core operations.

 Regulatory and regional requirements

Modern CPMS development also becomes more complex when the platform must support protocols such as OCPP 2.0.1, OCPI, and ISO 15118. OCPP 2.0.1 expands charger communication requirements, OCPI adds roaming-related data exchange with external networks, and ISO 15118 introduces Plug&Charge workflows with certificate handling and secure vehicle-to-charger authentication. 
Regulatory and regional requirements manifest as:

Variations in tariff calculation and reporting
Region-specific data storage and compliance requirements
Ongoing updates that require system changes

Why it matters: The system must adapt to local constraints while preserving global consistency. 

 Scalability, uptime, and support expectations

As the network grows, the volume of devices, sessions, and events increases significantly. This introduces the following challenges:

Higher load and concurrency across the system
Wider impact of failures across multiple locations
Stricter expectations for availability and recovery

Why it matters: System failures scale with growth if the architecture is not designed to handle load and recovery.
Developing a smart power management system: Intelliarts’ success story

As a showcase of a custom EV development and associated complexities, let’s take a look at the smart power management platform created by Intelliarts for an EV company. 
Challenges: This development was complex due to the need to balance energy demand across multiple EV charging stations while staying within grid and infrastructure limits. Variability in charging loads, differences in measurement units such as kilowatts and amps, and peak-hour electricity costs created additional challenges. The system also needed to comply with regulatory requirements and support diverse hardware configurations, making it difficult to maintain stability, efficiency, and consistent control over energy consumption.
Solution: Intelliarts developed a custom smart power management system that enables scheduling power limits and dynamically adjusting load across charging stations. The solution integrated data analytics, cost tracking, and API-based control, allowing users to optimize energy use and reduce peak demand. 
Outcomes: The customer achieved lower electricity costs, improved infrastructure stability, and gained full visibility into consumption patterns. Altogether, it resulted in more efficient and scalable EV charging operations.
How should operators evaluate ROI?
ROI for a CPMS comes from two levers: cost reduction and revenue improvement. Cost reduction is typically measured as Ongoing Operational Costs (OPEX), which means operational expenditure and includes ongoing costs such as support teams, monitoring, maintenance, and daily operations.

 Operational efficiency

A CPMS engine reduces manual effort across monitoring, support, and billing by automating repetitive workflows.

Fewer manual checks and interventions
Less time spent on resolving routine issues
More processes handled automatically

Impact on ROI: Lower Ongoing Operational Costs.
How to calculate: OPEX Savings = (Time saved per operation × Number of operations × Hourly cost) 

 Faster issue resolution

Improved monitoring and workflows reduce the time it takes to detect and fix issues.

Faster detection of charger failures
Shorter time to resolve incidents
Fewer disrupted charging sessions

Impact on ROI: More completed sessions and less lost revenue.
How to calculate: Recovered Revenue = (Failed Sessions Before − Failed Sessions After) × Avg Revenue per Session 

 Better control over service differentiation

A flexible CPMS allows operators to implement pricing and service models that match real demand.

Dynamic pricing and tariff adjustments
Fleet and partner-specific agreements
New monetization models

Impact on ROI: Increased revenue per session and new revenue streams.
How to calculate: Revenue Uplift = (Revenue per Session After − Revenue per Session Before) × Total Sessions 

 Lower long-term platform constraints

A scalable CPMS reduces the need for costly changes as the business grows.

Less reliance on workarounds
Fewer system replacements or migrations
Easier integration of new services

Impact on ROI: Lower long-term operational and infrastructure costs.
How to calculate: Avoided Cost = (Cost of Future Replatforming + Cost of Additional Integrations + Cost of Workarounds) − Cost with Current CPMS Approach
See all the formulas gathered in a single infographics below:

In practice, ROI is driven by a combination of reduced operational costs and improved revenue capture across the charging network.
Final take
Custom CPMS development becomes relevant when operational complexity starts to limit control, scalability, and revenue potential. As networks grow, challenges shift from infrastructure to orchestration, integrations, and reliability. Operators must evaluate whether adapting, integrating, or building a CPMS aligns with long-term goals. In practice, ROI is driven by efficiency gains, reduced failures, and better monetization. The right approach ensures scalable operations without introducing additional system constraints. 
Over the course of 26 years delivering custom AI and conventional software, the Intelliarts team acquired industry-leading expertise and experience across domains, including EV. With 54% of in-house engineers being senior-level and 90% customer return rate, we are ready, willing, and able to contribute to your best CPMS development project. 
Discuss Your CPMS Strategy with IntelliartsEvaluate CPMS architecture options and define the right delivery approach with our experts.Contact Us

#### Custom Large Language Model Development Cost: 2026 Benchmarks
Enterprise adoption of large language models is accelerating rapidly. According to McKinsey’s 2025 State of AI report, 78% of organizations already use AI in at least one business function, while generative AI usage alone has reached 71% adoption across enterprises. 
Despite this growth, many organizations still struggle to implement and, most importantly, scale AI effectively beyond general assistance with limited usage and an off-the-shelf database. This blog post will guide you through large language model development cost factors and drivers, as well as indicate key development stages. It will also show three approaches to LLM projects, which are to buy from a third party, fine-tune, and custom-build.
What is custom large language model development and why does cost vary?
A custom Large Language Model (LLM) is an AI system adapted to a specific business use case, where outputs, data access, and behavior align with defined tasks and workflows.
Examples of such LLMs include:

Morgan Stanley GPT, built with OpenAI
Khanmigo by Khan Academy
GitHub Copilot by GitHub and Microsoft
Duolingo Max by Duolingo 

In practice, custom large language model development covers the creation of a full business solution, not a single AI model. After all, having an external or even your own AI model, like OpenAI or an Anthropic model, is basically having a backend engine rather than a ready-to-use product. 
Custom LLM product development may include the following layers:

Business use case definition and expected outputs
Domain data selection, access rules, and structuring
Model approach selection, like API-based, RAG, or fine-tuning
Retrieval logic and response generation flow
Integration with systems like CRMs, internal tools, or databases
Output evaluation, validation rules, and guardrails
Deployment setup across cloud or internal infrastructure
Post-launch updates, monitoring, and iteration

The scope depends on how the system is used in operations. A narrow assistant that answers questions from a single dataset requires limited setup. A system that supports decision-making across multiple tools and data sources requires deeper integration, stricter validation, and ongoing tuning.
Now, let’s move on to discussing the cost of creating such a solution. The image below indicates all the factors that influence language model development cost:

In this context, LLM development cost reflects the amount of engineering and product work needed to deliver a system that performs reliably within real business workflows.
Important note: The image shows what exactly affects the development price. It doesn’t take into consideration any possible measures to cut costs, as well as any unpredictable or unnecessary expenses that may arise. This cost factor overview will be complemented by a cost breakdown in the following sections. 
Some teams underestimate how much of LLM development is not about the model. The value, cost, and actual effort lie in making outputs usable inside actual business workflows. — Alexander Barinov, a managing partner at Intelliarts.
What drives large language model development costs in 2026 additionally?
Now that you know the factors that comprise the budget for enterprise AI implementation, let’s find out more about possible project complications leading to extra expenses. 
#1. Unusable data that required additional preparation
Acquired data for training and internal data for operation often cannot be used directly. Teams face:

Fragmented sources like tickets, documents, and chats
Inconsistent formats across systems
Missing context required for correct outputs

This shifts effort toward building data pipelines and retrieval structures, which can take more time than model setup.
Learn extra details on data preparation in ML for a full insight into the LLM development challenge. 
#2. Unmanaged accuracy expectations
From Intelliarts experience, it may be tricky to confirm the desired AI model accuracy. While an MVP of an enterprise LLM may seem to provide a suitable accuracy, actual usage often reveals the need for higher accuracy. This is exactly what may lead to extra rework during Large Language Model development. 
Higher accuracy requirements change how the system is built. A basic setup may work for internal exploration, while business-critical use cases require controlled outputs, repeatability, and validation. This introduces additional layers such as structured retrieval, response formatting, and evaluation datasets tied to real scenarios.
#3. Integrations introduce hidden system complexity
LLM systems derive value from interaction with existing tools. For example, working with a CRM like Salesforce requires structured inputs, permission handling, and alignment with business rules. Integration logic becomes part of the core system, not a separate layer.
#4. Expanding scope during stakeholder feedback cycles
Once stakeholders see early versions, they are eager to add new requirements. These often include additional data sources, edge cases, or stricter accuracy expectations. Without strict scope control, teams continuously expand the system, which increases development cost beyond initial estimates.
#5. Selecting a model approach before defining output requirements
It happens fairly often that teams choose between API-based models, fine-tuning, or custom setups too early, based on assumptions or budget constraints rather than real output requirements. Once tested in real workflows, gaps appear. For example, API setups may struggle with structured outputs, while fine-tuned models may not handle dynamic data well. This leads to rework in retrieval design or even full architecture changes.
You may be additionally interested in exploring the full list of Large Language Models from another blog post by Intelliarts.
How does a custom LLM development workflow typically look?
When getting started with an LLM development project, you typically have a planned workflow. While the exact sequence of some steps and their scope may differ, let’s review a general flow as an example. The following workflow aligns with Gen AI lifecycle guides by Google, Azure architecture center documentations, and other industry practices, while also incorporating Intelliarts’ expertise.
Step 1. Discovery and use case definition
Input: Business goal, target users, initial idea
Teams convert a high-level request into a bounded interaction model. The focus is on defining how the system behaves under real conditions and how outputs are consumed downstream. This is also where testing requirements are defined early.
What must be fixed at this stage:

Input structure, like user query, document, or system-triggered request
Output contract, like free text, JSON schema, or action payload
Acceptance criteria, including required fields and failure conditions

Deliverables: Use case specification document, input/output schema document, acceptance criteria and test requirements document
Step 2. Data assessment and preparation
Input: Internal data sources, documents, system access
Teams validate whether real queries can be answered with available data. The work focuses on making data usable for retrieval.
Core data work includes:

Aligning data structure with how queries are asked
Defining chunking based on information boundaries
Adding metadata for filtering, ranking, and access control

This step often determines system quality more than model choice.
Deliverables: Data inventory and source mapping document, retrieval and indexing specification, metadata schema and access control definition
Step 3. Model strategy selection and validation
Input: Output requirements, data characteristics, constraints
Teams test different approaches using real queries and evaluate how outputs behave under constraints. The goal is to understand failure patterns before committing.
Validation focuses on:

Ability to follow required output structure
Stability across similar inputs
Sensitivity to prompt or retrieval changes

This avoids committing to an approach that later requires system redesign.
Deliverables: Model evaluation report, comparison matrix of tested approaches, selected model strategy document
Step 4. System development and pipeline implementation
Input: Selected model approach, data design, architecture decisions
This is the core development step where the system is actually built. Teams implement how requests are processed end to end, including retrieval, model interaction, and response handling.
Development work includes:

Building retrieval pipelines and context assembly logic
Implementing prompt templates and the model interaction layer
Developing orchestration logic for multi-step flows
Creating APIs or services that expose the system

This step turns design decisions into working components.
Deliverables: Backend services and APIs, retrieval pipeline implementation, prompt and orchestration logic, integration-ready endpoints
Step 5. Solution architecture and integration planning
Input: Use case definition, data design, model behavior
Teams define how requests move through the system and where control is applied. This includes context construction, model invocation, and output handling.
Critical design points:

Where retrieval is triggered and how context is assembled
Where outputs are validated before use
How errors are handled across system boundaries

Integration requires contract-level alignment. For example, working with a CRM like Salesforce requires strict schema compliance and permission-aware execution.
Deliverables: System architecture diagram, integration specification document, orchestration, and data flow design
Step 6. Evaluation setup, testing, and guardrails
Input: Real or simulated queries, expected outputs, and defined acceptance criteria
Teams build an evaluation layer that reflects real usage and defines measurable quality.
Key elements include:

Test datasets based on real query patterns
Defined failure categories tied to business impact
Metrics for accuracy, completeness, and format compliance

Guardrails enforce constraints at runtime, ensuring outputs meet system requirements.
Deliverables: Evaluation dataset and test suite, quality metrics definition document, guardrails and validation rules specification
Step 7. Deployment, monitoring, and iteration
Input: Production usage, real queries, system logs
Teams analyze real behavior and adjust the system based on observed failures. The focus is on improving reliability under actual usage conditions.
Iteration targets:

Retrieval failures and missing context
Output inconsistencies or format violations
Usage patterns that increase latency or cost

Deliverables: Monitoring and logging setup, performance and usage reports, iteration backlog, and improvement roadmap
Important note: Despite the presented workflow being a step-based one, in practice, these phases often run in parallel. Teams iterate across discovery, data, and evaluation while refining the system in short cycles. Agile delivery overlaps these stages to reduce rework and validate decisions early.
The following diagram shows a simplified LLM workflow spanning across key stages that influence the development cost:

Build vs buy vs fine-tune: Which option is most cost-efficient?
Cost efficiency in LLM development depends on how well the approach matches output constraints, data behavior, and system integration requirements. The wrong choice usually does not fail immediately. It rather creates hidden costs later through rework in retrieval, orchestration, and integration layers. Here are three main options and how they are justified:
When off-the-shelf tools are enough
Off-the-shelf solutions are cost-efficient when the desired solution is a standalone assistant for general working purposes. 
They are suitable when:

Outputs are for human usage, not other software systems
No strict schema or deterministic behavior is required
Data is either static or passed directly in prompts
The system does not trigger actions or workflows

In these conditions, the system remains shallow. There is no need for retrieval pipelines, validation layers, or integration logic. Cost stays low because complexity is limited to prompt design and basic application logic.
Where teams lose cost efficiency is when they try to extend these tools into operational workflows. 
When fine-tuning is justified
Fine-tuning becomes cost-efficient when limitations of off-the-shelf solutions become blockers, and extensive customization and RAG as an add-on is needed. 
Typically, fine-tuning is used when the model must produce stable outputs across similar inputs without relying heavily on external retrieval. Building on top is justified when:

Output format must be consistent across repeated tasks
Domain-specific language or structure is not handled well by base models
The same patterns appear across large volumes of similar queries

In such cases, it’s possible to develop a validation layer, a dataset, fine-tune features, add integrations, or make other adjustments on top of the existing solution.
It’s safe to claim that fine-tuning of the existing LLM for specific business needs is a balanced approach that offers both exceptional results and cost-efficiency, if approached in a proper manner. 
Looking for an easy way to fine-tune your ChatGPT assistant in place?Intelliarts offers a range of consulting, integration, development, and automation services.Check ChatGPT dev services
However, it introduces a different cost structure. Teams must:

Curate and maintain training datasets
Validate outputs across edge cases
Retrain as requirements evolve

It also does not solve dynamic data problems. Overall, fine-tuning is a way to solve some niche problems related to workflows, address off-the-shelf LLM-related challenges, or provide some additional usages without resorting to custom development. 
Fine-tuning solves consistency issues, but it doesn’t remove the need for proper data access and retrieval when the use case depends on live information. — Andrii Shutka, DS Engineer and MLOps at Intelliarts.
When a custom LLM route makes business sense
A custom LLM approach becomes cost-efficient for enterprises with high budgets that intend to have a unique product as a part of the core business infrastructure.
Custom development is justified when:

Sensitive outputs must follow strict schemas and be consumed by other systems
The system interacts with multiple internal tools and data sources
Decisions depend on real-life data
The scale of potential LLM utilization is huge

In this setup, teams build a full system around the model:

Retrieval pipelines aligned with query patterns
Orchestration logic for multi-step flows
Validation layers to enforce output structure
Integration contracts with internal systems

Upfront cost is higher because the system is engineered for stability and control. Over time, this reduces the cost of change. 
Unlike off-the-shelf and fine-tuning solutions, custom LLM development is fully suitable for further scaling and can comply with any security requirements. From the experience of Intelliarts engineers, scaling and data handling are the core issues that medium and enterprise businesses face. That’s why, for such businesses, custom is typically preferred. 
Read about support automation with LLM for customer service in another blog post for additional insights on custom LLM applications. 
What actually drives cost efficiency across all three, based on where technical complexity sits:

Off-the-shelf → complexity stays outside the system
Fine-tuning → complexity moves into model behavior
Custom LLM → complexity is handled at the system level

Cost efficiency in LLM development comes from placing complexity in the layer that is easiest to control and maintain for a given business scenario while offering desirable results. 
See the simple build vs buy LLM comparison table in the image below:

Intelliarts success story: Enhancing expert assessment with a custom AI agent

As an example of how custom LLMs solve business problems, in one of the case studies from our portfolio, the Intelliarts team built an AI-powered agent for Valency, a global leader in project assurance. The agent processes expert feedback and converts it into structured, consistent assessments. The solution applies prompt engineering, model evaluation, and validation layers.
As a result, assessment quality became more consistent, and decision-makers received clear, actionable insights. It also reduced processing time from 5 minutes per query to a few seconds. Built as a microservice, the solution is ready for integration into Valency’s cloud environment.
Explore the AI agent for smarter expert assessments success story.
Which cost components should buyers include in a realistic budget?
A realistic LLM development cost is distributed across several system-level components. The model itself is only one part of the total investment.
#1 Discovery and requirements
This stage defines what the system must produce in a form that engineering can implement. Cost comes from aligning stakeholders on exact outputs, edge cases, and failure handling before build starts. If skipped, teams later rewrite prompts, retrieval, and integrations after seeing real queries.
Typical share of total cost: 5–10%
#2 Data engineering and preparation
This is where most of the time goes. Cleansing data pursues the goal of making it retrievable and valuable for learning. Custom LLM development cost here comes from building pipelines, defining chunking that matches query behavior, and adding metadata for filtering and ranking. Without this, retrieval returns the wrong context and breaks output quality as the garbage-in-garbage-out (GIGO) rule. 
Typical share of total cost: 25–40%
#3 Model development or adaptation
This is not about picking a model. It is about making outputs usable. Cost comes from enforcing structure, reducing variability, and handling edge cases where the model breaks format or logic. Fine-tuning adds dataset creation and repeated validation cycles.
Typical share of total cost: 10–20%
#4 Infrastructure and MLOps
Cost comes from making the system stable under real usage. Teams implement request routing, manage concurrency, and control token usage. Logging and tracing are required to debug failures that cannot be reproduced easily. These are not optional once the system handles production traffic.
Typical share of total cost: 10–20%
#5 Security, compliance, and governance
Cost appears when outputs must be controlled and auditable. Teams implement access rules at the query level, ensure data is filtered correctly, and track how outputs were generated. In regulated environments, this requires additional validation and logging layers that affect system design.
Typical share of total cost: 5–15%
#6 QA, evaluation, and post-launch support
Cost comes from building a system that can be tested repeatedly. Teams create datasets based on real queries, define failure categories, and track output quality over time. Without this, every issue requires manual investigation, which slows development and increases long-term cost.
Typical share of total cost: 10–20%

Important note: The specified cost shares are the insight of Intelliarts’ experts. For every business, cost distribution will vary because of the varying priorities and needs, especially in terms of infrastructure and AI model development. 
Looking for specialized development assistance? Don’t hesitate to explore our AI development services.
What business factors increase or reduce total cost?
Now let’s find out what factors most influence custom LLM implementation cost in 2026, with both external and internal ones specified: 
Internal cost factors

Use cases and LLM applications. Cost depends on how outputs are used. Simple text generation is low effort, while structured outputs, actions, and multi-step workflows require orchestration, validation, and deeper testing.
Data availability and structure. Fragmented or unstructured data increases cost because teams must build retrieval pipelines and align data with real query patterns before the system works reliably.
Required integrations. Each integration adds constraints on data flow, permissions, and output format. Complexity grows with the number of connected systems and their dependencies.
Internal team maturity. Experienced teams reduce cost through better early decisions. Less experienced teams often revisit architecture, which leads to expensive rework.
Time-to-market expectations. Short timelines increase cost due to parallel development and later fixes. Longer timelines allow validation and reduce post-launch corrections.

External cost factors

Geographic and regulatory scope. Compliance requirements affect how data is stored, accessed, and processed. This adds constraints that increase development effort.
Model and vendor dependency. Choice of providers affects cost through pricing, limitations, and flexibility. Changing vendors later can require system adjustments.
Market and competitive pressure. Pressure to launch quickly leads to reduced validation and optimization, which increases long-term cost.
Infrastructure and cloud environment. Cloud pricing, scaling needs, and regional availability influence both development and operational costs, especially for high-load systems.

In practice, these factors rarely act in isolation. Constraints overlap, such as complex LLM applications combined with fragmented data, multiple integrations, and strict regulatory requirements. On the other hand, cost remains more predictable when the use case is clearly scoped, data is accessible, and the system operates with limited dependencies.
Discuss LLM project scope, architecture, and delivery approach.Contact us
How to estimate ROI from custom LLM development
ROI from custom LLM development equals the total measurable business value generated by the system compared to its full cost.
In practice, it reflects the impact of four things combined, relative to the amount of investment:

Efficiency gains → time saved per task × volume × cost per hour
Workflow automation → number of tasks replaced × cost per task
Quality improvements → reduction in errors × cost of fixing them
Strategic impact → faster decisions and better use of internal data 

In other words, ROI captures how much value the system produces across these four core operations.
A higher ROI indicates that the development resulted in additional value from accelerated processes or better LLM outputs. And this value is big enough to justify the investment made. 
See the core and LLM development-specific calculations, as well as the example of ROI estimation in the infographics below:

Final take
Custom large language model development cost depends mainly on system architecture and process design. Data quality, integrations, and output requirements are only a few of the suite of factors that impact both effort needed and ROI. Off-the-shelf and fine-tuned solutions serve limited cases, while custom LLMs support scalable, integrated workflows. Cost efficiency comes from aligning architecture with real business needs. 
Intelliarts has been on the market for more than 26 years, delivering top-notch ML, LLM, and other AI solutions for businesses. With over 90% customer return rate, and 92% of senior staff engineers in-house, we can contribute to your custom LLM project regardless of its complexity. Don’t hesitate to reach out for consultation, advice, or a quote. 
Contact us

#### Custom LLM Development vs Using APIs: How to Make the Build or Buy Decision
TL;DR

API-first LLM development is usually the fastest path for pilots, early copilots, and low-risk GenAI features.
Custom LLM development becomes more relevant when cost, data control, latency, compliance, or product differentiation begin to affect the business case.
Fully custom models are rarely the first step. Most enterprise teams benefit more from RAG, fine-tuning, model routing, or self-hosted deployment.
Based on Intelliarts’ project experience, the strongest strategy is often hybrid: validate fast, then add ownership where it improves long-term value.

Many enterprise GenAI pilots start with OpenAI, Anthropic, or other LLM APIs because they are fast to test and easy to connect. AI adoption has moved into core operations, as the Stanford AI Index 2025 reports that 78% of organizations used AI in 2024, up from 55% in 2023. 
As usage grows, so do questions about APIs, token spending, data residency, vendor dependence, latency, and compliance. For CTOs and VP Engineering, desired outcomes of custom LLM development and corresponding concerns are largely defined by the level of ownership the company needs across models, data, infrastructure, and governance. 
This article explains how to compare LLM APIs with custom large language model development across cost, control, security, and delivery speed. It also shows how to structure a build-or-buy decision for enterprise use cases.
Have an AI project in mind, but unsure which path to take?Let’s discuss your options together
Why is “custom LLM vs API” a strategic question?
API-first GenAI works well for pilots. Teams can test copilots, chat interfaces, and document workflows without building model infrastructure.
The problem starts when pilots move into production. Then API access becomes an architecture decision: cost, vendor dependence, data control, latency, and compliance all matter.
Three factors are pushing leaders to revisit the build-or-buy LLM decision:

Open-source LLMs are becoming more practical. The Stanford AI Index 2025 points to rapid progress in model performance, tooling, and AI adoption, which gives enterprises more options beyond standard APIs.
AI regulation is getting stricter. The EU AI Act’s general-purpose AI rules started applying on August 2, 2025, increasing pressure around transparency, documentation, and risk controls.
LLMs are moving into core workflows. McKinsey’s 2025 State of AI survey says organizations are redesigning workflows as they deploy GenAI and assigning senior leaders to AI governance.

APIs are still useful for speed, variable workloads, and advanced capabilities that are hard to operate internally. Custom LLM development starts to matter when the use case needs more control over cost, data, behavior, or integration.
Before comparing both paths, leaders need to define what custom LLM development services actually cover. That’s something the Intelliarts expert more often than not helps to define before the beginning of new projects or throughout technology consultation sessions. 
What does custom LLM development mean?
Development of a custom LLM for enterprise applications is a spectrum of technical approaches to create, adapt, deploy, and operate LLM capabilities for a specific business case. 
At the lightest level, a company may still rely on external APIs but add custom prompts, retrieval, evaluation, and workflow logic around them. At the deeper end, it may fine-tune a proprietary or open-source model, host it privately, or continue pre-training a model on domain-specific data. Here are four main approaches aggregated:
[table id=19 /]

Important explanation: As you can see, even for enterprises, “custom LLM” does not mean building a GPT-scale model from zero. It usually means choosing the right foundation model, or, at the very maximum, selecting an algorithm, which may be an LLM or another AI-based one, to create an incredibly niche model. For example, Intelliarts did this with a two-model computer vision solution for car damage detection. 

So, the Intelliarts team can confidently state that the goal for almost every single AI, ML, and LLM project is adapting an existing model to the particular business task. Usually, it’s related to integrating it with enterprise data and making it up and running with proper MLOps.
Just a few of the backend layers underneath the model’s UI/UX include:

Model serving layer
Inference API
Request routing and orchestration
Data and vector database connections
Authentication and access control
Monitoring, logging, and tracing
Guardrails, fallback, and retry logic

Another important note here is that “build vs buy” is not a rigid decision in this scenario with LLM implementation services. The scale tips depending on how much ownership the company needs across the model, data, infrastructure, and governance layers. 
Searching for an AI and LLM software partner?Entrust the Intelliarts team with all your project development needs.Reach out
Custom LLM development vs using LLM APIs
You probably already concluded that APIs keep more responsibility on the provider side, while custom LLM development moves more responsibility to the enterprise team or its engineering partner. While this is a generalization, such a statement is mainly correct. 
See how a custom LLM for enterprise applications compares to fulfilling the same business objective using LLM APIs integrations:
[table id=20 /]
Based on what the Intelliarts team observes, for early-stage use cases, LLM APIs and fine-tuned procedures usually keep delivery simple yet sufficient at a small scale and within a limited scope of usage. They also let teams compare model quality before making deeper infrastructure decisions or committing to a full-scale custom LLM development process. 
However, for mature workloads, the shift toward LLM TCO (total cost of ownership) that would prioritize control and operational readiness is reasonable. That is where enterprise LLM development is already both an engineering and a business task, with full responsibility across the entire technology solution on the owner’s side. 
Evaluation framework: 6 questions to decide, build, or buy
Before choosing between APIs, custom LLM development process, or a hybrid setup, teams need to pressure-test each use case against business and technical constraints. These six questions help narrow the decision before architecture, budget, and delivery planning begin. 

How sensitive are your data and compliance requirements?
Sensitivity is the first hard filter. If the system will process regulated records, confidential contracts, claims, medical notes, internal IP, or government data, the choice often shifts toward custom LLM development and, respectively, VPC-hosted, self-hosted, or tightly controlled architectures. 
APIs can still work when the provider’s enterprise terms, retention policy, regional processing, and audit options match the risk profile. For enterprise teams, the key question is whether the full data flow can pass legal, LLM security, compliance, and procurement review.
For enterprise teams, this question usually comes down to three checks:

Processing boundary: where prompts, retrieved context, logs, and outputs are processed.
Control boundary: who can access the data, model outputs, audit trails, and configuration.
Regulatory boundary: which data residency, retention, explainability, or sector-specific rules apply.

From Intelliarts’ experience, this filter narrows the architecture early. A marketing content assistant may stay API-based. A claims review, underwriting, legal analysis, or healthcare workflow usually needs a stronger ownership model.
What are your volume and cost-per-request expectations?
High-volume workloads can make custom LLM development services more attractive. Low-volume, seasonal, or experimental use cases usually stay cheaper and easier with APIs.
The rough breakeven point appears when monthly API invoices become higher than the cost of running infrastructure, maintaining the model stack, and supporting production operations. This threshold depends on token volume, context size, latency needs, and workload predictability.
For enterprise teams, LLM TCO should account for:

Inference cost: API usage, GPU infrastructure, hosting, or managed deployment fees.
Engineering cost: integration, model updates, backend maintenance, and incident response.
Quality cost: evaluation sets, regression testing, guardrails, and human review.
Monitoring cost: usage tracking, latency checks, output quality review, and cost controls.

A support copilot used by thousands of agents can justify a custom or self-hosted setup once request patterns become stable. A small assistant used by one department may remain API-based for years because infrastructure ownership adds more cost than value.
As a custom LLM development company, we can confirm that in such digital projects, the stronger signal is repeatable volume with clear quality requirements and a predictable cost-per-request target.
“To craft an enterprise LLM system that would cater to business objectives, we build around workflows, permissions, evaluation, and feedback loops. Of course, model choice matters, but the infrastructure, data transformation, and integrations deliver the key value that stakeholders can observe.” 
— Alexander Barinov, a managing partner at Intelliarts.
How much differentiation do you need from your competitors?
If the LLM feature is part of your competitive edge, API access alone may not be enough. The same model can power many similar tools, so defensibility usually comes from what the company builds around it.
For enterprise products, differentiation often comes from the following sources:

Proprietary workflows: the way the system supports domain-specific decisions, approvals, reviews, or exceptions.
Private data: historical records, expert knowledge, user behavior, internal policies, or product-specific context.
Integration depth: how well the LLM connects to permissions, business systems, data sources, and operational logic.

This is where the custom LLM development process gives teams more control. An insurance copilot can reflect claims handling rules. A legal research assistant can follow a firm’s review standards. An industrial analytics product can connect model outputs to equipment data, maintenance history, and risk thresholds.
The timing still matters. If users have not validated the workflow yet, building custom LLMs can lock the team into expensive assumptions. APIs, especially those based on open-source LLMs, are often better for proving demand first. Once the feature becomes strategic, custom models can support stronger IP, more consistent behavior, and deeper product differentiation.
Learn about our ML solution for lead scoring in insurance. Read the case study.
How quickly do you need to iterate?
APIs are usually the better fit when requirements are still changing. They let teams test prompts, workflows, UX, and model behavior before committing to deeper architecture choices.
A practical path often looks like this:

Prototype with APIs: launch the first version quickly and compare model quality across realistic tasks.
Validate value: measure adoption, output quality, cost per task, and failure patterns.
Segment use cases: separate experimental, long-tail, sensitive, and repeatable workloads.
Build selectively: move stable, high-ROI workloads into the custom LLM development process when the business case is clear.

This phased approach reduces the risk of overbuilding too early. It also helps teams build long-term capability around the workloads that deserve more control, instead of treating every GenAI feature as a custom infrastructure project from day one.
What is your tolerance for operational complexity?
Custom LLM development requires more than model integration. The team also needs MLOps, SRE, security, and support capacity to keep the system reliable after launch.
API-first is safer when the organization cannot yet own that operational load. Custom LLM application development firms handle much of the model serving, scaling, updates, and infrastructure resilience.
For a custom or self-hosted setup, the main operational questions are:

Scaling: Can the system handle usage spikes without breaking latency targets?
Observability: Can the team trace failed answers, slow requests, retrieval issues, and cost anomalies?
Model updates: Can changes be tested before they affect production quality?
Incident response: Who owns failures when outputs are wrong, unsafe, delayed, or unavailable?
Security operations: Can the team manage access, audit logs, secrets, and data exposure risks?

In Intelliarts’ projects, we sometimes see companies underestimate the long-term cost of owning the LLM stack. The initial build may look manageable, while monitoring, regression testing, updates, and support become the real workload, which we typically help to optimize. 
If the team already has strong platform engineering practices, custom LLMs can be realistic. If that maturity is missing, APIs can reduce operational risk while the use case matures.
Want a clearer picture of LLM costs?Download our 2026 LLM cost guide to explore pricing trends and hidden costs.Get your copy
How important is multi-model flexibility for you?
We’ve found that multi-model flexibility matters when the product may need different models for critically distinct tasks, like code testing vs customer support. One workflow may need a GPT-class model, another may need an open-source LLM, and a third may need a domain-specific model with stricter deployment rules.
The custom LLM development architecture should avoid hardwiring the product to one provider. A model routing layer gives teams a pluggable backend for API models, custom models, and self-hosted deployments.
This helps teams control switching costs in three ways:

Provider changes: replace or add vendors without rebuilding the product layer.
Use-case optimization: choose cheaper, faster, or more accurate models for specific workloads.
Risk management: keep sensitive requests on controlled backends while sending lower-risk tasks to external APIs.

This flexibility becomes more important as the scope of self-hosted LLMs grows. Without it, every model change can turn into a product, backend, and compliance update at the same time.
Important tip: These questions, taken together, are useful because they force teams to evaluate each use case separately. A low-risk assistant may stay on APIs, while a regulated document workflow may require more ownership from day one. 
Once leaders see these differences clearly, the build-or-buy discussion becomes less abstract and easier to connect to architecture, budget, and delivery priorities. 
API-first strategy: When “buy” is the right answer
API-first is the right answer when the team needs speed, low upfront investment, and access to strong general-purpose models. It works best when data sensitivity is manageable and expected usage does not yet justify deeper model ownership.
In which scenarios do LLM APIs make the most sense?
One thing the Intelliarts experts consistently observed is that LLM APIs are usually the best starting point when the use case still needs validation. Teams can launch faster, compare model quality, and avoid early infrastructure decisions.
Common examples include:

Prototyping new AI features or pilots. APIs help teams test copilots, search assistants, summarization tools, or document workflows before making larger architecture commitments. These use cases are usually exploratory, so speed and flexibility matter more than infrastructure ownership.
Customer-facing chatbots and copilots without highly sensitive data. This approach works when the provider’s security controls match the risk level of the use case. It is a good fit for product guidance, FAQ support, onboarding flows, or other interactions that do not require strict control over private records.
Low-to-medium scale internal assistants across departments. Teams can support HR, sales, operations, or engineering workflows without building a dedicated model stack. These assistants usually handle routine knowledge access, drafting, summarization, or workflow support across a manageable number of users.
Specialized, semi-internal AI assistants. Such solutions may include limited-functionality bots that help users access specific, usually technical features of a vendor’s platform. Lots of other applications are possible, yet they are limited to the main platform or website functionalities and are incapable of providing answers to customer queries. 

For these scenarios, LLM integration services, or simply put, buying model access, keep the focus on workflow design, user adoption, and business validation. That is often more valuable than investing too early in a custom LLM development process.
What are the hidden risks of an API-only approach?
An API-only setup can become limiting when a pilot turns into a production dependency. The risks usually appear around cost, control, and compliance, for example, as per the above-mentioned EU AI Act. 
Key risks include:

Vendor pricing changes. A workload that looks affordable during testing can become expensive as token volume, user count, or document size grows.
Tighter rate limits or usage policies. Provider-side limits can affect reliability, regional availability, or supported use cases.
Regulatory shifts. New data residency, audit, or AI governance requirements may force teams to control more of the data flow.
Model changes. Providers may update, deprecate, or replace models, which can affect output quality, latency, pricing, or prompt behavior in production.
Limited control over optimization. API-only setups give teams less room to tune infrastructure, inference cost, latency, and behavior for repeatable domain-specific workloads.

These risks can be managed without abandoning APIs. A practical API-first architecture should include an API abstraction layer, basic multi-vendor testing, clear data-sharing rules, and an exit plan for workloads that may later need an enterprise LLM strategy and development.
Custom LLM development: When “build” starts to pay off
An easy answer here, based on Intelliarts’ expertise, is that when building starts to make sense, API-based delivery becomes too expensive, restrictive, or risky for a specific workload. This usually happens when the use case has high volume, high business value, or strict data requirements. Let’s elaborate on these considerations: 
Which use cases justify custom LLMs?
Custom LLM development is easier to justify when the model supports one or more of the core workflows already, rather than being a supporting functionality. 
Common product examples of generative AI model development include:

Domain-specialized copilots for regulated industries. Financial, healthcare, legal, or insurance teams may need assistants who understand domain language and internal policies. These copilots may support analysts, claims teams, compliance officers, or customer support agents while keeping sensitive data inside approved environments.
Large-scale document processing pipelines. A company may process contracts, claims, invoices, tickets, or reports at a volume where API costs become harder to predict. Custom models can help classify documents, extract fields, flag exceptions, or route cases to the right team.
Products where the LLM is central IP. If the model experience is part of the product’s competitive value, generic API behavior may limit differentiation. This can apply to legal research tools, industry copilots, AI analytics products, or domain-specific knowledge platforms.
Repeatable classification or extraction workflows. With custom LLM development services, stable, high-volume tasks can often benefit from a smaller optimized model. This works especially well when the task does not need broad general reasoning, such as ticket tagging, intent detection, entity extraction, or compliance triage.
Private knowledge assistants for enterprise teams. Some companies need assistants who work with internal documentation, policies, engineering tickets, or customer records. In this case, the main value comes from secure retrieval, access control, and reliable answers inside the company’s own context.
AI workflows with strict latency or uptime needs. Some production systems cannot depend only on external provider availability or changing rate limits. A custom setup can give engineering teams more control over inference speed, fallback logic, and service-level requirements.

These use cases do not always require training from scratch. In most cases, enterprise LLM development starts with an existing model and adds the right data, infrastructure, evaluation, and governance around it.
“Just to prevent any possible confusion. For one, a claims assistant, a legal review tool, and a marketing copilot can all use LLMs. It still doesn’t mean that they should share the same software architecture or even AI model by default.” — DS/ML Engineer at Intelliarts. 
What does a realistic custom LLM stack look like?
A realistic stack usually combines open-source LLMs, RAG systems, fine-tuning where needed, and production-grade MLOps. In practice, LLM software development services cover the backend, data, monitoring, evaluation, and governance layers that make the model usable in production. 
That stack usually includes:
#1. A model decision. The team chooses whether the workload needs an API model, a fine-tuned provider model, an adapted open-source model, or a self‑hosted LLM to control inference location, latency, data exposure, and model behavior.

Stack elements: Model provider, fine-tuning setup, open-source model deployment, or private inference endpoint.

#2. A data path. The system needs a controlled way to pull approved data from documents, databases, tickets, policies, or product systems without exposing more context than the model needs.

Stack elements: CRM, ERP, DMS, ticketing system, knowledge base, database, or data warehouse connectors.

#3. A runtime layer. The product needs a backend that routes requests between the application, data layer, and model while managing context, retries, latency, and usage.

Stack elements: Model gateway, inference API, request router, prompt/context builder, cache, retry logic, and fallback route.

#4. A quality control loop. Teams need a way to catch wrong, irrelevant, unsafe, or expensive answers before model or prompt changes affect production quality.

Stack elements: Evaluation dataset, golden questions, answer scoring, regression tests, hallucination checks, and human review queue.

#5. A governance layer. The company needs rules and controls for regulated or sensitive workflows, including access, retention, auditability, approvals, and policy enforcement.

Stack elements: SSO, RBAC, audit logs, PII detection, data retention rules, approval flows, and usage policies.

Custom LLM development pays off when the workload is stable enough to optimize and important enough to own. Otherwise, APIs may still be the more efficient choice.
If you are looking for a technical explanation of how enterprise solutions for LLMs work, specifically RAG architecture and best practices, take a look at another of our blog posts. 
Hybrid LLM strategies: Best of both worlds
A hybrid LLM strategy works when processes need different model paths. In practice, this means splitting workloads by purpose:

APIs when flexibility, model breadth, or fast iteration matter most.
Custom LLMs, when control, cost predictability, data boundaries, or repeatable quality matter more.

The infographic below shows how this routing logic works in practice.

At the end of the day, hybrid strategies work best when each workload is matched to the model path that fits its risk, cost, and performance profile. 
Decision matrix: Example scenarios mapped to LLM strategy
Use this matrix to map each use case to the right delivery path. The goal is to compare demand certainty, data sensitivity, workload volume, and ownership needs without overengineering the first decision.
[table id=21 /]
The matrix should be revisited as usage grows. A use case can start API-first, move to hybrid, and justify custom ownership once value, volume, and risk become clear.
Looking for custom LLM development services from a trusted partner?Request LLM services
How Intelliarts helps enterprises navigate the build or buy LLM decision
Intelliarts, as a provider of custom LLM development services, helps enterprises with their build-or-buy LLM question based on the investment decision tied to value, risk, and long-term ownership factors. The work starts with business priorities: which workflows create measurable value, which data can be used safely, and which risks would block production adoption.
Our expertise combines AI/ML engineering, data engineering, enterprise software development, LLM development, and RAG system integration. 
While it’s hard to specify exact project stages before discussing business needs, our general LLM development, with or without API-based integrations, is usually shaped around four phases:

Use case discovery. We define the workflows that can benefit from LLMs, the users involved, the data they need, and the business metrics that will prove value. The result is a prioritized set of use cases with clear constraints.
Build-or-buy modelling. We compare LLM APIs, RAG, fine-tuning, self-hosted models, and hybrid options against cost, latency, risk, security, and maintenance needs. The result is a practical enterprise LLM strategy, with LLM TCO and risk assumptions visible before implementation starts.
Pilot implementation. We build a focused proof of value with real workflows, integrations, and evaluation criteria. The result is measurable evidence on output quality, cost per request, user adoption, and failure patterns.
Production scale-up. We add monitoring, guardrails, evaluation pipelines, cost controls, and reliability practices. The result is an LLM architecture that can support broader enterprise use without losing control over quality or operations.

This gives leadership a clearer roadmap before major engineering investment begins. Teams can validate GenAI ideas quickly, keep early delivery lean, and move deeper ownership only into workloads where it improves cost, reliability, security, or product differentiation.
With Intelliarts’ LLM development services and RAG development services, enterprises can plan, build, and scale LLM systems with the right balance of speed and control.
Conclusion
Build or buy is a portfolio decision. LLM APIs are useful for pilots, variable demand, multimodal workloads, and low-risk assistants. Custom LLM development becomes more relevant when workloads are stable, sensitive, expensive at scale, or tied to product differentiation. 
The middle ground is often a hybrid enterprise LLM strategy with model routing, where each request goes to the backend that fits its risk, cost, latency, and quality needs. 
Whether you lean more toward custom or API-based development, you need a trusted custom LLM development company specializing in AI/ML development. With more than 26 years of experience delivering software solutions and 90% customer return rate, the Intelliarts team is ready, willing, and able to contribute to your best project. 
Contact us

#### Customer Support Automation with Large Language Models
Customer support is known for many resource-intensive processes, from handling data to addressing customers’ inquiries. Automation in customer support is one of the ways how businesses attempt to reduce expenses and enhance satisfaction. Large Language Models (LLM) is exactly the technology that offers a solution.
Recent studies reveal that existing implementations of LLMs contribute to a 10% increase in customer support efficiency. Another research shows that 78% of customer service specialists claim that AI and automation affect their efficiency in the workplace positively. 
In this post, you’ll discover how LLMs are integrated with customer service operations and what business benefits they can bring. Besides, you’ll review key LLM customer service applications and figure out how to understand whether your business can actually benefit from adopting LLMs. 
LLM integration with customer service operations
First, let’s get to know several key concepts:
An LLM (Large Language Model) is an advanced artificial intelligence system trained on vast amounts of text data to understand and generate human-like language.
You may familiarize yourself with the large language models list in another blog post by Intelliarts. 
At the cornerstone of this blog post is the usage of LLMs to boost customer service operations, which is defined as the following:
The usage of LLMs in customer service is the integration of large language models into customer service operations with the prospect of enhancing tedious tasks and automating labor-intensive processes. 
The integration is a multi-step procedure aimed at connecting AI-based software solutions with business processes and putting the resulting solution to good use. 
The basic LLM for customer service integration steps include:

Identify use cases: Define tasks like FAQs, ticket routing, or multilingual support.
Select an LLM: Choose a suitable model like GPT that fits your needs.
Train and fine-tune: Customize the model with industry-specific data.
Integrate into platforms: Connect to your CRM, chatbots, or helpdesk tools.
Monitor and optimize: Continuously track performance and refine workflows.

Note that LLM integration is a complex business and a technology task that typically requires the involvement of a trusted engineering partner. Should you need custom ML development services, don’t hesitate to reach out to Intelliarts. 
Key applications of LLMs in customer support
Here’s a scope of well-adopted LLMs in customer support and pain points that businesses aim to address with this technology:

#1 Chatbots and virtual assistants
LLM-powered chatbots handle routine queries and simple tasks, offering instant, consistent responses and 24/7 availability. They improve customer service efficiency while reducing the need for human agents in repetitive interactions.
Pain points addressed:

Long response times for basic queries.
Overburdened support teams handling repetitive tasks.
High operational costs for 24/7 staffing.
Inconsistent service delivery.
Limited scalability of traditional support teams.

Examples:

Duolingo’s AI Chatbots for language learning assistance
Mastercard’s AI Assistant (KAI) for financial transactions
AI-driven assistant for students by Intelliarts

#2 Automated ticketing systems
LLMs in automated ticketing classify, prioritize, and route support tickets based on content, urgency, or customer history to ensure faster resolution. They minimize manual effort while improving ticket management efficiency.
Pain points addressed:

Slow response to critical issues.
Inefficient ticket routing causing delays.
Inaccurate categorization of support requests.
High reliance on human agents for routine tasks.
Difficulty in scaling ticket management operations.

Examples:

Zendesk AI-Powered Ticketing for automated ticket categorization
Zoho Desk Zia AI for ticketing optimization

  White PaperExplore practical RAG applications and drive efficiency in your AI systems. Explore our RAG for LLMs white paper
#3 Sentiment analysis
LLMs for sentiment analysis assess customer emotions and intent within messages, tailoring responses or escalating cases as needed. This enables personalized support and improved customer satisfaction.
Pain points addressed:

Inability to identify and address frustrated customers promptly.
Lack of personalized responses.
Missed opportunities for proactive problem resolution.
Ineffective escalation processes for sensitive cases.
Difficulty in maintaining customer loyalty during negative experiences.

Examples:

Sprinklr AI Sentiment Analysis for real-time emotion tracking
Salesforce Einstein Sentiment for understanding customer moods

#4 Knowledge base management
Using LLMs for customer support, businesses can efficiently manage and retrieve information from knowledge bases, providing accurate and contextually relevant answers to customer queries. This ensures faster, more reliable support.
Pain points addressed:

Difficulty in accessing specific information from large knowledge bases.
Inconsistent or incorrect responses to customer questions.
Wasted time on manual knowledge retrieval.
Overburdened support agents due to repetitive information queries.
Inefficient knowledge base updates.

Examples:

Intercom Articles for instant knowledge base recommendations.
Freshdesk Freddy AI for automated knowledge base self-help articles and guide generation.

Explore how Freshdesk Freddy AI enables self-help for customers in the video below:

#5 Proactive issue resolution
LLMs can transform customer service by identifying potential issues through data analysis and alerting teams before customers report them. This ensures faster resolutions and improved customer trust.
Pain points addressed:

Reactive support leading to customer frustration.
Extended downtime or unresolved issues.
Lack of insights into recurring problems.
Missed opportunities to enhance customer loyalty.
Increased operational costs from preventable issues.

Examples:

ServiceNow Predictive Intelligence for proactive IT support.
IBM Watson Assistant for predicting and resolving customer issues.

You may be interested in exploring the usage of text generation models in business since it complements insights into using LLMs in customer support. 
#6 Multilingual support
LLMs for customer support enable seamless communication in multiple languages and even regional dialects, ensuring accessibility for a global customer base. This reduces language barriers without the need for human translation.
Pain points addressed:

Inability to serve non-English-speaking customers efficiently.
High costs of hiring multilingual support agents.
Delayed responses due to translation needs.
Inconsistent translations affecting customer experience.
Limited scalability in global markets.

Examples:

Microsoft Translator with AI integrated into support systems.
Shopify’s Multilingual Chat Support with AI for serving merchants and buyers in their preferred languages.

Apply for expert LLM development assistance  Transform customer support with tailored LLM solutions Contact us
The benefits and challenges of automating customer support with LLMs
There are many value-adding benefits to having automated customer support. The most impactful ones are provided in the image below:

Any technology innovation naturally comes with certain risks and potential issues. Here are common challenges related to using LLM for customer support and potential solutions to them:
#1 Accuracy and reliability: LLMs may generate incorrect or irrelevant responses.

Solution: Implement continuous training with high-quality data and monitor outputs.

#2 Bias and fairness: Models can inherit biases from training data.

Solution: Regularly audit outputs and use diverse, unbiased datasets.

#3 Data privacy: Handling sensitive customer information raises privacy concerns.

Solution: Use encrypted data storage and comply with privacy regulations like GDPR.

#4 Context understanding: LLMs may misinterpret nuanced customer queries.

Solution: Train models with domain-specific data and test for contextual accuracy.

#5 Cost management: LLM training and deployment can be expensive.

Solution: Use pre-trained models and fine-tune only where necessary.

#6 Language and dialects: LLMs may struggle with regional dialects or less common languages.

Solution: Customize models with localized datasets and ongoing updates.

Looking for assistance from an AI engineering company?Reach out to Intelliarts, and let’s discuss your project.Let's talk
Among the mentioned challenges are ones related to data preprocessing in ml. Explore how to process data in ML in another blog post written by Intelliarts experts. 
How to understand whether it is worth implementing LLMs into your customer support
The research reveals that integrating LLMs can decrease the time agents spend per customer interaction by approximately 10%, resulting in considerable annual savings and enhancing customer satisfaction. As the usage and uselessness of large language models in customer service grows, even higher results should be achievable. 
You are probably curious whether the development of an LLM for customer service is worth the investment and effort. Well, you can estimate whether the ROI of this initiative will likely be positive or negative by assessing the potential for automation with LLM in your business environment. 
Here are the scenarios when it’s advised to consider implementing LLM for customer service:

High volume of repetitive queries: If your team handles a large number of routine customer inquiries, LLMs can automate responses, saving time and resources.
Need for 24/7 support: LLMs enable round-the-clock customer engagement without requiring human agents at all hours.
Multilingual support requirements: If you serve a global audience, LLMs excel in providing responses in multiple languages efficiently.
Complex query resolution: LLMs can assist agents in resolving sophisticated customer issues by analyzing context and generating relevant solutions.
Scalability is a priority: For businesses aiming to scale their operations rapidly and without considerable expenses added, LLMs provide a cost-effective way to manage growing support demands.

At the same time, here are the scenarios where it’s recommended to consider alternative solutions:

Highly personalized service needs: If your customers require deeply empathetic or personalized interactions, human agents are irreplaceable.
Limited budget for training LLMs: Implementing LLMs requires upfront costs for training and fine-tuning. Smaller budgets might benefit from non-AI-driven automation tools.
Low query volume: For businesses with minimal customer queries, investing in LLMs may not provide significant ROI compared to other options.
Highly specialized domains: If your industry involves niche expertise or regulatory compliance, traditional expert systems may be more reliable. 

Note. Depending on the nature of the niche and the problem, it’s possible to fine-tune a custom LLM-based solution so it can still be of use in this scenario. 

Data privacy concerns: Businesses handling sensitive customer data may need to prioritize compliance and consider less data-intensive solutions.

As you can observe, using LLMs for customer service is not a one-size-fits-all solution. However, scenarios where LLM-driven instruments can prove themselves useful vastly outnumber scenarios where such solutions may have limited impact. 
Intelliarts’ expertise in LLMs
Let’s get through two examples of using LLM technology in custom software development:
LLM-enabled document review and compliance assistant

Challenge:
The legal firm faced difficulties manually reviewing a high volume of legal documents for compliance, which was time-consuming and prone to human error. They needed a solution to analyze contracts efficiently and identify regulatory discrepancies, so they reached out to Intelliarts as a trusted software engineering partner for assistance.
Solution:
Intelliarts designed a ChatGPT-powered AI assistant, which is basically an LLM model that uses Retrieval Augmented Generation (RAG) methods to enhance document analysis. The assistant identifies compliance gaps, suggests amendments, and handles both textual and tabular data. It’s trained on legal terminology and jurisdictional guidelines, ensuring accurate and context-aware responses. 
Results:
The AI assistant reduced document analysis time from several days to a few hours, improving operational efficiency. It minimized errors in compliance checks and enabled employees to focus on high-value tasks. The solution was seamlessly integrated into the firm’s workflow. 
Explore success story
GPT-driven solution for shipping contract analysis

Challenge:
Our customer, a startup in the logistics sector, aimed to simplify the complex process of comparing shipping contracts from major players like UPS and FedEx. These contracts often feature inconsistent formats, varying abbreviations, and diverse discount structures, making it difficult for small- and medium-sized businesses to identify the most cost-effective options. The customer sought a Proof-of-Concept (PoC) to validate their AI-powered business idea for contract analysis and cost optimization.
Solution:
Using the LLM technology, Intelliarts developed a PoC using GPT technology and OCR tools to extract and standardize critical contract details. The application analyzes shipping contracts, identifies key cost factors, and recommends optimal options through a user-friendly interface.
Results:
The created PoC showed 75-80% accuracy in analyzing contracts, validating the feasibility of the solution. The app simplified decision-making by highlighting weights, zones, packaging, and billing options, saving time and reducing shipping costs. Full-scale implementation is underway following customer testing.
Explore success story
Future of LLMs in customer support
One of the best things about LLMs is how well they interconnect with other technologies and their vast potential to improve customer support on multiple levels. Here are some of the trends of LLM for customer service that are currently gaining momentum:

It’s difficult to claim for sure what future trends in using Large Language Models for customer support will become commonly widespread. After all, it boils down to your business priorities and needs. Don’t hesitate to start adopting LLMs in your customer service operations where you see the opportunity.
Final take
In essence, LLMs have already revolutionized customer support by enhancing efficiency, scalability, and personalization. Whether automating repetitive tasks or enabling multilingual interactions, their versatility is unmatched. As automated customer service with large language models evolve, it starts reshaping how businesses engage with their customers, driving innovation and improving the overall customer experience.
In a complex technology project, such as creating a customer support solution using LLMs, having the right engineering partner is half the battle. Should you need assistance, don’t hesitate to reach out to Intelliarts. With more than 24 years of experience on the market delivering on-demand development, we can resolve your business needs with flying colors.
Automate your customer support with our expert LLM development services.

#### Data Analysis as the Key to Business Success
Data analysis is increasingly critical in modern business operations. Upon a proper analysis, it can provide more than enough actionable insights to drive decision making, shaping the path toward success in a niche. 
The total Business Intelligence market size is projected to reach $59.7 billion by 2025, with larger companies with over 5,000 employees showing adoption rates above 80%. The fastest-growing segment, cloud analytics, is expected to grow from $23.2 billion in 2020 to $65.4 billion by 2025 due to its flexibility and scalability. 
In this post, you’ll discover the role of data analytics in business decision making on a high level and the key benefits of using data analysis for business. Besides, you’ll explore challenges in data analytics and solutions to them. We’ll also review list staking, IoT data processing, and improving lead quality cases by Intelliarts. 
The role of data analytics in business decision making
First, let’s get to know what is data analysis in business:
Data analytics is the process of examining datasets to uncover patterns, extract insights, and support decision-making.
Data analytics with a simple data visualization instrument or an advanced ML algorithm is just assistance to human analysts who cannot comprehend and interpret thousands of data entries. Basically, the importance of data analytics in business lies in making use of the vast data businesses gather over time in their operations, contacts with customers, observations of the market, competitor research, and more. Data analytics help decision makers to make appropriate decisions that help improve business productivity and get positive growth in the market.
You can discover what data analytics techniques and tools are there from the infographic below:

Looking for consulting services at a trusted data science consulting and engineering firm? Drop the Intelliarts team a line. 
Now, let’s get to know what areas of strategic planning and decision-making data analysis in business can contribute to:
#1 Market entry

Geographic expansion: Companies utilize data analysis to identify potential new markets by evaluating demographic data and consumer behavior.
Competitive analysis: Data on competitors’ market share and customer reviews can assess the feasibility of entering a market.

Metrics to measure: Market growth rate, customer acquisition cost, price elasticity, Net Promoter Score (NPS)
#2 Product development

Customer feedback: Data analysis helps refine products by evaluating user feedback. 
Trend analysis: Companies anticipate market needs by studying consumer behavior trends. 

Metrics to measure: Customer satisfaction scores, feature usage rates, time on page for product features, adoption rates of new products, and feedback response rates.
#3 Resource allocation

Budget optimization: Analyzing historical spending helps businesses allocate budgets more effectively, identifying areas for cost reduction or increased investment, as well as determining whether an investor deck may be needed in the near future.
Operational efficiency: Data from production processes can identify inefficiencies, enabling companies to implement automation measures wisely.

Metrics to measure: Return on investment (ROI), cost savings, budget variance, efficiency ratio, and capital expenditure efficiency.
#4 Risk management

Predictive analytics: Companies predict risks using data analysis, like financial institutions assessing credit risk through historical loan performance.
Crisis management: In emergencies, real-time data analysis allows companies to quickly evaluate situations and adapt, such as adjusting supply chains during disruptions.

Metrics to measure: Risk exposure, default rates, predictive accuracy, crisis response time, and impact mitigation effectiveness.
Read also: IoT in Supply Chain
#5 Marketing strategy

Customer segmentation and targeting: Data analysis can segment customers by purchase behavior and preferences. This helps to tailor marketing efforts.
Effectiveness of marketing campaigns: Analyzing marketing channel data helps optimize marketing spending by identifying the most effective campaigns.

Metrics to measure: Conversion rates, engagement rates, customer lifetime value (CLV), and marketing ROI.
As we can see, types of data analysis in business, as well as ways to utilize them, are many. At the same time, for many companies, the main issue is how to gather data that powers up data analytics. 
You can discover what big data collection is and how it works from another blog post by Intelliarts. 
Key benefits of data analysis for business
As explained above, data analysis can be utilized in various forms of business decision making. Here are some of the benefits that data analysis brings to business: 

Cost savings. If approached wisely, data analysis significantly contributes to cost efficiency by identifying areas where expenses can be reduced without compromising on quality or performance. This is crucial for maintaining profitability, especially in highly competitive or low-margin industries.

Enhanced operational efficiency. By examining data from various business processes, organizations can identify bottlenecks and inefficiencies, leading to streamlined operations and reduced costs.

Deeper customer understanding. Through data analysis, businesses can gain a profound comprehension of customer preferences, behaviors, and needs. This insight is crucial for effective customer segmentation and targeting in marketing strategies.

Competitive advantage. Utilizing data analysis helps to understand market dynamics. It also fosters improvement based on consumer insights. This leads to stronger market positioning and the ability to adapt quickly.

Better innovation and product development capabilities. Data-driven insights highlight emerging trends. This allows companies to define the need for a new product or a feature, this way enhancing product development and lifecycle management.

In truth, it’s difficult to go without data analysis at all. The only question is how properly the processes are established in the company and how much value it can extract from data. 
On a basic level, decision-making can overview basic indicators like the number of purchases, percentage of recurring clients, and so on, make assumptions, and create strategy upon it. At the same time, data analysis helps to make sense of even seemingly unusable data and find dependencies in most areas that have been hidden previously. 
You may be interested to discover strategies of data science for business in another blog post by Intelliarts. 
Challenges in data analytics and how to overcome them
Now, let’s move to the review of common obstacles to setting up solid data analytics processes and what’s possible to do to avoid them:
#1 Data accessibility and integration
Challenge: Seamless access and integration of data across various platforms is often hindered by different data formats, incompatible systems, and data silos, complicating the aggregation and analysis of data.
Solution: Implementing robust data integration tools and adopting standards like APIs can ensure smooth data flow and accessibility, breaking down silos and enabling comprehensive analysis.
#2 Data skills and talent
Challenge: The rapid evolution of data technology outpaces the availability of skilled professionals, limiting a company’s ability to fully leverage data analytics for strategic advantage.
Solution: Investing in training programs and establishing partnerships with educational institutions can build a pipeline of skilled data scientists and enhance the data competency of existing staff.
#3 Data security and privacy
Challenge: With increasing data breaches and stringent regulatory requirements, ensuring data security and compliance is critical for maintaining trust and legal compliance.
Solution: Adopting advanced security technologies, conducting regular security audits, and ensuring adherence to data protection regulations like GDPR can safeguard sensitive information.
#4 Data quality management
Challenge: Inaccurate or poor-quality data can skew analytics, which brings misleading insights and potentially costly business decisions.
Solution: Implementing comprehensive data quality management frameworks and continuous monitoring can maintain high data standards, ensuring that analyses are based on accurate and reliable data.
#5 Cost management
Challenge: Managing the high costs of data storage, processing, and analysis requires efficient resource management, especially as data volumes grow exponentially.
Solution: Leveraging cloud storage and efficient data management practices, such as data deduplication and archival strategies, can significantly reduce costs associated with large-scale data operations.
You can discover more expert insights into data analytics challenges from the video below:

Data analytics in business: Success stories by Intelliarts
Here at Intelliarts, we have substantial experience implementing data analytics solutions to various business needs. Here are some of our related past cases:
ML-powered list stacking and predictive modeling for the real estate business
The Intelliarts’ customer, an established real-estate agency, reached out to us with the request to help to make use of their vast real estate market and homeowners data. The goal was to predict the homeowners that are most likely to sell their houses to boost the productivity of real estate agents.
In this project, we created a complex ML solution consisting of the realtor and the investor ML models. It was trained on a dataset of 60 million data records,1400 property characteristics, demographic data, and skip tracing data.
The resulting solution successfully helps to detect the most motivated sellers with an accuracy of over 70%, which is a great performance for such a technology application. It boosted Intelliarts’ customer’s monthly revenue by 600%. 
Explore ML-power list staking and predictive modeling case
Processing IoT data for OptiMEAS
In this case, the customer, who is the distributor of intelligent IoT, reached out to Intelliarts with the request to help use the full potential of IoT data they collected.
Our team created a powerful data processing pipeline that helps to gather data more effectively as well as to process, analyze, and visualize it. The solution was then transformed into a B2B SaaS platform capable of drawing insights from vast data in different formats. Examples of insights are energy consumption, customer usage habits, downtime patterns, and more. Now, the OptiMEAS team successfully utilizes a more data-driven approach to decision-making. 
Explore IoT data processing and analysing case
Improving lead quality and agent efficiency through data analytics

Our customer, who is a health insurance company, requested assistance from Intelliarts as an insurance software development company with data analytics. They needed to optimize lead quality and agent efficiency and increase sales conversions.
In this case, our data scientists conducted sales data analysis based on destination sectional center facility (SCF) codes to predict the average profit in the regions and focus on those most profitable. After that, our team of engineers created a two-part ML solution that can help detect regions with the most promising leads while also assigning those leads to high-performing insurance agents. 
As a result, the end solution contributed to a 5% increase in the customer’s lead quality and enhanced insurance agent efficiency by 3%. It also helps spend less time on regions with low predicted sales. 
Explore improving lead quality and agent efficiency case
How to use data analytics in business decision making?
You can discover what steps to follow to become a data-driven organization in the infographic below:
 
While the provided steps form the cornerstone of data analysis implementation, there is much more to add. Here are some higher-level tips, strategies, and advice to follow while shaping your path to becoming a data-driven business:

Identifying key data points

Before diving into analytics, it’s essential to identify which data points are the most relevant to your business objectives. These might include sales figures, customer engagement metrics, operational costs, or supply chain data. Focus on data that directly impacts your critical performance indicators.
Pro tip: Don’t attempt to start tracking all the indicators possible. In such occasions, minor statistics often interfere with larger-scale, more important numbers, making it more difficult to shape vision and extract actionable insights. Start from several core indicators, and then, add more upon it. Always prioritize the indicators that evidence the same pattern. From the strongest to the weakest indicator, you need to always know how to prioritize stats in case there are any discrepancies. 

Utilizing analytical tools and techniques

While the choice of exact instrument to initialize depends on the strategy, business model, data type, and more, there are several tools and techniques that are always useful in analyzing most data points:
Microsoft Power BI: Useful for creating dashboards and interactive reports.
Tableau: Known for its powerful data visualization capabilities.
Google Analytics: Best for tracking and understanding website traffic and user behavior.
SQL: Essential for data manipulation and retrieval from databases.

Developing predictive models

Predictive modeling involves using statistical techniques to forecast future outcomes based on historical data. Using tools like Python’s Scikit-Learn library or R’s caret package can help to develop models that predict customer behavior, sales trends, and other important business metrics. Techniques such as regression analysis, decision trees, or neural networks are commonly used depending on data complexity and the prediction required.

Incorporating real-time data for agility

In opposite to historical data incorporating and acting upon real-time data into business processes allows for quicker and more informed decision-making. This approach also supports agile decision-making as it provides a continuous flow of data that can highlight trends and anomalies almost instantly, thus empowering businesses to act swiftly and efficiently without the delay typically associated with batch data processing.

Fostering a data-driven culture

It’s advisable to go beyond merely equipping teams with analytical tools and training them on using these instruments. Here’s how you can cultivate an organizational mindset that later on will pay back with better outcomes of data analysis:

Provide in-depth education that goes beyond the role responsibilities
Ensure accessibility of data within the allowed restrictions, providing the broader picture for every worker involved
Encourage experimentation on both real and abstract datasets 

Regular reviewing and adjusting data analysis approaches

Regularly reviewing the outcomes of data-driven decisions and the models used is more than essential. This will ensure that you’re moving in the right direction and the outcomes of data analysis are beneficial rather than detrimental overall. This may involve adjusting models, refining data collection methods, or redefining key metrics in response to changes in the business environment or strategy.
We recommend additionally exploring how to use big data and AI in business, as this topic is complementary to data analysis. 
Final take
Data analytics supports business decision-making by helping to save costs, improve operations, and understand customers better. Addressing challenges like data integration, skill gaps, and data quality enables companies to use data analytics effectively. A data-driven culture ensures continuous improvement and agility, keeping companies competitive.
Here at Intelliarts, we have been on the market for more than 24 years offering big data consulting services and helping companies to translate their business data into actionable insights and transfer them to value. Our expert engineers, data scientists, and data analysts are ready, willing, and able to address your business needs. 
Turn your business data into actionable insightsOur experts are here to helpContact Us

#### Data Pipeline Components and Use Cases
In the highly digitalized world, the usage of information is key to business success. For a company to effectively handle both structured and unstructured data from various sources, it requires a set of tools and processes, or to put it simply, a data pipeline. 
As a C-level executive or a software engineer, you may be particularly interested in the usage of different types of data pipelines architecture for your company’s benefit. This post allows you to delve into the concept of data pipelines and their architecture, explore key features these processing methods may have, what business value their usage has to offer and data pipeline examples.
Data pipeline vs. big data pipeline
Data pipeline and big data pipeline are two similar concepts, which are often used interchangeably, which is not exactly correct.  Let’s delve into what is a data pipeline and big data pipeline, what is the difference between them and how both types of data pipeline works:

Data pipeline refers to a set of processes and systems involved in moving and integrating data from various sources. This comprehensive system is created to collect, structure, and transmit data for the purpose of obtaining precise and useful insights, with diverse data pipeline use cases.
Big data pipeline is the same data pipeline designed to handle much larger volumes of data and more complex data processing tasks. It often deals with data that is too voluminous, diverse, or fast-moving to be processed with traditional data processing tools. They may process data in batch, stream processing, or other methods. Data pipeline software is instrumental in managing these tasks efficiently.

Both data processing concepts involve steps of data collection, data transformation, and data storage. In the case of big data processing pipelines, the last step is obligatory data analysis, which is often done with the assistance of ML models. Regular data pipelines allow access to data immediately after its storage.
The most important difference between big data pipelines and regular pipelines is the flexibility to process massive amounts of data.
A data pipeline needs to be scalable to meet the organization’s big data needs. Without proper scalability, the system’s processing time may significantly increase, potentially taking days or even weeks to complete the task.
You may discover more about the role of data pipelines in an ML project from our other post.
Data pipeline components and building blocks
In this section, let’s deepen into how a data pipeline works and explore data pipeline architecture examples. Its architecture is designed to efficiently manage all data events, simplifying analysis, reporting, and usage. Such a digital system as a data pipeline encompasses the following six components and processes:

Data sources or origin
Origin is the initial data entry point, forming the backbone of the data collection pipeline. It may encompass a wide variety of data sources such as transaction processing applications, IoT devices, social media, APIs, public datasets, and storage systems.
Data destination
The destination is the endpoint of the data pipeline where data is transferred, and it is use-case specific. Data might be directed to power data visualization tools and analytics applications or stored in data lakes or other types of data storage, shaping data pipeline infrastructure. 
Data storage
Data storage refers to systems where data is preserved at different stages as it moves through the pipeline. Examples include data lakes and lakehouses, data warehouses, databases, local storage vs cloud storage, and Hadoop Distributed File System (HDFS), forming the backbone of data pipeline structure. Data storage choices depend on various factors, for example, the volume of data, frequency, and volume of queries to a storage system, uses of data, etc. Investing in proper data warehouse development ensures that these storage solutions remain efficient, scalable, and secure.
Incorporate big data into your processes.Harness the power of data, make informed decisions, and boost performanceLearn more
Data processing
Processing encompasses the series of operations involved in ingesting data from sources, forming the core of data engineering pipeline architecture. It also concerns data pipeline processes of storing, transforming, and delivering data to the specified location. 
You can also explore our Data Engineering Consulting Services.
While processing is intertwined with dataflow, it focuses on the mechanisms used to facilitate this movement. Techniques of pipeline data processing may include data extraction from source systems, database replication, data streaming, etc.
Data workflow
Data workflow defines a sequence of processes or tasks and their dependence on each other in a data pipeline. Key concepts include job, upstream, and downstream. A job is a discrete work unit performing a specified task on data. Upstream refers to the data source entering the pipeline, whereas downstream refers to the output of the pipeline.
Data monitoring
The monitoring component aims to evaluate the functionality and efficiency of the data pipeline and its individual data pipeline stages. It checks the pipeline’s scalability with increasing data loads, maintains data accuracy and consistency throughout processing stages, and ensures data integrity.
Should you need a trusted partner for building a data pipeline or big data pipeline, don’t hesitate to reach out to ML engineers from Intelliarts AI.
Types of data pipeline architectures
Let’s review six common data pipeline examples to understand better what a data pipeline is. 
ETL and ELT data pipelines
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) data pipelines are two approaches for collecting, processing and storing data. As mechanisms of handling data, they belong to the processing component, as detailed previously. These types of data pipelines are foundational to an entire system for data handling. Here’s their concise overview:

ETL data pipeline

In an ETL pipeline, the software first extracts data from different source systems, then applies transformations to make it suitable for analysis. Means of analysis may include aggregating data or resolving inconsistencies. Finally, the software loads data into a data warehouse or data mart. 
This method is commonly used when there is a need to regulate the transformation process and when the data warehouse cannot handle the transformation itself.

ELT data pipeline

The ELT pipeline retrieves data from source systems, transfers it to the data warehouse in its raw form, and then applies transformations to it. 
This method utilizes the advanced computing abilities of contemporary data warehouses to transform data. It is commonly employed when the data for analysis should be left in an unprocessed state. When choosing a platform for this approach, an ETL tools comparison should include factors like connector coverage, transformation flexibility, and cost at scale.

The choice between ETL and ELT largely depends on the specific needs of a project or organization, including the volume and variety of data, the computational power of the data warehouse, the desired speed of data availability, and the need to store data in its raw form.
Batch and stream data pipelines
Batch and stream data pipelines represent two distinct approaches to data processing. They define the way in which data is accumulated and moved through the pipeline. Here’s a brief overview of these mechanisms:

Batch data pipeline

This type of data pipeline process data in large, discrete batches. It collects data over a certain time period or until a certain amount has been accumulated and then processes it all at once. 
This method is suitable for non-time-sensitive operations and for cases when a business has to deal with massive volumes of data. Possible scenarios for using batch processing are daily or weekly reporting, data synchronization, or large-scale data transformation.

Stream data pipeline

Stream processing pipelines handle data in real-time or near-real-time. It’s achieved by processing each data item individually as it arrives in the pipeline, which allows for continuous, instantaneous data processing.
This method is particularly suitable for real-time analytics, real-time decision-making, and event-driven applications. Possible scenarios for using stream processing are fraud detection, live monitoring, and real-time personalization systems. 

Cloud and on-premise data pipelines
Cloud and on-premise data pipelines represent two types of infrastructure setups for handling data processing.

Cloud data pipeline

Cloud data pipelines refer to the infrastructure that is hosted on the cloud by third-party service providers. This setup can provide various advantages such as scalability, reduced operational cost, flexibility, and accessibility from any location. 
This method is particularly beneficial for smaller organizations or startups as it doesn’t require significant upfront infrastructure costs. Examples of cloud services include AWS Data Pipeline, Google Cloud Dataflow, and Azure Data Factory.

On-premise data pipeline

On-premise data pipelines are set up within an organization’s own infrastructure. They typically offer more control over data and processing since the organization owns the servers and other related hardware. 
This method can offer enhanced data security and privacy, which is particularly important for organizations dealing with sensitive data or operating under strict regulatory environments. However, it comes with higher upfront costs and the need for regular maintenance and upgrades, which the organization must handle itself.
It’s also worth mentioning that combinations of the listed above data handling and data processing mechanisms and infrastructure setups are possible. These are called hybrid pipelines. They may be as complex as the business require them to be. Most intricate systems can be easily designed to combine elements of ETL and ELT, batch and stream, and have both on-premise and cloud infrastructure. 
You may also be interested to learn about ML-based data extraction in the insurance business.
Key features of big data pipelines
Now, let’s proceed with learning or refreshing the knowledge about eight main attributes that big data pipelines may have. Here we will also mention the data pipeline architecture best practices.

Scalable cloud-based data pipeline architecture. It enables dynamic adjustment of computational resources based on data volume. Such architecture grants flexibility in handling varying data loads efficiently, without compromising speed or performance. It is essential for managing big data in the cloud.
Fault-tolerant architecture. It ensures continuous operation of the data pipeline even in the event of component failure. It automatically recovers or switches to backup systems, preventing data loss and maintaining the pipeline’s reliability and consistency.
Transforming high volumes of data. This involves the efficient processing of large data sets. Big data pipelines transform, clean, and normalize data at scale, making it suitable for further analysis and aiding in data-driven decision-making.
Real-time analytics and data processing. This allows immediate data processing and analysis as soon as it enters the pipeline. This feature enables businesses to gain instant insights from their data, supporting real-time decision-making.
Self-service management. It empowers users to manage their data workflows with minimal technical support. It allows non-technical users to access, prepare, and analyze data, democratizing data use this way.
Streamlined data pipeline development. This grounds on DataOps — a methodology that combines diverse technologies and processes in order to reduce development and delivery times. Streamlining pipeline development and deployment makes it easier to modify or scale pipelines as well as accommodate new data sources.
Exactly-once processing. This means that dataflow commits every record to storage exactly once, eliminating duplicates. Exactly-once processing is crucial for maintaining data integrity and accuracy and for ensuring consistent analytical outcomes.
Automated data quality checks. They automatically deliver and address data anomalies, inconsistencies, or missing values. This way, it preserves the integrity of the dataset, ensuring the accuracy and reliability of data throughout the pipeline.

It’s advised to create data pipelines with these best practices in mind. This way, you’ll ensure the high performance of your data processing infrastructure and minimize the risks associated with its potential malfunctioning. 
Should you require assistance with building a data pipeline or big data pipeline architecture, don’t hesitate to reach out to our team of well-seasoned AI and ML engineers.
Business use cases of data pipelines
Here are some of the ways in which data pipelines can help businesses get a competitive edge, enhance their operational performance, and meet industry standards:

Business Intelligence

Data pipelines provide a holistic view of business operations. This critical insight enables leaders to spot trends, make informed strategic decisions, and optimize overall performance.

Predictive Analytics

Data pipelines can power predictive analytics tools to forecast trends, customer behaviors, and market movements. This foresight helps executives to take proactive measures.

Fraud Detection

With data pipelines, businesses can rapidly process and analyze vast transactional data making it possible to detect fraudulent activities in real-time. This can protect the company’s assets and maintain customer trust.

Real-time Reporting

Data pipelines can stream real-time data, providing up-to-the-minute information. It helps to maintain the pulse of operations, track performance metrics instantly, and respond to issues as they arise.

Customer Segmentation

Data pipelines aggregate data from various customer touchpoints, enabling advanced segmentation. This helps in personalizing marketing strategies which leads to increased customer loyalty and improved sales performance.

Supply Chain Optimization

Data pipelines can uncover hidden inefficiencies and bottlenecks in the supply chain. Such insights facilitate more effective IoT supply chain optimization, and resource allocation, improve logistics and inventory management.

Risk Management

Data pipeline helps to quickly process diverse data sets to identify potential threats and vulnerabilities. These insights help businesses anticipate risks, enforce preventative measures, and ensure regulatory compliance.
6 incredible advantages of using data pipelines in business
Making use of data pipelines for small to large-scale businesses is totally recommended. Here are some of the advantages of data pipeline:

Need help with applying data pipelines to your business tools?
In one of our past projects, the Intelliarts team of engineers built an end-to-end big data pipeline for a US-based data-driven market research company. We developed a solution that enables the customer to manage data from the moment of data collection to delivery.
Here at Intelliarts, we specialize in data pipeline development that is tailored to your business needs. Our team of experienced professionals can design and implement efficient data pipelines, transforming your data into actionable insights. Our efforts in this niche were recognized by TechReviewer.co which included Intelliarts in their list of the Top Big Data Analytics Companies in 2023.
Building and managing data pipelines
There’s nothing more helpful for grasping something new than concrete tips and advice. Let’s review some of the best insights on building and managing data pipelines that practicing data engineers at Intelliarts can share:

Design with scalability and modularity in mind

Ensure your data pipeline can handle both current and projected data loads. Use distributed data processing frameworks (e.g., Apache Spark, Apache Kafka). Build your pipeline in modular chunks, allowing for easy testing, debugging, and replacement of individual components.

Ensure data quality and integrity

Implement data validation checks at every stage of the pipeline. For instance, ensure that data types, ranges, and formats match the expected schema. Actively monitor your pipeline for failures, delays, or anomalies. Tools like Apache Airflow or Apache NiFi provide monitoring capabilities out-of-the-box.

Maintain idem potency and retries

Design processes so that they can be safely retried without creating duplicate or incorrect results. Incorporate automated retry mechanisms, especially when interfacing with external services that might occasionally fail.

Document everything

Clearly document the design, architecture, and purpose of each component of your pipeline. Ensure that configuration details, schema definitions, and assumptions about the data are documented. Metadata management tools can also help keep track of lineage, schemas, and transformations.
Surely, there is much more to add about data pipeline management. But the detailed above can help you break the barrier to entry.
Final take
As can be concluded, data pipelines heavily impact how businesses handle the data in their digital environment. The detailed six types of data pipeline architectures and their features can be combined to create a perfect-fit solution to a company’s needs. The resulting system should be able to flawlessly handle unstructured data of varying types, be scalable, and reliable.

 Building data pipelines for your business is a task that requires high expertise and proficiency in ML. As a C-level executive, you may consider contacting a trusted ML development company. This way, you will get professional help with developing and integrating data pipelines into your infrastructure, which may result in numerous operational advantages for your company.
Streamline your data flow with a custom pipelineIntelliarts data engineers are here to help.Contact us

#### Data Preprocessing in Machine Learning [6 Best Practices]
In machine learning (ML), data preprocessing is one of the essential steps that largely impact the outcomes of the development and usage of any created ML model. So, for the management of tech companies and ML enthusiasts, having a proper insight into nuances and best practices of data preprocessing is essential for optimal project planning and execution.
The ML market size is expected to grow at a CAGR of 17.15% up to 2030. It clearly indicates the strong tendency of companies worldwide to join digital transformation, investing in ML-based solutions to diverse business needs. This directly implies the growing importance of solid data preprocessing.
In this post, you’ll discover more about data preprocessing and its actual importance. Besides, you’ll learn 6 best practices for data preprocessing, which are based on the vast experience of Intelliarts with pre-processing diverse data for various projects such as DDMR data marketplace or analyzing IoT data for OptiMEAS. Additionally, you’ll review real-life case studies of successful data preprocessing.
Maximize ML model accuracyQuality data preprocessing leads to better insightsLearn more
What is preprocessing in machine learning and why it’s due
Preprocessing in machine learning is the preparation of data through cleaning, normalization, and feature engineering to improve model performance and accuracy.
You may wonder why you even need data preprocessing when you already have real-life, seemingly suitable data. Here are the reasons why training and then utilizing a model can bring results that are far from optimal if the preprocessing step is excluded:

Lack of sufficient data
Poor quality data
Overfitting
Underfitting
Bad choice of algorithm
Hyperparameter tuning
Bias in the dataset

Basically, for these and possibly other reasons, you will not be able to build a model that shows expected performance in actual business processes. So, the data preprocessing step is rather unavoidable.
You can see what place data preprocessing occupies in the data preparation process in the infographic below:

Additional information on machine learning advantages can be found in another Intelliarts blog post.
Importance of Preprocessing in Machine Learning
Let’s take a look at three main values that preprocessing has to offer to the overall data preparation procedure crucial for machine learning development:
Impact on model performance
Cleaning and preparing the data beforehand ensures the models are trained on high-quality, relevant data. This process includes:

Normalization. Scaling data to a uniform range.
Feature encoding. Transforming categorical data into a machine-readable format.
Handling missing values. Filling in or omitting gaps in data to prevent bias and improve model reliability.

Accurate preprocessing can significantly increase the accuracy of the model while reducing the time and computational resources needed for training.
Avoiding garbage in, garbage out (GIGO)
The principle of “garbage in, garbage out” is particularly relevant in the context of machine learning. Without proper preprocessing, even the most sophisticated algorithms can yield inaccurate or irrelevant results. The main idea here:
The inaccuracies in real-life ML cases often result from inadequate training datasets because machine learning models learn patterns from the data they are fed.
If the data is incomplete, inconsistent, or laden with errors, the models will internalize these flaws, leading to poor performance. Preprocessing ensures the data is clean and consistent, laying a solid foundation for the models to learn from.
Enhancing data quality
Preprocessing directly enhances the quality of data, making it more suitable for machine learning models. Possible measures here are:

Noise reduction techniques. Removing irrelevant or redundant features to focus the model’s attention on the most informative aspects of data.
Data transformation. Applying mathematical transformations like log or square root to stabilize variances.
Encoding categorical variables. Converting categorical data into numerical format for model compatibility.
Dimensionality reduction. Techniques like Principal Component Analysis (PCA) reduce data dimensionality.
Data sampling. Balancing class distribution in imbalanced datasets.

By improving data quality, preprocessing ensures that machine learning models can extract the most value from data, leading to more accurate and generalizable insights.
If you’re interested in data processing and machine learning, you may also be interested in exploring the post on the topic of big data and AI.
Top 6 best practices for data preprocessing in machine learning

Now when you’re aware of what preprocessing serves for, let’s find out what best practices for ML development are there to handle one or another data preprocessing technique in a proper way:
#1 Data cleaning
Proper data cleaning is foundational to any machine learning project, as it ensures the dataset is free of errors and inconsistencies that could skew results. Here is what the Intelliarts team of big data engineers has to advise you:

Handling missing values: Impute missing values using statistical methods (mean, median) for numerical data or mode for categorical data, or remove rows/columns with excessive missing values to maintain data integrity.
Correcting outliers: Identify outliers through statistical tests or visualization techniques and correct them using capping, transformation, or removal to prevent distortion of the dataset.
Standardizing formatting: Ensure consistent data formats across the dataset, including date formats, capitalization, and units of measurement, to avoid discrepancies during analysis.

Conclusively, clean data is the bedrock of reliable machine learning models, making meticulous cleaning an indispensable step.
#2 Data transformation
Transforming data into a format that can be easily analyzed is vital for machine learning algorithms to perform optimally. Here are the practices for every data transformation technique:

Normalization and scaling: Apply normalization (e.g., Min-Max scaling) or standardization (Z-score normalization) to scale features, ensuring no attribute dominates others due to its scale.
Feature encoding: Convert categorical data into numerical formats using one-hot or label encoding, allowing algorithms to process and learn from such data effectively.
Skewness correction: Apply logarithmic, square root, or Box-Cox transformations to reduce skewness in data distributions, enhancing model accuracy.

Effective data transformation facilitates the smooth and efficient training of machine learning models by making the data more amenable for analysis.
#3 Data reduction
Reducing the dimensionality of the dataset can significantly speed up training times and improve model performance by eliminating redundant features.

Dimensionality reduction: Employ techniques like PCA to reduce the number of features while retaining most of the variability in the data.
Feature selection: Use methods such as mutual information, chi-square test, or feature importance scores to keep only the most relevant features.
Data sampling: In cases of large datasets, use sampling methods to create a manageable subset that accurately represents the whole dataset.

Data reduction should not only simplify the model but also enhance its interpretability and reduce the risk of overfitting.
Looking for a trusted expert in machine learning and big data? Drop engineers from Intelliarts a line, and let’s discuss opportunities for your business.
 Research PaperBoost Your Insurance Sales: Key Strategies for Effective Predictive Lead Scoring Download now
#4 Handling imbalanced data
Imbalanced datasets can lead to biased models that perform poorly on minority classes, making it crucial to address this imbalance.

Resampling techniques: Balance the dataset by either oversampling the minority class or undersampling the majority class to prevent model bias.
Synthetic data generation: Tools like SMOTE can generate synthetic samples for the minority class, enhancing the diversity of data available for model training.
Algorithmic adjustments: Opt for algorithms that are less sensitive to class imbalance or adjust the algorithm’s weights to make it more attentive to the minority class.

Addressing data imbalance is key for developing fair and accurate models, especially in applications where minority class predictions are critical.
#5 Data integration
Combining data from various sources can enrich the dataset but requires careful handling to ensure consistency and quality.

Schema and entity resolution: Reconcile differences in data structure and entity representation across sources to prevent conflicts.
ETL processes: Use extract, transform, and load techniques to clean, merge, and integrate data from different sources seamlessly.
Duplicate elimination: Identify and remove duplicate records to maintain the integrity of the dataset, preventing skewed analysis and results.

Effective data integration provides a comprehensive view of the dataset, enabling more nuanced insights and predictions from machine learning models.
#6 Data splitting
Correctly splitting the data into training, validation, and testing sets is crucial for assessing the model’s performance accurately.

Representative splitting: Ensure each split reflects the overall distribution of the dataset, including stratification for classification problems to maintain class ratios.
Time-series split: For time-dependent data, use chronological splits to preserve the temporal order and avoid future data leakage into the training set.
Validation techniques: Employ techniques like cross-validation to utilize the dataset efficiently for training and validation, ensuring robust model evaluation.

Proper data splitting strategies are essential for validating model performance and generalizability to unseen data, thereby enhancing the reliability of machine learning applications.
Intelliarts case studies
Let’s take a look at some of Intelliarts’ success stories related to data preprocessing in machine learning:
Improving lead quality and insurance agent efficiency

The customer requested help detecting leads who are more likely to purchase a health insurance policy. They provided their vast databases for us for this purpose.
The Intelliarts, as an insurance software development company, created an ML-based solution that preprocesses data and then utilizes it to determine leads that are more prepared to make a purchase based on multiple factors like demographics, region, age, gender, and more.
The application of machine learning for insurance by Intelliarts team contributed to a 5% increase in the customer’s lead quality and increased agent efficiency by 3%.
Discover more about the improving lead quality case study.
Processing and analyzing IoT data for OptiMEAS

The customer’s request was to help utilize the data they gather from their IoT devices more effectively.
To help the customer make use of the vast IoT data it gathers, Intelliarts built a fully-fledged data processing pipeline. The end solution can collect, analyze, process, and visualize big data effectively.
With the help of the data pipeline we built, OptiMEAS can now transform its vast data into actionable insights and make informed decisions.
Discover more about the OptiMEAS case study.
Building a B2B DEI-compliant job sourcing platform for ProvenBase

The customer’s request was to help them establish ML-based processes for automated CV parsing and candidate searching and scoring.
To help the customer, Intelliarts created an ML solution composed of several trained ML models. One of them is an AI-based job description analyzer that can derive key information for sourced candidates to be used for scoring later.
Our solution enabled automated candidate profile sourcing and candidate profile matching. The end model shows over 90% accuracy.
Discover more about the ProvenBase case study.
Final take
It’s impossible to go without data preprocessing in nearly any ML development project. Successful gathering and processing of vast amounts of data significantly increases the chance of building a working solution after a minimum number of iterations and with the best operational outcomes on real-life data. Following best practices in data preprocessing is how you maximize the value of the effort put into preparing your data.
Having the right tech partner for running your ML project is half the battle. Consider reaching out to expert data science and ML engineers from Intelliarts. With 24+ years of experience, hundreds of projects successfully delivered, and substantial experience with businesses from numerous industries, we are ready, willing, and able to contribute to your best project.
Skilled ML experts at your serviceWe use advanced machine learning to drive innovation for your businessTalk to us

#### Deep Learning for Predictive Maintenance
The global predictive maintenance market is projected to hit $60.13 billion by 2030, growing at a CAGR of 29.5% from 2023 to 2030. At the heart of this momentum is deep learning for predictive maintenance, a powerful approach that enables businesses to detect equipment issues before failures occur, minimize downtime, and expand asset lifespans.
At Intelliarts, we’ve seen first-hand how advanced machine learning models transform maintenance strategies. For instance, in our asset failure prediction project, we helped a partner prevent costly equipment breakdowns using advanced ML models. In this article, we’ll share our experience and explore main deep learning techniques for predictive maintenance, comparing their advantages, disadvantages, and use cases.
Deep Learning Techniques for Predictive Maintenance Solutions
Deep learning can be considered a subset of machine learning (ML); however, its capabilities and business implementations are different. The key differences between predictive maintenance and machine learning include:

Data & compute: Machine learning works with smaller, structured datasets (e.g., sensor logs), while deep learning uses much larger and more complex data (like images or sound) and typically needs stronger hardware.
Hyperparameters: Classical machine learning has a limited capability for hyper-parameter tuning, while deep learning can be tuned in various ways so it can uncover deeper patterns.
Outputs: Machine learning usually provides clear outputs like risk scores or failure probabilities, while deep learning can detect hidden anomalies and recognize issues from unstructured data such as vibration signals or equipment images.

With these differences in mind, let’s look at the main deep learning approaches used in predictive maintenance and how each works in practice.
Auto-Encoder
The first deep learning method to be discussed is Auto-Encoder. This is a type of unsupervised artificial neural network that learns how to compress and encode data effectively and then reconstruct it from a reduced encoded representation to a form as close to the original as possible. Auto-Encoder is built to reduce data dimension by figuring out how to reduce the noise in the data. Here is a picture to illustrate how it works:

Among their popular usages are anomaly detection and dimensionality reduction. In predictive maintenance, Auto-Encoders have proven especially useful when labeled fault data are scarce. Researchers increasingly combine stacked auto-encoders (SAEs) with health indicators to capture degradation stages of bearings, batteries, and compressors. Studies show they are effective at extracting meaningful features directly from raw signals without heavy manual feature engineering.
Use Case of Deep Learning: Remaining Useful Life (RUL) Prediction of Bearings
One of the key reasons for failures in industrial machinery is the degradation of bearings. Having a correct prediction for degradation is crucial for introducing the best maintenance strategy, which will help manufacturers lower overall costs and eliminate unwanted downtime or even casualties. There is an Auto-Encoder-based solution for RUL prediction of bearings.
Human experts usually base approaches for RUL prediction on manual feature extraction and selection. However, it is possible to leverage a groundbreaking two-stage automated approach using Deep Neural Networks (DNNs) to estimate the RUL of bearings.
First, it’s recommended to implement a denoising Auto-Encoder-based DNN to classify acquired signals of the target bearings into multiple degradation phases. The representative features are received directly from the raw signal after the training process of the Deep Neural Network.
Second, based on shallow neural networks, the regression models can be developed for each separate degradation phase.
The resulting remaining useful life is calculated by smoothing regression results from multiple deep learning predictive models. This approach already showed satisfactory performance and delivered precise predictions for real-life bearing degradation datasets during different operating conditions.
Recurrent Neural Networks
Another deep learning for predictive maintenance technique is Recurrent Neural Network (RNN). RNNs are a class of artificial neural networks where the connections between units form a directed graph along a sequence. This factor enables RNN to exhibit dynamic temporal behavior for a time sequence. RNNs can use their internal memory to process sequences of inputs, which is impossible for feed-forward neural networks. That’s why they are perfect for such challenging tasks as speech recognition or unsegmented, connected handwriting.
When your model needs a context to generate an output based on input, a Recurrent Neural Network can solve this challenge. Sometimes, the context is a deciding factor in the correct output of the model.
To make an analogy, let’s say you read the book and the whole time through you have the context because you remembered all the events from the very beginning, and you can relate to events correctly. Just like that, Recurrent Neural Network remembers events in context, unlike other neural networks, in which inputs are being processed independently of each other.
If we look at the text recognition example, RNN remembers the relation among words in a given sentence to achieve better results in predicting a missing word. This understanding by RNN is achieved during the training process and is possible because of the creation of smaller networks with loops in them:

This is how the loop structure looks. Thanks to this structure, the neural network is able to process a sequence of inputs.

The network takes the x(0) and outputs h(0), which combined with x(1) is the input for the next step. After that, the same principle repeats as many times as needed. As illustrated, a Recurrent Neural Network works by remembering context from the previous output.
 How to reach zero downtime, reduce costs, and optimize your manufacturing processPredictive Maintenance White Paper Download the white paper
Use Case of Deep Learning: Predictive Maintenance of Mill Fan Systems
There is a solution that allows for predicting the amplitude of vibration for a mill fan system. In the case study, successful results were achieved thanks to monitoring the vibration of the rotor bearing block located the closest to the mill. The observation lasted for an entire calendar year, with the replacement of the rotor of the mill fan happening in the first month. The rotor worked after the replacement for nearly 380 hours. Having the data before and after the replacement, the experts concluded that the new rotor has higher amplitudes compared to the old one.
So, based on preliminary analysis of sensor data the nearest to the mill rotor bearing block, it was decided to build a nonlinear model that would enable the timely prediction of vibrations tendencies that would signal on system breaking down. The expert team used two types of Recurrent Neural Networks in a comparison. The first one was historical Elman architecture, and the second one was the newly developed type of RNN called Echo state networks (ESN).
Early analysis by the expert team showed the advantages of ESN over the Elman network. In fact, Normalized Root-Mean-Square Error (NRMSE) for Elman architecture equaled 0.52 on a training set and 0.69 on a testing set. The total training time took over 5 hours. For ESN architecture, however, NRMSE on a training set was 0.48 and 0.68 on a testing set, with a total training time of around 10 minutes. The obtained results demonstrated that ESN architecture works better in terms of accuracy and training time. With this approach, we can effectively predict the amplitude of vibration for a mill fan system.
LSTM Networks
This is an expansion of the idea of a Recurrent Neural Network. Long Short Term Memory networks (LSTMs) are a type of RNN that can learn long-term dependencies. Hochreiter & Schmidhuber introduced this concept in 1997, and since then many other experts have popularized and improved it.
LSTM networks have proven to be great in solving a lot of complex challenges in areas like speech recognition, time series prediction, robot control, market prediction, and others. They are built to remember information for long periods by design.
Recurrent Neural Networks, as we covered above, look like a chain of repeating modules. Standard RNNs have a simple structure with a single tanh layer:

LSTMs structure is also chain-like, but each repeating module has four neural network layers instead of one:

This is what the notation inside a module means:

Each line carries an entire vector from the output of one node to the input of others. Pink circles stand for pointwise operations, while yellow boxes include learned neural network layers. The merging line denotes concatenation, and a forking line denotes its content being copied, and the copies are transferred to different locations.
The main idea behind LSTMs is the cell state. As you see, the horizontal line (the cell state) is running through each module at the top of the diagram. It runs through an entire chain with minor interactions, making the information flow along the entire chain easily while staying unchanged:

The LSTM can remove from or add information to the cell state, which is regulated by structures called “gates”:

Recent research confirms that deep recurrent neural networks (DRNNs), particularly LSTMs and GRUs, are among the most widely used deep learning predictive models for Remaining Useful Life (RUL) prediction. They excel at discovering hidden dependencies in long sequences of vibration, current, and temperature signals.
Use Case of Deep Learning: Predict the current state of an engine
LSTMs can be efficient in estimating the degradation level of engines. And here is one of the case studies on this.
Before building a model, the following equation for percentage residual life of engine was used:
R(t) = (Time to failure – Current Age) / Time to failure
When R(t) equals one, it shows that the remaining life of an engine is at 100% point. When R(t) equals zero, it means that the engine has broken down and its remaining life is at 0%. The values of R(t) were divided into the following classes to indicate the life conditions of the components:

Healthy – the engine is at the very beginning of its lifetime
Caution – the engine is operating intensively and needs to be taken care of. Action is needed to avoid the Repair state
Repair – the engine needs repair
Failure – this is the most unwanted state that leads to a breakdown

The solution was to set up an alert mechanism before a component moves from Caution to a Repair condition. This solution would allow fixing or replacing the component, avoiding the stoppage of the system.
LSTM algorithm was implemented to build an efficient remaining life of an engine prediction model. After performing preliminary calculations and data analysis, the following feature selection process took place:

The total temperature at fan inlet (°R), pressure at fan inlet (psia), demanded fan speed (rpm), demanded corrected fan speed (rpm) sensor measurements were dropped from the input because of having zero variance.
The total temperature at HPC outlet (°R), total temperature at LPT outlet (°R), total pressure in bypass-duct (psia), total pressure at HPC outlet (psia), ratio of fuel flow to Ps30 (pps/psi), bleed enthalpy, HPT coolant bleed (lbm/s) sensor measurements were dropped due to the high correlation with other sensors.
The remaining sensor measurements were used as input variables: the total temperature at LPC outlet (°R), physical fan speed (rpm), physical core speed (rpm), engine pressure ratio (P50/P2), engine pressure ratio (P50/P2), corrected fan speed (rpm), corrected core speed (rpm), bypass ratio, burner fuel-air ratio, LPT coolant bleed (lbm/s).

After training the LSTM model on selected inputs, the resulting model was able to achieve an impressive 85% training accuracy of RUL prediction.
Convolutional Neural Networks (CNNs)
CNNs are another important architecture in predictive maintenance. They are designed to extract local features from grid-like data such as images or spectrograms. The method is used in deep learning for predictive maintenance because vibration signals can be transformed into spectrograms or other 2D representations. This allows CNNs to detect degradation patterns automatically.

Research indicates that CNNs are most widely used for damage, failure, and fault identification and RUL prediction. But also they can prove useful in degradation assessment and failure prediction. Their strength lies in capturing both local and global features in multi-dimensional data. In return, this helps detect small but significant changes in equipment condition.
Use Case of Deep Learning: Bearing Fault Detection
In one study, raw vibration data from bearings was converted into 2D images using Markov transition fields. A pre-trained CNN was then fine-tuned to detect faults. The model successfully identified degradation stages even in imbalanced datasets where fault cases were rare. Researchers note that CNNs can overcome limitations of manual feature engineering by learning discriminative patterns directly from raw data.
Transformers and Temporal Convolutional Networks
While Auto-Encoders, RNNs, LSTMs, and CNNs are well-established, recent years have seen growing interest in attention-based architectures and transformers and temporal convolutional networks (TCNs). To explain:

Transformers use a self-attention mechanism to model long sequences more efficiently than RNNs. In deep learning for predictive maintenance, they are applied to multivariate sensor data and RUL forecasting, especially where sequences are very long or inputs come from multiple sensors. Researchers also experiment with physics-informed transformers to integrate domain knowledge.

TCNs are convolutional networks adapted for sequence modeling. They capture long-term dependencies through dilated convolutions and train faster than RNNs. In some studies, TCNs matched or outperformed LSTMs for vibration signal forecasting.

Both techniques are emerging as practical alternatives when datasets are large, complex, or multimodal.
Advantages and Disadvantages of Deep Learning Algorithms
Predictive maintenance using deep learning may be tricky when you come to choosing the right algorithm. Each technique suits one case better than the other and has its own strong and weak points. See the advantages and disadvantages of ML-based deep learning methods in the table below.

Conclusion
We covered three deep learning-based techniques that can solve a Predictive Maintenance problem. It is impossible to name the sole best technique because each unique case requires an individual approach. Experts in ML-based deep learning know what algorithm to choose based on their experience and a specific business domain.
While all three deep learning techniques mentioned in this article can become a powerful solution to maintenance problems, they still have some major challenges. The biggest ones are probably the amount of data you need to have for a deep learning solution to work properly and the amount of computing power. Incorporating automatic data extraction from PDF can significantly streamline data processing, enhancing the accuracy and speed of analytical tasks. Similarly, vehicle damage detection systems benefit from large, well-processed datasets to accurately identify damage and assess repair needs, making them highly effective in the automotive insurance sector.
Sometimes, a less powerful machine learning algorithm can be much more efficient and cost-effective for your predictive maintenance project. In any scenario, it makes sense to contact experts in machine learning and deep learning for proper evaluation of your situation that will help with actionable strategies to improve your processes.
We at Intelliarts love to help companies to solve challenges with data strategy design and implementation, so if you are interested in how AI used to predict machinery failure in particular or other areas of machine learning — feel free to reach out.

#### Digital Twin Manufacturing Innovations
What if your manufacturing company could keep the machinery running properly, well-maintained, and secure — and all this without providing an actual site inspection? Or what if you could develop multiple product iterations to check which design and materials would suit you better without building the product and spending real-life resources?
Both are the promise of digital twins, a new tool in manufacturers’ arsenal that’s disrupting the industry by connecting the physical and digital worlds and allowing companies to create a virtual replica of their physical environment — processes, products, and facilities. So they transform into a digital twin company.
This article dives deep into the technology of digital twins in the manufacturing industry. Aside from learning about what the technology is in general, you can read about its major use cases and digital twins examples in manufacturing.

#### Electric Truck Fast Charging: Challenges & Solutions
Over the past decade, electric vehicles have gained an increased presence and even some local domination in the passenger transportation market. That’s due to a combination of sustainability concerns and regulatory changes. But is this true for EV charging trucks as well, and can truck EV charging potentially be an additional opportunity for the energy infrastructure business?
In this post, you’ll discover more about the state of the EV truck industry and why electric truck charging is a game-changer for charging businesses. Besides, you’ll find out about solutions for electric truck charging infrastructure as well as key business challenges and possible solutions to them. 
Intelliarts has crafted a white paper on e-mobility technology trends, covering EV fleets and electric trucks. Access it for up-to-date information on the industry. 
Why electric trucks are gaining momentum
Electric trucks are commercial or utility vehicles powered by electric motors instead of internal combustion engines. They are powered through electric truck charging, which is, in a broad sense, a higher-power EV charging.  
Just to clarify the type of vehicles this post covers, let’s take a look at three categories of electric trucks:

Light-duty electric trucks (LD e-trucks). Electric pickups, small delivery vans, and utility service vehicles. Mostly used for urban delivery, personal transport, and light hauling.
Medium-duty electric trucks (MD e-trucks). Box trucks and cargo vans, refuse trucks, utility and construction trucks, and airport ground service vehicles. Ideal for city logistics and municipal operations.
Heavy-duty electric trucks (HD e-trucks). Electric semis and tractor-trailers, long-haul and regional delivery trucks, construction haulers, cement mixers, and mining/logging trucks. Designed for industrial and long-range heavy transport.

Before proceeding any further, let’s find out how advantageous EV trucks are compared to traditional trucks in simple bullet points:

Lower fuel and maintenance costs
Zero tailpipe emissions 
Quiet operation
Financial EV incentives
Suitable for stop-and-go driving 

What’s also essential to understand is how electric trucks are different from other passenger EVs, from a positive perspective:

Bigger batteries allow handling heavier loads and often covering longer distances, which is especially true for light-duty vehicles.
Handle longer routes suitable for deliveries or freight across cities or regions.
Higher power needs due to weight, towing, and industrial functions.
Stronger chassis and components that are built for durability under heavy use.
Slower acceleration and less agility compared to smaller passenger EVs.

However, every advantage comes with a limitation. That’s why, when building a business, including EV charging one, around electric trucks, you need to keep in mind the following cons:

High upfront investment cost 
Notably longer charging times
Shorter driving range under heavy load for most medium and heavy-duty vehicles
Faster battery degradation 
Higher power input requirements, e.g., up to 250 kW for Tesla Model 3 supercharger vs. up to 1,000 kW for Tesla Semi electric truck megacharger

As you can conclude, EV trucks are looking as a promising replacement for traditional vehicles, while naturally being more suited to anything related to moving cargo or fulfilling utility duties compared to lightweight regular and EV vehicles.
Explore the EV fleet management software success story by Intelliarts. 
Electric trucks industry overview
With the above information in mind, we need confirmation that fast charging of electric trucks is a promising niche. Here’s a breakdown of stats revealing that electric trucks are indeed growing in popularity as a passenger transportation solution:
 
Why fast EV truck charging is a game-changer
It’s easy to overlook the significance of fast electric truck charging solutions. That’s because most businesses are used to heavy-duty machinery equipped mostly with diesel engines, which don’t face downtime issues. However, the considerations are vastly different when it comes to electric truck charging.
The need to charge electric trucks faster
Fast charging for electric truck fleets directly impacts uptime, delivery efficiency, and overall profitability. Unlike passenger EVs, trucks are commercial assets expected to operate for long hours with minimal interruptions. The infographics below reveal all points that evidence the strong need to charge electric trucks faster:

Where are we today?
The need to have fast electric truck charging is well understood already. However, we also need to have an insight into where the world is, as of now, in terms of covering EV charging needs and EV charging solutions. Here are the stats to consider:

China, as a country accounting for most EV truck usage, has a share of fast chargers of only around 45%.
The U.S. had approximately 204,000 public and workplace chargers by the end of 2024, with DC fast chargers increasing by 56% from the previous year.
BP Pulse has established Europe’s first public charging corridor for electric trucks along the Rhine-Alpine route, featuring ultra-fast 300kW chargers capable of adding up to 200 km of range in 45 minutes.

As we can conclude, the situation with electric truck charging infrastructure is not ideal, but it’s gradually improving. There are plenty of opportunities for charging businesses to enter the niche and start offering fast electric truck charging solutions themselves. 
Top 4 emerging fast truck charging solutions
For the main take, let’s focus on four technology solutions for charging electric trucks. This will help to understand the niche better, as for 2025 and potentially beyond, and delve into related critical considerations. 
Truck charging standards
In the early days of EV charging, the interoperability of connectors was problematic. At the same time, chargers with sufficient power were difficult to acquire. That’s when standards stepped in. As of now, there’s a range of protocols and standards that manufacturers of EV chargers follow and businesses utilize to select suitable chargers for their electric truck charging infrastructure. Here they are:

MCS (Megawatt Charging System). MCS is developed for heavy-duty trucks to enable ultra-fast DC charging up to 3.75 MW. It is expected to become the global standard for long-haul electric freight.
CCS (Combined Charging System). CCS supports both AC and DC charging through one connector and is widely used for passenger EVs and medium-duty trucks. It’s a foundational standard in Europe and North America for public and depot charging.
OCPP (Open Charge Point Protocol). OCPP is a communication standard that enables interoperability between EV chargers and backend systems. It allows charging stations from different vendors to be managed from a single platform.
SAE J1772. SAE J1772 specifies the AC Level 1 and Level 2 charging connectors used in North America for light- and medium-duty EVs. It is the default connector for most non-Tesla EVs in the U.S. and Canada.
IEC 61851. IEC 61851 is the international standard for EV conductive charging systems, covering safety and charging modes. It establishes how EVs and chargers should interact to ensure reliable and safe operation.

Explore Intelliarts’ success story on openADR implementation for load management for an extra insight into EV solutions from expert developers. 
Take Charge of Your EV Charging InfrastructureManage and optimize your charging points throughout their lifecycle.Talk to our team
Smarter energy resource usage on multiple levels
For operators planning electric heavy duty truck charging solutions, smarter energy orchestration is the difference between theoretical capacity and dependable day-to-day uptime. While “smarter” usage may seem like a rather vague term, it actually hints at a range of technology solutions that electric truck charging businesses may consider implementing. Here they are:
#1 On-site energy storage
On-site energy storage refers to a system, typically using large batteries, installed at a facility (like an EV truck charging station) to store electricity for later use. 
These systems charge using electricity from the grid or renewable sources (like solar) during low-demand or low-cost periods, making them a valuable element of electric truck and heavy duty charging market development. This stored energy can then be used during high-demand periods, reducing peak load charges and enhancing grid resilience. It also ensures continued operation during outages or instability.
#2 Solar integration
Solar integration means installing solar panels at a facility, such as an EV truck charging station, to generate electricity from sunlight. 
An on-site renewable energy source can power chargers directly or charge an energy storage system. It helps reduce dependence on the grid, cut energy costs, and lower carbon emissions. Solar systems can operate alone or alongside batteries, providing clean, sustainable energy for daily charging operations. But clean energy offers more than cost efficiency. Actually, solar integration may make a business eligible for better tariffs or tax reductions. 
#3 Smart load balancing
Smart load balancing is a system that automatically distributes electricity across multiple EV chargers based on real-time demand. 
Smart load balancing distributes available electrical capacity across multiple chargers based on real-time demand and usage patterns. This prevents overloading the grid connection and ensures efficient charging without the need for costly infrastructure upgrades. It allows priority scheduling for certain vehicles and optimizes power usage dynamically, which is critical when managing fleets with varying arrival times and charging needs. 
Explore load balancing additionally in another blog post by Intelliarts experts. 
Mobile and temporary charging units
Mobile charging units are portable EV chargers that don’t require permanent infrastructure and are ideal for temporary fast charging for trucks, especially in remote depots or logistics hubs. These systems can be deployed quickly at depots, events, or construction sites to serve temporary needs. They’re especially useful during fleet transitions, infrastructure upgrades, or when operating in remote areas without grid access. Let’s get through the main use cases of mobile charging units:
Use cases:

Fleet pilots without infrastructure commitment
Temporary charging at new or remote depots
EV support for construction and infrastructure projects
Power for leased or seasonal delivery fleets
Event-based logistics and pop-up operations
Emergency relief in disaster zones or grid outages
OEM truck demos or mobile EV service stations

Integrated fleet management platforms
Integrated fleet management platforms combine EV charging control with vehicle tracking, energy usage, and operational planning, turning charging from a manual task into a data-driven advantage. These platforms help logistics operators reduce costs, avoid charging bottlenecks, and optimize route scheduling based on real-time vehicle and charger availability.  So, are there fast charging options for electric trucks? Yes, integrated platforms like these provide visibility and management across the full charging ecosystem.
Examples of platforms:
#1 Geotab
Offers real-time EV battery data, smart charging analytics, and route planning tools tailored to electrified fleets.
Website: www.geotab.com
#2 ChargePoint Fleet
Combines hardware and software to manage charging, vehicle readiness, and energy usage at scale — used by Amazon and FedEx.
Website: https://www.chargepoint.com/ 
#3 Shell Telematics (formerly Greenlots)
Supports load balancing, depot optimization, and integrates with major fleet platforms via open APIs.
Website: www.shell.com/
Note. An alternative to using a management platform available on the market is custom solution development. Regardless of the choice, you will still need a trusted provider like Intelliarts to integrate or develop from scratch and then integrate a platform into your charging infrastructure and IT system. 
Discover how Intelliarts created an EV charging management platform for EVIQ just in 6 months, which is significantly faster than the average industry standard.
Key challenges for EV truck businesses and solutions to them

Any business project comes with possible challenges and difficulties. Let’s review the top four issues that providers of electric truck charging solutions and infrastructure may face within their business model:
#1 Establishing power infrastructure and the grid usage conditions
To charge electric trucks at scale, especially with rapid charging solutions for e-truck fleets, businesses must navigate the limitations of existing electrical infrastructure. Most commercial sites aren’t equipped to handle the energy demands of high-power charging. Therefore, installing an electric truck charging station often requires significant upgrades, such as new substations, high-capacity transformers, and power lines.
Solution: Start by conducting a site readiness assessment with your utility provider. Explore grants or incentives for infrastructure upgrades. For large fleets, consider adding battery storage systems to manage peak demand and reduce pressure on the grid.
#2 Battery stress from fast charging
Frequent use of fast chargers without proper energy management can shorten the lifespan of truck batteries. This is a key concern in electric truck charging solutions since charging too quickly can cause overheating and degrade cells, especially in older or lower-grade batteries. 
Solution: For owners of EV vehicles, it can be advised to choose smart electric truck chargers that automatically adjust charging speeds based on battery condition and temperature. As for charging businesses, it’s recommended to offer mixed charging speeds (Level 2 and DC fast) to balance efficiency with battery longevity.
#3 Difficulties with site selection and real estate constraints
Finding locations suitable for electric truck charging stations is not as simple as placing a charger in a parking lot. The ideal site must have sufficient space for trucks to maneuver, enough electrical capacity, and convenient access to major routes or logistics hubs. Urban environments may pose additional challenges due to land costs and zoning restrictions.
Solution: Target underutilized commercial spaces near logistics corridors or warehouse districts. Partner with property owners who can benefit from added foot traffic or new revenue streams. Consider shared-use models with other fleet operators to increase utilization rates.
#4 Bad customer experience and low charger availability
Whether you operate a public station or a depot, broken chargers or long wait times reduce customer satisfaction and trust in your electric truck charging solutions. That’s usually the lack of real-time data that leads to such issues in pre-planning and in scaling business stages when decisions regarding data analytics systems and their usage are made.
Solution: For an electric truck charging business, having data analytics software is obligatory. Using data analytics, businesses can monitor charger status in real time, predict maintenance needs, and balance loads to avoid peak-hour delays. This helps deliver a better customer experience and improve ROI as your network grows.
Final take
The electric truck charging market is quickly evolving, driven by sustainability goals, fleet electrification, and regulatory shifts. Fast charging, smart energy management (like storage, solar, and load balancing), and data-driven infrastructure planning are key to success. Although challenges such as grid upgrades and battery stress remain, businesses that embrace innovation and strategic site development can capture this emerging opportunity.
Looking for a trusted service provider to help you with software for your electric truck charging stations? Don’t hesitate to reach out to Intelliarts. With 90% of recurring customers, over 25 years of presence in the software industry, and a focus on long-term partnership, we can cover your development needs. 

#### Engineering Maturity Meets Agentic Software Development: Practices That Make AI Coding Safe
TL;DR

Code generation speed was never the limiting factor. Requirements and architecture account for 70% of a feature cycle, while AI accelerates only the 15% spent on syntax.
Agents fail in predictable, structural ways: degraded recall in long files, outdated documentation, unverified external sources, token noise, and conflicting specifications.
Context belongs in the repository, not in chat history. Files like DESIGN.md and CLAUDE.md, along with versioned specs, give agents a stable source of truth.
Verification needs to happen before a human reviews the output. Compilers, linters, and a testing pyramid lock behavior in place before code reaches review.
Modular architecture and strict boundaries limit the scope of any single agent edit.
Guardrails, sandboxing, and local CI keep autonomous agents contained and reduce feedback time from minutes to seconds.

According to the Stack Overflow 2025 Developer Survey, 84% of developers now use or plan to use AI tools in their work, up from 76% the year before. DORA’s 2025 report found 90% team adoption. The numbers on trust move in the opposite direction. That same survey found only 29% of developers trust AI output to be accurate, down from 40% in 2024. 
AI tools changed who writes code and how fast. Yet, the factors that determine whether that code is reliable, e.g., clear requirements and rigorous verification, haven’t changed at all. 
In this article, I share insights into software engineering practices for agentic development. I’ll explain how to make AI coding agents reliable, outline the new tech stack, and provide reasons why many AI-based agents fail, as well as how to get the most value from any agent-driven development.
About the Author
Oleksandr Stefanovskyi has a strong background in data science and software engineering and currently leads the Intelliarts cross-functional R&D team. In the Intelliarts blog, he shares his insights on building end-to-end ML pipelines and agentic systems.
His expertise in data science and AI comes from more than 10 years in software engineering, including 4 years leading ML projects. He follows the CRISP-DM methodology in AI and data-heavy projects and holds an AWS certification.
His experience also includes software architecture design and building data lakes and data warehouses for data-intensive applications.
Learn more about AI-assisted SDLC at IntelliartsContact us to see how AI-driven development can speed up delivery, cut costs, and improve code quality.Get in touch
Typing Was Never the Bottleneck
Agentic development gets framed as a break from how software has always been built. That framing rests on one assumption: writing code was the constraint holding teams back. The data on where a feature cycle actually spends its time says otherwise.
The myth of the “AI-disrupted” SDLC
In a recent webinar on engineering practices for agentic development, I broke down where a typical software lifecycle actually spends its time:

Requirements & Intent — 40% of cycle time
Architecture & Boundaries — 30%
Testing & Verification — 15%
Syntax (Coding) — 15%

The big idea here is that the 15% slice is the only piece that generative AI meaningfully accelerates. The other 85%, the reasoning about what to build, how to structure it, and whether it solves the right problem, stays entirely human work.
This reframes what “10x productivity” claims usually mean in practice. Speeding up 15% of a cycle, even by a large factor, still leaves the other 85% untouched. When requirements and architecture already eat most of a sprint, that’s where the real leverage sits.
“Measure your team’s cycle time, then target the architectural-design bottleneck.”
The new role: humans orchestrating agents
If typing was never the bottleneck, the job of a senior engineer was never about typing either. What’s shifted is how visible that fact has become. 
The hardest work has always been reasoning around a feature, and deciding whether it should exist at all. LLMs automate typing; they cannot determine intent.

The idea behind an autonomous individual directing agents comes down to this: one person now holds the authority that used to sit with an entire team. That person doesn’t write most of the code anymore. They decide what an agent should attempt, review what it produces, and pull the plug when it drifts off course.

Two very different modes of contribution now sit inside a single workflow:

Syntax generation. Sub-second, high-volume, prone to local drift, incapable of verifying whether the output matches what was actually meant.
Reasoning & intent. Defines what gets built and whether it should be built, enforces the invariants that hold a system together, and remains “the primary human responsibility”.

The agent can implement a function in seconds. Yet, it cannot tell you whether that function belongs in the codebase, whether it breaks an assumption three files away, or whether the user even asked for it. Someone still has to catch that, and increasingly, it’s one engineer instead of a chain of specialists.

The multi-role engineer diagram puts it visually: one engineer, plus agents, sits at the center, connected to a Product Manager, an Architect/Tech Lead, a QA Engineer, and an Engineering Manager, four roles orbiting one person instead of four separate desks.
This matches how we actually hire at Intelliarts. Our senior technical interviews weight design and product reasoning far above raw syntax recall:

Design Session — 35%, evaluating how a candidate partitions systems and separates concerns
Product Session — 30%, focused on business constraints and trade-offs
Engineering Values — 20%, covering ownership, quality bar, and pragmatism
Technical Code Skills — 15%, the part AI now accelerates

“Spend the first 10 minutes of every feature cycle acting explicitly as a Product Manager, slice the requirement, before you invoke an agent.”
The new toolset landscape (Cursor, Claude Code & Co.)
Agentic development tools, as of 2026, can be split into two main categories. IDE-native agents that operate inside the editor and CLI-native agents that run from the command line with broader system access. 
The level of autonomy of each category is different, so let’s find out which one can serve a particular project better:
IDE-native vs CLI-native agents
Cursor and Claude Code take different approaches to how an agent interacts with a codebase:

Cursor keeps a developer in the loop for each suggestion, which suits tasks where visual context matters and scope stays narrow. 
Claude Code operates with less supervision, executing multi-step tasks across many files before reporting back.

“Task complexity determines the tool, because a developer can only verify so much in real time. Cursor suits changes reviewable inline, one file at a time, while Claude Code is for scenarios where changes should be verified post-factum, across many files at once.”
A component refactor with tight visual dependencies favors an IDE-native agent that keeps a developer watching each change. A batch migration against a defined contract favors a CLI-native agent that can run unattended and report results.
Different UX, same need for engineering maturity
Cursor keeps a developer inside the editor for every step, showing suggestions inline before they land. Claude Code removes that visibility. A developer issues an instruction from the terminal, and the agent works through multiple files and test runs on its own, reporting back only once it’s done, with review happening against a finished diff rather than a step-by-step approval screen.
This is why I think of this split as moving from “ad-hoc chat prompting to systematic repository configuration”. 
The UX gap is there, but it doesn’t change what makes either tool safe to run. An agent with more autonomy just needs more of the safety net built in advance, since nothing catches a mistake mid-task the way Cursor’s interface does.
Safety concerns are the same regardless of which tool exposes the gap:

No lint rules or type checks. An agent ships code that compiles but violates conventions nobody enforced, in an editor sidebar or a terminal window alike.
No module boundaries. An agent reaches across a codebase and touches files outside its intended scope, since it has no way to infer architectural lines that a human hasn’t defined.
No repository-level context files. Without an AGENTS.md or equivalent, the agent reconstructs its understanding of the project from scratch each session.

The fix is the same across both tools: context design, verification loops, and guardrails, each covered in detail later in this piece. Claude Code, with less built-in supervision than Cursor, simply needs that safety net in place before the work starts.

Important note. The choice between agents boils down primarily to the repository configuration, with prospects of having a stack that is safe to run.

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The real bottleneck: Thinking, not syntax
So, if requirements and architecture eat 70% of a cycle, the people who handle that work carry the actual weight of a project, and their value is never in how fast they can type.
Why senior engineers are hired for partitioning, not recall
Partitioning a system, deciding where one module ends and another begins, has always separated a senior engineer from a junior one. 

Big idea: An agent can produce syntax at any skill level, so syntax stopped working as a filter for seniority.

That’s how Intelliarts weights our senior technical interviews:

Design Session — 35%, evaluating how a candidate partitions systems and separates concerns
Product Session — 30%, focused on business constraints and trade-offs
Engineering Values — 20%, covering ownership, quality bar, and pragmatism
Technical Code Skills — 15%, the part AI now accelerates

“We do not assess seniors on syntax recall. The Design and Product sessions evaluate how a candidate partitions systems, separates concerns, and reasons about trade-offs.”
Recall is used to correlate with experience, since a developer who had solved a problem before could reproduce the solution faster. An agent retrieves any syntax pattern instantly, regardless of who’s asking, so that correlation no longer holds. 
Judging whether a given partition holds up under load, or whether a boundary drawn today will trap the team in six months, still falls to a person. That judgment is what the interview weighting is built to catch.
Cheap code, expensive review: the democratization paradox
Lowering the cost of writing code shifts the cost downstream, toward review and architectural oversight. I name this shift directly: “cheaper code raises the bar.”
The mechanism is straightforward. When code generation becomes frictionless, the volume of code produced increases. An agent optimizes for a task passing its immediate check, not for how the code fits the system around it, so a large share of that volume ends up poorly structured. The demand for architectural review and supervision grows in proportion to how much code gets generated.
“Zero tolerance for undocumented agent modules; every agent PR must include human-reviewed architectural justification.”
That zero-tolerance stance is the practical response to the paradox. A team that lets agent-generated code merge without architectural review treats faster output as the win, when the bottleneck has already moved to the review stage. Senior engineers spend more time reading diffs because code has become cheap to produce.
Why agents fail: Five context pathologies
AI coding agents are deterministic token-parsing engines, so their failures follow predictable patterns rather than random mistakes. I group these patterns into two families: context pathologies and interface deficits. 
The first family covers five distinct ways an agent’s context window can work against it, each with its own root cause, symptom, and fix.
[table id=22 /]
Before breaking down each one individually, the five pathologies share a common structure worth noting upfront:

Each starts with a specific, identifiable root cause: a file that’s too long, a document that’s gone stale, a source that was never verified
Each produces a symptom that looks like the agent being careless, when the actual cause is structural
Each has a single, concrete fix that removes the root cause rather than patching the symptom

Lost in the middle
“LLMs recall the start and end of a context window strongly, but retrieval collapses in the middle. Long contexts (100k+ tokens) degrade reasoning and invite hallucinations.”
Recall accuracy holds near 95% at the start and end of a context window, then drops to roughly 20% in the middle. A requirement has a real chance of getting ignored because the agent’s attention degrades with distance from the edges. 
The fix suggested by our team: Keep files under roughly 300 lines, so nothing important ever lands in the collapsed middle of a long sequence.
Context rot (dead docs)
Outdated specifications rarely get deleted from a repository. They stay in the indexed path next to current code, and an agent has no reliable way to distinguish a contract that shipped last week from one abandoned eight months ago. Every document in the scan path carries equal weight by default, so a stale README can override a schema file it directly contradicts.
To explore how to deal with context rot through AI-based technologies, read our blog post on how RAG systems improve LLM.
A few practical markers separate live context from dead context:

Deprecated API specs still sitting in the scan path
Old design proposals that contradict shipped code
Documentation with no recent commit activity is a signal that the agent has no way to check on its own

The remedy is a docs/archive/ folder outside the agent’s scan path, with outdated specs moved there immediately, and the folder added to ignore files.
Context poisoning (unverified external data)
“Feeding the agent unverified external docs, outdated tutorials, or buggy StackOverflow snippets injects security holes, deprecated auth patterns, and dependency creep.”
That’s how this poisoning enters an agent’s workflow in three steps:

A poisoned source gets fetched: an outdated tutorial, a buggy forum snippet, a deprecated library reference.
The agent writes the deprecated or vulnerable pattern directly into the codebase.
Even when a sandbox catches and blocks the resulting code, the poisoned reference stays in the agent’s context for the rest of the session.

That third step is the part teams tend to miss. Blocking a single bad output doesn’t clear the contaminated context behind it. Typo-squatted packages, vulnerable snippets, and hallucinated dependencies can all resurface later in the same session, even after the original suggestion got rejected.
The fix is a root AGENTS.md file listing approved, pinned dependencies. That gives the agent a trusted reference to check against, instead of trusting whatever it happens to fetch from an unverified source. Teams building LLM-powered applications face this same risk at the model layer.
Context noise (logs and build spam)
“Scanning build outputs, logs, local databases, and node_modules can burn ~80% of your token budget on non-functional noise, diluting attention.”
A typical repository splits into two zones: source directories like /src, /tests, and /docs that carry the actual signal, and generated or transient directories like /dist and /node_modules that carry none. 
The second category can easily outweigh the first by volume: a node_modules folder alone commonly holds thousands of dependencies and hundreds of megabytes, none of it relevant to the task an agent was given.
Basically, my point here is that the 80% figure changes how a team should think about ignore files. An agent configured with a .claudecodeignore or .cursorignore works from a clean, high-signal view of the codebase. Without one, it scans through gigabytes of transpiled bundles and dependency trees to locate the handful of files that actually matter.
Context conflict (contradictions and loops)
The most disruptive pathology happens when two trusted sources disagree. An active TypeScript interface defines a field as a string, while a stale architecture document, last edited eight months earlier, defines the same field as a number. The agent tries to reconcile both, produces a hybrid type, breaks compilation, and retries.
An agent stuck in this loop doesn’t fail loudly. It keeps attempting fixes, each one wrong in a slightly different way, until something interrupts the cycle. Common triggers behind this pattern include:

A stale README describing a schema that code has since outgrown
Two ADRs written months apart, neither one marked as superseding the other
A type definition duplicated in both code and markdown, with the two falling out of sync

From what we at Intelliarts observed, the solution in a nutshell is to maintain one source of truth for schemas and delete any markdown that duplicates a type definition already present in code.
Taken together, these five pathologies point to the same lesson: an agent fails because its context window sometimes contains something not actually intended, resulting in poor output as per the Garbage-In-Garbage-Out (GIGO) rule. 
Designing context the machine can actually use
A repository structured so that an agent can find the right context without guessing resolves the five pathologies above. That structure belongs inside the codebase.

Bringing “why” into the repo
“Raw code explains how a feature works, but never the business rules, the users, or the rationale that governs the system. Those layers sit above the syntax.” 
Source code represents the visible fraction of what a working system depends on. Developer intent, business constraints, architectural conventions, and a team’s unwritten conventions sit beneath that layer, inaccessible to an agent reading a function body.
At the same time, code communicates how something works to an agent. From my perspective, the rationale behind a design decision, and the business rule it enforces, remains undocumented unless a team records it explicitly. A concise DESIGN.md, covering user personas, domain boundaries, and constraints a change must not violate, addresses that gap.
The same context-design discipline governs retrieval-augmented generation systems. Take a look at a corresponding blog post by Intelliarts for more information. 
From chat prompts to versioned specs
Instructions entered into a chat interface disappear once the session closes, along with any record for the next developer or the next agent run.
“Long instructions typed into a chat vanish when the session closes. Design specs, ADRs, and rules belong inside the repository, where they evolve with the code.”
Moving that instruction into a versioned file changes its properties measurably:

Retention shifts from ephemeral to permanent, evolving alongside the codebase
Traceability shifts from none to full visibility in commit logs
Consistency shifts from highly variable results to deterministic, repeatable output

A CLAUDE.md file in the project root, documenting setup, run commands, and style conventions, establishes a consistent starting point for every session, reducing the need to re-explain project context repeatedly.
Contracts that survive agent runs
A specification committed alongside the code it describes remains accessible to a CLI agent. A specification stored only in institutional memory or an external wiki stays out of reach.
This creates a strict connection: every commit carries both the code change and the spec or ADR that justifies it, giving a reviewer, or an agent initiating a new session, direct access to the reasoning behind the change.
The same applies to API contracts. An agent can alter a JSON payload or database schema in ways that break downstream consumers, and a passing test suite may not detect it. 
Static schema diffing addresses this: an automated tool compares a proposed change against the base contract, rejects changes that introduce breaking modifications, and allows compatible changes to proceed to review.
“An agent can silently alter a JSON payload or database schema and break every frontend consumer. Static schema diffs compare generated output against the base contract.”
Running that diff as part of a pre-commit hook prevents an undocumented, breaking API change from reaching a branch.
Verification: Feedback loops agents self-correct against
Structuring context keeps an agent working from correct information. Confirming its output is correct comes from a separate layer: feedback loops the agent checks itself against, before a human ever reviews the diff.
Compiler and type system as ground truth
A compiler enforces its rules without exception, which makes it a natural first gate for agent output.
“The compiler and type checker are an absolute, non-negotiable feedback channel; the agent self-corrects against them before a human ever looks.”

Here’s the mechanism: a compiler error identifies the exact file, line, and column where a type violates its contract. An extractor tool turns that location into a targeted prompt. The agent applies a scoped fix until the code recompiles cleanly.
This loop resolves compiler-detectable errors without human involvement. Catching a broken type by hand costs a reviewer minutes per instance. An agent checking its output against tsc –noEmit catches the same error in seconds, clearing type mismatches from the diff before a reviewer opens it.
Linters, AST rules, and auto-formatting
A compiler is intended to detect broken types. A circular import, a private module exposed across a boundary, or an unenforced style convention needs a different layer of static analysis.
There is a three-gate sequence an agent’s edit passes through before it’s considered safe (Slide 21):

AST parser. A structural pre-check that catches architectural violations, like a deep import reaching into another module’s internals
tsc –noEmit. Type safety enforcement, the same compiler gate described above
ESLint. Style and anti-pattern checks that keep the codebase consistent regardless of which developer, or which agent, wrote a given file

Based on what we observed in our projects, formatting plays a smaller but measurable role in this same pipeline. I describe Prettier as a token strategy: unformatted code produces high diff noise from tabs, braces, and spacing changes unrelated to logic, which wastes tokens and slows review. Strict formatting strips that noise, so a reviewer or an agent reading a diff spends attention on the change that matters.
The testing ladder of confidence
Static checks confirm code compiles and follows conventions. Confirming the code does what it’s supposed to do is the job of tests, and the presentation frames them as a pyramid of increasingly broad, increasingly expensive guarantees, the same discipline our software engineering services apply to every project. 

Unit tests. Secure local invariants and edge cases, fast and cheap to run
Component API tests. Guard module contracts and boundaries
Integration tests. Catch secondary side effects that isolated tests miss
E2E tests. Validate a complete user workflow end to end

Unit tests carry the most weight for agent-written code specifically, because they lock behavior against regression. The mechanism is simple: a test file sits in a boundary that an agent is not permitted to edit. Only the implementation file stays open for changes. A failing test defines the requirement in concrete terms, and the agent iterates against that fixed target until the suite passes.
“Never tell an agent ‘it’s broken.’ Write a unit test that fails under the broken condition, and let the agent fix it green.”
Telling an agent a feature is broken invites a guess at what “broken” means. A failing test defines success in concrete terms, leaving no room for the agent to misinterpret it.
Component API tests extend that locking mechanism to module boundaries. Integration and E2E tests extend it further still. Each layer catches what the one below it structurally cannot see:

An ESLint rule blocks a deep import past a module’s public gateway. Every consumer goes through one versioned interface instead.
A user workflow, sign-in through checkout to confirmation, reveals side effects once the full sequence runs together.

Running that full sequence with a fast, headless E2E runner after a major agent edit confirms the whole workflow still holds, not just the pieces tested in isolation.
Architecture that gives agents room to work
Verification catches bad output after the fact. The structure of a codebase determines how much bad output an agent can produce in the first place, and that structure comes from architecture decisions made before any agent touches the repository.
Small modules, clear boundaries
A codebase that’s hard for a human to navigate produces worse results for an agent. A tightly-coupled system, where a single change ripples across a dozen files with unclear dependencies, gives an agent no reliable way to scope its own edit.
“If a human struggles to navigate a tightly-coupled codebase, an agent fails spectacularly. Decoupled modules cut the number of files an agent must read to make a change.”

Compare two outcomes:

A tightly-coupled repository with six modules cross-referencing each other leaves an agent failing within two minutes. 
The same task in a modular repository, split into clean domain, api, and infra folders with their own tests and docs, ships in five.

Small modules extend that same principle to the class level. Let’s set a concrete ceiling: 150 lines per class, 25 lines per function. Enforcing that ceiling keeps a change locked inside a single module. A class or function within those limits rarely accumulates the kind of hidden dependencies that spread a change across a repository.

A focused edit stays contained to the file it touches
A module beyond scope stays explicitly blocked, with an instruction like “refactor orders, do not touch files outside src/orders/”
The blast radius of a mistake shrinks to whatever that single module contains

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Designing “safe zones” for autonomous changes
Some parts of a codebase tolerate an agent working with minimal supervision. Others don’t, and mixing the two without a clear boundary invites unscoped edits.
A safe zone, in practice, is a directory an agent has explicit permission to modify, paired with directories it’s explicitly blocked from touching. I define an active scope: src/orders/ marked open for edits, while src/payments/, src/users/, src/notifications/, and src/shared/ stay locked for the same task.
That boundary sets a hard limit on what a mistake can cost:

Without a boundary, an agent fixing an unrelated bug can drift into payment logic.
With a boundary, the same agent has no path to touch anything outside its assigned directory, regardless of how far a flawed fix drifts.

That restriction pairs naturally with the module boundaries from the previous section. A codebase already split into domain, api, and infra gives a team a ready-made set of safe zones. Each folder already represents a self-contained unit an agent can work inside without touching the others.
Git and review discipline in the agentic era
Architecture defines the scope of an agent’s access. Git discipline governs the pace and reversibility of its changes, determining how quickly a team can identify and undo a mistake once one occurs.
Commit hygiene for humans and agents
“Letting an agent touch 20 files over two hours and then committing once is an anti-pattern. Commit early and often; every commit is an instant rollback checkpoint when the agent loops.”

Here I outline a hook-based implementation of this principle: a post-write hook runs the unit test suite after every commit. A passing run advances the branch. A failing run triggers an automatic git reset –hard HEAD~1, restoring the workspace and prompting the agent to retry with the failing-test context intact.
This cycle establishes a checkpoint at every commit, limiting the scope of any single rollback:

A commit after each passing test run keeps the rollback distance minimal
A blocked git push –force at the pre-command hook prevents an agent from overwriting protected history

Squashing and history that’s still readable
Frequent commits address the rollback problem, but introduce a separate one: a main branch accumulating dozens of “wip” commits that carry little meaning for anyone reviewing the history later.
We address this with a standard pattern: an agent’s iterations remain on a feature branch, often six or more small commits deep, and a single squash merge consolidates them into one clean entry on main.
“Dozens of micro-commits from agent iterations clutter the history. Squash on merge: the feature branch keeps the detailed steps, while main stays clean and auditable.”
The detailed record remains available on the feature branch for anyone tracing an agent’s iterations. The main branch reflects the outcome, which serves most readers of the history. Branch-protection rules requiring squash-and-merge on every pull request enforce this as a default rather than a manual step.
Human-underwritten PRs
An agent generates code faster than a human can review it. We can quantify that gap: an agent produces 1,000 lines in two minutes, while a human requires hours to audit the same diff for safety.

Review quality holds near 90% up to roughly 200 lines, then declines sharply beyond that threshold, according to the chart. A reviewer examining a 1,000-line diff identifies a fraction of the issues the same reviewer would catch in a 200-line diff.
Our engineering practice shows that capping PR size at 200 lines keeps a diff within the range where review quality holds. Large features still ship, structured as a sequence of smaller, independently reviewable branches rather than a single diff too large to audit properly. A pre-push hook enforces the cap by rejecting any push that exceeds the line budget.
“A lot of failures we’ve reviewed came from an undefined standard in coding and QA. That’s basically a variation of any scenario describing communication and project scope errors.”
Guardrails for autonomous agents
Architecture and Git discipline shape how an agent’s code moves through a repository. Neither one restricts what an agent can execute directly on a system, and a CLI agent with shell access can run commands, write files, and index a filesystem well beyond the scope of a single task.
Permissions, ignore lists, and lifecycle hooks
“Autonomous CLI agents can run shell commands, write files, and index the system. Three layers of guardrails contain them: environment isolation, active ignoring, and lifecycle hooks.” 
You can structure those layers as concentric rings around agent execution:

A lint check forms the outer ring, catching static safety issues
A dangerous-command checker sits inside that, matching patterns like curl, wget, or rm -rf before they run
A filesystem boundary enforces scope constraints
A compliance auditor at the center logs every action the agent takes

Ignore files handle a related but distinct problem: defining what an agent is permitted to see in the first place. Filtering node_modules/, dist/, log files, .env, and .git/ out of an agent’s context reduces the visible codebase to roughly 20% of the raw repository, all of it relevant source.
Lifecycle hooks tie both mechanisms to specific moments in an agent’s workflow. I break the sequence into three stages:

Pre-command hook. Audits and validates a proposed action, blocking commands like rm -rf / or git push –force before execution
Agent action. The actual write, run, or edit
Post-command hook. Verifies and formats the result, running Prettier, a typecheck, and a security scan like trivy fs .

Sandboxed execution and pre-commit gates
Permissions and ignore lists constrain what an agent can see and attempt. A sandbox constrains what an agent’s mistakes can reach, even if a guardrail fails to catch one.
I draw that boundary directly: a Docker container mounts only a clone of the repository, with no access to host credentials, SSH keys, or other projects on the machine. An agent running inside that container has full toolset access, shell, write, index, but no path to exfiltrate a credential or touch a file outside the mounted clone.
Pre-commit gates add a second layer of enforcement, catching what a sandbox alone doesn’t verify: whether the code an agent produced actually meets the repository’s baseline standards.
“No code reaches the branch unless it meets basic repository invariants. The hook runs linting, typechecking, and core unit tests, and blocks the commit on any failure.”
A commit attempt runs through three checks before it’s accepted:

Lint, catching style and anti-pattern violations
Typecheck, catching broken contracts between modules
Core unit tests, catching regressions in existing behavior

A failure at any stage blocks the commit and returns the agent to fix and retry, rather than letting broken code reach a branch where a human has to catch it manually.
Local CI mindset 
Guardrails and pre-commit gates catch bad code before it merges. The speed at which an agent learns its code failed determines whether that check accelerates work or introduces friction.

That latency gap produces two distinct outcomes:

A remote pipeline failure surfaces after the agent has already moved to a different task, requiring a context switch to diagnose the issue
A local pipeline failure surfaces within seconds of the edit, while the agent retains the relevant context, enabling immediate correction

The resulting workflow: an agent runs the local script immediately after an edit, resolves any failures, and pushes only code that clears the full local suite. 
Big idea: Because of the 10 to 30-second local check, verification is handled inside the agent’s own workflow. Therefore, it’s checked and corrected before a human or a remote pipeline ever sees the result. 
So, this brings us to an insight and an action: local CI resolves mechanical errors before a human ever opens the diff, leaving one question for a reviewer to answer: whether the change belongs in the codebase at all. 
Conclusion 
Reducing the search space, enforcing boundaries, and verifying against a compiler or test suite: these are the same practices covered across this piece, from context design and architecture to guardrails and local CI. None of them are new to software engineering. 
What changed is the volume of code moving through them and the speed at which it arrives. Writing code stopped being a scarce skill once an agent could produce it on demand. 
Directing that agent, setting its boundaries, and verifying what it built remains the work only a person, or a technology consulting partner, can do. 
Where to go next
The practices covered here come from my personal experiences. Should you have any concerns left or require any consulting or development assistance, proceed with a trusted AI development partner.
The Intelliarts team has been on the market for more than 26 years, providing AI/ML and software development services to businesses worldwide. With a 90% customer return rate and a focus on business objectives, we can offer assistance with projects of any complexity.
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#### Enhancing Large Language Models with RAG
As of 2024 and potentially beyond, Retrieval-augmented generation (RAG) models are gaining traction. As evidence, Databricks reports that 60% of their LLMs use RAG. And, since the global LLM market, valued at $4.35 billion, shows an extremely fast compound annual growth rate (CAGR) equal to 35.9%, using RAG seems more than beneficial. 
In this post, you’ll learn basic concepts related to retrieval-augmented generation. Besides, you’ll find out how it works in relationship with LLMs, what tools are used to integrate RAG, and what the workflow of processing a user’s query looks like when there’s RAG technology involved. 
What is retrieval-augmented generation and why do you need it?
Retrieval-Augmented generation or simply RAG is a generative AI framework that augments or simply updates and improves Large Language Models (LLMs) with relevant data from chosen sources. 
RAG technique is primarily suited to Natural Language Processing (NLP) applications. RAG combines retrieval-based methods with generation-based models, leveraging the strengths of both approaches and putting them to use. 
For a detailed comparison of AI models like Claude and ChatGPT, which complement RAG systems in enhancing text generation capabilities, check out our article on Claude vs. ChatGPT.
Here’s why developers widely used RAG with LLMs:

Enhanced accuracy of LLMs’ predictions
Usage of up-to-day information in real-time
Higher-quality outputs that are both contextually accurate and creatively generated
Better efficiency when working with high volumes of data
Better capabilities for operating with highly customized responses

Using the RAG approach has no cons except for making the system more complex, and, therefore somewhat harder to develop and maintain. 
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How does RAG work in LLMs?
RAG utilizes pre-trained LLMs, which allows it to generate text. At the same time, the technology incorporates a retrieval mechanism that allows the model to access external knowledge sources. This way, RAG integrates retrieval into the generation processes.
For a detailed look at best practices and strategies for implementing RAG systems in enterprise environments, check out our article on enterprise RAG.
The knowledge base can be composed of any information-containing artifacts, including:

Internal company wiki pages
Files in GitHub
Messages in collaboration tools
Topics in product documentation, including long text blocks
Text passages in databases supporting SQL queries
Document stores with collections of files, like PDF legal contracts
Customer support tickets in content management systems

As for the RAG’s retrieval mechanism, it operates by retrieving relevant texts from external sources, which are later used as inputs to the generative model. The latter synthesizes the information and produces coherent and contextually appropriate responses.
Here’s the complete two-step procedure for the model to learn how to select the best documents and generate appropriate responses based on them simultaneously:
Step 1: Document retrieval with DPR techniques
RAG frameworks start by exploiting the dense passage retrieval (DPR) techniques. DPR encodes both the input query and the external documents into dense vectors using a transformer-based model. Such dense vectors represent the query and the documents in a high-dimensional space. The model retrieves documents by calculating the cosine similarity between the query vector and document vectors, selecting the ones with the highest scores. This technique ensures the retrieval of documents that are semantically related to the query, even if there are no direct keyword matches.
Alternatively, a RAG system can function with more than just plain document chunks by integrating a graph database. This enables the system to capture the relationships between chunks and operate at various levels of granularity, enhancing the retrieval process.
Step 2: Generating responses with LLM
Once the relevant documents are retrieved, they are used to condition the response generation. This is accomplished with a model like GPT-4, which generates responses based on the input query and the retrieved documents. The documents act as extensions to the input, providing the model with additional context for more informed responses. Integrating DPR within the RAG framework improves the model’s ability to handle complex tasks that demand a deeper understanding of the subject matter.
Integrating DPR within the RAG framework enhances the model’s knowledge base, making it exceptionally adept at handling tasks that demand a profound comprehension of the subject matter.

The RAG-LLM relationship in executing retrieval and generative functions
On the backend, RAGs execute retrieval and generative models in 5 key steps. Here’s the detailed procedure description:
#1 Sourcing
Sourcing from internal text documents and enterprise systems is the starting point of an RAG solution. Here, the unstructured and structured source data is searched and queried. The outcome of the sourcing stage is the pool of relevant information. 
Pro tip: To ensure accurate and diverse data sourcing, it’s essential to maintain up-to-date metadata and minimize data redundancy.
Read also about generative AI in marketing.
#2 Pre-processing data for retrieval
It’s essential to organize multi-source data and metadata for real-time access, which includes integration and unification. Here are the possible types of data:

Customer master data. Customer ID, name, contact information, and account information
Transactional data. Service requests, purchases, payments, and invoices
Interaction data. Emails, chats, and phone call transcripts

Depending on the use case, data may need to be arranged by other business entities, such as employees, products, or suppliers.
#3 Pre-processing documents for retrieval
Unstructured data, such as textual documents, must be divided into smaller, manageable pieces through chunking. 
Chunking is the process of breaking down large pieces of unstructured data, such as textual documents, into smaller, more manageable units called “chunks.”
Textual chunks are then transformed into vectors for storage in a vector database, enabling efficient semantic search. This embedding process, using an LLM-based embedding model, links the embeddings back to the source, ensuring more accurate and meaningful responses.
  White PaperElevate Your LLM Performance at least by 40% with RAG Systems Read RAG for LLM white paper
#4 Protecting the data
Here, Role-Based Access Controls (RBAC) and Dynamic Data Masking are the primary protection measures. 
RBAC is a security mechanism that restricts access to information based on the roles of individual users within an organization.
At the same time:
Dynamic data masking is a technique used to hide sensitive data in real-time, ensuring that unauthorized users see only obfuscated or anonymized information.
By using RBAC and dynamic data masking, as well as Privileged Access Management, a company ensures that sensitive customer information is protected and only accessible by authorized personnel. Thereby it complies with GDPR requirements for data minimization and access control and provides the required security level. 
#5 Engineering the prompt from enterprise data
After enterprise application data is retrieved, the retrieval-augmented generation model generates an enriched prompt by building a “story” out of the retrieved 360-degree data. 
Ongoing prompt engineering, ideally aided by machine learning models and advanced chain-of-thought prompting techniques, enhances the prompt’s quality. This might involve reflecting on the data to determine additional information needs or checking if the user has finished the conversation.
Bonus Example
Let’s review a more practical instance, revealing how the RAG with LLM relationship works on user queries. The RAG architecture diagram below illustrates the retrieval-augmented generation framework and data flow from the user prompt to response:
So, in practice, the simplified procedure encompasses these steps:

The user enters a prompt.
The retrieval model accesses the company’s internal sources for structured data and unstructured documents.
The retrieval model selects parts of documents that will help to answer user prompts as contextual information.
The retrieval model enriches the user’s prompt with additional contextual information.
The enriched prompt is passed to the generation model (LLM).
The LLM uses the augmented prompt to generate a more accurate and relevant response.
The response is provided to the user.

Integrating RAG with language models
Here’s the basic, step-by-step procedure for integrating RAG with any of the top large language models:
1. Setup prerequisites
Hugging Face Account: Sign up for a free Hugging Face account to access embedding models and download the Llama-2 model. Recommended options and ways of their usage are:

Llama-2: Request access to Meta’s Llama-2 model via Meta’s website for integration.
LlamaIndex: Use this framework to load data and feed it into Llama-2.
Chroma: Employ this embedding database for fast vector similarity search and retrieval to store the index.

2. Install required libraries
Install the necessary libraries for the project.
3. Import necessary modules
Import the required modules from the libraries to set up vector indexing, embeddings, storage functionalities, and other computations.
4. Provide context to the model
Load the data into the model. For this demonstration, a PDF research paper is used. However, other data sources like Arxiv Papers Loader can also be used.
5. Download and configure Llama-2
Set up an account with Meta and have your access token ready. Configure the model to fit into constrained memory environments, such as Google Colab, by using 4-bit quantization.
6. Initialize the language model
Initialize the Llama-2 model with the necessary configurations, including the model name, tokenizer name, query wrapper prompt, and quantization settings.
7. Compare outputs without context
Test the model by asking questions without providing any additional context to see how it responds and identify hallucinations.
8. Create a ChromaDB client and collection
Set up a client to interact with the ChromaDB database and create a new collection to hold the vector index.
9. Initialize the embedding model
Set up the HuggingFaceEmbedding class with a pre-trained model for embedding the text into vectors.
10. Index-embedded document vectors
Set up the vector store and use it to index the embedded document vectors, allowing quick retrieval of relevant passages for a given query.
11. Establish a summary index
Create a SummaryIndex to enable the model to summarize the data efficiently and retrieve pertinent information.
12. Query with indexed data
Ask the model the same question, this time using the indexed data to provide relevant context and observe the improved response quality.
13. Enable interactive conversations
Set up a chat engine to have a back-and-forth conversation with the model about the content of the PDF file and ask follow-up questions to support more productive and natural interactions.
As a result, you should obtain an instrument capable of answering user queries with data that is reinforced with additional information from the database in real-time. 
Looking for software engineering services from a trusted provider? Drop Intelliarts a line and let’s discuss. 
Key tools for integrating RAG with language models
You can see the comparison of the key tools for integrating RAG in the table below:

Here are the Gitgub repositories for each of the provided tools:

Weaviate repository
Pinecone repository
Txtai repository
Qdrant repository
Haystack repository
Ragchain repository
Metal repository

Intelliarts experience with retrieval augmented generation language models
Data extraction chatbot development using ChatGPT
The customer, who is a non-governmental organization that advocates for gun control, requested to create a software solution. It was intended to allow employees to extract data from their internal knowledge database in an easy way. 
To assist the customer, the Intelliarts team built a chatbot using the GPT-4 model reinforced with the RAG model specifically for empowering the report requests. The chatbot was trained on the customer’s data. It can carry on a conversation exclusively about gun safety and related topics. It also can provide references as additional information.
The end solution which is custom and well-tailored to the customer’s workflow and content topics offers better productivity, automation, streamlined decision making, and ease of use. 
Discover more about the chatbot development case study.
AI assistant to help students pass the cybersecurity course

The Intelliarts’ customer, who is a provider of cybersecurity training, reached out to Intelliarts with the request to create an AI agent. The solution was intended to help analyze students’ work in virtual environments and provide recommendations. 
As a solution, the Intelliarts team provided recommendations regarding data collection. We researched and consulted the company on building an AI assistant by combining a classification ML approach and an LLM with an RAG model. Our team completed the discovery phase and built the PoC proved to perform the intended tasks. Now we’re moving to the full-scale solution implementation. 
As for business outcomes, the Intelliarts team proved the feasibility of such an AI agent and provided the customer with recommendations for the next project steps, including data pipeline and data type requirements. 
Discover more about the AI assistant development case study.
Final take
RAG enhances the capabilities of LLMs by combining retrieval-based methods with generative models. This innovative approach provides accurate, contextually rich responses and supports high data volumes. Businesses adopting RAG can achieve better decision-making, efficiency, and customer satisfaction. 
Should you need RAG-based solution development or another AI/ ML assistance, don’t hesitate to reach out to the Intelliarts team. With more than 24 years in the market and hundreds of completed projects under our belt, we are ready, willing, and able to address your needs with flying colors. 
Need an AI / ML expert consultation?Talk to an expert

#### Ethical Data Collection in the Digital Age
In 2022, a lawsuit was filed against Amazon for illegal voice data collection via the Alexa virtual assistant. This case illustrates the growing importance of ethical data collection. In an era where data misuse and data breaches happen here and there bringing severe reputational damage to the company, an ethical approach to data collection becomes a necessity, not an option anymore. It’s more than a matter of legal compliance or measures against misuse of sensitive information. Here we’re speaking about corporal responsibility and customer trust, as evident in Amazon’s case, when the company hit privacy charges, worker walkouts, and strong criticism of publicity.
Being experts in data collection and analysis, we decided to publish this article to walk you through the jungle of ethics in data collection and explain its key principles, regulatory framework, and best practices.
Key principles of ethical data collection
As more businesses adopt a data-centric approach, they need to consider the ethical aspects of their data practices, including data collection. Below we speak about 5 key principles of ethical data collection, although there are more of them:

1. Consent
Informed consent is probably the most important when it comes to the ethics of data collection. From 2008 to 2010, Google Street View cars gathered email and password data from unencrypted Wi-Fi networks. And even though Google did this unintentionally while capturing images, the situation invited a slew of criticism because of absent consent.
As a part of the agreement established between the data owner and the business, consent is taken before the actual data collection process starts. It’s also critical to articulate the request in a clear and understandable language, so the data subject knows what they’re granting consent for.
2. Data minimization
The principle of data minimization entails that businesses have to collect the data they need for a specific purpose. It’s a bad idea to gather excessive information, which increases the risk of data breaches and privacy infringement.
3. Transparency
Being open and transparent about data collection practices is also important. This way, businesses communicate to data providers what specific information is being collected, how it’s going to be used, and with whom it might be shared. Let’s learn from the best: following Apple’s latest iOS update, many consumers gained insight into the extent of their data usage. Here is how Craig Federighi, Apple’s senior vice president of software engineering, explains their latest focus on data privacy, which also intends transparency in data collection:
“Privacy means peace of mind, it means security, and it means you are in the driver’s seat when it comes to your own data.”
The data owner should also feel enough control over their data. This means if for any reason they decide to stop the usage and sharing of data, they should have the option to easily opt out.
4. Fairness
You as a business executive should make sure that your data collection contributes neither to inequality nor injustice. This means being mindful of potential biases and prejudices in data gathering, which can then affect algorithmic decision-making.
The story of the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm teaches that data collection and AI can easily perpetuate discrimination if not managed properly. Because of biased data collection, the model used in the US court system predicted twice as many false positives for recidivism in black offenders (45%) as compared to white ones (23%).
5. Accountability
Last but not least, businesses should stay accountable for their data collection methods. If a data breach or any other problem occurs as a consequence of your action, you’d better take responsibility and try to remedy the situation. If a company follows this principle, it will likely have a risk mitigation strategy, which makes it easier to solve any challenges.
Integrating these principles into your data collection practices simplifies it for businesses to navigate through the intricacies of data ethics. This also benefits the business itself because data providers are confident their data is being collected responsibly and are more inclined to share it. Still, we can discuss the benefits of ethical data collection in detail.
Business benefits of ethical data collection

If a company pays attention to ethics in data collection, it’ll likely benefit from:

Greater trust and reputation: Customers, partners, and the public, in general, will trust the company more eagerly if they know this business prioritizes ethics of data collection. As a result, the company could count on a positive brand reputation and loyal stakeholders, which makes a strong foundation for long-term sustainability and growth.
Compliance: If the company complies with data protection and privacy regulations when it comes to data usage and data collection, it avoids any possible legal consequences, from fines to reputational damage.
Data quality: More focus on ethical considerations in data collection can enhance data quality and, thus, your model results. As said, if your company’s data collection methods are trustworthy, others are more likely to share their data.
Cost efficiency: Businesses can expect significant cost savings in case they prevent data breaches and legal and financial consequences that usually follow.
Competitive advantage: With more emphasis on ethical data collection practices, it’s also possible to set yourself apart from the competitors. Stakeholders are more ready to work with the companies that put ethics first.

Challenges and risks in ethical data collection
As evident, data ethics, including data collection, extends beyond a moral obligation; it also brings many growth opportunities for businesses. At the same time, complying with ethical standards isn’t easy — below are some ethical dilemmas and risks that you could meet.

Accuracy vs. privacy: The biggest issue in ethical data collection lies in the conflict between privacy and effective analytics. AI and ML technologies need lots of data to be more accurate in results, but this also poses more data privacy risks. The healthcare sector with sensitive patient data is the prime example here. In 2018, the personal information of 2.65 million patients was leaked, with names, addresses, social security numbers, etc. falling into the wrong hands due to a lack of proper data management.
Legality vs. stakeholder expectations: Another interesting matter in digital data collection is what’s legally permissible and what your stakeholders believe is fair. Sometimes, companies can gather more information than they actually need, even though they act within the existing regulations. This could raise uncomfortable questions from their stakeholders, especially if the company didn’t warn the partners about their plans.
Third-party risks: Don’t ignore ethical issues in data collection posed by third parties. Your company could be fully transparent in data collection methods, and then you decide to buy data from a third-party vendor whose practices are less secure. Since businesses have limited control over their partners’ policies, this could create a new ethical challenge for you.

Each of these challenges demands thoughtful consideration, but we’ll cover some ideas on how to mitigate these risks later. Meanwhile, we can talk about the specific laws and regulations that impact your data collection.
Turn Data into Business ValueBoost quality and efficiency with our data science consulting.Learn more
Regulatory framework and compliance
Here we mention the most well-known compliance standards and regulations for data protection and privacy:

General Data Protection Regulation (GDPR) enacted in Europe is acknowledged as one of the strictest data privacy laws. Its idea is to make businesses maintain the strictest control over user data as well as empower users to delete or remove their data from the systems where it was uploaded. On the matter of data collection, the law says that certain data can be collected and stored only if it remains anonymous. It also shouldn’t be kept longer than you need it. (See the infographic below to learn more about GDPR.)

For Mac users, a convenient way to manage local data while preserving iCloud content is to delete using CleanMyMac, which simplifies device-level photo cleanup without affecting cloud backups.
 

California Consumer Privacy Act of 2018 (CCPA) empowers users with greater control over personal data collected by businesses. Covering California residents and companies that do business within the state, the law extends to 1) the right to be informed about personal information that’s being collected, how it’s used and shared; 2) the right to correct inaccurate data collected about the user; 3) the right to delete the collected information (with some exceptions); 4) the right to opt-out of the sale and sharing of personal data; 5) the right to non-discriminate when exercising the CCPA rights; and others. To a large extent, CCPA was modeled off of the GDPR.
The Data Protection Act of 2018 represents the UK’s version of the GDPR. The act regulates the use of personal data by businesses, organizations, and the government. Among other things, it proclaims the following data protection principles: data collection and use in a fair, lawful, and transparent manner; data collection for specific, explicitly stated purposes; data retention for no longer than necessary; data collection in a way that is adequate, relevant, and limited to what is necessary; etc.
Health Insurance Portability and Accountability Act (HIPAA) is the US federal law that aims to protect health data or information related to patient care. The law mainly targets hospitals, insurance companies, and other businesses that work with patient data prohibiting disclosure of health data to third parties unless a patient gives clear, well-documented consent.
The Children’s Online Privacy Protection Act (COPPA) is enacted in the US to protect children’s data. This framework outlines guidelines, including dos and don’ts for data collection and processing of children under 13. For example, it covers the topics when consent from a guardian is required or when data collection is prohibited.

Businesses that don’t adhere to these standards and regulations can be subject to legal liability like fines and other penalties. Even harsher risks of non-compliance include damage to the reputation, challenges in securing investments, lower profits, and others.
 White PaperMachine Learning for Insurance Business Download now
Best practices for ethical data collection
As we made it clear why data ethics is important, the next logical step is to answer how to achieve this. Consider the best practices for ethical data collection:
1. Develop a policy for ethical data collection
Start by setting up company-specific rules for data collection. This could be a part of a larger framework for data usage that employs a shared vision and mission regarding the company’s use of data and, thus, speeds up decision-making.
Here are some pieces of advice to make your data policy more applicable:

Make sure the C-level executives participate in the process of policymaking.
Tailor your data collection rules to your specific industry, as well as the products or services offered. For example, if you work with patient data, you might need some extra rules, such as about the use of HIPAA-friendly solutions.
Ensure the policy is accessible to all stakeholders of yours, including employees and partners.
Revisit and update the rules regularly to adapt them to changes in business and technology.

2. Comply with regulations
Compliance is a critical aspect of ethical data collection, which has to be strongly rooted in your data policy. Data protection laws, such as the GDPR, CCPA, etc., should be the foundation for your company’s rules. Also, make sure to stay updated about the latest changes within these regulations, as they are reviewed regularly. For example, monitor any updates regarding extra consent needed from individuals or new security practices.
Think also about regular audits and compliance checks within your organization. From time to time, organize an independent examination of data collection to check how this process aligns with the applicable laws as well as your internal policy.
3. Cultivate a responsible data culture
It’s easier to use transparent and fair data collection practices in a company with a responsible data culture. First, build a privacy-first mindset in your company, prioritizing privacy rights and data protection in your operations. Secondly, conduct regular data ethics training, where you explain the importance of ethical data collection, the potential risks and ethical implications in case of non-compliance. Training programs could also help you educate employees on ethical data collection methods and ethical data handling in general.

4. Mind your data collection methods
On a lower level, make sure your data scientists employ ethical data collection methods like:

Using encryption for sensitive data to enhance security. Overall, when dealing with any sensitive data like identification details and health records, it’s recommended to use precautions to ensure the data won’t be leaked.
Safeguarding the data from unauthorized access by using strong passwords, keys, and authentication methods.
Implementing data anonymization whenever possible. This typically means removing any personally identifiable data from the dataset or, if it’s impossible, separating the data from sensitive information. Besides, we recommend choosing an anonymization technique based on your specific project requirements. For example, k-anonymity protects your data by grouping similar individuals and generalizing data fields with identifying information. This technique suits perfectly for patient, marketing, census, or credit card data.
Categorizing your data according to its sensitivity before making it available for ML models. For example, businesses can classify data as normal, sensitive, highly sensitive, and strictly confidential. Tokenization should be used for the two last categories, especially if the data is stored in the cloud.
Being conscious about your data sources. It’s always more secure to use publicly available sources like market research studies, GitHub repositories, social media, etc. In other cases, you might need private information received from IoT, CRM software, or ERP systems — make extra security measures in this scenario.
Using APIs for web scraping. Avoid this data collection method if possible since it involves duplicating the company’s data and can potentially lead to copyright infringement.
Backing up your data regularly and using reputable and trustworthy cloud or on-premise storage providers. For example, a simple MacBook backup strategy can safeguard business-critical files and protect against accidental loss, breaches, or errors. Also, monitor your data storage in case you see any anomalies, breaches, or errors to react immediately.

5. Prevent discrimination and biases
To follow the principle of fairness, your company should avoid discrimination and biases in data collection. For example, training algorithms on partial data usually risks getting biased and misleading results. Ensure the data collected is diverse and representative, as well as it doesn’t marginalize or unfairly target any specific groups.
The data should accurately represent the demographic that the ML model is intended to affect. So if it’s the AI solution developed for women specifically, the data couldn’t be based on the male audience as it makes it irrelevant.
The bottom line
Ethics in data collection and usage overall is gaining more prominence in today’s data-driven environment. And this impact goes far beyond legal responsibility; it’s about cultivating trust and credibility in the eyes of customers, partners, and other stakeholders. Ethics of data collection also refers to protecting privacy and mitigating reputational and financial risks.
What’s more, the role of ethics in data collection and analysis is expected to grow further. As the amount of data increases over time, so does the public awareness of data privacy issues. Consequently, businesses might want to prioritize ethical data collection practices, keeping up with the evolving data privacy regulations and frameworks.
Planning to kick off a data science project? As a big data consulting firm with vast expertise in data science and machine learning, Intelliarts knows everything about data ethics and follows ethical data collection principles thoroughly. We’ll gladly help you transform your data into valuable insights.
Let's discuss your data projectContact Us

#### EV Charging Data Analytics for Smarter Energy Management
Stats reveal that electric vehicle (EV) adoption has surged in recent years, with EVs accounting for over 17 million sales globally in 2024, representing more than 20% of new passenger car sales. Considering that, it is not surprising that business owners often regard data as an untraditional asset due to its objective value, and EV data analytics has gained adoption.
Jumping ahead, let’s recap several pain points that EV charging optimization aims to address: Suboptimal charging station locations, inefficient operations and maintenance, and inability to scale. But there’s more to it. 
In this post, you’ll explore the role of data in the EV charging business and review physical devices that can be used in such a business model. Besides, you’ll find out how to optimize charging operations through data analytics step-by-step, find out more about common challenges and solutions, and have a look at the potential future of EV charging data analytics. 
Power Up with Data-Driven EV InsightsLearn what the top 10 EV tech trends mean for your charging operations.Read the white paper
The role of data in EV charging site selection
The best way to reveal the usefulness of having the right amount of high-quality data is to delve into its application on real examples. Within this topic, for instance, we can freely explore the factors taken into account in EV charging site selection. The list goes as follows:

Traffic flow. Such data shows where vehicles most frequently travel or stop. High-traffic corridors (e.g., highways, urban intersections) indicate strong potential for station usage.
Population density. This factor identifies areas with enough people — and therefore vehicles — to generate consistent demand. Dense urban zones may need more charging spots per square mile.
EV adoption rates. Such information reveals where EV users are most concentrated. Regions with fast-growing EV ownership offer immediate customer bases and higher ROI potential.
Dwell time alignment. This metric considers how long people stay at a location. Sites near shopping centers, restaurants, or offices allow vehicles to charge during natural pauses in activity.
Land availability and zoning. This factor helps to determine whether a site can legally and physically accommodate charging infrastructure. Permissive zoning laws and open space speed up development.
Charger network gaps. This data shows underserved areas lacking nearby stations. Charging sites located in such areas may cover the demand successfully. 

The big idea here, as you might note, is that having the necessary data can help EV charging projects. Take a look at the exact benefits of EV data analytics in the infographics below:

Discover tips to streamline EV fleet management in business in another of our blog posts. 
Devices and software solutions used to collect data for EV charging data analytics
Here’s the scope of devices you may need to implement EV charging data and analytics, along with examples of real instruments available on the market:
#1 Remote sensing instruments:
They are used for capturing aerial and satellite imagery to assess environmental and infrastructure conditions.

DJI Phantom 4 RTK: A drone equipped with a high-precision RTK module for aerial mapping.​
MicaSense RedEdge-MX: A multispectral camera designed for agricultural and environmental remote sensing.​

#2 Surveying equipment:
It measures precise distances, angles, and positions for terrain mapping and infrastructure planning.

Leica Total Stations: High-accuracy instruments for surveying and construction measurements.​
Topcon GT Series: Robotic total stations offering advanced surveying capabilities.​

#3 Data loggers:
They record environmental or operational data over time for analysis and reporting.

Campbell Scientific CR1000X: A versatile data logger for environmental and industrial applications.​
Onset HOBO Data Loggers: Devices for monitoring environmental conditions like temperature and humidity.​

For a better understanding of how data loggers work and how to use their input in mapping EV charging station accessibility, take a look at the video below:

#4 Sensors:
Such devices detect and transmit physical parameters like temperature, humidity, motion, and pressure in real-time.

Bosch BME280: An integrated environmental sensor measuring temperature, humidity, and pressure.
Libelium Waspmote: A sensor platform for wireless sensor networks and IoT applications.​

#5 Geospatial Analytics Software:
These instruments process spatial data to reveal patterns, optimize infrastructure, and support decision-making.

CARTO: A cloud-native location intelligence platform for spatial analysis. ​
ArcGIS by Esri: A comprehensive suite for mapping and spatial analytics.

IoT in the EV IndustryDiscover best practices of implementing IoT in electric vehiclesRead our blog post
How to optimize charging station operations through data analytics: Step by step
Let’s get down to the very implementation of data analytics for EV charging:
Step 1: Evaluate existing infrastructure, IoT, and data analytics needs
Conduct a comprehensive review of your current EV charging infrastructure, identify technical limitations, and define the business and operational goals that data analytics should support. This step sets the foundation for all subsequent efforts.
Action points:

Audit hardware and software infrastructure
Review current data sources and flow
Identify missing sensors or data points
Define business goals for analytics
Map regulatory and security requirements

Expertise needed: Infrastructure engineer, IoT specialist, data analyst, compliance officer
Step outcome: A clear understanding of what needs to be upgraded or added to enable data-driven optimization.
Step 2: Prepare charging infrastructure by reinforcing it with IoT solutions
Upgrade your charging stations with IoT devices to enable real-time data collection, remote diagnostics, and dynamic control. This step ensures your infrastructure is digitally connected and ready for intelligent monitoring.
Action points:

Install IoT devices (sensors, meters, controllers)
Ensure secure connectivity (e.g., 4G/5G, Wi-Fi, LoRa)
Enable data logging and timestamping
Integrate with charging station software
Test IoT data transmission and reliability

Expertise needed: IoT engineer, hardware technician, network engineer
Step outcome: A connected charging infrastructure capable of real-time data capture.
Step 3: Establish means to collect, clean, and centralize data
Implement a data architecture that consolidates data from all stations and sensors into a centralized, structured system. Cleaning and validation ensure the accuracy and reliability of data before it’s used for analytics.
Action points:

Set up a cloud-based data warehouse
Develop ETL (extract, transform, load) processes
Implement data validation and error detection
Automate regular data collection
Comply with data protection standards

Expertise needed: Data engineer, cloud architect, cybersecurity specialist
Step outcome: Reliable, structured, and secure access to unified operational data.
Step 4: Choose and integrate business intelligence, data analytics, and data visualization software
Select and configure the right tools to analyze trends, measure performance, and visualize operations in real time. Integration ensures that insights can flow seamlessly into decision-making processes.
Action points:

Evaluate BI and analytics platforms (e.g., Power BI, Tableau)
Set up dashboards and KPIs
Connect tools to the data warehouse
Configure access for stakeholders
Ensure scalability for growing datasets

Expertise needed: BI developer, data analyst, software integrator
Step outcome: An operational analytics suite that transforms raw data into actionable insights.
Step 5: Train team on working with IoT-reinforced charging infrastructure and data analytics solutions
Equip your team with the necessary skills to operate IoT-powered stations and interpret analytics tools. Training ensures adoption, minimizes errors, and empowers staff to make data-informed decisions.
Action points:

Conduct training sessions for hardware operation
Provide data interpretation workshops
Create documentation and SOPs
Appoint internal analytics champions
Set up a feedback and support channel

Expertise needed: Technical trainer, operations manager, analytics consultant
Step outcome: A knowledgeable team empowered to use data and IoT tools in daily operations.
Step 6: Launch real-time monitoring and data analytics processes, improve, and iterate
Deploy your live monitoring and analytics systems, start generating insights, and establish a culture of continuous improvement by analyzing results and refining strategies based on what the data reveals.
Action points:

Deploy real-time dashboards
Monitor for anomalies or inefficiencies
Use predictive analytics for maintenance and demand
Gather feedback and analyze trends
Adjust algorithms and processes regularly

Expertise needed: Data scientist, operations analyst, DevOps engineer
Step outcome:
An adaptive, intelligent system that continuously improves station efficiency and user satisfaction.
Explore EV big data services by Intelliarts at our respective service page, so you can both explore how our offering stands out and reach out to us in a few clicks! 
Here, we would like to reveal our case on exactly the usage of data analytics for EV charging infrastructure. 

Challenge:
EV Connect needed to prevent unexpected charger downtimes and improve service reliability but lacked structured data for predictive maintenance.
Solution:
Intelliarts analyzed historical EV charger data, cleaned and restructured it for ML use, and applied anomaly detection algorithms to identify failure patterns. We provided detailed recommendations for improving data quality, collection pipelines, and storage.
Results:
EV Connect gained strategic insights into charger behavior, optimized maintenance planning, and prepared data infrastructure for a future predictive maintenance system. This resulted in enhanced uptime, reduced service interruptions, and smarter, data-driven EV charging operations.
Explore success story 
Challenges in EV charging data analytics and solutions to them
As with any innovation and initiative, there are difficulties related to adopting EV charging and data analytics and applying it to real business processes. Here are the main concerns, and ways to address them: 

Challenge 1: Collecting high-quality, standardized data across multiple stations
EV charging infrastructure often involves various hardware vendors and network operators, leading to inconsistent data formats and metrics. This fragmentation limits the effectiveness of EV charging station data analysis and hinders network-wide insights. In the meanwhile, according to the Garbage-In-Garbage-Out (GIGO) rule, the quality of output largely depends on input data quality. 
Solution: Implementing a sufficient big data analytics pipeline and having a unified data model can ensure compatibility across systems. Data-driven EV charging solutions that normalize incoming data streams enable effective EV charging analytics and network-wide benchmarking.
Take a look at our data collection guide for a complete perspective. 
Challenge 2: Privacy and security issues related to sensitive user data
User-level information collected through EV charging analytics — like location, preferences, and payment details — poses risks if not properly protected. Data breaches can compromise customer trust and violate compliance laws.
Solution: Leverage encrypted data storage and role-based access within EV data analytics systems. It can also be recommended to implement privacy-by-design into smart EV charging analytics platforms. 
Challenge 3: Difficulty in forecasting demand and optimizing station locations
Without having predictive capabilities, it’s challenging to know where to expand or how to balance charger loads, leading to overuse or underuse of assets. This impacts ROI and customer experience and basically mitigates the entire purpose of integrating smart EV charging with IoT. 
Solution: Apply predictive data analytics in electric vehicles using machine learning models rather than through conventional data analytics and visualization means. This would lead to having better forecasting capabilities and is basically an intended way of implementing smart EV charging reinforced with data analytics. 
Challenge 4: Privacy and security issues related to sensitive user data
Many charging network operators and infrastructure providers lack in-house expertise in handling advanced EV charging data analytics. This limits their ability to extract value from data, model demand accurately, or optimize performance effectively.
Solution: It’s recommended to collaborate with external data science partners to cover every tech aspect. Besides, we recommend using custom or out-of-box EV charging software solutions to overcome the skill gap of employees intending to work with EV charging and data analytics. 
Discover more about big data trends in another blog post by Intelliarts. 
It’s often that difficulties boil down to three main constraints: expertise availability, budget, and time. Luckily, partnering with a trusted tech partner is a solution to all three pain points, as it’s more cost-effective for non-development companies to utilize other vendors’ expertise rather than grow their own. 
The future of predictive & autonomous networks with Intelliarts’ insights
And now, let’s have a glance at what the future of EV charging networks reinforced with data analytics may look like:
#1 Reducing wait times through charging pattern insights
Modern EV charging analytics tracks when, where, and how long drivers charge their vehicles. With this data, providers can apply data-driven EV charging optimization to:

Analyze charging frequency and dwell time
Predict congestion and recommend nearby available stations
Implement smart EV charging analytics with dynamic queue management and pricing models

The result? Reduced wait times, higher utilization, and a smoother charging experience.
#2 Big data enables autonomous load balancing
EV charging infrastructure is now powered by data analytics in electric vehicles to dynamically manage energy across multiple sites. By leveraging big data:

Load is distributed intelligently to avoid grid overload
Charging sessions are scheduled based on grid conditions and pricing
Predictive models trigger automated fault detection and maintenance

This form of EV charging station data analysis does not only improve efficiency but enables autonomous systems to run without constant human oversight.
#3 Integrated insights to reduce costs and waste
EV charging data and analytics become even more powerful when layered with other datasets, such as:

Weather forecasts (e.g., solar output predictions)
Real-time energy pricing and availability
Charger usage data across locations

You may be interested in exploring renewable energy solutions development services by Intelliarts. 
This comprehensive approach to optimizing EV charging networks can shift high-energy sessions to off-peak times, align with renewable energy output, and cut operational costs — all through intelligent automation.
What Tesla did for cars, data analytics is doing for infrastructure. We already could see a reduction in operational costs upon the integration of real-time demand data and local grid inputs in actual cases. — Alexander Barinov, Managing Partner at Intelliarts 
Take a look at a sample use case on EV charging data and analytics optimization: 

Our customers used to manage chargers based on a reactive approach to complaints or failures. With EV charging data analytics, we can now offer to anticipate issues, balance grid loads, and serve more vehicles without adding new stations. It’s not just optimization; it’s transformation. — Yuliia Zabudska, EV expert at Intelliarts 
 
Final take
EV charging data analytics plays a critical role in optimizing site selection, station performance, and user experience. By integrating IoT devices, big data tools, and predictive models, businesses can improve decision-making and enable proactive maintenance. 
Though challenges like data quality, privacy, and expertise gaps remain, a structured data-driven approach positions EV infrastructure for smarter, scalable, and more efficient operations in the rapidly growing electric mobility market.
The Intelliarts team has substantial expertise in creating data analytics solutions for renewable energy and EV markets. With more than 24 years’ of experience in software engineering, 80+ large projects delivered, and a 90% customer retention rate, we can deliver a top-notch software solution for specific EV charging data needs, so don’t hesitate to reach out to us with your request.
Struggling with EV Charging Analytics? Let Intelliarts Help.Let's talk

#### EV Charging Software Architecture Best Practices: What to Take into Account
Recently, IEA calculated that the global number of public EV charging points exceeded 5 million, with more than 1.3 million chargers added in a single year. Since EV infrastructure worldwide is expanding, and more businesses are entering the niche, it’s useful to know relevant software practices. After all, virtually every company in the EV industry uses applications to obtain data and improve its services.
In this guide, you’ll explore types of EV software design paradigms and find which one is the most suitable for your projects. Besides, you’ll explore core layers of electric vehicle solutions, protocols and standards, common mistakes, and learn how software is designed for scalability from day one. 
What is EV charging software architecture and why does it matter?
EV charging software architecture is the structural design of how charging stations, backend services, and applications connect and exchange data within an EV charging network. 
Typically, software architecture options include monolithic, modular, and hybrid approaches. 

In a monolithic architecture, all system functions run as a single application where components are tightly coupled. 
A modular architecture organizes the platform into separate functional modules that remain logically independent but may still run within the same system. It’s similar to a microservices alternative, where architecture components are independent, but usually belong to different external systems. 
A hybrid architecture combines both approaches, keeping some tightly integrated components while separating others into independent modules or microservices. 

Important note: Monolith architecture is typically used for cybersecurity, banking, government, and similar apps. EV charging software usually has either modular or hybrid paradigms, so this blog post elaborates on patterns and specifics with only these two architectures in mind. 
Here’s a list of core capabilities that most off-the-shelf and custom EV charging software have:

Device management to connect chargers to the platform and manage configuration, firmware, and diagnostics.
Session control to start and stop charging sessions and record energy usage.
Billing and payments to calculate tariffs and process transactions.
User authentication to authorize drivers through apps, Radio Frequency Identification (RFID) cards, or accounts.
Load balancing to distribute power across chargers and prevent grid overload.
Smart charging to adjust charging behavior based on grid signals or pricing.
Monitoring and alerts to track charger status and detect faults.
Analytics and reporting to analyze usage, utilization, and revenue.
API integration to connect the platform with mobility, energy, and business systems.

You might be asking yourself: “How will architecture impact the uptime and user experience of my future app?” and: “How are core capabilities related to architecture usability?” 
From the business standpoint, the EV charging platform architecture basically determines how reliably the platform handles session data and system failures. Architecture also affects how quickly the system reacts to charger events. Therefore:
When one component or feature fails, proper EV charging software architecture enables the rest of the platform to continue operating, which prevents network-wide outages.
Leading the industry requires trusted dev partnersFind out how the Intelliarts team handles EV projects of any complexity for our customers.Explore industry expertise
The following blog post sections will show you why you should and how exactly you can achieve this desirable outcome.  
What core layers should a modern EV charging platform include?

Let’s proceed by taking a good look at an EV fleet managed charging software structure, layer by layer and module by module. That will help to see where the backend and the capabilities listed above are contained. It will also indicate what other critical components the modular or hybrid architecture is composed of:
#1 Device layer
This layer includes software that runs on charging stations or interacts directly with charger hardware. It handles firmware control, diagnostics, and charger communication with the backend through protocols such as OCPP, enabling reliable device connectivity within an EV charging platform architecture.
Separating this layer allows the platform to support different charger models while keeping a consistent device interface for the rest of the EV charging software system.
Firmware & diagnostics module

Firmware lifecycle management
Secure firmware updates
Charger diagnostics and fault codes
Local logs and health checks
Meter interface and energy measurements

OCPP device client module

OCPP message creation and parsing
Charger state management
Connection keep-alive and heartbeats
Local retry and reconnect logic

#2 Edge layer
This layer operates at the charging site level and manages groups of chargers locally. It relies on edge computing to maintain stable charging operations when connectivity to the backend is unstable and to execute fast on-site decisions.
Edge components reduce backend dependency by enforcing local policies and buffering communication with the central EV charging management software.
Site controller module

Local authorization cache
Offline mode operation
Message buffering
Store-and-forward queues
Ordered message replay

Local power control module

Local load balancing rules
Site power limit enforcement
Charger power allocation
Fallback smart charging schedules

#3 Backend layer
This layer forms the operational core of EV charging software. It manages chargers, charging sessions, and operational workflows through services such as a charge point management system (CPMS).
Most core capabilities of EV charging network software, including device management, session control, and monitoring, run within this layer.
CPMS operations module

Charger provisioning
Device configuration management
Firmware rollout coordination
Site and asset management
Remote commands (unlock, reboot, availability)

Session control module

Session start authorization
Session stop handling
Session state machine
Meter value ingestion
Energy consumption tracking

Monitoring & alerts module

Charger status tracking
Fault detection rules
Availability calculation
Operational alerts and notifications

Identity & access module

Driver authentication (app, RFID, QR)
Operator role management
Permission policies
Audit logging

The difference between a pilot network and a real charging business is software reliability. When the backend goes down, the whole network goes down. — Jurij Aulikh, an eMobility Software Engineer at Intelliarts. 
#4 Integration layer
This layer connects the platform with external services such as roaming networks, payment systems, fleet platforms, and utilities. These integrations allow EV fleet charging software and mobility services to interact with the charging network through standardized API integration.
Separating this layer simplifies partner integrations and enables interoperability across different charging ecosystems.
Roaming module 

Open Charge Point Interface protocol (OCPI) endpoint management
Partner authentication
Roaming session exchange
Tariff publication
Settlement data export

Payments & billing connectors module

Payment gateway integration
Transaction processing
Invoice generation
Tax and VAT calculation

Fleet and energy integrations module

Fleet management system integration
Vehicle and driver mapping
Utility signal ingestion
Energy price feed integration

#5 Data & analytics layer
This layer processes operational data generated by chargers and backend services. It enables real-time data processing, analytics dashboards, and reporting used by operators to evaluate network performance and usage patterns. It also helps to implement measures to optimize electric fleet charging.
Separating analytics workloads prevents reporting processes from affecting the operational performance of EV charging software.
Real-time data processing module

Event ingestion
Telemetry processing
Data aggregation
KPI calculation

Dashboards & reporting module

Utilization dashboards
Availability reporting
Revenue analytics
Data export APIs

Important note: Not every module should look like this in every EV charging software architecture. Capabilities may be combined in different clusters and have a varying scope. 
The pyramid from the beginning of the section shows a dependency-based viewpoint on software architecture. The rules are that upper layers depend on lower layers and lower layers provide capabilities used by the layers above. That’s why hardware and related modules go first, then backend, then the components in which apps interface, and finally, UX and data output. 
Looking for a trusted provider of EV software development services to handle the development for your best EV project? Don’t hesitate to reach out to Intelliarts.
How should you design for scalability from day one?
As mentioned above, any trusted software development partner, like Intelliarts, would advise you to go either a modular or a hybrid architecture route for your EV charging platform. Here are several main considerations, with indications of exact technologies covering one or another business need:
#1. Rapid station expansion
Rapid station expansion refers to the ability of EV charging software to onboard and operate large numbers of new charging stations. The platform should be able to do so quickly without disrupting existing network operations.
Technologies that support rapid expansion

Microservices architecture
Cloud-native infrastructure (Kubernetes, containers)
Event-driven architecture (Kafka, RabbitMQ)
Horizontal API scaling and load balancers
Automated device provisioning pipelines
Multi-tenant platform design

#2. Peak load and real-time session spikes
Peak load and real-time session spikes occur when large numbers of vehicles start or stop charging simultaneously, creating sudden bursts of session traffic and telemetry across the EV charging network software.
Technologies that support peak load handling:

Event-driven architecture (Kafka, RabbitMQ)
Horizontal service scaling
Load balancing across services
In-memory data stores (Redis)
Streaming data processing
Asynchronous message queues

#3. Multi-region deployments
Multi-region deployments enable EV charging software to operate reliably across different geographic regions while maintaining low latency, regulatory compliance, and high availability.
Technologies that support multi-region deployments:

Multi-region cloud infrastructure
Global load balancing
Distributed databases
Region-aware service routing
Data replication and failover mechanisms
Content Delivery Network (CDN) and edge gateways

Need a technical consultation on choosing the right EV charging software architecture?Let Intelliarts experts assist
Now, let’s combine all bits of knowledge, layers, architectures, and key considerations into one insight. The table below shows how exactly these two approaches, which share a core of similar layers and modules, compare to each other and to a rigid, monolithic architecture:

As you can see, modular and hybrid approaches are fairly similar, and both are superior, in the EV software scenario, to a monolith. Hybrid is more favored when there’s a core structure, probably the legacy monolithic code, or when there are business and compliance considerations involved. 
The main take: You design for scalability from day one by sticking to either a modular or a hybrid approach and utilizing best practices in software architecture design.
In practice, the modular architecture and scaling opportunities it provides are what appeal to businesses in the niche the most. — Ihor Rudnyk, an eMobility Tech Specialist at Intelliarts.
What protocols and standards must be considered?
Modern EV charging software must support several industry protocols and standards. These specifications determine how devices exchange operational messages, how charging networks interoperate, and more. See the short overview of protocols and their main provisions below:
#1 OCPP (Open Charge Point Protocol)
OCPP is the primary protocol governing communication between charging stations and the backend charge point management system (CPMS). It standardizes how chargers report status and how backend systems control device operations across the charging network.
Regulatory organization: Open Charge Alliance (OCA) 
Key provisions defined by the protocol:

Charger boot notifications
Device authorization requests
Charging session start and stop events
Meter value reporting
Remote commands (start/stop, reset, unlock)
Firmware update management
Smart charging profiles and power limits

Explore how Intelliarts can help your business navigate global standardization frameworks and achieve optimal implementation results. Read the white paper.
#2 OCPI (Open Charge Point Interface)
OCPI is the interoperability standard used for roaming between charging networks and mobility service providers. It defines how operators exchange operational and billing information across different platforms.
Regulatory organization: EV Roaming Foundation
Key provisions defined by the protocol:

Charger location and availability data exchange
Tariff and pricing publication
Charging session information exchange
Token authorization for roaming users
CPO–eMSP settlement records
Partner credential and role management

#3 ISO 15118
ISO 15118 defines the communication framework between electric vehicles and charging stations. It enables plug & charge functionality and secure authentication without requiring driver interaction.
ISO 15118 standard also enables advanced energy scenarios such as Vehicle-to-Grid (V2G) technology, allowing EVs to act as distributed energy resources within the power system. Simply put, it allows EV batteries to return electricity to the grid to support energy balancing.
Regulatory organization: International Organization for Standardization (ISO) in cooperation with the International Electrotechnical Commission (IEC) 
Key provisions defined by the standard:

Vehicle authentication through digital certificates
Secure EV-to-charger communication channel
Contract certificate management
Plug & charge authorization workflows
Smart charging negotiation between EV and charger
Vehicle-to-grid communication support

#4 Payment standards
Payment standards regulate how financial transactions are processed within EV charging software, ensuring secure handling of payment credentials and billing data.
Regulatory organization: PCI Security Standards Council (PCI SSC) 
Key provisions defined by these standards:

Secure card data handling requirements (PCI DSS)
Payment tokenization mechanisms
Transaction authorization flows
Fraud detection and monitoring controls
Secure integration with payment gateways
Compliance reporting and audit requirements

And here’s a brief overview of the impact of protocols on the architecture design:

How do you ensure security and compliance in EV charging systems?
In the displayed software architecture, security & identity are not a separate layer, module, or microservice. Instead, it’s a cross-cutting concern. A quick example, encryption, data security, secure updates, compliance, etc., are all parts of a broader security concept, despite being distributed across nearly all layers and modules.
The infographics below summarize the sensitive aspects of security covered by one or another technology implementation:

What does an optimal EV charging workflow look like?
So, given all the EV software architecture choices and design paradigm constraints, what does a workflow with an EV charger look like? The diagram below shows the flow of data from an EV being charged to the data and analytics layer, through the EV charging infrastructure, backend, and all the external systems:

Basically, data is generated during EV charging, and then, in full compliance with regulations, passed through a suite of layers comprising an EV software architecture. That’s how you end up with clear electricity consumption patterns and actionable insights. 
Looking for a trusted dev partner to design your EV charging management system? Don’t hesitate to reach out to Intelliarts.
When should you build custom EV charging software vs use an off-the-shelf platform?
The choice between custom development and an off-the-shelf platform determines how much control an operator has over its EV charging software and how easily the system can evolve as the charging network grows.
#1 Custom EV charging software advantages:
Custom development allows operators to design the EV charging platform architecture around specific operational needs rather than adapting business processes to a predefined product. This approach is particularly useful for companies that require deep integrations, differentiated services, or specialized charging logic.

Greater architectural flexibility
Full control over system integrations
Ability to build differentiated charging features
Easier adaptation to new standards and regulations

#2 Off-the-shelf platform advantages:
Off-the-shelf solutions allow companies to launch charging services faster without building a full backend platform from scratch. These platforms provide core functionality out of the box and reduce the initial engineering investment.

Faster deployment of charging infrastructure
Lower upfront development cost
Prebuilt charger management capabilities
Standard protocol support, such as OCPP

See the decision framework table that can guide you in choosing between a custom EV fleet charging software and off-the-shelf solutions available in the market:

Important note: While for many small-scale or beginning projects an off-the-shelf software may seem like a better choice, money and effort-wise, it’s slightly more complicated than that. Off-the-shelf solutions are inefficient and not always possible to scale. 
At the same time, custom development services may offer a tailored MVP at an affordable cost and scale easily once the idea is validated. As another alternative, Intelliarts offers to add customization to a pre-built solution of your choosing. It’s usually a good trade-off in terms of time and money. 
Common architecture mistakes in EV charging projects
Building EV charging software means dealing with distributed devices and connectivity challenges on top of regular software architecture complexities. Here are some of the most common mistakes that the Intelliarts team sees occurring in projects based on our experience:

Ignoring offline scenarios because chargers regularly lose connectivity, which can cause interrupted sessions and delayed synchronization with the backend system.
Underestimating real-time data load, especially when there is insufficient event processing capacity, resulting in delayed monitoring and unstable session handling.
Poor API design in cases when charging platforms must integrate with apps and inconsistent interfaces make integrations hard to use. 
Vendor lock-in that occurs due to tightly coupling the system to proprietary infrastructure limits architectural flexibility and makes scaling significantly more difficult.
Treating security as an afterthought, especially when it comes to delayed implementation of device authentication, encryption, and monitoring, which results in higher risks.

Success in avoiding these mistakes usually boils down to the experience of a development team and their expertise with EV solutions specifically. That’s exactly why we recommend assistance from trusted EV software development agencies like Intelliarts. 
Intelliarts’ Experience: EV Charging Management Software for EVIQ
Our customer, EVIQ, needed software to support a rapidly expanding EV charging network in Saudi Arabia while meeting strict regional requirements. The platform had to enable station monitoring, charger lifecycle management, user payments, and fast deployment within an ambitious six-month development timeline.
Intelliarts designed and built custom EV charging software using a scalable AWS serverless architecture, which is basically a hybrid solution. The platform was launched on time and now supports thousands of EV drivers. The mobile app achieved strong ratings on Android and iOS, while the scalable architecture allows EVIQ to expand its nationwide charging network.
Should you need to learn more about our experience, take a look at the full EV charging case study in our portfolio. 
 

Final take
Well-designed EV charging software architecture determines whether a charging network can grow without operational instability. As charger fleets expand, platforms must handle thousands of devices without downtime.
Modular or hybrid architectures make this possible by separating critical services such as CPMS operations, session control, and data processing. Combined with standards like OCPP and OCPI, this approach allows operators to scale infrastructure while maintaining reliability and control.
Here at Intelliarts, we are proud to have more than 25 years of experience delivering custom software solutions. We also possess substantial expertise in EV and AI development. With over 80 successful large-scale projects completed, 54% senior staff engineers, and a 90% customer return rate, we are ready, willing, and able to contribute to your next EV charging software project.
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#### EV Charging Standards and Protocols: Key Types and Why They Matter
EV charging has become a practical requirement for drivers, businesses, and energy providers. More stations appear each year, and each one must work with different vehicles, software systems, and power conditions. In 2024 alone, more than 1.3 million public charging points were added globally, a growth of over 30% compared to the previous year. Clear standards help avoid confusion and technical mismatches, especially as countries use different connectors and communication rules. 
In this post, you’ll explore the main EV charging standards and protocols. The guide will familiarize you with their provision, indicate the main benefits of complying with them, and show the future of EV charging standards development in 2026 and beyond.
Navigate EV Charging Standards with EaseUnderstand how global EV charging standards connect to real regulatory requirements and what this means for scalable, compliant charging platforms.Read our white paper
What are EV charging standards and protocols?
First, let’s get through the topic-related concepts:
EV Charging standards are technical guidelines created by international standards bodies that define requirements for charging electric cars. 
Groups like ISO, IEC, SAE, and the European Commission define standards like connector types, safety rules, power levels, and more. Respectively, automakers and charging networks then have to adopt these requirements to keep vehicles and chargers fully compatible and their operation compliant.
At the same time:
EV Charging Protocols are communication rules that define how a charger and an electric vehicle exchange data during a charging session.
Unlike EV charging optimization standards, which focus on physical hardware and safety rules, protocols define the intelligence of the system. The standards are provided by ISO, IEC, SAE, OCA, and CharIN. They design these charging frameworks so that charging networks can coordinate thousands of chargers. Automated monitoring, integrated roaming, and consistent uptime are only a few of the goals of electric vehicle charging standards implementation. 
The table below shows the distinctions between EV charging standards for electric vehicles and EV charging protocols:

Key EV charging standards in the industry
When countries follow the same electric vehicle charging standards, networks grow faster, and users face fewer adaptation barriers. Here are the main industry standards, as well as details on governing organizations and their main provisions:
Combined Charging System (CCS)
Authority: https://www.ieee.org
Description: CCS is the dominant electric car charging standard in Europe and North America, combining AC and DC pins into one connector. It supports a broad power spectrum and relies on ISO 15118 as its main EV charger communication protocol, enabling features like Plug & Charge and smart-charging control. CCS is governed collectively through IEC, ISO, and SAE documents
Main provisions:

Supports both AC (Type 1/2) and DC fast charging
Power levels from 22 kW AC up to 350 kW DC
Uses ISO 15118 for authentication and communication
Adopted as the European EV charging standard

CHAdeMO charging standard
Authority: https://www.chademo.com/
Description: CHAdeMO is a Japanese-origin EV charging standard used by Nissan and several Asian automakers. It has historically led innovations such as vehicle-to-grid (V2G), though CCS is now more globally widespread.
Main provisions:

DC charging up to 200 kW (current CHAdeMO 2.0)
Native support for V2G bidirectional energy flow
Widely deployed across Japan and parts of Asia
Independent communication channel separate from AC circuits

Type 1 and Type 2 (AC Charging) standard
Authority: https://www.iec.ch
Description: These AC connectors define the baseline for EV charging station specifications. Type 1 is common in the U.S., while Type 2 dominates Europe thanks to EU regulations mandating it.
Main provisions:

Type 1: single-phase AC up to ~7 kW
Type 2: three-phase AC up to 22 kW (public AC up to 43 kW)
Required interface for most European AC chargers
Forms the AC component of CCS2

GB/T Standard (China)
Authority: http://www.sac.gov.cn/ 
Description: China’s electric car charging standards for electric vehicles operate under GB/T requirements with separate AC and DC connectors. A next-generation “ChaoJi” system is under co-development with CHAdeMO Association to unify high-power DC charging across Asia.
Main provisions:

Government-mandated standard for all Chinese EVs
AC up to 7–11 kW, DC up to 250 kW (higher in ChaoJi)
Strong emphasis on grid-integrated smart charging
Unique communication protocol not compatible with CCS

North American Charging Standard (NACS)
Authority: https://www.sae.org/standards
Description: Originally developed by Tesla and now standardized by SAE as US EV charging standards, NACS is compact, lightweight, and gaining adoption among major automakers in North America.
Main provisions:

AC and DC in a single compact plug
DC fast charging beyond 250 kW
Backward compatibility across Tesla vehicles and networks
Rapid growth among U.S. charge point operators

Clear standards shape how networks expand across regions and how EV smart charging regulations evolve. They also help operators avoid costly compatibility problems and ensure reliable charging access for users.
See the consequences of not following these EV charging station standards in the image below:

Limited hardware compatibility between chargers and EVs
Higher infrastructure costs due to multiple connector types
Slower deployment of global charging networks
Increased failure rates due to inconsistent EV charger communication protocols
Barriers to roaming, billing interoperability, and cross-border mobility
Regulatory penalties for violating EV charging station specifications
Delayed certification approvals and market entry issues
Greater cybersecurity risks when communication standards aren’t met

Key EV charging protocols in the industry
Clear EV charger communication protocol design protects users from failed charging sessions and supports global adoption of electric car charging standards. Here’s a list of the industry’s widely adopted protocols:
Open Charge Point Protocol (OCPP)
Authority: https://www.openchargealliance.org/ 
Description: OCPP is the most common communication system between a charging station and a central energy management platform. Operators use it to control chargers, update firmware, and monitor performance. OCPP is open and allows networks to mix hardware from different manufacturers. This supports the goals of modern electric vehicle charging standards and reduces vendor lock-in.
Main provisions:

Open protocol for charger-to-platform communication
Versions 1.6 and 2.0.1 supported by industry
Tools for remote monitoring and fault handling
Support for smart charging and tariff control

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ISO 15118
Authority: https://www.iso.org/
Description: ISO 15118 defines secure communication between an electric vehicle and a charging station. One of its core features is Plug and Charge. This function identifies the vehicle automatically and starts charging without cards or mobile apps. ISO 15118 improves user comfort and supports future vehicle-to-grid functions that appear in EV smart charging regulations.
Main provisions:

Automatic authentication through Plug and Charge
Secure channel between vehicle and charger
Structure for power control messages
Preparation for V2G functions

These protocols operate around the charging ecosystem. They support roaming, grid coordination, and large infrastructure projects. They also help unify EV charging station specifications across regions.
Standardization feels like an engineering component only, but it’s actually a part of a business strategy. The more compliant your system is, the faster you can expand, certify, and form roaming partnerships. — Yurii Aulikh, Software Engineer at Intelliarts
IEC 61850
Authority: https://www.iec.ch/
Description: IEC 61850 defines communication rules for smart grids and substations. It is useful for large high-power charging sites. The protocol helps operators coordinate charging loads with local grid equipment.
Main provisions:

Structured data exchange in grid systems
Support for high-power infrastructure
Tools for grid stability
Integration with smart energy systems

These protocols are a valuable addition to global EV charging station standards. They are intended to help EV charging providers improve the reliability of their EV networks. 

Take a look at how Intelliarts, a development service provider, handles regulations in our EV fleet management software success story. 
OCPI (Open Charge Point Interface)
Authority: https://ocpi-protocol.org/
Description: OCPI creates communication between different charging networks. It sends data about prices, availability, and session details. OCPI allows roaming, so users can charge across networks without separate accounts.
Main provisions:

Roaming support between operators
Data exchange for pricing and availability
Session sharing for billing and settlement
Simple integration across platforms

IEEE 2030.5
Authority: https://standards.ieee.org/
Description: IEEE 2030.5 controls communication between energy systems and distributed resources. It appears in home charging systems, grid-aware chargers, and utility control programs. It helps regions maintain stable power grids while supporting fast EV adoption.
Main provisions:

Utility-to-device communication
Support for demand-response programs
Load control for residential and workplace charging
Tools for grid-friendly operation

The consequences of not following these EV charging protocols are shown in the image below:

Failed charging sessions
Incompatibility between chargers and vehicles
Limited roaming access across networks
Higher cost of maintenance and integration
Unreliable billing and authentication

Delays in certification and approval
Higher infrastructure costs due to custom solutions
Restricted access to regulated markets
Increased security risks in communication systems

Why Standardization Matters for the EV Ecosystem: Key Benefits
Standardization gives the EV ecosystem a stable foundation. Charging networks grow faster when every part of the system follows the same rules. It also helps operators avoid compatibility issues that slow adoption. Clear electric car charging standards and mature EV charging protocols create a predictable environment for manufacturers, utilities, and consumers.
Key benefits of strong standardization:

Interoperability across different hardware and software vendors. This reduces integration problems and keeps charging networks accessible to all EV models.
Lower development complexity and reduced long-term costs. Companies do not need custom hardware for every market, which supports reliable global deployment.
Better data quality and scalable analytics. Standard protocols make it easier to collect, analyze, and use data for predictive maintenance, grid coordination, and load balancing.
Higher consumer trust and smoother user experience. Drivers can rely on roaming, simple billing, and consistent smart-charging behavior between networks.
Faster certification and easier regulatory compliance. Standardized equipment enters the market more quickly and stays aligned with new policy updates.

In a nutshell, standardization helps every part of the EV ecosystem work more smoothly. It cuts unnecessary complexity, keeps networks reliable, and gives users a consistent experience wherever they charge. When companies follow the same rules, it becomes easier for every party involved to expand infrastructure and support new technologies without disruptions.
Looking for an experienced tech partner who has worked with lots of EV projects? Explore the EV charging management platform success story by Intelliarts. 

The future of EV charging standards
EV smart charging regulations progress as infrastructure expands and regulatory pressure increases. The next stage depends on handling identity, energy coordination, and EV charging data analytics at scale. These four trends signal where the industry is heading and what constraints it must address.
#1 Growing adoption of Plug and Charge (ISO 15118)
Plug and Charge moves the authentication step into the vehicle. Chargers must read certificates and confirm identity without apps or cards. Networks that do not support ISO 15118 will struggle to connect newer EV models.
Indications:

Adoption noted in the GAC European EV charging report.
More EV models scheduled to support ISO 15118 in 2025–2026.

You may be additionally interested to explore EV charging management software platform market trends in another blog post by Intelliarts.  
#2 Convergence toward global standards
The rise of large charging networks makes fragmented regional rules harder to maintain. Manufacturers want fewer connector formats, and regulators want simpler cross-border compatibility. The industry is moving toward shared technical baselines.
Indications:

The IEA’s Global EV Outlook 2025 shows growth levels that intensify convergence needs.
Cross-region alignment discussions continue in major industry groups.

#3 AI-based optimization and data-driven management
Large charging networks rely on forecasting and automated decisions. AI tools need clean, consistent data from chargers. This pushes future standards to define clearer data structures and communication rules.
Indications:

An MDPI study shows how AI is reshaping load management.
Providers invest in real-time data systems instead of static backends.

These trends show a shift in what actually limits the growth of charging networks. Hardware is no longer the main obstacle. The real challenge is software that can process new rules, handle grid signals, and work with large amounts of data. Standards will keep changing, so the networks that adjust their software fastest will shape how the next stage of EV charging develops.
Over the past couple of years, we have seen that getting new chargers isn’t the biggest bottleneck. Data management and grid coordination are. Future standards will be written with AI-driven load balancing in mind. — Alexander Barinov, Managing Partner at Intelliarts
#4 Integration with smart grids and renewable energy
Charging is now tied to grid stability. Operators expect chargers to react to grid load, frequency, and renewable output. Standards must adapt so chargers can follow live grid signals and handle new forms of energy exchange.
Explore the renewable energy software development expertise of Intelliarts. 
Indications:

A 2025 study, Grid-Aware Scheduling and Control of Electric Vehicle Charging Stations, demonstrates how chargers follow day-ahead grid plans and adapt in real time to support network stability.
Utilities continue testing programs that link EV charging with solar and wind output.

Final take
EV charging depends on more than compatible hardware. Modern networks rely on shared standards, stable EV charger communication protocols, and software that adapts to new technical rules. Plug and Charge adoption, growing alignment between regions, and the shift toward grid-aware operation all point in the same direction. At the same time, companies that follow recognized standards now place themselves in a stronger position for the next stage of the EV industry.
Should you need an EV charging solution, make sure to partner only with experienced software vendors. Here at Intelliarts, with 24+ years of providing tech services to customers worldwide, we can help you with EV protocols and standards-compliant development. Don’t hesitate to reach out to our team of senior engineers to have your challenges covered and find out why we have a 90% customer return rate. 
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#### EV Fleet Management: Tips to Track Your Fleet
Maintaining a fleet of vehicles is a tricky task for any business. An increased number of electric vehicles (EV) nowadays adds another layer of complexity to this matter. 
Companies report an average reduction of 20-30% in total cost ownership (TCO) when switching to EVs. Besides, the EV fleet management market has grown at a compound annual growth rate (GAGR) of 15% over the past 5 years. As a consolidated observation, the EV fleet management approach is increasing in the business value it has to offer. 
In this post, you’ll discover the concept and components of EV fleet management. Besides, you’ll explore critical electric fleet management considerations, as well as what telematics and IoT in EV applications can be used to track related metrics like energy consumption or battery condition. Finally, you’ll get to know the top 5 leading providers supporting electric fleet operations.
EV fleet management definition and challenges
Electric vehicle (EV) fleet management is the process of overseeing and optimizing the operation, charging, maintenance, and efficiency of a fleet of electric vehicles using specialized tools and technologies.
While not considered a fleet, one vehicle under management still requires optimization to operate effectively. Implemented on a large scale, fleet management brings substantial cost benefits. 
You can discover what components EV fleet management comprises in the infographics below:

When it comes to managing electric vehicle fleets, there’s a range of high-level challenges that most businesses face:

High operating and maintenance costs

Lack of infrastructure

Difficulty in integrating EVs into your business processes

Regulatory compliance

Poor performance of operators and drivers

Instead of listing specific technologies, we will provide key considerations and tips for optimized EV management in the next section. Upon implementation of these pieces of advice, you will have most, if not all, of the detailed challenges resolved automatically. 
Optimizing Electric Vehicle Fleet ManagementLearn about EV fleet management as one of top 10 e-mobility trends by 2030.Get the white paper
Key factors to consider to manage an EV fleet effectively
The Intelliarts team has substantial experience with implementing fleet management technologies and addressing nuances related to effective electric fleet management. From what we could observe in our projects, key factors to consider to manage an EV fleet optimally are:
#1 Charging infrastructure
The typical difficulty and the main restriction is the absence of publicly available charging stations. Since no business can afford to develop a complete net of charging stations on its own, you need to find a balance between utilizing public charging options and setting up your own ones where possible. 
Pro tip: You may consider partnering with another company with an EV fleet. For example, you may agree on the mutual use of private charging stations in your energy network. Or you may rent another company’s charging stations. 
#2 Energy management
Electric vehicles are known for overconsumption of energy at times. It is both deteriorating for their batteries’ health and cost-ineffective for your business. You may consider implementing physical and digital tools designed to support EV maintenance and energy efficiency. 
Pro tip: The easiest way to ensure optimal energy consumption and track both EVs and chargers’ health is smart power technology. By analyzing charging reports with its help, you can spot anomalies and fix them swiftly.
You can discover extensive information on smart power software development by Intelliarts in our success story. 
Here’s what reports provided by smart energy management setup may look like: 

#3 Vehicle selection
Needless to mention, there’s a vast range of EV models available on the market. The very core of good EV fleet maintenance and management is a selection of optimal vehicles based on operational needs and travel distance requirements.
Pro tip: While the selection of vehicles is guided by many factors, we recommend paying attention to the total cost of ownership, maintenance costs, energy consumption, reliability of vehicles, and their battery lifespan. 
#4 Maintenance and battery health
EV battery degrades by approximately 2.3% per year. Their estimated lifespan is about 8 to 12 years, so you need to catch the moment when it’s not energy-efficient to use your electric vehicles any longer. It’s especially the case when you have second hand vehicles in your fleet. 
Pro tip: Regularly monitor vehicle and battery health, either through sensors or connected technologies, to prevent breakdowns. If it’s impossible, shorten the periodicity of scheduled maintenance procedures. 
#5 Route planning
This consideration is among the primary ones. Based on the type of vehicle, cargo, distance to the destination, infrastructure, and other factors, your operators need to have all routes estimated. It prevents cases with a lack of battery energy or, in the opposite, with frequent fast charging. 
Pro tip: You don’t actually need a team of operators to manually calculate the best routes. There are multiple tools on the market designed for route optimization. Most of them are totally suitable for electric fleet management. Just to mention a few, Onfleet, and RoadWarrior are among popular options. They can be used even by drivers themselves. However, if you have an extended fleet, you can hardly ensure effective management without operators at all.
#6 Telematics and data analytics
A considerable component of EV fleet management is tracking and analyzing the performance of vehicles and energy stations. For it, you need to install IoT sensors and specialized tools, gather data, extract reports, and then analyze historical information to detect any potential inefficiencies in order to fix them.
Pro tip: You don’t need lots of IoT sensors installed in your electronic vehicles and energy stations. Based on your business model and actual EV management needs, limit yourself to 2 to 4 sensors and, respectively, the types of reports that you gather and analyze. For example, as a transportation company or a regular business that provides employees with electric cars, you don’t need to track acceleration or braking patterns or tire pressure, as the latter is normally done by mechanics at a certain periodicity. 
Types of telematics for choosing and data to be gathered are provided in the following section. 

Intelliarts delivers custom IoT solutions to help you implement, manage, and scale connected environments with ease.

#7 Driver and operator training
Even with your infrastructure, vehicle, and digital tools in place, there’s still room for mistakes or inefficiencies. They are mostly attributed to the work of operators and drivers, who need to be well-familiarized with your new approach to EV vehicle management. 
Pro tip: Consider conducting a series of training sessions for your employees. Ideally, your technology providers who supported your EV fleet and energy management setup should share knowledge of how to use the tools and best management practices.
#8 Technical issues
There’s one more problem. Rapid increase in charging EVs can cause load profile distortion, voltage deviation, harmonic injection, frequency imbalance, overloading, and a bunch of other technical issues. 
Pro tip: While providers of energy infrastructure aim to reduce the probability of such issue occurrence, you, as a business, can also contribute to minimization of technical issues. As such, implementing smart charging technology optimizes key parameters, leading to reduced power loss, decreased voltage deviation, and a remarkable 96% decrease in the grid’s peak power.
To assist you, the Intelliarts team has prepared a checklist for maximizing range in your EV fleet. You may consider sharing it with drivers, car mechanics, and operators, so they can play their respective roles in ensuring the best conditions for driving. 

You can also download the checklist for maximizing the range in your EV fleet in the PDF. 
Tracking EV fleet performance
As mentioned previously, there’s a range of performance metrics to track using specialized tools and IoT sensors. Here’s a table encompassing all common approaches and providing the types of reports you can gather for further analysis:

6 Leading EV fleet management companies
Now you’re probably wondering what the providers of electronic fleet management on the market are. Here’s our list of 5 leading providers of charging stations and electric fleet management systems:
#1 EV Connect
EV Connect is a leading provider of cloud-based technology for managing electric vehicle charging networks. Founded in 2009, the company specializes in helping businesses deploy, manage, and optimize EV charging infrastructure with a focus on scalability and reliability.
Key benefits:

Comprehensive tools for EV charging station management
Real-time monitoring and reporting capabilities for charging operations
Scalable offerings tailored for large enterprises
White label framework for the creation and launch of a driver mobile application and management portal
Easy integration with existing energy management setup
APIs for custom charging and EV fleet management functionality
Comprehensive 24/7 support for clients and their drivers

#2 EVIQ
EVIQ is a Saudi-based electric vehicle infrastructure company that focuses on building a nationwide fast-charging network for electric vehicles (EVs) to support the Kingdom’s transition to sustainable transportation. A joint venture between the Public Investment Fund (PIF) and the Saudi Electricity Company (SEC), Eviq aims to accelerate EV adoption in Saudi Arabia.
Key benefits:

A nationwide network of 5,000 EV chargers across 1,000 strategic locations is planned by 2030
The first public DC fast-charging site launched in Riyadh, with high-power chargers providing over 100 kW per unit
Collaboration with local partners for installation, maintenance, and support to enhance EV infrastructure
Focus on providing reliable, accessible charging to drive consumer confidence in EV ownership
Aligned with Saudi Arabia’s Vision 2030 for sustainability and economic diversification

#3 Samsara
Samsara offers advanced fleet management that integrates real-time GPS tracking, vehicle diagnostics, and driver behavior monitoring. Established in 2015, the company is known for its holistic approach to fleet management, including its strong support for electrification.
Key benefits:

Real-time GPS tracking and vehicle diagnostics
EV suitability assessments and route planning
Automated workflows to enhance operational efficiency
Comprehensive fuel and energy usage analytics

Looking for trusted technology consulting to power your EV?Intelliarts experts are here to discuss your needs.Learn more
#4 Geotab
Founded in 2000, Geotab is a global leader in fleet management and telematics, providing robust capabilities for both electric and traditional vehicle fleets. Their tool is known for its scalability, making it suitable for businesses of all sizes.
Key benefits:

GPS tracking and driver behavior monitoring
Vehicle diagnostics and predictive maintenance
Support for emissions tracking and decarbonization efforts
Integration with a wide range of EV models

#5 ChargePoint ViriCiti
ChargePoint’s ViriCiti is a specialized fleet management tool focused on maximizing the efficiency and uptime of electric vehicles. ChargePoint, founded in 2007, has become one of the largest networks of independently owned EV charging stations, and its ViriCiti technology is an integral part of its comprehensive EV management suite.
Key benefits:

Advanced reporting and real-time fleet insights
Optimized charging schedules to reduce costs
Vehicle and station management for improved uptime
Enhanced battery health monitoring to extend vehicle life

#6 Verizon Connect
Verizon Connect, a division of Verizon Communications, offers comprehensive fleet management that leverages GPS tracking, telematics, and driver behavior monitoring. With its roots in telecommunications, Verizon Connect provides robust and reliable capabilities for managing both traditional and electric vehicle fleets.
Key benefits:

Real-time GPS tracking and fleet monitoring
Charging station location mapping and schedule management
Integrated video and dashcam features for enhanced driver safety
Scalable offerings suitable for various industries including transportation, logistics, and construction

How Intelliarts can be helpful in your EV fleet management
While the EV fleet management companies listed above can provide branded technologies, they still need to be integrated into your digital infrastructure. Alternatively, you may need smart power management, vehicle tracking, or a fully tailored setup developed from scratch. That’s why, oftentimes, you need consulting and dev assistance from trusted development partners like Intelliarts.
Here are two of our many success stories related to energy and EV fleet management:
Enabling smart, next-gen EV fleet operations

Challenge: The customer reached out to Intelliarts with a request to build a custom tool for managing electric vehicle fleets. This enterprise-level tool was intended to help operators keep track of fleets and plan charging effectively. 
Solution: We delivered the requested software that helps to ensure better fleet control and efficient charging. With this app, we provided means to optimize energy consumption and lower costs for EV fleets. We added features like EV charging status, EV charging data analytics and prediction, at-home charging options, and more. 
Outcomes: With our fleet management, the customer achieved greater operational efficiency and cost optimization. Besides, we managed to ensure the best charging flexibility possible.
Explore our EV fleet management case studySee the results
Smart power management for energy consumption optimization

Challenge: In this project, we were requested to help ensure that end users obtain more value from using our customer’s charging site goals. The goal was to optimize energy consumption, mitigate the effects of climate change, and maximize the cost-efficiency of stations. 
Solution: The Intelliarts team built a power management setup that allows for scheduling power limits for any time period for one or more stations. The algorithms utilized made it possible to automatically shift the charging load based on the dynamic electricity grid and renewable supplies. We included data analytics features as well as off-peak charging and random delay capabilities. 
Outcomes: With our energy management system, Intelliarts’ customers now enjoy optimized use of energy, greater cost-efficiency, longer lifetime of their electrical infrastructure, and higher quality service.
Streamlined service verification setup for a vehicle service provider
Challenge:
Intelliarts’ customer, who is a leading vehicle service provider, faced a significant challenge with their service verification process. After a mechanic completed a service, a quality team had to manually check whether the service was performed correctly based on certain tabular data and the mechanic’s comments. This resulted in delays in billing customers, as the quality team had to validate every service before approval.
Solution:
To address this, we developed an innovative system that ranks services based on the likelihood of errors. Our solution leverages data analytics to prioritize services from most to least probable for mistakes. This approach enables the customer to immediately bill customers for services falling below a certain error threshold while allowing the quality team to concentrate their efforts on verifying potentially faulty services.
Outcomes:
With our service verification, our customer has significantly reduced the time spent on billing processes and enhanced operational efficiency. The quality team now focuses on services that require attention, leading to faster service turnaround and increased customer satisfaction.
Final take
As it can be concluded, effective EV fleet management is crucial for businesses looking to optimize their electric vehicle operations and reduce associated costs. By focusing on key factors such as charging infrastructure, energy management, vehicle selection, maintenance, route planning, telematics, and driver training, companies can overcome common challenges and ensure the sustainability and efficiency of their EV fleets. 
Implementation of EV fleet management requires substantial niche-specific expertise and experience. Should you need assistance, consider Intelliarts as your trusted tech partner. With over 24 years of experience in the market and dozens of renewable energy and EV-related projects under our belt, we are ready, willing, and able to address your business needs. 
 Need advice on EV fleet? Talk to an expert

#### EV Fleet Software: 8 Platforms Overview vs Custom Solution
Global electric vehicle adoption is expanding fast, with more than 17 million EVs sold worldwide over the past two years and EVs accounting for over 20% of new car sales. This growth makes it more complex and resource-demanding to operate for both businesses that use EV fleets and provide any related services. That’s exactly why there’s a need for ready-made and custom EV fleet solutions.
In this guide, you’ll find out how off-the-shelf EV platforms compare to custom solutions. Besides, you’ll review the top 8 EV fleet management platforms, delve into the limitations and advantages of software, and, finally, discover how to choose the right EV software for your business.
What is EV fleet software and what does it do?
EV fleet software is a digital solution that helps businesses optimize the way electric vehicles operate across multiple processes to bring measurable financial gains. 
EV fleet software is designed for the centralization of data obtained from vehicles, chargers, IoT devices, and drivers into one system. This enables real-time visibility into vehicle status, energy consumption, and usage patterns. EV fleet software is also used for strategic planning and cost control. 
From the leadership standpoint, EV fleet software solves the following suite of problems:

Fragmented fleet data
Inefficient charging management
Limited insight into battery health
Difficulties scaling EV infrastructure

Core capabilities of EV fleet software include:

Fleet telematics for real-time vehicle tracking and status monitoring
Charging management to control charging schedules, costs, and infrastructure usage
Route optimization to improve efficiency and reduce energy consumption
Battery health monitoring to extend battery lifespan and prevent failures
Analytics and reporting for performance insights and TCO analysis
Integrations with EV fleet management platforms, energy systems, and enterprise tools

Get additional insights into EV fleet management tips and challenges in another blog post by Intelliarts. 
Why companies choose ready-made EV fleet platforms
Before delving any further into the topic, it’s useful to understand why exactly companies even opt for solutions available on the market. After all, custom software is normally regarded as a superior solution based on functionality and long term margins. 
Intelliarts experience reveals several common considerations that make companies prioritize off-the-shelf apps:

Need for fast deployment of EV fleet software without long development cycles
Limited internal engineering resources to build and maintain custom systems
Lower upfront effort compared to custom EV fleet software
Access to predefined features for charging management, tracking, and analytics
Vendor-managed updates and support for the electric vehicle fleet management system

These pain points should be respected even if practice shows that it’s usually possible to address them with custom development. It’s also important to mention that not only new businesses would lean towards ready-made EV fleet management platforms. In fact, it’s often a matter of prioritization, tech expertise, and resource allocation rather than the business scale or the budget size. 
Overview of 8 popular EV fleet management platforms
By now, you are probably wondering what some of the popular electric vehicle fleet management systems on the market are. Probably, you are also looking for the reference of their smart charging features, before comparing what they have to offer for custom development. 
The table below provides an overview of popular EV fleet management platforms based on several key factors:

As you can see, there are some lucrative features of off-the-shelf EV fleet management platforms. Probably the best of them, aside from core functionalities, is the implementation speed.
Intelliarts, a software development partner, has taken part in the development of multiple EV fleet software platforms and adjacent products. As such, we provided OpenADR implementation for load management for EV Connect, listed in the table. Don’t hesitate to check the full case at our respective success story page.
The next section will focus on the limitations of using such ready-made software. 
What are the limitations of off-the-shelf EV fleet software?
From Intelliarts experience with customers considering off-the-shelf software or switching from one, the main concerns are the following: 

Vendor lock-in. Off-the-shelf EV fleet platforms tie fleets to a single vendor’s ecosystem, APIs, and roadmap. For example, when a fleet adopts new charger hardware, it may discover the software does not support it, forcing continued reliance on the original vendor.
Limited customization. Prebuilt EV fleet software supports generic workflows and restricts customization. A logistics operator may be unable to configure depot-specific charging rules aligned with shift schedules, resulting in inefficient charging windows and manual operational workarounds.
Scaling and cost constraints. As fleets grow, off-the-shelf pricing models scale per vehicle or charger. Expanding from hundreds to thousands of EVs can dramatically increase subscription costs while system performance and reporting flexibility remain unchanged.
Shallow integrations. Many off-the-shelf solutions offer limited integration with ERP, fleet management telematics, or energy systems. As a result, fleet managers often export charging data manually to reconcile costs, usage, and emissions reporting across disconnected platforms.
Limited control over data and compliance. Data storage, access, and retention policies are controlled by the vendor. For instance, a European fleet may be unable to enforce EU data residency or custom retention rules, creating compliance risks with internal or regulatory requirements.

As you can conclude, the main challenges of using a ready-made fleet and charging management solution boil down to data and compliance issues. They also reveal a basically limited ownership over the product that customers have. For businesses, again, it may be a deal breaker. 
Across EV fleet projects in which we participate, we consistently see charging-related factors determining operational stability and even end revenue. Fleet readiness and cost savings are the most impactful aspects here. — Ihor Rudnyk, an eMobility Tech Specialist at Intelliarts
You may be interested in learning additionally about Intelliarts’ emobility solution expertise.
What is a custom EV fleet software solution?
A custom EV fleet software solution is a purpose-built electric vehicle fleet management system designed around a company’s specific technical and regulatory requirements.
Unlike off-the-shelf EV fleet management platforms, a custom software is developed to match existing workflows, EV infrastructure, and data models rather than forcing the business to adapt to predefined features. This feat is both a challenge and an opportunity, depending on the perspective. 
From a leadership standpoint, custom EV fleet software addresses a different class of strategic and structural problems:

Rigid workflows imposed by standard EV fleet management platforms
Limited ability to unify fleet data with internal business systems
Inflexible charging logic that does not reflect real operational constraints
Lack of control over data ownership, security, and compliance
Scaling limitations when EV infrastructure, fleet size, or regions expand

Explore fleet management technology in greater detail in another blog post by Intelliarts. 
Additional capabilities of custom EV fleet software, present on top of regular functionality, include:

Custom charging management logic
Route optimization rules adapted to real-world business scenarios
Battery health monitoring models tuned to specific vehicle types and usage patterns
Custom analytics and dashboards supporting leadership-level TCO analysis
Deep integrations with ERP, TMS, energy systems, and other enterprise tools without platform constraints

Custom EV Fleet Software Development ServicesLooking for a trusted partner to build or modernize your EV fleet platform? Explore Intelliarts’ EV software development services.Don’t hesitate to reach out
Platforms vs custom EV fleet software: Side-by-side comparison
Choosing between software for your tech stack is always a business decision and should not be taken lightly. Let’s proceed by comparing off-the-shelf EV platforms with custom solutions across several key parameters:

Due to our 25+ years of experience and multiple technical consulting sessions, Intelliarts has formed strategic ideas that help to make value out of this comparison. Here they are: 

The decision is not purely technical. It reflects whether EV fleet operations are treated as a cost center or a strategic capability.
As EV infrastructure expands, system rigidity can become a hidden operational risk.
Data ownership and integration depth increasingly influence long-term competitiveness.
The more complex the fleet ecosystem, the stronger the case for custom architecture.
Long-term efficiency gains often outweigh short-term implementation savings.

Quick tip: Off-the-shelf solutions may be viewed as some sort of a stater kit. A good, fast decision for an MVP project or just starting a business. Then, later on, as a company expands, it may opt for a custom solution. For example, Intelliarts can build any custom instrument on top of a ready-made platform you might have in place, or smoothly transition you to a customized software built from scratch. 
When does a custom EV fleet solution make more sense?

Now that we have reached the end of the off-the-shelf vs custom electric vehicle fleet management system comparison, let’s review some use cases. As EV operations expand, standard platforms may no longer support operational depth or integration needs. The following list shows when the business situation require custom solution instead of opting for a ready-made one:

Large or complex fleets. Multi-site fleets with diverse vehicle types and charging models often outgrow standard EV fleet management platforms. Custom systems support scale, performance stability, and structured reporting across regions.
Unique operational workflows. Fleets with contract-based schedules, regulated routes, or internal process rules need system logic that reflects how they operate. Custom EV fleet software aligns charging and dispatch with real workflows.
Need for competitive differentiation. When fleet efficiency impacts margins or service levels, tailored route optimization and charging prioritization create a measurable advantage beyond generic EV fleet software features.
Advanced energy optimization requirements. Dynamic tariffs, site power limits, and renewable energy contracts require precise load management. Custom charging management logic supports energy cost control and smarter allocation.
Deep enterprise integrations. If fleet telematics must connect directly with ERP, finance, or planning systems, custom architecture ensures clean data exchange and unified reporting.
Multi-country compliance and data control. Cross-border operations require control over data storage, tax logic, and regulatory compliance. Custom systems provide flexibility beyond vendor infrastructure constraints.
Long-term infrastructure expansion plans. As EV infrastructure grows, subscription costs and vendor limits can scale quickly. Custom EV fleet software supports expansion without structural restrictions.
Strategic ownership of fleet data. Fleet data drives forecasting and TCO analysis. A custom electric vehicle fleet management system allows full ownership of analytics models and performance metrics.

In a nutshell, custom solutions make sense when fleet operations are strategic to business performance and require control, scalability, and long-term flexibility. 
Does your business’s situation fit any of the scenarios provided above? If so, don’t hesitate to contact Intellairts for a free strategy session. 
Case study by Intelliarts: Creating a next-gen EV management software

A great example of custom EV fleet management solutions in practice is the Intelliarts’ success story.
Background: The client, an established EV charging platform provider, aimed to expand into fleet operations by launching its own EV fleet software. The goal was to enter the fleet management market with a solution that supports at-home charging while improving energy optimization for commercial electric vehicle fleets.
Solution: Intelliarts developed a custom electric vehicle fleet management system integrated with telematics, energy management, and charging infrastructure. The platform enabled real-time EV status tracking, predictive charging prioritization, load balancing, and at-home charging reimbursement. 
Results: The solution improved operational efficiency through intelligent charging management and real-time analytics. Fleet operators gained better visibility into energy consumption, vehicle readiness, and infrastructure utilization. This strengthened the partner’s market position and expanded its offering within the EV fleet management market.
How to choose the right EV fleet software for your business
There has been a tendency for years. It shows that long-term competitiveness in electric mobility increasingly depends on data architecture decisions in EV products you primarily use. — Alexander Barinov, a Managing Partner at Intelliarts.
With all the insights on off-the-shelf and custom EV fleet software, you know what type of solution is needed. Now, you are likely choosing a particular product or a provider. The framework below covers several critical concerns that, based on Intelliarts experience, customers have before signing a deal: 
Decision checklist:

Fleet size. Platforms built for small fleets may struggle with performance when managing hundreds or thousands of vehicles across multiple depots. System latency, reporting delays, and pricing per asset can become operational bottlenecks at enterprise scale.
Growth plans. If expansion includes new regions or additional EV infrastructure, verify whether the system supports multi-site management and regional configurations. Some EV fleet management platforms increase fees significantly with each added charger or vehicle.
Integration requirements. Basic API access may not support structured data exchange with ERP, TMS, or energy billing systems. Poor integration leads to manual reconciliation and inconsistent reporting.
Reporting and analytics depth. Standard dashboards may not support custom TCO analysis or predictive maintenance modeling. Limited analytics restrict leadership visibility into cost and performance trends.
Data ownership and compliance. Fleet data may be stored in vendor-managed cloud environments located in specific countries. This affects data residency compliance, cybersecurity exposure, and access control. Limited data export options can also reduce long-term flexibility and vendor independence.

Hopefully, you now understand what type of solution, ready-made or custom, suits your business needs best. You should also know exactly what to look out for when comparing service or product offerings on the market. 

Final take
Choosing between ready-made EV fleet management platforms and custom EV fleet software depends on scale, integration depth, and long-term strategy. SaaS solutions provide speed and simplicity, while custom systems offer control, flexibility, and stronger alignment with complex operations. 
Here at Intelliarts, with more than 25 years of experience and 90% customer return rate, we can offer industry-leading expertise in EV fleet software. Our senior experts can both consult on the nuances of choosing ready-made platforms or a custom solution, as well as integrate one. Additionally, we can offer end-to-end development of your custom EV fleet platform from scratch.
Build an EV fleet solution tailored to your operations and growth goals.Contact us

#### Fault and Damage Detection In Solar Panels and Wind Turbines
More than 30% of all energy is already generated through renewable sources, including solar and wind. However, while trying to leverage renewable energy to the fullest extent, companies face engineering difficulties. 
About 20% of solar panels suffer from some form of degradation or defect within the first five years of exploitation, with 90% of faults attributed to micro-cracks and hotspots. Similarly, 25% of wind turbines’ blades suffer from damage within the first 5 years. Fortunately, ML-based solutions are proven to be useful in solar panel fault detection, preventing downtimes. They can reduce inspection time by 50% and increase fault detection accuracy by 25% compared to conventional methods.
In this post, you’ll discover how ML can reshape damage detection in wind turbine blades and solar panels with computer vision. Besides, you’ll explore what Intelliarts, an AI development company, can offer as a software solution to damage detection challenges. Moreover, you’ll review the phase-by-phase procedure for implementing a fault and damage detection system in business. 
Types of wind turbine and solar panel damage 
You may discover all the main forms of fault and damage that can occur to wind turbines and solar panels in the infographic below:
 
Mechanical damage and wear and tear damage are some of the most widespread deterioration forms, while structural damage, environmental damage, and natural disasters are rare. Fault detection in solar panels and wind turbines is what serves prevention of downtimes in such critical equipment.
Conventional ways of fault and damage detection in wind turbines and solar panels
Typically, in such equipment as wind turbines and solar panels, technical maintenance is conducted by human workers. They utilize specialized equipment and methods of visual and manual inspection. Integrating IoT for solar energy projects can mitigate these maintenance and damage detection challenges, which are often associated with labor safety risks, costly and lengthy maintenance times, and limited capability to cover all energy units within reasonable times.
To further compare ML-based and manual ways of wind turbines and solar panel fault detection systems, let’s first check all conventional approaches in both types of energy units:
In wind turbines:

Non-automated visual inspection: Manual checks for visible damage in turbines.
Non-automated thermal imaging: Identifying overheating components.
Vibration analysis: Monitoring for imbalance and wear.
Acoustic emission monitoring: Detecting invisible damages by monitoring short bursts of acoustic energy in metal with cracks or imperfections. 
Oil analysis: Checking for wear particles and contamination.
Load monitoring: Measuring stress on the turbine.
Non-automated blade inspection via drone: Using drones for detailed blade inspections.

In solar panels:

Non-automated visual inspection: Checking for cracks and physical damage in solar panels.
Non-automated thermal imaging: Detecting hot spots and faulty cells.
Electrical performance monitoring: Measuring electrical output for faults.
EL (Electroluminescence) imaging: Detecting micro-cracks in cells.
I-V curve tracing: Analyzing electrical performance.
Non-automated drone inspection: Using drones for large-scale inspections.
Cleaning and maintenance records: Tracking cleaning and maintenance activities.

As can be seen, there are both highly specialized forms of testing and less intricate, nevertheless labor-intensive inspection types. For complete insight, we need to understand well all the advantages and limitations of conventional damage and fault detection: 
Pros of conventional ways of fault and damage detection are the following:

Affordable and easy to implement
Established and proven techniques
Immediate results

Cons of sticking to conventional ways of fault and damage analysis only include:

Reliance on labor resources
Subjectivity
Safety risks
Not efficient with large volumes of equipment to be tested

You may be interested in exploring how AI changed renewable energy solutions in another blog post by Intelliarts. 
Fault and damage detection and prevention in renewable energy with computer vision
As an alternative to the labor-intensive and tedious process of conventional fault and damage detection in the renewable energy sector, it can be proposed to use ML-based technologies. In particular, computer vision comes into great use here.
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
You can discover extensive information about how computer vision works, and how computer vision can detect car damage in other our blog posts.
Here are ways of inspection that ML-based wind turbine blade damage detection and solar panel damage detection systems can offer:

Automated visual inspection: Checks for visible damage in turbines.
Automated thermal imaging: Identifying overheating components.
Automated blade inspection via drone: Using drones for detailed blade inspections.

Fault detection technologies for solar panels and wind turbines have the potential to detect all forms of visual damage, from mechanical and electrical to operation, structural, wear and tear, etc. 
The only issues are in the proper development of ML-based real-time damage detection systems and the implementation of the technology in actual business environments. Consequently, it brings us to the discussion of the strengths and limitations of such smart systems. 
You may discover more about how AI and ML, in particular, computer vision, can be applied to solve renewable energy challenges in the video below:

Benefits and limitations of ML-based automated detection systems in renewable energy
The advantages of using ML solutions as a complementary way of fault and damage detection in wind turbines and solar panels are the following:

Efficiency with large volumes of samples for analysis
Consistent and unbiased analysis
Operation 24/7 in case of real-time automated computer vision systems
Complete safety for workers in case of drone or camera-based method of taking images for analysis
Optimization of labor resource usage

At the same time, a few cons of AI-based fault detection in solar panels and wind turbines include:

Relatively high initial investment costs
High requirements for the technical expertise of engineers responsible for the implementation
Complete automation and sometimes integration with other software and ML systems can be technically challenging

You may additionally be interested in exploring IoT in smart grid tech to find out more about other renewable energy solutions in another blog post by Intelliarts. 
Intelliarts’ experience with fault and damage detection computer vision-based solutions
In this section, you’ll review two computer vision-based solutions for visible fault and damage detection, and test their demo versions. 
Fault and damage detection in solar panels

Recently, the ML team of Intelliarts prepared a demo of a computer-vision ML model intended to detect fault areas in solar panels. It is a part of our research on 2024 renewable energy challenges and fault detection technologies for solar panels and an attempt to contribute to maintaining a sustainable environment globally. With this ML solution, we intend to showcase to companies some innovative ways to solve sustainability, labor security, business operations, and other issues. 
In the case of the solar panel damage detection demo by Intelliarts, the model works with infrared images of solar panels. Upon processing, the model returns images with damaged areas and approximate damage detection accuracy indicated. In this instance, damaged areas are outlined by a blue square. 
In a similar way, an appropriately trained computer vision model can process regular solar panel images and wind turbine images for visible defects. 
You can access the demo of the computer vision model for damage detection in solar panels in the HuggingFace environment.
For this computer vision solution, we used a pre-trained model that is based on the YOLO image classification algorithm. We had a limited dataset, which consisted of 337 infrared images. That’s why we had to augment images to increase the number of instances in the dataset. So, given a more appropriate range of images for training a testing, a resulting solution can show even better results.
The prepared demo is rather a PoC than a finished product. As a potential business solution, it can be extended in numerous ways. For example, we can add:

Native analytics capabilities or integration with external analytics systems.
Detection of other types of faults in solar panels.
Detection of visible faults and damages in wind turbines.
Real-time automation.
Integration with the software of cameras and drones.
Real-time data exchange with a database connected to any external software.
Continuous improvement of detection accuracy.
Automated assessment of the probability of critical fault or combustion.
Automated assessment of the reduction of energy generation because of fault and damage.
Automated assessment of potential repair costs.

Need assistance with automated fault and damage detection?Our AI development experts are ready to help you implement a cutting-edge solution.Explore our AI services 
Fault and damage detection in wind turbines

The Intelliarts team created a model for fault and damage detection in wind turbines, which is similar to the model for fault and damage detection in solar panels.
In the case of the wind turbine fault and damage detection demo by Intelliarts, the model works with regular images of wind turbine blades. Upon processing, the model returns images with damaged areas and approximate damage detection accuracy indicated. Once again, damaged areas are outlined by a blue square. 
You can access the demo of the computer vision model for damage detection in wind turbines in the HuggingFace environment.
Important note: Ways to extend this demo to a complete business-level solution are the same as for the solar panel damage detection demo and are listed in the previous section. 
How to implement a fault and damage computer vision-based solution in business
The basic process of implementing a complex computer vision-enabled system boils down to the following key phases:
#1 Preparing the digital infrastructure for gathering data
Basically, you need to ensure that you systematically gather statistical and visual data for faults and damages in your wind turbines or solar panels as a part of your regular maintenance and repair procedures. This data will be used for training and testing an ML model later on. 
#2 Developing a computer vision-based model
In this stage, you, or your hired ML team, choose an algorithm for the model and then train and test it. After that, they build a computer vision solution based on the finished model, adding the necessary features to it.
#3 Integration of the ready computer vision solutions into the infrastructure
Next, you need to make the finished solution a part of your existing system for gathering and processing data related to faults and damages in wind turbines or solar panels. 
Naturally, you should have a repository of visual and statistical materials, a database with records for damages as well as maintenance and repair protocols. The goal is to enable the prepared computer vision system to work with such forms of data, both historical and supplied in real time. At the most basic level, you need to ensure that the computer vision model can:

Process newly uploaded images in real-time, periodically in batches, or by request, depending on how you want to work with the solution. 
Yield results in a suitable format for you to process further. 
Access the database with reference images and all other complementary images to support fault and damage detection and enable the continuous improvement of the model. 

#4 Integration of the ready computer vision solutions into the business operations
Finally, you need to make this computer vision solution an integral part of your business operations focused on technical maintenance and repair of wind turbines and solar panels. It includes training of the personnel, updating practices and procedures, acquisition of additional software instruments, implementation of other analytical systems, etc. 
The end result is a well-integrated and highly usable computer vision system that is supplied with images taken by drones, cameras, or human workers. It processes images and yields results that are then used for analysis in connected analytical systems. Eventually, the business can obtain fault and damage detection reports with repair needs, repair pricing estimations, predictive maintenance forecasts, wear and tear patterns, and many more. 
Needless to say, it’s hard to evaluate the end investment needed for such a large-scale project. The best way to obtain a general estimate is to request a quote from a trusted ML provider. 
In case you are looking to implement automated fault and damage detection solutions, don’t hesitate to contact Intelliarts. We can offer IT technology audit of your existing infrastructure, provide technology consulting services, and offer end-to-end development and implementation. 
Final take
Integrating ML-based computer vision systems is a solution to numerous solar panel and wind turbine maintenance challenges. Although the initial investment and technical expertise required may seem high, the long-term returns in terms of reliability, predictive maintenance, and operational continuity make it a worthwhile investment. 
Intelliarts team of expert software engineers can offer the development of a computer vision model and its integration with your IT infrastructure end-to-end. We are Clutch global leader for 2023 and a company with more than 24 years of experience in the market, delivering software solutions to business challenges. 
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#### Full Guide to Dynamic Load Management for EV Infrastructure
TL;DR

Dynamic load management distributes available site capacity across active EV chargers in real time.
It helps prevent overloads by keeping charging demand within grid, transformer, and site limits.
The system reduces peak-load pressure by adjusting charger output when demand or tariffs change.
Effective load management depends on software logic, charger telemetry, backend control, and OCPP smart charging.
Rule-based allocation is usually the starting point, while AI and ML can improve forecasting and optimization.
For CPOs, fleets, and public hubs, dynamic load management is essential for scaling EV infrastructure without unnecessary grid upgrades.

In 2025, nearly 1.8 million public charging points were added globally, and the number of fast or ultra-fast chargers reached 2.2 million. Higher-power sites create a harder constraint: grid connection capacity has to be shared between chargers, buildings, tariffs, and user demand. Dynamic load management solves this control problem by deciding how much power each charger can safely receive at any moment. 
This guide introduces dynamic load management in EVs, shows how the technology works, and outlines the benefits of using it in business. Besides, it highlights challenges and limitations as well as brings real-world use cases.
What is dynamic load management in EV charging?
Dynamic load management is a system that allocates available electrical capacity across multiple EV chargers in real time based on live demand, site limits, and operational priorities. 
In EV charging, the available power at a site is naturally limited. A fleet depot or a public charging hub may have more chargers than the grid connection can support at full power at the same time. Dynamic load management solves this by controlling how much power each charger receives at any given moment.
So, the core purpose of dynamic EV charging energy management can be defined as follows:
Keep total site consumption within safe capacity limits while distributing power efficiently and helping operators expand charging infrastructure without unnecessary grid upgrades. 
A load management system for EV infrastructure usually provides three core capabilities:

Monitoring: Tracks site load, charger activity, vehicle demand, and available electrical capacity.
Allocation: Distributes power between active chargers based on technical limits and priority rules.
Optimization: Adjusts charging behavior to improve energy use, reduce peak demand, and support network uptime.

Dynamic load management differs from static load management because it responds to real-time conditions instead of relying on fixed charger limits. The following sections will explain this distinction in more detail and show where each approach fits.
Looking for OCPP and OCPI assistance?Intelliarts team can provide you with a full spectrum of EV interoperability servicesRequest OCPP/ OCPI development
Why is load management critical for EV charging networks?
Based on Intelliarts’ consulting sessions with EV companies, the limiting factor is often usable power capacity. A typical scenario is when a site has enough parking spaces and installed EVSE units, yet still fails to support full-speed charging when demand rises. Therefore:
Load management, as a technology that orchestrates charging, is critical because it controls how that limited capacity is used across active sessions.
See how load management technology is a must-have for high-performing EV charging networks in the infographics below:

How does dynamic load management work?
Dynamic load management works as a closed-loop control process. The system monitors site conditions, analyzes charging demand, calculates safe power limits, sends commands to chargers, and keeps adjusting allocation as new data comes in.
Here are the common stages that comprise the load management process:
1. Real-time monitoring
Load management starts with live data collection to uncover how much power the site can safely use before it can define available EV charging capacity.
This monitoring layer usually tracks:

Grid or utility capacity: the site’s contracted or technical power limit
Building load: non-EV consumption from HVAC, lighting, elevators, or equipment
Site meter data: real-time total load across the location
Charger status: charger availability, energy use, session state, and faults

The key output is the available charging capacity. This is not the same as the site’s maximum grid connection. 
2. Demand analysis
Once available capacity is known, the system evaluates charging demand. It checks which chargers are active, which vehicles are requesting power, and which sessions should receive priority.
Demand analysis may include:

Active charging sessions
Pending charging requests
Charger power ratings
Vehicle charging capability
User or fleet priorities
Schedules, tariff windows, and business rules

This step prevents every session from being standardized by default.
3. Power allocation
The allocation engine calculates how much power each charger should receive, or, in proper terminology, per-charger power limits.
In a simple setup, this can be as simple as dividing available power across active chargers. In a more advanced setup, the algorithm can account for the following constraints to allow for customized per-charger power limits:

Site capacity limit
Charger ratings
Priority rules
Tariff windows
Minimum charging thresholds
Electrical safety limits

The output is a set of charging limits or schedules for each charger. These limits define how much power each EVSE unit can draw without pushing the site beyond safe capacity.
“Static limits often hide unused capacity. We have seen quite a few cases where chargers stay restricted while the site has available power that could be reassigned to active sessions.” — Ihor Rudnyk, eMobility Tech Specialist at Intelliarts
4. Charger-level control
After the allocation engine calculates limits, the EV charging backend or Charging Station Management System (CSMS) sends them to chargers. In most scalable EV charging platforms, this is handled through Open Charge Point Protocol (OCPP) smart charging. OCPP allows the backend to send charging profiles, current limits, schedules, and status requests to compatible chargers. 
You may discover more about OCPP and other EV protocols in another blog post by Intelliarts. 
At this stage, the calculated limits are enforced for each charger separately. 
Important note. The main risk is execution mismatch. Intelliarts’ experience with integrating load management software shows that some chargers, especially those from different suppliers, when combined in the same network, apply profiles differently. 
This can result in delayed responses or connection errors during the enforcement of charging limits. That’s why it’s recommended to account for validation logic, retries, and fallback limits in the backend.  
5. Continuous optimization
After the enforcement of limits, chargers keep sending telemetry back to the backend. This data is then used as input for stages 1 and 2 to re-evaluate allocation in real-time. 
Re-optimization can be triggered by:

A new vehicle plugging in
A vehicle unplugging
A change in building load
A tariff update
A charger status change
A charging session reaching its target
A fault or communication issue

This closed loop is what makes the process dynamic, as reflected in the name of the technology. 
See the diagram indicating the main stages of the dynamic load management process, as well as the flow of data below:

Static vs dynamic load management: What’s the difference?
Static load management is a fixed-capacity control method where each charger, charger group, or site zone receives a predefined power limit. 
The limit is usually configured during installation and does not change automatically based on live building consumption, charger utilization, or grid conditions. This approach can work when the site has predictable demand and a small number of chargers. 
However, it becomes inefficient when usage varies. If several chargers are idle, their reserved capacity may remain unused. If many vehicles plug in at once, the system may reduce charging speed too broadly because it cannot recalculate allocation based on live demand.
The table below compares both approaches from an infrastructure, cost, and operational perspective.

What algorithms and technologies power dynamic load management?
Dynamic load management depends on two layers: the decision logic that calculates power limits and the technical infrastructure that enforces those limits at the charger level. In practice, most systems combine rule-based allocation, OCPP smart charging, Energy Management System (EMS) integrations, and, in advanced cases, AI or machine learning models.
#1. Rule-based allocation
Rule-based allocation is the most common starting point for dynamic load management. It uses predefined operating rules to distribute already available EV charging capacity across active chargers.
Common rule types include:

Equal sharing: all active chargers receive the same power limit.
Priority-based sharing: selected users, vehicles, or fleets receive more power.
Minimum threshold rules: each active session receives enough current to continue charging.
Departure-based rules: vehicles with earlier departure times receive higher priority.
Tariff-based rules: charging power is reduced during expensive peak periods.

For equal sharing, the allocation logic can be expressed as:
Power per active charger = Available EV charging capacity ÷ Number of active charging sessions
For priority-based allocation, the system applies weights instead of splitting capacity evenly:
Allocated power = Available EV charging capacity × Session priority weight
Control power across your entire charging networkDynamic load management solutions for scalable EV infrastructureContact us
#2. OCPP smart charging
OCPP smart charging is the communication layer that allows the backend to control charger output. Without this layer, the allocation algorithm may calculate limits, but the system cannot reliably enforce them across chargers.
Through OCPP, the CSMS can send charging profiles, schedules, and current limits to compatible chargers. These commands define how much power a charger can draw during a specific time window or operating condition.
In a typical setup, the process looks like this:

The backend calculates a charger limit.
The CSMS sends a charging profile through OCPP.
The charger applies the limit locally.
The charger reports status and meter values back to the backend.
The backend updates the profile when site conditions change.

The technical challenge is consistency. Different charger models and firmware versions may interpret profiles differently, so the backend needs validation logic, retries, and fallback rules.
#3. EMS and grid system integrations
An Energy Management System gives the load management system access to wider site energy data. This is especially important when EV charging competes with building consumption, solar generation, battery storage, or industrial equipment.
Typical EMS inputs include:

Real-time building load
Grid connection limit
Transformer or panel load
Solar generation
Battery storage status
Demand response signals
Energy tariff data

A practical control formula can be:
Available EV charging capacity = Grid import limit + Usable on-site energy − Current building load − Safety reserve 
#4. Power allocation algorithms
More advanced systems use allocation algorithms that balance technical limits with business goals. These algorithms do more than split power evenly. They decide which sessions should receive more energy based on constraints, priorities, and expected demand.
The algorithm may optimize for:

Maximum charger utilization
Lower peak demand
Guaranteed fleet readiness
Lower energy cost
Phase balance
Fair distribution across users
Reduced waiting time

A fleet depot, for example, may allocate more power to vehicles with earlier route schedules. A public hub may prioritize fairness and session stability. A commercial site may reduce output during peak tariff periods and increase it when energy is cheaper.
A simplified priority-based formula can look like this:
Session priority score = Departure urgency + Required energy + User priority − Tariff penalty
#5. AI and machine learning optimization
AI and machine learning may be regarded as optional layers. However, they are usually utilized nevertheless. Here at Intelliarts, we utilize optimization when the customer has collected or is going to collect enough historical data to implement predictive control.
Common AI/ML use cases include:

Forecasting charging demand by hour, site, or user group
Predicting fleet energy needs before route departure
Estimating peak-load risk
Optimizing charging against dynamic tariffs
Detecting abnormal charger behavior
Recommending better charger schedules

Explore time series ML, which is exactly the method used for purposes of such optimization, in another one of our blog posts.
Yet, AI should not replace deterministic safety rules. Site capacity limits, electrical protection logic, and minimum charger thresholds should remain rule-based. ML works best as an optimization layer above those hard constraints.
See the formulas conveniently gathered in a single infographic below:

What are the benefits of dynamic load management?
Dynamic load management shows its value when charger utilization grows, and site capacity becomes a constraint. For operators, the positives are tied to infrastructure cost, uptime, and the ability to serve more vehicles without overloading the site. These value-adding benefits include the following:

Keeping total charging demand within safe grid, transformer, and site limits.
Reduced need for early grid, cabling, or switchgear upgrades.
Unused capacity moved from idle chargers to active charging sessions.
Support for more chargers within the same electrical connection.
Lowered charging load during utility, tariff, or demand-response events.
Reduced failed sessions caused by overloads, breaker trips, or unstable power.
Prioritized fleet vehicles by departure time, route schedule, or required energy.
Reduced demand spikes that can increase electricity bills.
More predictable charging performance during high-demand periods.
Support for infrastructure growth and delaying large grid upgrades.

Explore the cost-effectiveness of EV charging optimization and other advantages of having proper dynamic load management in another one of our blog posts. 
What are the challenges and limitations?
“An interesting approach to the cost-efficiency problem is to treat charging capacity as inventory. When power sits reserved for an idle charger while an active session is delayed, the platform is wasting something the operator could have sold.” — Alexander Barinov, a Managing Partner at Intelliarts.
Dynamic load management only works well when site data, charger behavior, and control logic stay aligned. The main limitations come from delays, inconsistent hardware behavior, and rules that must fit both technical and business priorities.

 Integration complexity

Dynamic load management connects the Charging Station Management System, meters, chargers, and sometimes an Energy Management System. The risk is mismatched data timing: one system may show current site load, while another still reports older charger data.
Solution: A reliable setup depends on clear data ownership, update frequency, and fallback logic for each integration.

 Real-time data accuracy

The system calculates limits from live site load and charger telemetry. If this data is delayed or inaccurate, the site may underuse capacity or move too close to its electrical limit.
Solution: Timestamped telemetry, validation rules, and a safety reserve help protect the site from short spikes and delayed readings.

 Charger compatibility

Open Charge Point Protocol support does not guarantee that every charger applies limits in the same way. Firmware, reconnect behavior, and current-limit handling can differ between models.
Solution: Charger compatibility planning should cover profile handling, response delays, reconnect cases, and fallback behavior before large-scale rollout.

 Priority logic conflicts

Fleet readiness, fair distribution, and peak-cost reduction can compete. A system that optimizes for tariff savings may still delay a vehicle that needs to leave soon.
Solution: The control model should separate hard safety limits from business priorities, then rank those priorities clearly.

 Regulatory and utility constraints

Grid rules, tariffs, demand-response programs, and reporting needs vary by market and utility. This can make one standard configuration unsuitable for every site.
Solution: Local grid and tariff rules work best as site-level configuration inside the load management system.

 Failure handling

If meter data, backend control, or charger connectivity fails, the site still needs safe operating limits. Poor fallback logic can either overload the site or charge too much.
Solution: Conservative fallback limits keep charging predictably until real-time control is restored.
Important note: The best mitigation strategy for most companies is rarely a particular fix. Instead, an array of targeted changes and best practices should be applied. Oftentimes, it’s best to partner with a trusted EV software development company like Intelliarts for comprehensive assistance.
Make every charging site easier to manageSoftware for monitoring, energy allocation, and multi-site EV network controlRequest EV development
How to implement dynamic load management in your EV charging system?
Dynamic load management implementation can be approached just like any other development. Yet, it has some specificities to account for, especially in terms of site power limits, charger communication protocols, and business requirements behind the EV infrastructure modification. 
The infographic below shows the implementation flow from capacity assessment to live optimization, with the main technical factors that make this process specific to EV charging systems.

Real-world use cases of dynamic load management
Dynamic load management is most useful where charging demand changes faster than electrical capacity can be expanded. In practice, this usually means sites with shared power limits, predictable peak periods, or many vehicles connected at the same time. 
Let’s get through exactly such use cases to see how the technology is applied in practice and what governmental initiatives support dynamic load management for EVs:

 Public charging hubs

Public charging hubs need to balance high utilization, grid limits, and driver experience because many vehicles can arrive within the same peak window. Dynamic load management helps distribute available power across active sessions to keep the site within its connection limit.
A practical example of this load management application is Intelliarts’ work with EVIQ, a company building a nationwide fast-charging network in Saudi Arabia. The EV management software platform Intelliarts developed supports station monitoring, charger lifecycle management, and charger onboarding/offboarding. 
As a result, EVIQ received the core components of a fully functional EV station management platform in six months, supported tens of thousands of active app users, and achieved strong app ratings of 4.1 on Android and 4.8 on iOS. The serverless architecture also helps the network scale without major infrastructure investments as EVIQ expands across Saudi Arabia.

Explore Intelliarts’ portfolio

 Commercial parking

In office parking, retail parking, and paid parking facilities, EV charging competes with building load. Dynamic load management helps keep charging available during business hours without sizing the electrical system for every charger running at full power. The 
Alternative Fuels Data Center explains how utilities can support infrastructure planning through power availability and capacity analysis and reduce grid impact through managed charging.

 Fleet depots

Fleet depots need vehicles ready by a specific departure window, so load management is tied to schedules and operational priority. The U.S. Department of Energy describes managed EV charging for federal fleets as a way to reduce installation costs, avoid peak pricing or demand charges, and help vehicles receive the energy they need.

 Residential complexes

In multifamily housing, the main constraint is shared electrical capacity across many residents and parking spaces. Dynamic load management helps distribute charging load without assigning full dedicated capacity to every EV space. The U.S. Department of Energy’s Multifamily EV-Ready Checklist reflects this logic: EVSE serving multiple spaces may use lower nameplate charging capacity per space when supported by an energy management system. 
Why choose Intelliarts for EV charging software development?

For successful dynamic load management, developers need to understand how EV charging platforms behave under site constraints. Therefore, hands-on, specialized expertise is required. 
At Intelliarts, we approach EV charging software and load management with regard to energy and infrastructure specificities and core business objectives. Our work in smart energy management systems is closely connected to this challenge because dynamic load management depends on live energy data, charging demand forecasts, and site-level control rules.
Our team also brings relevant experience in smart grid software development, where power usage optimization, dynamic limits, monitoring, and demand-side flexibility are core parts of the system logic. This matters for CPOs, fleet operators, and infrastructure companies that need charging software to work within grid constraints.
For dynamic load management projects, Intelliarts can help with:

Designing cloud, local, or hybrid load management architecture.
Building smart charging logic for charging profiles, current limits, and schedules.
Integrating chargers, meters, Energy Management Systems, fleet tools, and billing platforms.
Developing custom allocation algorithms for site limits, tariff windows, user priorities, and fleet schedules.
Planning fallback behavior for connection loss, delayed telemetry, and charger-specific execution issues.

This experience also connects to our broader work in renewable energy software development, where EV fleet management, charging schedules, load needs, and route optimization often intersect. This means that we have the expertise and knowledge to drive an EV charging project with load management end-to-end without any third-party providers required.
Don’t hesitate to explore our software engineering services on our website. 
Final take
Dynamic load management helps EV charging operators control limited site capacity without sacrificing reliability or expansion plans. It connects real-time monitoring, demand analysis, allocation logic, OCPP-based charger control, and continuous optimization into one operating model.
For CPOs, fleets, public hubs, and residential sites, this means safer power distribution, better use of existing infrastructure, and a more practical path to scaling charging networks.
The Intelliarts team has more than 26 years of experience delivering custom software solutions, including EV charging platforms and dynamic load management integrations. We can create software from scratch or add necessary development on top of an existing platform. With more than half of our senior staff engineers in-house and 90% of our customers, we are ready, willing, and able to contribute to your best project. 
Contact us

#### Generative AI in Marketing: Key Applications and Best Practices
AI promises usefulness where teams often face challenges related to task automation, data analytics, content creation, and others. Since the potential of generative AI for marketing is immense, it’s totally worth delving into its usage and adoption. 
In this post, you’ll discover a suite of top applications of Generative AI in marketing, explore three best practices for generative AI marketing strategies for businesses, and review challenges you may face during an AI system implementation. Besides, you’ll get to know some of the most notable Generative AI in marketing trends, which may soon become common applications. 
Real-world examples of generative AI in marketing
For starters, let’s have a quick look at stats and numbers revealing the impact of Generative AI.

Curious about what are the key applications of generative AI in marketing? Now, with an understanding of how huge and valuable the role of Generative AI is, let’s explore how to use generative AI in marketing through practical applications in modern-day strategies.
#1 Personalized marketing campaigns
Generative AI enables the creation of hyper-targeted campaigns due to its vast capabilities to analyze consumer behavior and preferences based on big data. It helps to deliver personalized content at scale, leading to improved customer engagement and higher conversion rates. When considering tools for implementing such strategies, understanding the differences between Claude vs. ChatGPT can be beneficial.
How Generative AI assists:

Automated customer segmentation based on demographics, behaviors, and purchasing history.
Predictive analytics for content based on customer preferences and predicted behaviors.
Automated personalized email and ad generation, such as order confirmation email automation that drives post-purchase engagement
Real-time interaction personalization, i.e., adaptation of content dynamically based on user interactions and engagement patterns.
Behavioral trend analysis.

Real-life example:
Starbucks uses its Deep Brew AI platform that enables it to analyze customer data and preferences with the prospect of personalizing customer interactions based on purchase history.
#2 Dynamic advertisement creation and optimization
Generative AI enhances advertising effectiveness by automating the creation of dynamic and contextually relevant ads. Leveraging real-time data and audience insights, generative AI transforms marketing campaigns, ensuring higher engagement and improved ROI. It is especially valuable for brand awareness advertising, helping brands reach wider audiences with personalized and memorable messages.
How Generative AI assists:

Automated A/B testing for quick evaluation of ad variants.
Real-time performance monitoring with content and targeting adjustments.
Context-aware content adaptation, including location- or device-specific modifications.
Automated multilingual ad generation to target diverse global audiences.
Dynamic ad template creation tailored to specific platforms and audience segments.

Real-life example:
Coca-Cola announced a collaboration with OpenAI and has started using generative tools to create dynamic branding campaigns. They leverage a vast suite of Generative AI capabilities, from an advanced image generation solution and AI search chatbots to creating out-of-home advertising experiences.
#3 Customer journey mapping and predictive analytics
Generative AI empowers businesses to visualize and predict customer journeys by analyzing vast datasets. It enables proactive decision-making, ensuring tailored experiences across touchpoints while boosting customer satisfaction and retention rates.
How Generative AI assists:

Automated customer journey mapping with actionable insights.
Prediction of drop-off points and customer behaviors.
Identification of high-value touchpoints for personalized interactions.
Real-time recommendations for upselling and cross-selling opportunities.
Sentiment analysis to refine customer engagement strategies.

Real-life example:
Amazon offers SageMaker, which is an AWS ML and AI platform that provides unified access to data and a vast range of data analytics capabilities. Among many usages of this instrument, it offers merchants using the Amazon platform data-driven options to anticipate customer needs, with Amazon offering timely product recommendations based on browsing and purchase patterns.
From AI Experiments to Measurable Business ValueDiscover how companies evaluate LLM costs and optimize AI systems without overspending.Read the white paper
#4 Creative design and visual content generation
Generative AI revolutionizes creative processes by producing high-quality visual assets at scale. It supports marketing efforts with customized designs, saving time and reducing costs while maintaining brand consistency. Tools like this image generation tool offer a quick and scalable way to create visuals for various platforms and audiences.
How Generative AI assists:

Automated generation of logos, banners, and ad visuals.
Creation of reusable banner templates that streamline campaign design and ensure brand consistency across platforms.
Creation of hyper-personalized content for different demographics.
Dynamic video editing and animation based on campaign goals.
Style transfer for content aligned with specific aesthetics or brand guidelines. A well-defined brand architecture framework helps maintain consistency and clarity across all content and communications.
Real-time adjustments for multi-platform compatibility.

Real-life example:
Nike utilizes a blend of proprietary generative AI models, Large Language Models (LLMs), 3D printing, and computational design in their brand product design initiatives. They also run AI-driven advertising campaigns and use a variety of generated image and video content in their marketing. 
#5 Automated content creation
Generative AI streamlines content creation by automating the production of high-quality, engaging, and relevant materials. Whether you’re crafting social media captions, ad copy, or starting a blog, AI tools like Breeze can help marketers to maintain consistency across campaigns while scaling up content efforts, saving time and resources. 
How Generative AI assists:

Generation of blog posts, social media captions, and ad copy.
Automated drafting of product descriptions tailored to target audiences.
SEO-optimized content creation using keyword analysis.
Dynamic creation of multilingual content for global campaigns.
AI-assisted scriptwriting for videos and presentations.

Real-life example:
Associated Press is one of the first news organizations to start leveraging AI. The company utilizes AI to produce thousands of financial and sports reports automatically. They are looking for ways to deploy AI in news production and distribution to ensure timely and accurate content delivery to their audience.
  White PaperAcquire extra details on utilizing Generative AI reinforced with RAG in business Download the RAG white paper
3 best practices for implementing Generative AI in marketing
Surely, the nuances of adopting AI are vast. However, let’s focus on three Generative AI use cases for marketing, which, if left overlooked, may make it impossible to use customized marketing solutions reinforced with AI. 
#1 Map AI capabilities to specific goals
Essentially, you need to align generative AI for marketing with clearly defined objectives to maximize its potential. For example, Gen AI can offer capabilities for targeted audience segmentation, personalized content creation, or campaign analytics. Mapping listed and other capabilities, when keeping in mind limitations, ensures that strategies are well-thought-out. This drives better resource allocation and enables businesses to drive impactful generative marketing and advertising campaigns while achieving desired outcomes efficiently.
Microsoft provides learning materials to educate all enthusiasts on creating AI/ML Capabilities Map intended to tailor AI solutions to business goals. Don’t hesitate to utilize such guidelines when crafting your Gen AI for marketing strategy. 
#2 Ensure that your data management is optimal
The success of generative AI in marketing hinges on the quality and management of input data. That’s why it’s essential to mind the data you feed your Generative AI for marketing. Having a solid big data collection process and clean, well-structured datasets ensure that AI models produce precise, contextually relevant outputs, while compromised quality of data may lead to critical errors in customer communications and marketing campaigns. Also, for content-driven SEO strategies, combining clean datasets with techniques like Python for NLP SEO can help extract semantic insights from large volumes of text, enabling smarter keyword targeting and better search visibility.
You may explore Garbage-In-Garbage-Out (GIGO) data management in ML rule and best data preparation practices in another blog post by Intelliarts. 
#3 Establish the continuous learning and optimization flow
AI models used in generative AI and marketing must undergo regular updates, retraining, and testing to remain effective. These Generative AI marketing strategies reduces the adverse effects of feeding an AI model that applies continuous learning with real-life data since it may result in bias cumulated over time. It ensures that AI stays relevant, aligns with changing customer behaviors, addresses emerging trends, and 
Need ML services to develop or refine your Generative AI model?Intelliarts experts are here to helpLearn more
Challenges and risks of using Generative AI for marketing
The World Intellectual Property Organization (WIPO) expresses concerns regarding the usage of AI in business. They mention issues of bias, transparency, accountability, and privacy. In that regard, let’s explore business risks that, if left unhinged, may lead exactly to concerns coming true:

Technical challenges: Developing and integrating generative market AI solutions can be complex, requiring high-quality data and seamless system integration. That’s why poor implementation may lead to inaccurate outputs, inefficiencies, or costly delays in marketing workflows.
Ethical and legal risks: Generative AI models can inadvertently perpetuate biases, raising ethical concerns. Additionally, managing data privacy and ensuring compliance with GDPR provisions on AI and recommendations like European ethics guidelines for trustworthy AI is essential to avoid legal repercussions.

You may be interested in exploring Intelliarts’ blog post on the ethics of data collection for additional insight into the corresponding risks. 

Over-reliance on AI: Relying too heavily on AI without human oversight can lead to unchecked errors or inappropriate content generation. Balancing automation with human review ensures quality and context alignment.
Cost and scalability issues: Implementing AI solutions can be resource-intensive, with high upfront costs and ongoing maintenance. Smaller businesses may struggle to scale AI effectively within budget constraints, limiting accessibility.

Each company is expected to figure out its own way of addressing challenges and risks associated with utilizing Gen AI on multiple levels across departments, including marketing. A basic framework for identifying and addressing risks is provided in the infographic below:

Should you need assistance with identifying Generative AI risks and creating ethical solutions, don’t hesitate to apply for technology consulting and AI development services by Intelliarts. 
Future trends in Generative AI and marketing

It’s safe to claim that the IT industry is fast-paced. The technologies that were introduced as early concepts only recently are gaining traction in the present. So, let’s find out what Generative AI advanced applications and modifications the marketing team may face in a few years: 

Immersive marketing with generative AI and AR/VR. The fusion of generative AI with AR/VR allows brands to craft experiences that feel real and interactive. Imagine virtual stores where AI generates tailored product recommendations based on your browsing history, or immersive storytelling campaigns where AR environments are dynamically created in real time.
Hyper-personalization through big data. By leveraging big data insights, generative AI can analyze consumer behavior at an unprecedented depth, generating targeted ads, emails, and content that resonate with individual preferences. This goes beyond demographic segmentation into true personalization, boosting both engagement and ROI.
Enhanced predictive insights with ML. When paired with machine learning in business, generative AI improves predictive modeling for marketing campaigns. It can foresee trends, customer needs, and market shifts, enabling businesses to stay ahead of the competition with data-driven strategies.
Sustainable AI practices and ethical marketing. As generative AI gains traction, the emphasis on sustainability and ethical use is becoming critical. Sustainable AI practices, such as reducing the carbon footprint of large language models, are also a growing concern in the industry.

Emerging trends highlight how AI tools for business growth will transform marketing strategies. Besides, we should expect further increase in both the adoption of AI in marketing and the usefulness of this advanced technology on multiple levels. 
For additional insights into practical implementations of the latest AI innovations, explore our AI assistant case study and AI-powered equipment consulting case study by Intelliarts. 
Final take
Generative AI is revolutionizing marketing with its ability to personalize campaigns, optimize advertisements, and streamline content creation. By integrating AI thoughtfully and upon addressing challenges like data management and ethical concerns, businesses can harness its full potential in marketing for better engagement and ROI. 
Here at Intelliarts, we specialize in creating tailored AI solutions for diverse business needs, from AI chatbots to AI car damage detection. We have worked, among our many other clients, with such famous brands as HubSpot, Nowsite, and Verity. 
Build AI Solutions for Your Marketing Department Leverage Intelliarts' 24+ years of expertise to bring your idea to life.Contact Us

#### GoodFirms Interview with Intelliarts CEO
This is a guest post from GoodFirms. The detailed interview can be read on the GoodFirms site.

Intelliarts is an AI (Artificial Intelligence) consulting and software development company that excels in AI & big data analytics. The organization’s innovative approach makes them an unmatched software development solutions provider. The company was founded in 1999, headquartered in Lviv, Ukraine & also has a branch office in Lublin, Poland.
Intelliarts possesses experienced & talented engineers who create data-driven and ML-powered software solutions. As one of the leading technology consulting firms, the company offers complete digital business transformation for startups, midsize companies, and large enterprises worldwide. The team helps its customers to create value by reinforcing companies with innovation.
Intelliarts is a renowned IT company with significant expertise in the following areas:

Big Data Consulting
Data Science
Machine Learning
Cloud Development
SaaS Development
IoT Solutions
Business Intelligence Solutions

The expert team offers exceptional technology consulting, R&D (Research and Development), and software development services. The excellent client reviews vouch for their incredible efforts and strong engineering prowess.

The GoodFirms team had an opportunity to speak with Andriy Zakharchuk – CEO of Intelliarts – recently. Andriy speaks at length about the story behind launching Intelliarts, the types of services, and his expectations for the company’s future.
Right at the start, he mentions that he is fairly passionate as a CEO and loves motivating all his team members by sharing his professional experience and thereby helping to lead, manage, and support them.
Talking about the initial days, Andriy recalls that the company was initiated by passionate people who were propelled by empathy & enthusiasm to run the business. Such an approach inspired the newcomers to treat the customers’ goals & objectives as their own. As a consequence, each team member strives to come up with the best possible solution keeping in mind the customer’s business from the entire perspective.
Andriy added that Intelliarts was founded to mark an influential impression on the IT sector through a values-based approach. The company’s business communication is entirely transparent, which helps customers to make better decisions. The value-based approach to business allowed Intelliarts to become the most sought-after software development firm in Artificial Intelligence & Big Data.
Further, Andriy talked about the company’s business model, which focuses on providing consulting services and building dedicated teams of highly-skilled engineers that operate nearly as close as customers’ on-site engineers. The company provides experts who comprehend customers’ business requirements and help them during the development process, including planning, deployment, integration, and predictive maintenance. They hire experienced, professional, and dedicated developers for long-term contracts.
Moreover, he added that Intelliarts strives to integrate the team members with the client’s team as much as possible, which in a way, strengthens the project and improves the reliability of its success. The collaboration ensures the exchange of values and culture with the customers and helps form a solid bond with them.
What’s more, by sharing about the company’s cultural values that differentiate them from its competitors, Andriy clearly states that client satisfaction is the most critical asset for Intelliarts. The team is determined to provide ultimate success to their client’s businesses through honesty, integrity, and reliability.
Plus, employees love to extend their knowledge boundaries and improve the work processes accordingly, which helps customers to keep the relationship for the long term. The developers’ team ensures each project scales in keeping with those ethical values.
Intelliarts’ team offers exceptional service to their customers that helps maintain the business bond reliability through robust software development solutions. Since 1999, the company’s objective approach has led them to reach greater heights in IT services globally. Not surprisingly, their client roster is at its peak, encouraging them to keep consistency in what they do!
The company caters to the following industries:

Manufacturing
Insurance
Digital Marketing
Energy & Utilities
Information technology
Agritech
and other

A sense of ownership and responsibility of the team members helps Intelliarts to provide excellent solutions to their customers.
While focusing on creating complex data-driven ML-powered solutions for the business, they also ensure that the product is built according to high-quality standards, operates in a fault-tolerant and resilient manner, and of course is user-friendly.
Thus, Intelliarts has been listed among the top artificial intelligence companies in Ukraine at GoodFirms.
The company’s most demanded services include AI development solutions, software engineering, R&D (Research & Development), and data engineering services. CEO of Intelliarts added that the organization recently extended its expertise in the machine learning domain, and hopefully, more ML-related projects will be on board soon.

Andriy mentioned that more than 90% of their customers come back to them with new projects and ideas. He firmly believes that the real success of his company is the happiness of customers.

Andriy also outlined the steps to achieve customer satisfaction. His special recipe includes:

a friendly culture backed by solid professional values

product quality

solid support system

open and constant communication

All of the above takes effect when the dedicated team thoroughly learns the client’s business strengths and weaknesses and takes action accordingly to achieve massive success.
The support system of Intelliarts is reliable in helping their customers from the initial stages to the end of the project and the team may cooperate with the customers as long as they are needed. On project completion, the team always welcomes their customers to reach out for any queries or help. 
Andriy has revealed the company’s payment structure, which is a flexible billing model. The budget depends on the project’s requirements, and the company offers a reasonable budget for each business size. They prefer monthly invoices more than any other structure, which is convenient for customers. Besides, fixed-price projects are also profitable for some in terms of convenience.
Intelliarts’ team works with all types of businesses that have growth potential. They ensure that communication is transparent with their customers, and the budget should not be an obstacle to finalizing the business deal. This means that Intelliarts may assist anyone from large to small-sized businesses.
The company focuses on the customer’s business growth rather than the budget. The professional team model allows work with annual revenue per client’s metric, and prices are according to the team size. Andriy said that in 2021, the price range was between $60,000 and $1,200,000. 
When asked about the company’s ten-year plan, Andriy stated that the company’s vision for the upcoming years is to influence more IT-enthusiastic people, create a better world through innovative machine learning technologies, and help all sizes of businesses grow.
 Need a consultation? Contact us
About GoodFirms
Washington, D.C.-based GoodFirms is an innovative B2B Research and Reviews Company that extensively combes the market to find business services agencies amongst many other technology firms that offer the best services to their customers. GoodFirms’ extensive research process ranks the companies, boosts their online reputation, and helps service seekers pick the right technology partner that meets their business needs.

#### How EV Charging Software Transforms Energy & Cost Management
Over the past decade, EV adoption has surged, forcing an expansion of charging infrastructure. In 2024 alone, over 1.3 million public charging points were added globally, resulting in a 30% year-on-year growth. Yet, rapid scale-up brings operational and grid challenges, including peak demand, charger downtime, and billing complexity.
EV charging management software is regarded as a critical enabler to tame niche complexities. Owners, network operators, fleet managers, and enterprises all stand to benefit from using such software. 
In this post, you’ll explore how EV charging management software works in great detail. Besides, you’ll find out why exactly businesses need EV solutions based on niche-specific use cases, and discover what the future will likely bring for companies in the industry. 
What Is EV Charging Management Software? 
EV charging management software is a digital platform that enables operators to monitor, control, and optimize electric vehicle charging stations. 
Basically, EV management software is a centralized tool that offers automated and semi-automated capabilities to manage energy distribution, user access, billing, maintenance, analytics, and other functionalities that a company considers aggregating and using. 
Without further ado, we continue delving into the technicalities of EV charging optimization software by reviewing features of such tools and their expected impact on ROI. 
Why do owners and operators need EV charging management software: Key 5 features
It may be a bit tricky to assess the positive impact of utilizing EV fleet management software in digital transformation. However, certain research and case studies indicate the high impact of corresponding EV platform features on ROI:
#1 Dynamic load management and smart energy balancing
This feature automatically distributes available power among connected chargers in real time, ensuring optimal energy use and preventing overloads. It’s a key element of energy management for EVs, helping balance grid demand and station performance.
ROI impact: Barclays Capital, using EVoke’s smart charging APIs, reduced its daily charging load by 200 kW through advanced load scheduling. The project demonstrated how energy management for EVs can prevent grid overloads, lower demand charges, and significantly reduce operating costs.
#2 Usage analytics & utilization forecasting
Built-in analytics tools within EV charging optimization software track charging sessions, identify idle periods, and predict demand trends. This helps operators understand when, where, and how customers charge their vehicles.
ROI impact: The International ZEV Alliance report found that increasing average charger utilization from 10% to 20% can halve the payback period for EV infrastructure investments. This highlights how EV charging optimization software directly improves ROI by supporting data-driven expansion and load planning.
#3 Automated billing & payment system
Automated billing focuses on energy metering, accurate invoicing, and flexible payment options through apps or RFID at smart EV charging stations. 
ROI impact: Industry ROI benchmarks emphasize that automating billing, metering, and payment processes significantly reduces administrative overhead and revenue leakage. CPOs implementing smart EV charging station platforms typically see measurable improvements in accuracy, cost efficiency, and overall profit margins.
Learn more about cost-effective EV charging optimization in our blog article.
#4 Multi-protocol interoperability (OCPP / OCPI, etc.)
Open Charge Point Protocol (OCPP) is an essential component of any EV smart charging system. The interoperability that this technology offers is intended to drive seamless communication between chargers and management software, regardless of brand or network.
ROI impact: For example, in a paper on OCPP interoperability, it’s noted that open protocols reduce complexity and cut costs by avoiding vendor lock-in and enabling the use of diverse hardware. By avoiding stranded assets and enabling hardware flexibility, interoperability can reduce replacement or retrofit costs.
#5 Smart pricing & tariff flexibility
Smart pricing approaches allow CPOs to dynamically adjust tariffs based on energy demand, time-of-use, or user type. It encourages off-peak charging and better grid alignment.
ROI impact: According to Solidstudio’s 2025 analysis, optimized pricing models and demand-based tariffs increase station profit margins by 10–30%, depending on utilization and energy costs. These smart charging solutions help operators balance grid loads while maximizing revenue through adaptive tariffs.

How does EV charging management software work? 
EV charging management software is not exactly a standalone application, but rather a multiple-component platform with several logic layers and connections with external devices and third parties. Companies often use product roadmap software alongside the platform to plan feature development, prioritize updates, and ensure smooth integration of new charging functionalities.
Here’s how it works in a regular EV vehicle charging flow:
Step 1: IoT and sensor data сollection: The hardware–software bridge
Each EV charger acts as a smart IoT device equipped with meters, controllers, and sensors. Additional sensors may be installed in an electric vehicle. Altogether, they gather and send the following types of data:

Energy delivered, voltage, and current
Connector and charger status (available, charging, faulted)
Temperature and load for safety control
User or vehicle identification (via RFID, mobile app, or Plug & Charge)

This data is securely transmitted via OCPP or API to the cloud platform, where it’s aggregated from all connected chargers. The cloud layer forms the backbone for real-time monitoring, diagnostics, and data sharing across the network.
What really makes EV charging networks efficient isn’t just the hardware. It’s how well the energy is managed. Smart software keeps demand balanced and makes sure every kilowatt is used with purpose. — Ihor Rudnyk, an e-Mobility Solution Expert at Intelliarts
Step 2: Cloud platform and AI analytics: Intelligence layer
Within the cloud, collected data is processed by AI-powered analytics engines that continuously evaluate station performance and grid demand. Intelligence layer covers the following responsibilities:

Predictive maintenance: Identifies anomalies such as overheating or voltage drops before they lead to outages.
Load balancing: Automatically distributes power across active chargers to prevent overloads.
Energy forecasting: Learns from historical use to predict future charging demand.

If, for instance, multiple chargers start simultaneously, the cloud engine automatically regulates current distribution to avoid exceeding facility limits. This is the stage where operational efficiency is achieved — less downtime, fewer technician visits, and optimized use of every kilowatt delivered.
Step 3: Integration with IT/OT systems and the energy grid
EV charging software doesn’t work in isolation. It integrates with IT systems, including billing, CRM, reporting, and OT systems, which form the bridge between the digital platform and real-world energy operations.
Above the cloud layer, the system connects with operational management modules such as:

Energy management: Communicates with utilities, grid operators, and building systems to optimize power flow, balance demand, and integrate renewables.
Charging management: Controls the real-time operation of chargers, manages queues, and coordinates sessions across sites.
Vehicle management: Monitors EV status, battery levels, and fleet readiness, prioritizing vehicles that must depart soon.

This upper OT layer uses insights from the cloud’s AI and analytics to coordinate physical infrastructure. In turn, it sends back live operational data, creating a two-way feedback loop between the digital platform and the grid.
Step 4: Billing, authentication, and smart pricing: Business logic layer
The business logic layer connects the technical and financial sides of operation, including:

Authentication: RFID, or Plug & Charge authentication.
Billing and payments: Automatically generated invoices, prepaid accounts, and fleet settlements.
Dynamic pricing: Tariffs adjusted by energy cost, time of day, or user group to balance demand.

All financial data flows through the same cloud infrastructure, ensuring that users are billed accurately and operators maintain transparent, real-time revenue control. The result — simplified workflows, minimal errors, and stronger monetization through adaptive pricing.
Step 5: Continuous monitoring and control loop
The described system often operates as a closed feedback loop to ensure continuous updates. Here’s how it may work:

The IoT layer sends live charger data to the cloud.
Analytical software processes and visualizes data for operators.
Commands and updates (like restarting chargers, throttling power, or changing tariffs) flow back down through the same path.

This loop ensures fast responses to faults, dynamic energy control, and a consistent user experience.
Take a look at the infographic, including all the components of the charging procedure with an EV charging management software, and data flows: 
 
Note: The infographic and the instructions illustrate one of many possible approaches to structuring a flow of data with an EV charging management software in place. In certain scenarios, some components like mobile app integration or vehicle management may be absent, while others, like regulatory compliance, may be added. 
Why do business owners and operators need EV charging management software? 
Now, let’s focus more on the potential concerns of EV businesses and find out how exactly smart EV charging solutions address them. Information is available via the following infographics:
 
While indicated solutions already cover most of the EV and energy concerns, the actual usefulness of EV charging optimization software extends far beyond. 
Niche-specific use cases of EV charging management software in business
While searching for a practical business solution, it’s best to leverage others’ experience. The following several cases reveal how exactly EV charging management software is used in practice:
Creating next-gen EV fleet management software: Success story by Intelliarts

Challenge: The client sought to expand into the EV fleet management market but lacked software to efficiently control and optimize vehicle charging across diverse locations — depots, routes, and drivers’ homes. 
Solution: Intelliarts built a next-gen EV charging management system integrating telematics, smart energy scheduling, and predictive analytics. The platform tracks EV status, charging sessions, and energy consumption in real time while prioritizing vehicles based on routes, schedules, and battery levels.
Results: The solution enhanced fleet efficiency, minimized charging downtime, optimized electricity use, enabled automated compensation for at-home charging, and provided analytics-driven insights for cost-effective, data-informed decision-making.
Explore success story
EV charging management platform for EVIQ: Success story by Intelliarts
EVIQ Solution Architecture
Challenge: EVIQ, a leading Saudi e-mobility company, needed a custom EV charging management platform to oversee station performance, enable payments, and ensure ZATCA-compliant invoicing — all within a tight 6-month deadline. The solution had to scale with their growing nationwide charging network and meet local regulations and user experience standards.
Solution: Intelliarts designed and developed a serverless AWS-based EV charging management system, integrating local payment gateways, ZATCA e-invoicing, and OCPI protocol for future roaming. The platform enables real-time station monitoring, lifecycle management, and seamless charger onboarding/offboarding, paired with a bilingual mobile app in Arabic and English.
Results: Our team delivered the platform’s core modules faster than industry standards. More than that, we ensured full compliance, scalability, and smooth user experience. EVIQ now operates a robust, future-ready e-mobility ecosystem that supports rapid network expansion and long-term operational efficiency across Saudi Arabia.
Explore success story
Amazon & PowerFlex: Smart overnight fleet charging for a logistics business

Challenge: Amazon sought to electrify its delivery fleet while controlling depot energy use. The company needed a scalable solution to optimize overnight EV charging across hundreds of sites without overloading local grids.
Solution: PowerFlex implemented a smart load management system for 20,000+ chargers, using adaptive scheduling and real-time monitoring to balance charging demand and energy costs while ensuring all vans are ready by morning.
Results: Reduced peak demand, improved fleet reliability, and enabled cost-efficient overnight charging across 150+ logistics depots.
Barnet Council & Trojan Energy: Municipal charging hubs

Challenge: Barnet Council aimed to expand EV access for residents without home chargers by creating municipal charging hubs through public-private collaboration.
Solution: Partnering with Trojan Energy, the council deployed 500 streetlights and 793 ground-level chargers under a £5.19M OZEV-funded program, ensuring inclusive, on-street charging access.
Results: Enabled broad EV adoption, strengthened public-private infrastructure cooperation, and made sustainable mobility accessible to thousands of urban residents.
Choosing the right EV charging management software 
Since you are already well-versed with EV smart charging systems, the time is just right to explore how to choose such a software solution for your business. Using our vast experience, the Intelliarts team has gathered essential properties and features that a sufficient EV software should have, as well as tips for selecting software strategically. 
#1 Define your EV charging management needs
Software usually differs in its monetization approaches, advantages, and weaknesses. Selecting your priorities correctly can help filter the least suitable options at the very beginning. 

Scalability. The number of stations the software can support and the future growth opportunities available.
Use case. Public, fleet, workplace, or residential charging — each requires a different setup.
Business model. Subscription, pay-per-use, or B2B management approach for your network.
Regional specifics. Grid setup, energy tariffs, and local compliance requirements.
User experience. Ease of use for operators and drivers, including app and payment flow.
Operational control. Automation level in load balancing, alerts, and maintenance.
Data and analytics. Depth of insights for usage, revenue, and performance monitoring

A platform built for public networks won’t necessarily fit fleet operations or depot charging. Realizing that the development should be purposeful and address core needs early can save months of rework. — Jurij Aulikh, an e-Mobility Software Engineer at Intelliarts. 
#2 Core evaluation criteria
Ask vendors these questions:

How do you handle charger onboarding and monitoring?
Does your platform support real-time analytics and load management?
What’s your system uptime and maintenance support record?
Can it be white-labeled or customized for different clients?
How do you ensure data privacy and cybersecurity?

#3 Must-have integrations
Your software should be able to connect with or have support for the following:

PCI DSS-compliant billing and payments apps
Software APIs and third-party tool
OCPP & OCPI protocols
Renewable energy or smart grid APIs
CRM and customer portals
Fleet management systems

#4 Compliance & security
Here are some non-negotiable aspects concerning the cybersecurity of an EV charging management software:

Data protection. GDPR, SOC 2, ISO 27001
Payment security. PCI DSS certification
Audit readiness. Built-in reporting and traceability
Access control. User authentication and role management

#5 Future-ready features
Ideally, your solution should support, or have in its development roadmap plans for the support of the following: 

AI-driven energy forecasting
Vehicle-to-grid (V2G) readiness
Cloud-native architecture
ESG & carbon tracking modules
User-friendly mobile app for drivers and admins

#6 Vendor partnership & support
Ensure that your provider has the following:

Dedicated customer success manager
24/7 technical support and incident response
Regular feature updates and roadmap transparency
Strong client references and proven deployments

 
Important: Off-the-shelf EV charging management software may lack some functionalities you are looking for, while charging your business for features you may not even use. Not to mention that an external tool still requires custom software integration with your platform. That’s why it’s often recommended to strategize for having a custom EV charging management solution. 
Need an AI-Driven EV Charging Management Solution?Intelliarts provides the best-fit development options.Contact Us
Future Trends in EV Charging Management Software 
The EV industry is evolving rapidly. As of the end of 2025, we can note predominant digitalization, centralization, and attentiveness to Earth sustainability. Here are the main trends in the industry that are shaping the future decade:

AI-driven energy forecasting. This trend focuses on using artificial intelligence to predict charging demand, electricity prices, and grid loads in advance. It helps optimize charging schedules, reduce costs, and prevent overloads.
Vehicle-to-grid (V2G) integration. V2G technology allows electric vehicles to return stored energy to the grid or buildings. It turns EVs into mobile energy resources that support grid stability and efficiency.
Cloud-native charging management platforms. Such systems are built entirely in the cloud. They are needed to enable real-time monitoring, scalability, faster updates, and easier integration with third-party services.
Cybersecurity and user authentication. Here, the technology is applied to ensure the protection of charging infrastructure and user data. It relies on encryption, identity verification, and compliance with cybersecurity standards.
EV fleet electrification and ESG reporting. Electrification is another approach to digitalization. It focuses on transitioning to electric fleets while simultaneously tracking carbon savings and energy use. This way, the technology can support sustainability metrics and reporting on environmental, social, and governance (ESG) goals.

Additional information on e-mobility technology trends is available in a white paper by Intelliarts.

Final take
EV charging management software centralizes control over energy use, billing, analytics, and integrations, this way making charging networks smarter and more profitable. Key ROI drivers include load balancing, predictive analytics, automation, interoperability, and dynamic pricing. The technology relies on IoT, AI, and cloud platforms to ensure seamless grid interaction.
Here at Intelliarts, we have over 25 years of experience delivering custom AI-powered solutions, including EV smart charging systems. With a focus on long-term partnerships — some lasting more than 15 years, and a team composed of 92% senior engineers, we’re well-qualified to take on your next project.

#### How to Automate PDF Data Extraction with Machine Learning
Data extraction from documents is a real goldmine for businesses across different domains.  Client agreements, contracts, reports, financial statements, or regulatory compliances — all this gets documented in everyday business operations. Unfortunately, at least 80% of this data is collected and stored in unstructured formats, PDFs included, complicating access and affecting decision-making processes.
With strong machine learning and data engineering expertise, Intelliarts can help companies with automating PDF data extraction in a matter of seconds. By leveraging machine learning (ML), professionals can quickly access critical data without manually sifting through thousands of pages.

#### How to Build and Train an AI Knowledge Base Chatbot
TL:DR

AI knowledge base chatbots combine LLMs with RAG to answer questions using company-approved data.
They retrieve relevant information from help centers, PDFs, wikis, tickets, databases, and product docs before generating a response.
The core architecture includes data ingestion, chunking, embeddings, a vector database, a retriever, and an LLM.
RAG is great for fine-tuning because it keeps answers connected to current business knowledge.
The strongest use cases include customer support, internal knowledge search, HR assistance, developer documentation, and workflow automation.

Introduction
Knowledge is of actual business value when both workers and customers can access and use it in a time-effective manner. Yet in many companies, critical answers sit across help centers, PDFs, tickets, internal wikis, databases, and product documentation. This is one reason AI adoption keeps accelerating. 
McKinsey reported in 2025 that 88% of organizations used AI in at least one business function, up from 72% in early 2024. At the same time, Salesforce’s 2025 State of Service report says service teams estimate AI already handles 30% of cases, with 50% expected by 2027.
This guide explains how to build an AI knowledge base chatbot reinforced with Retrieval-Augmented Generation (RAG). It also indicates what tools and technologies to use and what the common challenges are, as well as brings up real-world success stories.
What is an AI knowledge base chatbot?
An AI knowledge base chatbot is a conversational system that retrieves information from structured or unstructured data sources and generates context-aware answers using LLMs. 
By and large, AI knowledge chatbots are used to help users find reliable information across the following sources of information:

Help centers
Internal wikis
Product documentation
Databases
PDFs
Support tickets

Traditional chatbots usually depend on scripted flows, keyword matching, or predefined FAQ logic. This works for simple requests, but it becomes limited when users ask questions that require context from several documents.
A RAG chatbot addresses this limitation by combining retrieval with response generation. First, the system searches the connected knowledge base and selects the most relevant content. Then, the LLM uses that context to generate a direct answer based on approved company information.
Core capabilities usually include:

Retrieval: finding relevant information across connected data sources.
Reasoning: interpreting user intent and connecting related context.
Contextual responses: generating answers that match the user’s question and available evidence.
Source grounding: reducing unsupported answers by relying on retrieved content.

For enterprises, an AI knowledge base chatbot becomes a practical knowledge access layer. It can support customers, employees, developers, HR teams, and operations staff without forcing them to search across disconnected systems manually.
“The strategic value of an AI chatbot emerges when it’s already a part of an organization’s knowledge infrastructure, making information easier to access and govern.” — Volodymyr Mudryi, a data scientist and ML expert at Intelliarts. 
Why build a knowledge base chatbot instead of using traditional search?
A RAG chatbot is most useful when users need a direct answer grounded in company knowledge and supported by relevant information from approved sources.
A conversational AI knowledge base handles this through retrieval before generation. It searches approved sources first, such as help center articles, product documentation, internal policies, or support tickets. Then, the LLM uses that context to generate a focused answer. 
This approach helps teams solve the common knowledge access issues:

Poor search relevance. An AI chatbot retrieves content based on semantic meaning, not only exact keywords.
Lack of context awareness. A smart assistant connects the user’s question with relevant documents and business rules.
Manual information retrieval. AI reduces the need to open several files, tabs, or systems.
Inefficient support workflows. A RAG chatbot helps agents answer repetitive questions faster and more consistently.

Important note. Intelliarts’ experience shows that having both traditional manual access to the knowledge base and a RAG-enabled chatbot is even more beneficial than relying on only one method. 
Looking to leverage industry-leading AI expertise?We specialize in AI, ML, RAG, big data, and custom software solutions for distinct business needs.Request RAG development services
How does an AI knowledge base chatbot work?
An AI knowledge base chatbot works by retrieving relevant information from connected data sources and using that context to generate an answer with an LLM. In most enterprise use cases, this workflow is based on retrieval-augmented generation, which helps the system produce answers grounded in approved company knowledge.
The workflow usually includes these stages:

Data ingestion: the system collects content from sources such as product documentation, FAQs, internal wikis, support tickets, or databases.
Chunking and preprocessing: long documents are split into smaller text sections. The content is also cleaned, deduplicated, and prepared for more accurate retrieval.
Embedding generation: each text chunk is converted into embeddings, which represent the meaning of the content in numerical form.
Vector database storage: embeddings are stored in a vector database, where the system can search for semantically relevant content.
Query retrieval: when a user asks a question, the retriever finds the most relevant chunks based on meaning and context.
LLM response generation: the selected context is passed to the LLM, which generates a clear answer based on retrieved information.

See a simplified RAG architecture for an AI knowledge base chatbot split into a workflow from user input to RAG-driven AI output in the infographics below:

RAG vs fine-tuning: What’s the best approach?
For most AI knowledge base chatbot projects, RAG is the better starting point because it connects the chatbot to current company knowledge without model retraining. Fine-tuning can help in specific cases, especially when the chatbot needs a controlled tone, format, or task behavior.
RAG works well when answers must come from help centers, policies, product documentation, internal wikis, or support tickets. LLM fine-tuning is more useful when the model needs to follow a specific response pattern, classify requests, or use domain-specific phrasing more consistently.

Looking for a technical consultation on RAG development? Don’t hesitate to reach out.
What tools and technologies do you need?
An AI knowledge base chatbot depends on how well its technical stack supports the RAG workflow for an LLM. The large language model may generate the final response, but answer quality also depends on retrieval, data preparation, access control, and integrations with real company systems. 
For a practical implementation, the stack usually includes the following four main layers:

 LLM for answer generation

Such LLMs provide responses as per the retrieved context based on users’ inquiries. API-based models are useful for faster deployment because teams do not need to manage model hosting, scaling, or infrastructure updates themselves.
Common LLM providers include:

OpenAI for strong general-purpose reasoning and fast implementation
Anthropic for long-context workflows and enterprise-grade response generation
Google Gemini for teams already building on Google Cloud
Azure OpenAI for companies that need Azure-native deployment and governance
Open-source models, such as Llama or Mistral, for private hosting, cost control, or deeper customization

Open-source LLMs for customer service can fit projects with stricter requirements for private hosting, data control, or domain-specific customization. However, they usually require more internal MLOps and infrastructure support.

 Vector database for retrieval

A RAG chatbot needs a vector database to find relevant content by meaning. This helps the chatbot match questions with document sections, even when users do not repeat the same terms used in the original source.
Common vector database options include:

Pinecone for managed vector search
Weaviate for semantic search and metadata support
FAISS for lightweight local search
Milvus for larger-scale vector search infrastructure
Qdrant for open-source deployment and flexible filtering

This layer matters because retrieval quality directly affects answer quality. Poor chunking, weak metadata, or irrelevant matches can lead to vague answers, even when the LLM itself is strong.
Explore automated data extraction in great detail on the example of ChatGPT AI in another blog post by Intelliarts. 

 Backend layer for orchestration

The backend connects the workflow. It handles ingestion, chunking, embeddings, retrieval, prompt assembly, authentication, logs, and feedback.
Teams often use:

LangChain for chaining retrieval, prompt engineering, tools, and LLM calls
LlamaIndex for document ingestion and document indexing, as well as RAG pipeline development
FastAPI for Python-based backend APIs
Node.js for web application logic and real-time interfaces
Custom services when the chatbot needs complex permissions, workflows, or integrations

This layer also controls business logic. For example, it can check user permissions before retrieval, apply metadata filters, route questions by topic, and store feedback for later improvements.

 Integrations for business value

The chatbot becomes useful when it connects to real knowledge sources, such as documentation, tickets, files, CRM records, databases, or internal systems. Without integrations, the chatbot may produce fluent answers but miss operational context.
Common integrations include:

Confluence and SharePoint for internal documentation
Google Drive and OneDrive for business files
Zendesk, Intercom, Freshdesk, and Jira Service Management for support data
Salesforce and HubSpot for CRM context
PostgreSQL, MySQL, MongoDB, and Snowflake for structured business data
Slack and Microsoft Teams for employee access
Internal APIs for product, billing, account, or workflow data

For enterprise chatbot implementation, integrations should also respect permissions. A chatbot connected to HR, finance, or customer records must retrieve only the information each user is allowed to access.
Interested in improving your AI chatbot’s accuracy by 40%?We describe how to drive efficiency with RAG & LLMs in our recent research.Read white paper
Step-by-step: How to build an AI knowledge base chatbot
Building a knowledge base chatbot should begin with a business problem and then move into architecture. Here’s a simplified step-by-step plan:
1. Define the use case
Good starting points include customer support automation, internal policy search, developer documentation, onboarding, or product troubleshooting.
Before development starts, define:

User group: Customers, support agents, employees, developers, or sales teams
Question types: Troubleshooting, policy lookup, product guidance, onboarding, or technical documentation
Source of truth: Help center articles, internal wikis, PDFs, tickets, databases, or APIs
Answer boundaries: Questions the chatbot can answer, refuse, or escalate
Success criteria: Faster resolution, fewer repetitive tickets, higher answer accuracy, or reduced manual search time

2. Prepare the data
As per the Garbage-in-Garbage-Out (GIGO) rule, a RAG chatbot depends on the quality of the knowledge it retrieves. 
Prepare the data by checking:

Content quality: Remove obsolete pages, duplicates, and unclear instructions
Document ownership: Assign owners for policies, product docs, and support articles
Metadata: Add product, version, region, department, role, source type, and last update date
Access rules: Define which users can retrieve HR, finance, customer, or internal product data
Update frequency: Decide whether sources should sync daily, weekly, or after every content change

For technical teams, this stage also includes chunking. Each document should be split into sections that are small enough for precise retrieval, but large enough to preserve a useful context window 
Learn about data preparation in Machine Learning in another one of our blog posts. 
3. Choose the architecture
For most knowledge base chatbot development projects, RAG is the best starting architecture because it lets the chatbot answer from the current sources. Your options here are the following:

Basic RAG: Best for clean FAQs, help center articles, policies, and product documentation.
Hybrid RAG: Best when exact terms matter, such as error codes, product names, SKUs, account types, legal terms, or API names. It combines semantic search with keyword search.
RAG with reranking: Best for large knowledge bases with many similar documents. The retriever finds possible matches, then a reranker sorts them before the LLM receives context.
Permission-aware RAG: Best for enterprise use cases. The system checks user permissions before retrieval, so restricted information does not enter the prompt.
RAG plus LLM fine-tuning: Best when retrieval already works well, but the chatbot needs a stricter answer format, tone, or domain-specific response pattern.

4. Build the pipeline
A practical first version of the pipeline should use a limited document set and a real question set from support tickets, search logs, helpdesk requests, or internal chats.
A good data pipeline for an AI chatbot typically includes:

Ingestion: Pulls content from approved documents, databases, and apps
Preprocessing: Cleans text, removes duplicates, and applies metadata
Chunking: Splits content into retrievable sections
Embedding generation: Converts text chunks into vectors
Vector storage: Stores embeddings with metadata and source links
Retrieval: Finds relevant chunks for each user query
Reranking: Prioritizes the strongest matches when needed
Prompt assembly: Combines system instructions, user query, and retrieved context
LLM generation: Produces the final answer
Logging: Stores queries, retrieved chunks, answers, errors, and feedback

If the retrieved context is weak, the chatbot should say that it cannot answer from available sources and offer escalation.
5. Integrate the UI
The UI should fit the place where users already ask questions. For customers, this may be a help center widget, website chat, or in-product assistant. For employees, it may be Slack, Microsoft Teams, an internal portal, or a service desk interface.
A good chatbot UI should include:

Clear input field
Source links
Feedback buttons
Escalation path
Conversation history
Access-aware results

6. Test and optimize
Intelliarts’ experience with custom AI projects shows that post-production support and optimization heavily rely on concrete metrics. In the case of AI knowledge chatbot development, the usual technical metrics are the following:

Correct source found rate: How often the chatbot retrieves the document that contains the right answer
Top-result accuracy: How often the first retrieved chunk is useful
Answer groundedness: Whether the final answer is supported by the retrieved context
Unsupported answer rate: How often the chatbot answers without enough evidence
Response latency: How long users wait for an answer
Cost per answer: Model, retrieval, storage, and infrastructure cost per interaction

At the same time, it’s also important to track business metrics such as:

Ticket deflection rate: How many support requests the chatbot resolves without human help
Escalation rate: How often users still need an agent
User satisfaction: Whether users rate answers as helpful
Repeat question rate: Whether users ask the same question again
Average handling time reduction: How much time support or operations teams save
Resolution rate: How often users solve their issue after using the chatbot

Technical optimization usually happens in the retrieval layer. At the same time, meeting business metrics requires continuous maintenance and regular updates to the solution over time.

How do you train and improve a knowledge base chatbot?
Reflecting on the need to test and improve a chatbot, as mentioned above, you may start wondering how to do so. An expert AI development team like Intelliarts uses a combination of strategies in the production and post-production steps to obtain measurable improvements in technical performance as well as in business metrics. 
See the infographic on the general process of training and improving a knowledge base AI chatbot below:

The biggest gains in accuracy, which is the biggest quality metric for an AI chatbot, usually come from stronger retrieval, better data sources, and regular review of user queries. By and large, development measures from the previous section, paired with subsequent training and improvement procedures, all work towards the same business goals. 
What are the common challenges and how to solve them
An AI knowledge base chatbot can fail in production when retrieval, data quality, latency, or access control are overlooked. See the common business and technical challenges as well as mitigation strategies in the image below:

Real-world use cases of AI knowledge base chatbots
An AI knowledge base chatbot can support different workflows depending on which sources it retrieves from, who uses it, and what output the business expects. The strongest use cases are usually tied to repeated questions, scattered documentation, or slow manual search.
1. Expert assessment and audit intelligence assistants
An expert assessment assistant helps consulting, audit, and advisory teams turn raw expert feedback into structured business insights. Technically, it works by processing expert comments, scores, categories, and subcategories through LLM-based workflows. 

Output: Assessment summaries, scored category overviews, benchmark comparisons, management-ready insights, source-based references, and quality metadata.
Users: Consultants, auditors, project assurance teams, risk specialists, operations leaders, and company management.
Software example: Intelliarts built an AI-powered agent for Valency, a project assurance company in the construction industry. 
The project also shows what this type of chatbot or AI agent needs in production. Intelliarts used structured prompts, iterative prompt optimization, SME review, quality checks with Pydantic and LangChain, and model evaluation across Claude, GPT, Llama, and AWS NOVA.
Looking for a trusted partner to handle the development of your AI chatbot?Talk to our AI experts
2. Customer support automation
A customer support chatbot retrieves answers from help center articles, FAQs, product documentation, and past tickets. Technically, it works as a RAG layer connected to support knowledge sources and escalation workflows.
Output: Direct answers, troubleshooting steps, source links, ticket summaries, and escalation suggestions.
Users: Customers, support agents, customer success teams.
Software examples: Intercom Fin is built for customer service and uses knowledge sources to power AI agents and self-serve support.
3. Internal knowledge assistants
An internal assistant retrieves information from company wikis, shared drives, policies, project docs, and collaboration tools. It helps employees ask questions in natural language instead of searching across multiple systems.
Output: Policy answers, project summaries, document references, internal process guidance.
Users: Employees, operations teams, managers, new hires.
Software examples: Glean connects to enterprise data and helps teams find answers, analyze data, and create content using company knowledge.
4. Developer documentation bots
A developer documentation bot retrieves from API docs, SDK guides, codebase context, issue trackers, and technical runbooks. It helps developers find implementation details without leaving their workflow.
Output: Code explanations, setup steps, API usage guidance, troubleshooting instructions.
Users: Software engineers, DevOps teams, solution architects, technical support.
Software examples: GitHub Copilot uses the editor and workspace context window to generate suggestions, while Copilot in GitHub Support provides configuration and troubleshooting steps based on official GitHub documentation.
5. HR assistants
An HR assistant retrieves from employee handbooks, benefits policies, payroll guidance, onboarding materials, and internal HR case data. Access control is critical because answers may depend on role, location, contract type, or department.
Output: HR policy answers, onboarding guidance, benefits explanations, case routing.
Users: Employees, HR teams, people operations, managers.
Software examples: Workday Help supports employee answers to HR questions, while Workday has described an AI-powered search that gives personalized HR and finance answers based on role, location, and needs.
“An interesting notion is that for enterprise teams, source grounding is perceived as a trust mechanism. Users need to understand where an answer comes from, and an AI knowledge base solution aids them vastly, cutting uncertainty and minimizing support tickets.” — Alexander Barinov, a Managing Partner at Intelliarts.  
Why choose Intelliarts for AI chatbot development?

Here at Intelliarts, we approach AI development as an all-in-one product engineering task. We strive to make architecture, data quality, integrations, and other components of an AI knowledge base chatbot building process work together to deliver measurable, desirable business outcomes. 
In our experience, a great product is not necessarily a technological wonder or an unnecessarily complicated piece of code. Bringing actual value to customers and their clients is what makes an AI solution worth the investment. 
Our team can provide assistance to businesses looking for end-to-end development as well as companies with an in-house team across the full development path. From RAG architecture and LLM selection to building data pipelines, model training, and enterprise integration, we can cover it all and more. 
Our ultimate goal is to contribute to your success through our RAG development, large language model development, and other services. 
Intelliarts’ delivery approach is shaped by long-term engineering ownership:

15 years is our longest partnership with the same customer
4+ years is the average partnership duration on one project
90% of customers return to us with new projects
80+ successful large projects have been completed

Should you be interested in exploring the success stories of our customers, don’t hesitate to visit our portfolio page. 
Final take
An AI knowledge base chatbot is useful when it connects real business questions with reliable company knowledge. RAG helps make this possible by retrieving relevant context before the LLM generates an answer. Still, the quality of the final system depends on data preparation, architecture choices, integrations, UI design, security, testing, and continuous improvement. 
When these parts are handled properly, the chatbot can support customer service, internal operations, expert assessment, HR, and developer workflows with faster access to validated answers.
The Intelliarts team has been on the market for more than 26 years, providing software with AI agents, AI chatbots, and many more solutions for businesses from different domains. Our senior staff engineers are ready, willing, and able to consult you on your intended AI knowledge base chatbot. They will provide you with any assistance needed to roll out your best project successfully. Contact us.

#### How to Choose an Agritech Software Development Company: 10 Questions to Ask Before You Sign
Agritech buyers are under pressure to make software decisions more carefully. The OECD-FAO Agricultural Outlook 2025-2034 projects global agricultural and fish production to grow by 14% by 2034, mainly through productivity gains. Meanwhile, the World Bank’s 2025 report on AI in agrifood systems maps 60 AI use cases across the agrifood value chain.
Still, the usage of additional technology software doesn’t automatically translate into better farming outcomes like extra yield. Based on Intelliarts’ expertise with agriculture businesses, software has to account for field data, hardware constraints, weak connectivity, and seasonal validation for a solid chance of having a good ROI. 
That is why choosing an agritech software development partner requires more than checking portfolios or rates. The questions below will help you clarify whether a vendor can handle the real operating conditions behind your product.
Why agritech software development is a different beast
An agritech product, even a digital one, gets tested under specific conditions in a field. It’s something that can hardly be replicated during QA. 
A platform can look convincing during a vendor call, then fail when it has to process delayed sensor readings, weak connectivity, inconsistent machinery records, or crop-specific workflows.
The main risks usually appear in several areas:

Field usability: Farmers and agronomists may use the app under bright sun, with gloves, dirty hands, older devices, or limited time between field tasks.
Data reliability: Soil sensors, satellite images, weather feeds, machinery records, and manual notes can be incomplete, delayed, or inconsistent across locations.
Local agronomic context: Growth predictions may fail if the model does not account for soil type, drainage, field history, crop variety, or microclimate.
Seasonal validation: A yield model, irrigation feature, or disease-risk alert may need a planting or harvest cycle before the team can prove value.
Connectivity limits: Field teams often work with weak internet, so the product needs offline workflows, local caching, and delayed-sync logic.
Traceability pressure: Agrifood companies may need proof of input use, product origin, sustainability metrics, or supplier activity.

Vendor selection should therefore extend beyond software expertise. Practical experience and actual agritech and renewable energy cases are high-priority consideration factors. 
A strong agritech app development company covers, among others, these agritech-related concerns early:

Where does the data come from, and how often does it fail?
Which soil, crop, and climate factors affect the recommendation?
Can users read and operate the product under field conditions?
What happens when the device goes offline?
Which decisions must be supported during this season?
How will the team prove that the product improved planning, yield, waste, or compliance?

This mindset is what eventually helps clients avoid overbuilt dashboards, unreliable predictions, and a bunch of other errors.
What to look for in an agritech app development company
Before you compare prices or team size, check whether the vendor can handle the core delivery dimensions of an agritech product. Using our expertise with agriculture projects, the Intelliarts team drafted the approximate scope of dimensions in the infographic below to help:
[table id=12 /]
After you check the delivery dimensions, use the Experience, Expertise, Authoritativeness, and Trustworthiness (EEAT) framework to judge whether the vendor’s claims are credible:

Experience: Look for proof that the company has worked with real agritech use cases, not only adjacent software projects. 
Expertise: Check whether the team can explain the logic behind the solution. For agritech, this may include soil data quality, crop models, model drift, seasonal validation, sensor calibration, offline sync, and explainable ML outputs.
Authoritativeness: Look for evidence that the company shares knowledge beyond sales calls. This can include detailed case studies, technical articles, conference talks, research work, or open-source contributions related to agritech, data engineering, IoT, or ML.
Trustworthiness: Pay attention to how the vendor talks about risks. A credible partner will discuss missing data, weak connectivity, hardware limits, adoption barriers, and pilot constraints before promising a full roadmap.

Important note: EEAT is an additional proof layer when choosing a provider. It doesn’t uncover whether a provider has sufficient technical feasibility for your particular project. However, it indicates whether an agritech software solutions and consulting partner is actually reliable and trustworthy. 
Keeping all that in mind, let’s proceed with questions used to clarify one or another such aspect.
Looking for a strategy session with industry experts?The Intelliarts team will guide you through any agritech project’s complexities.Request technology consulting
10 questions to ask before you sign with an agritech software development partner
Before committing to any contractual agreements, you naturally have a session with a representative of a shortlisted agritech software company. That’s the best time to have any of your potential concerns or business needs addressed before a provider would quote you, and you make a final decision. 
Important note: Normally, you don’t force a representative to answer every and each question in real life. The right call would be to evaluate what aspects had been covered through the website material of prior conversations first. 
Then, just ask the remaining questions in text. Request a meeting with a dedicated specialist if necessary to address some technical matters. The goal is to achieve mutual understanding rather than to examine a representative using the rigid list of questions. 
Based on Intelliarts’ experience, here are the questions we are often asked by clients, as well as the questions we would ask ourselves if we were in their place.

Question 1 – What real agritech experience can you show beyond generic case studies?
When shortlisting your potential vendors, a strong dev portfolio is naturally one of the main criteria. But ask for the details behind specific cases that fit your project conditions, if applicable.
As detailed above, in agritech software development, a case study matters more if it shows how the vendor handled agricultural constraints: crop variability, adoption of software, seasonal timing, region-specific considerations, and so on.
Don’t hesitate to ask what data the team used, who worked with the product, and where it was tested. 
A strong answer should cover:

The agricultural use case, such as farm planning, irrigation, livestock monitoring, crop scouting, or agrifood logistics
The field context, including offline use, sunlight-readable screens, hardware limits, or on-farm rollout
The measurable result, such as better forecast accuracy, reduced waste, faster inspections, or improved planning

The best vendors will also explain what went wrong. Real agritech projects often face missing sensor data, weak adoption, delayed integrations, or ML models that need adjustment after the first season.
Red flags: Vague experience, agriculture stock images, AI claims without data context, and case studies with no seasonality, no pilot results, and no clear business metric. 
Question 2 – How deep is your domain knowledge in agriculture and food supply chains?
A good vendor should understand who uses the product and how decisions move across the agricultural value chain. Ask a chosen agritech software development company about growers, agronomists, farm managers, machine operators, input suppliers, processors, distributors, or downstream buyers.
Domain knowledge matters because agriculture has too many context-specific workflows. Row crops, horticulture, greenhouse operations, livestock, and agrifood logistics all need different product logic. A greenhouse platform may depend on climate control and controlled inputs. An open-field solution has to deal with weather volatility, soil variation, machinery routes, and limited connectivity.
A strong answer should mention:

Domain experts, agronomists, or data scientists with agritech experience involved in discovery
Clear understanding of crop types, farm workflows, supply chain roles, and regional requirements
Ability to explain how product features change for growers, cooperatives, input suppliers, or buyers

Red flags: The vendor uses generic software terms rather than those specific to software in agritech. For example, they don’t articulate the difference between greenhouse and open-field workflows, or B2B input planning and downstream buyer needs.
“We note that our customers’ concerns primarily revolve around business goals and adoptions. Naturally, we put a lot of effort into discussing these aspects, especially when communicating with stakeholders.” — Alexander Barinov, a managing partner at Intelliarts. 
Question 3 – How do you approach data, sensors, and integrations in agritech projects?
A lot of agritech products depend on data that is messy before it reaches the application layer. Weather APIs, satellite images, soil sensors, machinery records, gateways, telemetry, and manual notes often arrive in different formats and update at different speeds. Some data may be missing for hours, while other sources may need calibration before they become useful.
Ask the vendor how they design for this reality. A strong agritech app development company should explain how they validate incoming data, handle gaps, and manage delayed sync. They should also prevent weak data from producing misleading recommendations.
Look for experience with:

Hardware integrations, such as sensors, gateways, telemetry devices, and field equipment
External data sources, such as OEM APIs, satellite imagery, weather providers, and farm management systems
Data architecture that can handle noisy, sparse, delayed, or region-specific data

It may happen that soil moisture readings need calibration in the field. Alternatively, satellite data may be blocked by cloud cover. Machinery data may arrive with inconsistent timestamps. A reliable partner should explain how the system behaves when data is incomplete, not only when every source works.
Red flags: Treating sensors as input sources only, skipping data quality checks, or ignoring offline use. Having no strategy for missing readings, delayed syncing, or unreliable field connectivity.
Explore satellite remote sensing and predictive analytics in agriculture in another one of our articles for additional insights.
Question 4 – What is your track record with agritech software solutions and consulting, not just coding?
In our experience, many agritech businesses start with an actual business problem or an objective without a clear specification at this stage. For example, they might be looking for ways to reduce water waste, improve yield forecasting, automate field reporting, or enforce traceability across suppliers through software. 
This is where agritech software development and consulting services become even more valuable. The team of a selected partner should test assumptions, review data availability, assess ML feasibility, and define what a useful pilot should prove. Without that step, the project may move fast, but build features that cannot be supported by real field data.
A strong answer should include:

Workshop-based discovery or product discovery sprints
Data audits, ML feasibility checks, or architecture reviews
PoC and pilot planning with clear success metrics
Examples where the vendor refined the scope after data review or early field feedback

For example, you, as an agriculture organizaiton, may want a full yield prediction platform, but the available historical data may only support a narrower planning tool at first. A good partner should say that early, explain the trade-off, and propose a staged roadmap.
Red flags: Fixed long-term scope before discovery, or no willingness to challenge assumptions. In agritech, weak early assumptions can cost more than delayed development because the next validation window may be months away.
Question 5 – How do you handle seasonality, field conditions, and offline use in product design?
Agritech products have to match the agricultural calendar. A feature released after planting, spraying, irrigation planning, or harvest may miss the moment when it can create value. Because of this, release planning should follow seasonal priorities, not only sprint capacity.
Field conditions also shape product design. A worker may use the app under direct sunlight, with gloves, dirty hands, a poor signal, or limited time between tasks. Dense dashboards, long forms, and always-online workflows can reduce adoption even when the backend works well. These concerns naturally should be discussed with a selected agritech app development company and addressed. 
A strong vendor should explain how they design for:

Offline-first mobile workflows and local caching
Delayed sync with conflict handling and clear status indicators
Sunlight-readable screens, fast input, and simple field actions
Seasonal release planning tied to planting, growing, harvest, or inspection windows

Ask what happens when connectivity drops during a field inspection or when data sync happens several hours later. The answer should include product behavior, user notifications, and data recovery logic.
Red flags: Include proposals based on constant internet access, office-style UX, or no distinction between field workers and office users. A capable agritech software development partner should know that field adoption depends on small practical details.
“To me, field adoption has always been one of the most interestings matters about agritech projects. Demands are ranging from readability under direct sunlight and offline sync to embedded, custom analytics with AI-powered, real-time forecasting. The impact of software is fascinating.” — Yurii Bondarenko, Software Engineer at Intelliarts

Question 6 – How do you manage data quality, ML models, and explainability for agritech use cases?
Prediction features can support better decisions only when the data behind them is reliable. Yield forecasting, disease-risk alerts, irrigation recommendations, and crop growth models depend on soil type, weather patterns, crop variety, field history, and farming practices.
Ask how the vendor checks data before it reaches the model. A serious agritech software development partner should address matters like missing values, outliers, delayed updates, inconsistent sensor readings, and seasonal retraining.
A strong answer should cover:

Data versioning, so the team knows which datasets trained each model
Model monitoring after new seasonal data arrives
Retraining plans based on crop cycles, regions, and field feedback
Farmer-friendly explanations for recommendations and risk alerts
Validation against real field outcomes across more than one season

Explainability matters because farmers and agronomists need to trust the output before they act on it. A disease-risk alert should show signals like humidity, temperature, crop stage, or recent observations. A yield forecast should show assumptions or confidence levels, especially when soil quality may affect accuracy. The scope of agritech software solutions and consulting should cover that. 
Red flags: “AI-powered” claims without data-quality checks, no model drift monitoring, no retraining plan, and no explanation of how predictions will be validated in real field conditions. A credible vendor should explain how the model behaves when data changes between seasons.
Explore AI use cases in agriculture in great detail in another blog post by Intelliarts. 
Question 7 – What is your approach to security, compliance, and data ownership in agritech?
Farm and supply chain data can expose commercially sensitive information. It may reveal yield performance, field locations, input use, livestock health, supplier relationships, production volumes, or sustainability metrics. When the product serves cooperatives, processors, or marketplaces, access control becomes even more important.
Ask who owns the data, who can access it, and how the system separates information between farms, partners, and tenants. A reliable vendor should answer this before development starts, especially when several value-chain actors use the same platform.
A strong answer should cover:

Data ownership and usage clauses in the project scope
Role-based access for growers, agronomists, managers, suppliers, and buyers
Multi-tenant data separation for cooperatives, platforms, or marketplace models
Audit logs for critical actions and data changes
Data residency, retention, and regional privacy requirements

Based on Intelliarts’ experience working with agricultural organizations, the scope of compliance is entirely dependent on the actual use case. Lots of AI usages in agriculture are still considered low or medium-risk if not involved with production processes. However, a traceability platform may still need reliable records for product origin and supplier activity. A sustainability reporting tool may need documented input, use, resource consumption, or emissions-related data.
Red flags: Vague answers related to cybersecurity options, no access model for different agricultural roles, no audit logs, no data residency discussion, and unclear data ownership. A credible agritech app development company should treat data rights as a product requirement from discovery.
Question 8 – How Will We Collaborate Day‑to‑Day (Team Setup, Communication, On‑Farm Presence)
One thing we’ve consistently observed is that agritech projects usually involve more stakeholders than a standard software build. One product may need input from growers, agronomists, farm managers, data scientists, hardware partners, operations teams, and executives. Without a clear collaboration model, field context gets lost before it reaches the backlog.
Ask who will be involved from the vendor side. The answer should go beyond the project manager and developers. For data-heavy or ML-based products, you may need a data engineer, ML engineer, QA specialist, solution architect, and someone who can translate domain feedback into product decisions.
A strong answer should cover:

Team setup with clear roles, such as PM, tech lead, domain expert, data engineer, ML engineer, and QA
Demo cadence tied to product decisions and field feedback
Communication channels for data issues, integration blockers, and user feedback
Discovery sessions with real users, such as agronomists, operators, or farm managers
Willingness to visit farms, processing sites, or pilot locations when needed

Collaboration with an agritech software solutions and consulting partner should also take into account seasonal aspects related to agriculture. During planting, harvest, or inspections, users may have limited time for long workshops. Here at Intelliarts, we firmly believe that a good partner adapts feedback loops to the agricultural calendar.
Red flags: Purely ticket-driven work, no domain expert on the vendor side, no direct access to field users, and demos that show screens without discussing real operating feedback. 
Question 9 – How do you estimate, price, and de-risk agritech software development projects?
Agritech projects often start with unknowns. The client may not know whether the data is complete enough, whether farmers will adopt the workflow, or whether hardware integrations will behave consistently in the field. Because of this, detailed long-term fixed quotes before discovery should raise concern.
A safer approach breaks the project into stages. Discovery defines the business goal, users, data sources, and constraints. A PoC tests technical feasibility. A pilot checks whether the product works in real operations. After that, full-scale development becomes easier to estimate.
A strong answer should cover:

A phased plan, such as discovery, PoC, pilot, and scale
Clear outcomes and go/no-go criteria for each phase
Transparent pricing, such as time and materials with caps or fixed scope for limited phases
Risk assumptions around data availability, hardware, adoption, and seasonality
A process for scope changes after pilot feedback

For example, a vendor may recommend testing one crop, region, or farm group before expanding the platform. That can reduce the risk of building a large product around weak assumptions.
Red flags: Fixed multi-year scope before data review, no PoC or pilot phase, no risk assumptions, and no criteria for stopping or changing direction. A mature agritech software development partner should price the work around evidence.
Looking to discuss your agritech project?Here at Intelliarts, we have expertise and experience for any of your requests.Contact us
Question 10 – What Happens After Go‑Live: Support, Iteration, and Long‑Term Roadmap?
Go-live is where agritech products start producing the most useful evidence. Field users reveal slow workflows, integrations expose data gaps, and models show whether they can handle new regions, seasons, or crop conditions. The post-launch plan matters because the first release rarely captures every field constraint.
Ask what support looks like after deployment. A strong partner should explain how issues are monitored, how incidents are prioritized, and how field feedback enters the roadmap. For ML-based products, they should also explain how model performance will be reviewed as new data arrives.
A solid answer should cover:

Support SLAs and escalation paths for critical issues
Monitoring for integrations, data pipelines, sync failures, and model performance
Issue response during high-pressure seasonal windows
Backlog management based on field evidence and user feedback
Roadmap planning for new crops, regions, integrations, or reporting needs

Seasonality should remain part of the roadmap. A feature needed before harvest cannot wait for a generic quarterly planning cycle. A reporting module tied to compliance deadlines needs the same timing discipline.
Red flags: No clear support model, ad hoc change requests only, no monitoring plan, no roadmap process, and no seasonal planning after launch. A credible agritech software development and consulting partner should help the product improve across seasons, regions, and user groups.
Comparison table: types of agritech software development partners
When it comes to agritech software development, you have an array of partners to choose from. You should be aware that specialization and technical feasibility may vary. Some focus on app delivery, some on hardware, and some on ML. 
For products with field data, integrations, seasonal validation, and scaling needs. You can see a general comparison across the main types of agritech software development companies that are offering corresponding services in the table below:
[table id=13 /]
The first option, which is a consulting and all-in-one agritech software development company, fits best when the project needs both strategic validation and delivery. 
Other partners can work well for narrower tasks, but they usually cover one product layer: the app, hardware, model, or implementation. Intelliarts’ experts have repeatedly observed that for agritech products that need to work in field conditions and scale over time, a narrow focus can leave critical gaps.
How Intelliarts thinks about agritech software solutions and consulting

Here at Intelliarts, we approach agritech projects as a sequence of validation steps framed around specified business objectives. We discuss potential risks like incomplete data, unavailable model inputs, weak field adoption, etc., early, and only then proceed.

The starting point is a clear product hypothesis: the team defines what the solution should improve and how that improvement will be measured. 
The next step is a data audit: the team checks what data already exists, where it comes from, how often it updates, who owns it, and which gaps may affect the product logic. 

Combining agritech software development with data and ML consulting is our specialization. Here are some of the measures we take to ensure that no expensive rework will be needed:

Starting with one crop, region, or decision workflow before scaling the product
Testing whether satellite, weather, soil, or machinery data is reliable enough for recommendations
Designing fallback logic for missing sensor readings or delayed sync
Checking whether an ML model can explain its output in a way agronomists and farmers can trust
Planning MLOps early, so models can be monitored and retrained after new seasonal data arrives

As a strong agritech software development and consulting firm, Intelliarts leverages digital technology, hardware, adoption, and compliance aspects to help reach business goals. 
Some of our agritech success stories demonstrating our expertise and experience include:

Case example 1: Sensor inventory management for agritech operations

In a sensor inventory management project, Intelliarts helped an agritech company replace fragmented tracking across spreadsheets, QR codes, dashboards, and SQL queries. The system centralized sensor lifecycle management across 2,000 sensors and 500+ fields, improving field updates, QA/QC checks, and operational scalability.

Case example 2: Data automation for Indigo Ag’s Carbon program

In a data automation project for Indigo Ag, Intelliarts automated agricultural data collection for carbon credit workflows. Our team built third-party integrations and ETL/ELT pipelines to reduce manual entry from up to 8 hours to several minutes while improving data consistency.
Final take
Choosing an agritech software development partner comes down to proof, not promises. The right team should understand field conditions, data quality, hardware limits, seasonal validation, ML risks, and agricultural workflows. 
Use these questions to clarify how an agritech software solutions and consulting vendor works, where their expertise is strongest, and whether their approach fits your roadmap. For complex products, prioritize partners that combine consulting, engineering, data, and long-term product thinking. 
Agritech software development is one of the main niches for the Intelliarts team. We combine high-tech, as well as AI and ML practices, to provide the best solutions for sustainable agriculture for businesses worldwide. With 92% senior developers in-house and a 90% customer return rate, we are well-equipped to contribute to your best project with all our knowledge and experience. 
Ready to build custom agritech software?We develop scalable AI, IoT, GIS, and data platforms that solve real agricultural challenges.Schedule a consultation

#### How to Get Started With Smart Manufacturing
We are living through major historical events. The breakthrough we are currently witnessing is the Industrial Revolution 4.0 (4IR). Unlike the First Industrial Revolution, which was focused on the transition from hand production to manufacture, the task of 4IR is the optimization of automated processes and a holistic approach to business. 
Therefore, industry leaders strive to empower the physical processes with digital technologies such as IoT, IIoT, AI, machine learning, AR/VR, digital twins, etc. The goal is to build a streamlined process on a global map. Bosch, Hitachi, Hugo Boss, and other manufacturers focus far beyond the factory floor and transform their companies into agile enterprises to succeed in a data and resource-driven environment. This approach is called a smart factory. In this article, we will talk about how to build a smart factory and turn your production into intelligent and efficient work.

#### How to Implement an EV Fleet Management System for Public Transport
With a population of nearly 18 million, Shenzhen was the first major city in the world to operate a fully electric bus fleet. China leads the world in this space, with electric buses making up 30% of its total bus fleet. Europe is catching up fast. From January through September 2025, EU countries registered 6,444 new electric buses. That is 49 percent more than in the same months in 2024. One in five new buses sold in Europe today is electric.
But swapping diesel for electricity is only the beginning. Fleet operators are learning this the hard way. Optimizing charging schedules, tracking battery health, planning routes around range limitations – none of this is manageable without a dedicated fleet management system. This article walks through a step-by-step implementation of an EV fleet management system for public transit.
Electric buses are scaling fast. Make sure your fleet operations scale with them.Explore our service
What is an EV fleet management system?
An EV fleet management system is a software platform used to manage and optimize a fleet of electric vehicles. It helps companies or organizations monitor, control, and improve how their EVs are used, charged, and maintained.
In the context of public transport, the system addresses specific challenges. City buses operate on fixed routes with strict schedules. Depot charging takes place overnight when the entire fleet returns to base. The system must distribute the load on the electrical grid so that every bus is fully charged and ready to depart by morning. Charging optimization alone involves multiple layers which we’ve explored in detail in our overview of smart charging platform features.
Demand for such solutions is growing alongside fleet electrification. The EV fleet management market is valued at $9.1 billion in 2025 and is projected to reach $32 billion by 2030. This growth reflects broader shifts in fleet management technology trends for 2026, including AI-driven analytics, predictive maintenance, and smart energy orchestration.
Core components of the EV fleet management solution
A typical EV fleet management solution consists of several interconnected modules. At its core, the system continuously collects data from vehicles, charging stations, and energy infrastructure, and turns it into operational decisions.
Here are its main features:

Real-time vehicle tracking gives operators a complete picture of the fleet at any given moment. 
Charging management handles the distribution of charging across vehicles. 
Battery health monitoring tracks the condition of each vehicle’s battery. The system analyzes charging cycles, depth of discharge, and degradation rate.
Route optimization takes the specifics of electric transport into account when planning routes. 
Finally, route optimization and analytics tie everything together. Operators receive reports on fleet efficiency and the performance of individual vehicles. Read more on the role of analytics in EV energy management.

Steps to implement an EV fleet management system for public transport
Intelliarts is a software development team with over 25 years of experience building complex digital systems across various industries. We have 9+ year expertise in the domain in particular, with industry leaders like EV Connect, EVIQ, and Greenline in our portfolio. 
Below is a framework we recommend using when designing an EV fleet management system. Please note that this is not a one-size-fits-all approach but rather a template to guide your process. You should discuss all the details with your chosen vendor throughout the implementation period.
1. Assess the fleet and existing infrastructure
The first step is to build a complete picture of the fleet’s current state. This includes the number of vehicles, route network, average daily mileage, operating schedules, and the age and technical condition of the vehicles.
In practice, energy infrastructure often becomes the main constraint on the system. That is why it is essential to understand from the outset what loads are feasible today and what limits exist for future scaling. This data gives your technical vendor a solid starting point.
2. Map daily operations to system requirements
The next stage involves formalizing operational requirements. Here, it is important not to rely solely on management’s vision but to involve the people who work with the fleet every day since this is where key usage scenarios are defined.
This stage answers the fundamental question of how your fleet management system will actually be used in day-to-day operations.
3. Plan charging infrastructure together with the platform
Charging infrastructure planning runs in parallel with the digital side of the project. The two most common scenarios are depot charging and opportunity charging.
Depot charging suits fleets that return to base at the end of a shift and can charge overnight. Opportunity charging is used for intensive routes where vehicles need top-ups during the day at terminals or hubs.

For each scenario, you calculate the number of charging stations, their capacity, usage schedules, and impact on the electrical grid. The main goal at this stage is to find a balance between the fleet’s operational needs and actual infrastructure constraints.
4. Decide on off-the-shelf vs custom software
Off-the-shelf solutions can work for standard cases. However, public transport often involves legacy systems, specific business processes, and regulatory requirements that are difficult to address with out-of-the-box products.
In such cases, custom development offers greater control over architecture and allows the system to adapt to real processes rather than forcing processes to fit the software. Regardless of the approach, it is critical to incorporate open APIs, support for standards such as OCPP, and the ability to scale without redesigning the entire system. For this reason, custom solutions tend to be more cost-effective in the long run. This approach is informed by years of building software for renewable energy and e-mobility sectors.
Project spotlight

In our recent US-based project for an EV charging provider entering the fleet market, the Intelliarts team built a custom EV fleet management solution tailored to real operational workflows. The system enabled:

Optimized depot, on-route, and at-home charging
Energy consumption control and cost reduction
Automated compensation for at-home charging
Real-time analytics and battery charge predictions

By integrating telematics and smart energy management, the platform helped fleet operators improve efficiency while keeping charging flexible and scalable. Respectively, we showed how custom architecture can help adapt to complex fleet requirements better.
5. Run a pilot on a limited scope
Before a full-scale launch, the system is typically deployed in pilot mode. The best approach is to implement it on a single route, for example. This allows you to test key hypotheses, collect real data, identify unusual scenarios, and train staff to work with the new tools.
6. Phased rollout and continuous improvement
After a successful pilot, the system scales in phases. As the volume of data grows, opportunities for deeper analytics emerge. From a technical standpoint, this means continuous system evolution. You release regular updates, ensure adaptation to new scenarios, and maintain close collaboration between the engineering team and the client’s operations side.
Helping EV companies become fleet platformsExplore how we built an end-to-end EV fleet management system with smart energy optimization.Read the case study
Key challenges and how to address them
Fleet electrification is not just a matter of technology. In practice, you may encounter a range of obstacles. Below we outline the most common ones and the approaches that typically work.

High upfront costs
Electric buses remain expensive. On average, they cost 1.5 to 2 times more than their diesel counterparts, with additional associated expenses on top of that. For municipal operators working with fixed budgets, this often feels like too risky a starting point.
However, if you look beyond the initial investment, the picture changes. E-buses have no fuel costs, fewer wearing components, and simpler maintenance requirements. Based on our observations, full payback typically occurs between the fifth and eighth year of operation.
It is also worth considering support programs. In the EU, the US, and other regions, governments often subsidize 20 to 60 percent of electric bus and infrastructure costs (for example, the federal EPA Clean School Bus Program in the United States).
Integration with legacy systems
Transport companies do not start from scratch. They already have fully established internal processes. Completely replacing the entire IT infrastructure for the sake of EVs sounds like an almost unrealistic scenario.
That is why EV fleet management software must fit into the existing environment. The practical approach involves open APIs and an integration layer that allows you to connect the new system without dismantling the old one. This is especially critical in public transportation fleet management systems where telematics, dispatch, and ticketing platforms must operate as one ecosystem.
At the same time, in more complex cases, middleware is used to synchronize data between legacy solutions and EV modules. The key principle here is simple: new software should not break processes that are already working.
A fleet management system only works when it truly reflects the day-to-day responsibilities of dispatchers and fits naturally into their workflows. Even the most advanced technology delivers no value if users don’t trust it or if it makes their work more complicated. 
– Ihor Rudnyk, E-Mobility Solution Expert
Range anxiety
For operators, this is a very real risk. A bus stopping mid-route can cause serious damage. The solution lies not in larger batteries but in more precise planning. An EV fleet management system works with historical route data, weather conditions, traffic patterns, and actual passenger loads.
In practice, this looks as follows: trips are scheduled with a buffer, and dispatchers receive alerts if projections approach critical levels.
Final take
Electric buses on their own do not solve any operational problems. Without a system that brings daily fleet operations into a unified logic, electric transport quickly becomes a source of constant compromises. A well-designed EV fleet management system for public transport turns electrification into a manageable process.
At Intelliarts, we bring to the table over 20 years of software engineering experience, deep expertise in e-mobility, and cross-functional teams that cover technology consulting through full-cycle development. We help companies design scalable, secure EV solutions, both from scratch and on top of existing platforms, depending on your needs. Our goal is to make sure that electrification delivers measurable business results for our partners, not extra complexity.

#### How to Reduce Operational Costs with Smart EV Charging Management
There was a time when the idea of “plugging in” your car sounded like a punchline. It used to be a futuristic experiment that surely wouldn’t survive in the real world. Even the worries seemed comical: What if the battery dies halfway? What then?
Well, jokes on us. Electric vehicles have moved from a curiosity to a daily necessity so seamlessly that it’s now hard to imagine the road without them. With global EV sales reaching around 14 million, another story unfolds just as quickly: the demand for charging points is rising far faster than the supply.
And that gap isn’t random. Installing chargers is expensive, technically demanding, and often slowed down by infrastructure limitations.
The Intelliarts team has developed EV charging software for the last 9 years, and we’re ready to share our knowledge. In this article, we’ll explore how your charging ecosystem can be optimized. How costs can be reduced, efficiency improved, and the path to widespread EV adoption made smoother for everyone.
Looking to bring EV charging costs under control?Build production-ready smart charging platforms with our expert engineering team.Explore our service
What EV costs are there to optimize?
But before exploring ways to reduce costs, it helps to understand what actually drives the price of an EV charging station. The overall cost typically consists of several key components:
1. Installation expenses
This covers both the cost of the charging hardware and the equipment required to set it up on-site. Depending on the location, the project may also involve upgrades to the electrical system (e.g., boosting power capacity). Labor, permitting, and construction requirements also affect final costs, but they are one-time investments. 
Hardware (and its maintenance, which we’ll cover later) remains the most significant single cost factor. Today’s charging stations generally fall into three categories:

Level 1 (AC 120V): Slow charging via standard household outlets
Level 2 (AC 240V): Faster charging commonly used in offices and public locations
Level 3 (DC fast charging): High-speed charging typically installed at fleet depots, highways, or dedicated hubs

For now, Level 1 and Level 2 chargers remain the most common. Why? According to McKinsey, around 48% of current EV owners globally already have access to private parking with a suitable power outlet. Another 35% say home charging would be possible with minor adjustments, while only 17% report that home charging isn’t an option at all.
Although if we look at potential EV owners, who will ultimately dictate market trends, the situation is very different. As electric vehicle adoption grows, public charging will play an increasingly critical role, as many future EV owners will lack sufficient space or electrical capacity at home to charge their vehicles. According to survey results, 42% of respondents who do not currently own an EV expect that home charging will not be available to them.
Also, McKinsey predicts “advanced battery technology specifications pairing long driving range with superfast recharging times, sometimes enabled by 800-volt system architecture. This means that technology is expected to advance to enable longer driving times and faster charging. In the near future, we may be able to charge our EVs as quickly as we refuel a tank.

However, before that time comes, it still makes a lot of sense to invest in charging infrastructure, as demand is far from being met. 
2. Maintenance requirements
To keep charging stations safe and reliable, routine maintenance is essential. This may involve updating software, fixing hardware, and ensuring the system runs smoothly for users. 

Hardware maintenance

Hardware failures typically cost a pretty penny. What can be done about it? This issue can be addressed with something called predictive maintenance. 
As opposed to reactive maintenance (in other words, fixing the machine that has already broken down), predictive maintenance will help you foresee that breakage and fix it before it even happens.

This approach relies on real-time data, sensors, and analytics to monitor equipment health and detect early signs of wear or malfunction. According to research data, predictive analytics and smart charging management systems can reduce operational costs by up to 35 %. By identifying anomalies before they escalate into failures, you can schedule maintenance at the right time, reduce unplanned downtime, extend hardware lifespan, and significantly cut repair and replacement costs.

Software maintenance

While hardware reliability is critical, it’s only part of the equation. The software running EV chargers plays an equally important role in their performance, security, and long-term stability. To keep EV chargers operating reliably, they need regular over-the-air (OTA) updates that deliver the latest features, performance improvements, and security fixes. Delivering them consistently requires strong in-house expertise, which can take years to build. If developing this capability isn’t your current priority, collaborating with a proven technology partner can be a faster and safer path forward.
A practical example of this approach is our work with EVIQ, where Intelliarts delivered the core components of an EV charging management platform in just 6 months, well ahead of industry norms. The solution now supports tens of thousands of active users, maintains high platform stability through continuous updates, and has earned 4.1 rating on Android and 4.8 rating on iOS. By handling OTA updates, monitoring, and long-term software evolution, we help operators keep charging networks reliable without building large in-house teams from scratch.
Read the full case study about EVIQ software development.
3. Energy expenses
Electricity pricing is a major driver of operational costs. High utility rates, peak-time charges, and demand fees can significantly increase the cost of running a charging site, especially in regions where energy is already expensive. However, these expenses can be reduced with the right charging approach, making EV ownership more affordable. How, you may ask? With smart EV charging management, which helps optimize how and when energy is used. 
For instance, charging can be scheduled during off-peak hours, when electricity is cheaper. Load balancing can ensure power limits aren’t exceeded when multiple vehicles or devices draw power simultaneously. Access to chargers can be restricted to authorized users only, and continuous monitoring of the charging process helps maintain safety, performance, and a smooth user experience.

Approaches to reduce costs with smart EV charging management
Let’s now review additional ways to optimize costs with smart EV charging management, along with real-world case studies shared by Intelliarts experts.
1. Smart power management
Electricity costs fluctuate during the day: power is typically cheapest late at night and early in the morning. The logic behind it is lower demand means lower cost. So, utility providers have introduced time-of-use (TOU) pricing to encourage people to shift their energy consumption to periods of lower demand. 
Smart power management leverages this pricing model by automatically scheduling charging sessions during off-peak hours, reducing electricity costs while easing strain on the grid. By aligning charging behavior with TOU tariffs, you can significantly lower energy expenses.
Case example: Power management software
Intelliarts experts have developed a power management solution for one of our clients. As a result, our client was able to achieve the following benefits:
The smart power management solution improved energy use, ensuring electricity was consumed at the right time and lowest cost. It helped charging operators avoid peak-demand spikes and gain better control over charging loads through a centralized platform and APIs.

By balancing power usage, site operators reduced electricity bills and avoided costly grid upgrades when adding more chargers. Better system monitoring extended infrastructure lifespan, reduced outages, and increased uptime. Built-in analytics gave operators clearer insight into energy usage, enabling smarter decisions. Overall, the solution supported more reliable service, lower costs, and improved sustainability.
Avoid peak-demand costs and scale charging - no grid upgrades required.Talk to expert
2. Load balancing
Electricity costs often rise when multiple EVs charge simultaneously or when charging coincides with other energy-intensive equipment, such as HVAC (Heating, Ventilation, and Air Conditioning) systems or industrial machinery. These simultaneous loads can push total consumption beyond contracted limits, triggering demand charges and higher utility fees. Load balancing addresses this by actively managing power distribution across all connected devices.
In practice, load balancing sets a maximum power threshold for a site and continuously adjusts charging output to stay within that limit. For example, if four EVs are plugged in at once, the system can reduce charging power per vehicle instead of allowing a sudden spike in demand. When one vehicle finishes charging or other equipment powers down, the available energy is redistributed automatically, increasing charging speeds where possible.

Load balancing software makes this possible by monitoring energy usage in real time and dynamically controlling each charger. It coordinates EV charging with other electrical loads, prevents peak spikes, and keeps total consumption within allowed limits. As a result, businesses avoid costly demand charges, reduce the risk of overloads, and optimize energy use without investing in expensive grid or infrastructure upgrades.
Smart charging is about managing grid capacity and reducing peak load. It also helps to align charging behavior with real-time electricity pricing. By shifting charging to off-peak hours and dynamically balancing load, operators can meaningfully cut costs and reduce stress on the energy system. 
– Ihor Rudnyk, eMobility Tech Specialist
3. Telematics for energy efficiency
Telematics plays a key role in improving charging efficiency by giving operators real-time visibility into how vehicles use power. Without this insight, EVs may charge more often than necessary, remain plugged in while fully charged, or follow inefficient routes that increase energy consumption and charging costs.
Telematics systems track battery state of charge, energy consumption patterns, driving behavior, and charging history. For example, operators can identify vehicles that consistently return with high battery levels and reduce unnecessary top-up charging, or spot inefficient driving and routing patterns that drain batteries faster than expected. Telematics data also supports smarter route planning, helping fleets complete trips with fewer charging sessions and lower overall energy demand.
Software makes this possible by continuously collecting and analyzing vehicle and charging data. AI-driven telematics platforms turn raw data into actionable insights, identify inefficiencies, recommend optimal charging and routing strategies, and support data-driven decisions that reduce energy waste, lower operating costs, and improve fleet performance.
Case example: Improving charging accuracy with telematics
Time for another case study, where telematics was one of the ways to address our client’s business pain. Our customer supports the setup, management, and optimization of EV charging stations. And they needed to improve their energy efficiency and charging accuracy. 

To do so, Intelliarts introduced telematics that enabled wireless collection of vehicle data, providing fleet operators with real-time insights into energy consumption and charging needs, essential for reducing waste and controlling costs.
In this case, our experts integrated Geotab to collect data such as battery state of charge, energy consumption, and driving behavior. This enabled more accurate predictions of charging demand and better alignment between vehicle usage and charging schedules. Since the fleet also included electric buses, the team expanded the setup by integrating NFI Connect, a telematics platform for public transit, to monitor bus locations and charging operations.
By combining telematics data across different vehicle types, operators gained a unified view of fleet performance. This resulted in smarter charging decisions, fewer unnecessary charging sessions, lower energy costs, and improved operational efficiency across the entire fleet.
4. Predictive maintenance
Predictive maintenance is one of the most effective ways to reduce the day-to-day operating costs of EV chargers. Instead of fixing equipment only after it breaks, this approach focuses on spotting problems early and dealing with them before they turn into failures.
By continuously monitoring charger health like temperature, power fluctuations, error logs, or communication issues, operators can see when a component is starting to wear out or behave unusually. This makes it possible to plan maintenance in advance, at a convenient time, rather than reacting to sudden breakdowns that cause downtime and frustrate drivers.
From an operational perspective, the benefits are very tangible. Fewer unexpected failures mean fewer emergency repairs, less downtime, and longer equipment lifespan. Maintenance teams spend less time traveling to sites unnecessarily, and chargers stay available more consistently. Over time, this translates into lower maintenance costs, better reliability, and a more predictable operating model for EV charging networks.
Case example: Reducing downtime with predictive maintenance
One of our clients, EV Connect, has partnered with us to do just that. They needed to reduce charger downtime and improve infrastructure reliability through predictive maintenance. By analyzing historical charger data and identifying abnormal charging patterns, the team uncovered early indicators of potential failures.
This work enabled EV Connect to better understand real-world charger behavior, improve data readiness for machine learning, and prepare a scalable predictive maintenance system. As a result, the company gained stronger control over charger performance, lower maintenance risks, and a solid foundation for more cost-efficient operations.
5. Remote monitoring and control
Managing charging stations on-site is time-consuming, costly, and difficult to scale. Remote monitoring solves this by giving operators real-time visibility into charger status, energy consumption, session activity, payments, and key performance indicators from a centralized dashboard.
With remote control capabilities, operators can detect issues such as offline chargers, failed sessions, or abnormal energy usage as soon as they occur and respond immediately. For example, many problems can be resolved remotely by restarting a charger, adjusting power limits, or updating configurations, eliminating the need for on-site maintenance visits.
As charging networks expand, implementing and maintaining smart charging capabilities becomes increasingly complex. Power management and all things software need to work together reliably and securely. For many businesses, this is where the right technology partner makes a real difference.
When choosing a vendor, it’s important to look beyond individual features and focus on long-term fit. A strong partner brings hands-on experience in EV charging software and infrastructure and understands the operational challenges of managing chargers at scale:

Security should be built in from the start, with proven practices such as secure authentication and encryption to protect remote updates and sensitive data.
Just as important is compatibility: the solution should support multiple charger models and communication protocols, allowing your network to grow without being locked into a single vendor.
Seamless integration with your existing charging management system is also key, as it reduces manual work and keeps day-to-day operations efficient.

Wrap-up
As EV adoption continues to grow, efficiently operating the charging infrastructure becomes just as important as expanding it. Energy costs, maintenance, and infrastructure constraints can quickly add up, making cost optimization an essential part of running a sustainable charging network.
Smart EV charging management brings predictability to these challenges. Using technology, operators can better align charging with real demand, reduce unnecessary energy costs, and maintain reliable operations as networks scale.
The Intelliarts case studies show how these approaches translate into measurable results: lower operating costs, improved uptime, and greater visibility across charging operations. For businesses investing in EV charging, a strong technology partnership can provide a solid foundation for long-term efficiency and growth.
Ready to optimize your EV charging operations and reduce costs? Contact us to learn how our solutions can help your business save and scale.

#### How We’ve Built an All-purpose Big Data Pipeline
It’s difficult to overestimate the importance of big data in today’s business environment. Companies around the globe use big data to improve customer service, optimize efficiency, and make marketing and sales more personalized and data-driven. 
To get value from big data, a company needs to know how to build scalable and efficient data pipelines. In this case study, we’re sharing the Intelliarts experience of creating a data pipeline from scratch, with all its ups and downs, petty nuances, and moments your company might not have foreseen before opting into a data project.
When you start with big data analytics, data science for business, or machine learning (ML), at least half of the effort goes to data collection, storage, processing, and delivery. To put it otherwise, a big data pipeline is what puts it all together and defines the long-term success of your data project. And that’s why it’s insightful to discover the hands-on experience of building data pipelines. Additionally, employing AI data extraction techniques can significantly streamline the process of gathering and analyzing vast amounts of information.
Read also: ML-based data extraction

#### How We’ve Won a Hack4Retail Hackathon for Data Scientists
1200 participants, 194 teams, 2 rounds, lots of brain-racking, and constant lack of sleep. This is a one-sentence description of how we took part in a Hack4Retail hackathon for data science experts organized by McKinsey and Fozzy Group – and we passed it with flying colors, scoring first in this hackathon.
Hack4Retail was a challenging but inspiring experience for our ArtPeople team. And since it ended for us with the joys of victory, we’ve decided to share this experience and tell about the solutions that our hackathon team came up with in this blog post.

#### Hydraulics Predictive Maintenance: The Fullest Guide
We continue our series of articles about predictive maintenance. In previous articles, we’ve discussed:
The difference between reactive maintenance, preventive maintenance, and predictive maintenance in part 1

How machine learning techniques can solve a maintenance problem in part 2
How deep learning can solve maintenance challenges for the organization in part 3

This time, we will talk about our own case study, in which we provided a solution for the preventive maintenance of hydraulic systems.

#### Implementing Object Detection in Business [Case Study]
Object counting is an important and challenging task in computer vision, which has lots of real-world applications. From product counting for conveyors and production lines to traffic flow monitoring, object counting aims at estimating the number of elements from a still image or video. As a result, business stakeholders get a chance to respond to events properly in real time. In the case study below, we describe our recent project when we helped our partner get more accurate results in counting objects and significantly contribute to its counting application.

#### Insurance Claims Processing with Machine Learning
Let us put forward a hypothetical situation that your insurance company should have already been to. A car accident happens to your customer, who registers an insurance claim and waits until the claim is processed. The experience seems nasty enough for the customer as it is: they don’t have any vehicles right now and need to repair or replace the car. Far worse, if they have to spend extra on medical care.
No wonder your customer may feel annoyed and disappointed by how slowly the insurer handles their claim. But what if you could reinvent your insurance business model by automating claims processing via ML?
Insurance claims processing automation with machine learning could bring lots of value to your organization. With less tedious paperwork and a reduced number of human-to-human interactions, you could deliver claims processing services faster to the customer and so improve your customer loyalty. Also, you can predict customer churn by using machine learning. 

#### Insurance Cold Calling Success Rate: Insurtech Case Study
Cold caller: “Hi Ben! I’m Alex, a sales representative from the X insurance agency. I’ve heard that you’re looking for a car insurance renewal, and I’d like to share with you a great offer we have. Do you have a couple of minutes to discuss this?”
What would you answer if you were Ben? The high chance is “no” if we trust the study conducted by Keller Research Center at Baylor University  that informs about 1% of cold call success rate statistics in insurance cold calling sales.
A common reason for all those nos is that many cold callers sound like spammers. However, an insurance company can improve its chances of making successful cold calls with the help of ML. Alike in the case study we describe below, ML provides the company with great insights into the relationship between spam calls and underperformance so the company could use this info to refine the strategy of cold calls. Therefore, we recommend that you take a closer look at our case of using machine learning in cold calls.

#### Insurance Fraud Detection With Machine Learning
Insurance fraud has been a challenge in the sector since… Always. Still, the problem has recently become more urgent due to the growth in global cybercrime and more sophisticated fraudulent schemes. The Covid-19 outbreak has also made a perfect storm for corporate fraud, placing an extra financial strain on businesses and forcing companies to go online.
Meanwhile, old-school fraud detection methods such as rule-based systems are not an option in insurance anymore. Companies need something more intelligent, and machine learning can become a great solution in this case. This article describes how insurers to do insurance fraud detection using machine learning to transform it into a powerful technological weapon and fight back against fraud more efficiently.

#### Intelliarts – One of The Most Reviewed Big Data Agency in Poland
Intelliarts was included in the list of the top 15 most reviewed Big Data companies in Poland according to The Manifest. The Manifest is a business resource platform that creates rankings of the top performers in various industries and technologies. These efforts work hand-in-hand with their awards program, which recognizes the teams that have positively impacted their customers.
Recently, Intelliarts opened an office in Poland to provide our big data services to even more companies from around the world.
Our company assists midsize and large organizations in using big data to become data-driven companies. Data scientists from Intelliarts help organizations deal with large volumes of real-time data to improve the cost-efficiency of their businesses and extract new insights.
Intelliarts has been operating on the market for more than 20 years already and has a proven track record of delivering high-quality software solutions to our customers. We were already acknowledged as one of the most reviewed Big Data companies in the US, and now we strive to deliver our services to more customers in the EU.

It’s a great honor for us to be among top big data companies in Poland. And we would like to express our sincere gratitude to everyone we were lucky to work with and to those companies we are working with right now. You helped us achieve this unique milestone in our company’s history. A special thank you is also for those of you who write us reviews on resources like Clutch. You show others how your trust in us and help us on our way to becoming a prominent big data analytics company in Poland.
By the way, here is feedback from one of our customers for whom we developed a big data solution:
“Intelliarts’ work is amazing. Thanks to their help, we’ve been able to process hundreds of terabytes of data per day. We’ve definitely seen an increase in our revenue since we started working with them.”
Jawad Laraqui, CEO @ DDMR
If you are looking for ways to improve your business processes and products with technologies like big data, machine learning, or artificial intelligence, we are happy to help you with developing a solution that solves your specific business case. Please contact us to discuss cooperation opportunities, we are always looking forward to partnering with innovative companies from around the globe.

#### Intelliarts among Top Big Data Companies by the Manifest
June 2026 — Intelliarts has been recognized by The Manifest as one of the top big data companies in Ukraine. The recognition highlights our expertise in building scalable data platforms and analytics solutions that help process complex data streams and make faster, data-driven decisions.
The Manifest is a B2B business platform that ranks technology service providers based on verified client reviews, market presence, technical expertise, and successful project delivery. Its industry reports help identify trusted partners with proven experience in solving complex engineering and business challenges.
Being included among the top big data companies reflects Intelliarts’ long-term focus on designing and implementing high-performance data ecosystems for companies operating in data-intensive industries.
Looking for a Technology Partner?We build scalable data platforms, AI-powered systems, and custom software solutions tailored to complex business environments.Explore our services
For more than 25 years, we have delivered software solutions that help businesses collect, process, analyze, and operationalize large volumes of data. Our software engineering teams build scalable cloud-native infrastructures, real-time data processing pipelines, AI-powered applications, and advanced analytics platforms tailored to complex enterprise environments. Today, data and AI engineering remain core areas of our expertise:

90% of our customers return with new projects.
Our teams support companies across e-mobility, agritech, insurance, digital marketing, and other data-driven industries.
We develop systems capable of processing millions of operational data points in real time.
We build end-to-end big data solutions and act as a one-stop vendor that covers everything from a proof of concept to design, implementation, and deployment.

This recognition also reflects our ability to deliver practical business outcomes through data-driven technology. We focus on building reliable systems that scale with business growth and create long-term value from data assets.
“Overall, we’re comfortable with Intelliarts. We know that whatever they work on will be of high value, which matters most.”
Frank Miller
President @Ryffine

Many thanks to our customers, partners, and team members whose trust and collaboration made this recognition possible. As businesses continue investing in AI and digital transformation, Intelliarts remains focused on helping you unlock the full value of your data.
About The Manifest
The Manifest is a business news and B2B ratings platform that publishes industry rankings, reports, and market research for technology service providers worldwide. Its ratings are based on verified client feedback, company expertise, service quality, and successful project delivery across industries.
About Intelliarts
Intelliarts is a technology consulting and software engineering partner specializing in full-cycle, data-driven, and AI-powered solutions. With more than 25 years of engineering experience, Intelliarts helps companies build scalable data infrastructures, intelligent applications, and custom software systems that solve complex operational challenges.

#### Intelliarts Among Top Software Dev Companies by TechReviewer
The highly regarded tech industry review platform and analytics hub, TechReviewer.co, has included Intelliarts in its list of the Top 100 Software Development Companies in 2023. This achievement proves Intelliarts as an industry leader and innovator within the competitive landscape of software engineering.
Intelliarts’ developers have been working with the client for over four years. Their team has played an integral part in releasing numerous high-quality product features on time. They are extremely effective and fully integrated into the development cycle, from ideation to delivery. – Director of Engineering @Hubspot
TechReviewer.co’s team of industry experts conducts an annual assessment of top software development agencies based on a range of factors, including company performance, customer feedback, market presence, and innovation. By making the top 100, Intelliarts joins a group of esteemed peers who are transforming industries and shaping the future of technology.
Earlier, Intelliarts also celebrated its inclusion into the list of the Top Custom Software Development Companies by Designrush, a leading B2B marketplace.

About TechReviewer.co
TechReviewer.co is an analytics hub that shares top IT services companies’ rankings and reviews. The reviewer conducts thorough research and publishes lists of the best software development companies in various business sectors.
About Intelliarts
Intelliarts is a trusted technology partner for businesses across the globe that promotes and helps with digital transformation and innovation initiatives. The company provides technology consulting and software engineering services and builds full-cycle data-driven and ML-powered solutions.
Additionally, Intelliarts’ expertise in data science and constructing efficient data pipelines ensures seamless data flow, empowering their clients to unlock the full potential of their data and drive informed decision-making through machine learning insights.

#### Intelliarts Joins the Open Charge Alliance
August 2025 — Intelliarts has joined the Open Charge Alliance (OCA), a global non-profit of 400+ members promoting open standards and driving innovation in the electric vehicle (EV) charging industry.
The OCA is best known for the development and adoption of the Open Charge Point Protocol (OCPP), an industry recognized standard enabling interoperability and seamless communication between EV charging stations, management systems, and service providers. Its members include companies across the globe from sectors like charging equipment manufacturers, software providers, charging network operators, and others.

At Intelliarts, we build software solutions that accelerate the transition to sustainable transportation. With extensive experience implementing OCPP in EV charging and fleet management projects, we see the Alliance as a natural step forward to deepen our contribution to the industry. By joining OCA, we reinforce our commitment to:

Supporting global interoperability standards that make sure our charging solutions remain scalable and future-ready
Collaborating with industry leaders to address the challenges of mass EV adoption
Contributing our expertise in software engineering and AI/ML to foster innovation in the e-mobility landscape

“Becoming part of the Open Charge Alliance is an important step for Intelliarts. It allows us to actively contribute to shaping the future of EV charging standards while bringing our engineering expertise and OCPP experience to a global community of innovators.”  — Ihor Rudnyk, E-Mobility Tech Specialist at Intelliarts
As we reach this milestone, we express gratitude to our customers, partners, and team members. Your trust and collaboration continue to shape Intelliarts into the company it is today.
About the Open Charge Alliance
The Open Charge Alliance (OCA) is a non-profit organization founded in 2014 whose mission is to foster the global development, adoption, and compliance of open communication protocols in EV charging, most notably the Open Charge Point Protocol (OCPP) and the Open Smart Charging Protocol (OSCP). Learn more at https://openchargealliance.org. 
About Intelliarts
Intelliarts is a trusted EV software development partner with 25 years of engineering experience and over 9 years in e-mobility. We work with leaders like Greenlane, EV Connect, and EVIQ to deliver EV charging management platforms, fleet and telematics solutions, and smart energy systems that meet global standards. With a 90% client return rate and 85+ large projects delivered, we accelerate the shift to sustainable, connected transportation.
Explore our e-mobility solutionsLearn more

#### Intelliarts Named a Top 100 Software Development Company
March 2025 — Intelliarts is proud to be recognized as one of the Top 100 Software Development Companies of 2025 by Techreviewer.co. This recognition highlights our commitment to delivering top-quality software solutions and driving digital transformation for businesses worldwide.
Every year, Techreviewer.co evaluates software engineering companies based on key factors such as technical expertise, project success rate, client satisfaction, and innovation. Being included in this prestigious list reflects the impact of our work across industries, including renewable energy, insurance, digital marketing, manufacturing, and agritech.
Another key factor behind this recognition is the success stories we’ve created with our customers. From enabling companies to process terabytes of data daily to developing next-gen electric vehicle fleet management solutions, our projects consistently demonstrate how technology can drive growth and efficiency. These achievements are not just about technical expertise — they stem from a deep understanding of our partners’ needs and a commitment to building solutions that align with their goals. 
This approach is already delivering results, with 90% of our customers returning to us with new projects.
Intelliarts genuinely cares about us and our product. There really is a sense of shared mission, success, and sacrifice within our teams.  
– Jawad Laraqui, CEO at DDMR
As we celebrate this milestone, we want to express our gratitude to everyone who has contributed to our success — our customers, partners, and team members. Your trust and collaboration have been pivotal in shaping Intelliarts into what it is today.
Looking for a Software Partner?We build tailored solutions to drive your success.Explore our services
About Techreviewer.co
Techreviewer.co is an analytics hub that shares top IT services companies’ rankings and reviews. The reviewer conducts thorough research and publishes lists of the best software development companies in various business sectors.
About Intelliarts
Intelliarts is a trusted technology partner, specializing in full-cycle, data-driven and ML-powered solutions. Our expertise spans technology consulting, software engineering, and AI/ML-powered solutions tailored to meet unique business needs.
 

#### Intelliarts Named Among Top AI Consulting Companies in 2026 by Techreviewer
April 2026 — We are proud to announce that Intelliarts has been included in Techreviewer’s 2026 ranking of top IT companies offering AI consulting. This recognition highlights our ability to turn AI strategies into scalable, production-ready solutions that deliver measurable business impact.
Each year, Techreviewer evaluates companies based on expertise, customer feedback, and market presence. This recognition reflects both the quality of our AI consulting and engineering services and the results we deliver, with 85+ large projects completed, 90% of returning rate, and long-term partnerships averaging over 4 years.

At Intelliarts, we focus on making AI practical. We help organizations define high-impact use cases, validate ideas through rapid PoCs delivered in 6–8 weeks, and scale solutions across the business. Our data-centric approach ensures that every AI initiative is built on a strong technical foundation and aligned with real business goals.
This approach is already delivering tangible outcomes. In one project, our predictive analytics solution processed 8TB of data and helped generate up to $5–7M in quarterly revenue. In others, we’ve reduced processing time from hours to minutes and achieved 90%+ model accuracy in production environments.
“Intelliarts’ work is amazing. Thanks to their help, we’ve been able to process hundreds of terabytes of data per day. We’ve definitely seen an increase in our revenue since we started working with them.” – Jawad Laraqui, CEO @DDMR
Our expertise covers a wide range of AI technologies, including natural language processing, computer vision, predictive analytics, and generative AI. These capabilities enable us to address diverse challenges, from automating repetitive tasks and improving customer experience to extracting insights from large datasets and solving complex business problems.
With 40% senior-level engineers and deep expertise across machine learning, data engineering, and system integration, we support the full AI lifecycle, from strategy and architecture design to deployment and optimization.
We see this inclusion as a reflection of our commitment to helping businesses adopt AI in a structured, scalable, and results-driven way.
Build Scalable AI Solutions with IntelliartsCheck our services
About Techreviewer
Techreviewer.co is an independent research and analytics platform that evaluates technology companies worldwide. It helps companies identify trusted partners for digital and AI-driven transformation
About Intelliarts
Intelliarts is a technology consulting and software engineering company specializing in data-driven and AI-powered solutions. We help organizations across industries turn complex data into actionable insights and scalable software systems.

#### Intelliarts Named as a Top Software Dev Company by SuperbCompanies
In January 2024, Intelliarts was recognized as one of the Leading Software Development Companies, according to SuperbCompanies. This award proves the commitment of the company to excellence and innovation in the dynamic field of software engineering.SuperbCompanies is an impartial research portal that publishes ratings of companies across software development, QA, design, and marketing. The 2024 rating was the result of thorough research where the SuperbCompanies team evaluated software engineering companies based on their experience, reputation, talent pool, scope of provided services, and others.
Intelliarts sincerely thanks SuperbCompanies for this esteemed recognition and remains steadfast in its commitment to raising software development standards and providing partners with exceptional solutions that drive their success and innovation.
The same month, Intelliarts was also honored as one of the top big data companies in Ukraine by Visual Objects.
About SuperbCompanies
SuperbCompanies is an independent market research and analytics company that hosts technology service providers. The platform helps business owners find trustworthy partners in various fields, from software development to consulting, testing, and design.
About Intelliarts
Intelliarts serves as a reliable technology partner that promotes digital transformation and innovation. The company offers technology consulting and software engineering services, building data-driven and ML-powered solutions throughout the entire development cycle. Their big data consulting services enable businesses to harness the power of vast data sets, providing valuable insights to drive informed decision-making and optimize operations.

#### Intelliarts Recognized Among the Top AI Integration Companies in 2026 by Techreviewer
July 2026 – We are proud to announce that Intelliarts has been included in Techreviewer’s 2026 ranking of top AI integration companies. This recognition reflects our ability to help companies turn AI ideas into solutions that support their everyday operations.
Techreviewer is an independent research platform that evaluates software and technology companies based on verified client reviews, technical expertise, and projects across CRM, ERP, cloud, and legacy environments. Its annual rankings help businesses find trusted technology partners.
We credit this recognition to the strength of our engineering track record: 75+ projects completed, 85% of clients returning with new work, and partnerships averaging 5+ years.

At Intelliarts, AI integration means more than shipping a model. We connect AI to the systems our clients already run on, APIs, databases, cloud infrastructure, and operational workflows, so new capabilities scale without disrupting the business.
Our approach spans architecture design, data pipeline readiness, deployment, and ongoing monitoring across the full AI lifecycle.
This shows in our long-running work with DDMR, a marketplace processing hundreds of terabytes of clickstream data daily. Our team built large-scale data transformation and augmentation systems that use AI to turn that raw data into insights DDMR’s business runs on.
“We hired Intelliarts to help us design, develop, and implement large-scale data transformation and data augmentation systems… Intelliarts has been critical in helping us process those data sets and transform them into valuable insights for our business using AI.” – Jawad Laraqui, CEO, DDMR
With experienced AI engineers, DevOps specialists, and full-stack developers, we support clients from initial architecture through deployment, monitoring, and optimization — the DDMR partnership alone has run since 2015.
We believe AI brings real value when it becomes part of the systems and processes businesses use every day. That’s the kind of work we focus on at Intelliarts.
Partner with Intelliarts to Scale AI, ReliablyExplore our services
About Techreviewer
Techreviewer.co is an independent research and analytics platform that evaluates technology companies worldwide, helping businesses identify trusted partners for digital and AI-driven transformation.
About Intelliarts
Intelliarts is a technology consulting and software engineering company. We build data-driven and AI-powered solutions. We help organizations turn data into insights, integrated directly into the tools they already use.

#### Intelliarts Recognized among Top 100 Software Development Companies 2026
January 2026 — Intelliarts has been included in the list of Top 100 Software Development Companies for 2026 by Techreviewer.co. This recognition highlights our focus on building quality, data-driven software solutions that deliver measurable business impact.

Every year, Techreviewer.co evaluates software development companies based on technical expertise, project outcomes, client feedback, and market presence. Being included in this year’s Top 100 reflects the results we’ve delivered for customers in emobility solutions, agriculture, digital marketing, insurance, and other data-intensive domains.
A key factor behind this recognition is the real-world value created through our projects. Our teams help organizations improve operational efficiency and automate complex workflows, creating AI agents, scalable data platforms, AI-powered solutions, and custom software for emobility use cases.
This approach is reinforced by long-term partnerships. 90% of our customers return with new projects, reflecting trust, continuity, and shared ownership of results.
“Working with Intelliarts felt like a true partnership. Despite the challenges we faced, the team stayed proactive and attentive to our needs. Their ability to respond quickly and manage expectations allowed us to move forward successfully and without involving administration.” — Panos Sakkos, Senior Engineering Manager at Panther
We’re grateful to our customers, partners, and team members whose collaboration and commitment made this recognition possible. As we move into 2026, we remain focused on helping businesses design smart digital solutions that support sustainable growth.
Looking for a Software Partner?We build tailored software solutions aligned with your business goals.Explore our services
About Techreviewer.co
Techreviewer.co is a research and analytics platform that evaluates and ranks software development companies worldwide. Its rankings are based on technical capabilities, project success, client reviews, and industry expertise.
About Intelliarts
Intelliarts is a technology consulting and software engineering partner specializing in full-cycle, data-driven, and AI-powered solutions. With over 25 years of engineering experience, we help companies across emobility, agritech, insurance, and other industries solve complex business challenges through custom software.

#### Intelliarts Recognized as a Clutch Global Leader for 2023
On Nov 15, 2023, Intelliarts announced its recognition as a 2023 Global Award winner on Clutch, the leading global marketplace of B2B service providers. For the sixth consecutive year, Clutch honors its top B2B companies with this award taking into account their industry expertise, ability to deliver, and the score calculated based on the client feedback from thousands of reviews published on the platform.
As a global provider of technology consulting and software engineering services, Intelliarts is proud to be recognized as a 2023 Fall Clutch Global Award winner. This award is a testament to the excellent client work we have delivered this year since recognized through the voice of our customers in their reviews on Clutch.
The most impressive thing about Intelliarts was their willingness to learn about our business. –
VP Product, Digital Insurance Agency
Earlier, Intelliarts also celebrated its inclusion into the list of the Top Software Development Companies in 2023.
About Clutch
Clutch is the leading global marketplace of B2B service providers. More than 1 million business leaders start at Clutch each month to read in-depth client interviews and discover trusted agency partners to meet their business needs. The company has been honored for the past 6 consecutive years as an Inc. 5000 fastest-growing company and by the Washington Business Journal as one of the 50 fastest-growing private companies in the DC metro area for 2023.
About Intelliarts
Intelliarts is a trusted technology partner for businesses across the globe that promotes and helps with digital transformation and innovation initiatives. The company provides technology consulting and software engineering services and builds full-cycle data-driven and ML-powered solutions.

#### Intelliarts to Attend ONS 2024
August, 2024 — Intelliarts, a trusted partner in technology consulting and software engineering services, is thrilled to announce its participation in the Offshore Northern Seas (ONS) 2024. Held in August 26-29 in Stavanger, Norway, the ONS Conference and Exhibition is one of the world’s most anticipated events in the energy industry, focusing on the latest technologies and innovations to shape the future of energy.
With extensive experience in machine learning and software engineering, Intelliarts will present its subject-matter expertise in developing full-cycle data-driven software solutions, particularly in renewable energy. We welcome attendees to visit us at Scale-Ups area in Hall 4 at 9:30-13:30 on the 28th of August, 2024 to learn more about Intelliarts’ expertise in areas such as demand forecasting, energy consumption optimization, smart grid solutions, and others. (Our experts will be present all four days of the conference, and we’d be glad to book a meeting at your convenience.)
Let’s schedule a quick meeting with our expert to discuss your specific needs and explore tailored renewable energy solutions.

Highlights of Intelliarts’ offerings at ONS 2024 include:

EV charging management to gain remote control over EV charging points, manage their lifecycle, and improve access and performance of charging stations
EV fleet management to automate EV fleet monitoring, from standard tasks like maintenance, repairs, and cost tracking to route optimization and management of charging schedules and load needs
Predictive maintenance to identify and resolve potential issues before they cause downtimes and system failures
Energy demand forecasting to predict energy needs based on past data and tap into opportunities for cost saving and efficient energy consumption
Smart grid solutions to manage and mitigate energy consumption during peak demands and reduce electricity rates, as well as to enhance grid performance and reliability
Power management systems that help to monitor and report energy use in real time and identify areas for improvement to maximize energy efficiency and optimize costs
Dynamic pricing solutions to adapt to market changes and automatically adjust prices based on real-time supply

Intelliarts genuinely cares about us and our product. There really is a sense of shared mission, success, and sacrifice within our teams.
Jawad Laraqui
CEO, DDMR

Meet our experts
 Yuliia Zabudska, Renewable Energy Expert
With over 7 years of experience in renewable energy, Yuliia drives business growth by implementing high-quality technical solutions. Her expertise spans power management systems, EV charging, smart grid solutions, and EV fleet management.

 Oleksandr Stefanovskyi, Head of R&D
Having a strong background in data science and software engineering, Oleksandr is heading the Intelliarts cross-functional R&D team. He gravitates toward data-heavy projects and follows the principles of CRISP-DM methodology.
 

About Intelliarts
Intelliarts is a trusted partner in technology consulting and software engineering services, driving digital transformation for businesses across the EU and US. With deep expertise in AI, data engineering, and machine learning, Intelliarts specializes in building full-cycle data-driven software tailored to the unique needs of industries such as renewable energy, manufacturing, insurance, and digital marketing. As a leading big data consulting firm, Intelliarts ensures that businesses can harness the power of data to optimize operations, enhance decision-making, and stay competitive in today’s data-driven landscape.
 Intelliarts has also been recognized by the Software Outsourcing Journal and included in the Top Software Outsourcing Companies in Ukraine list.

Visit us at ONS 2024
Join Intelliarts at Scale-Ups area in Hall 4 to explore how our custom solutions can empower your business to achieve greater efficiency, sustainability, and growth. Our team of experts will be available to discuss your specific challenges and demonstrate how Intelliarts can help you unlock the full potential of renewable energy.
Contact information:
Email: ons@intelliarts.com

#### Intelliarts Wins with Clutch Fall Global Award 2024
November, 2024 –  Intelliarts, a leading provider of AI development and software engineering services, is proud to announce its recognition as a Clutch Fall Global Award 2024 winner. For the second consecutive year, Intelliarts has earned this accolade, this time in the categories of Machine Learning and BI & Big Data.

Clutch, a renowned ratings and reviews platform, evaluates thousands of B2B companies annually using a rigorous methodology that combines industry expertise, client feedback, and delivery capabilities. This award recognizes companies that deliver exceptional results, and Intelliarts’ success reaffirms its position as a trusted leader in ML, BI, and Big Data innovation.
Reflecting on Intelliarts’ commitment to building meaningful partnerships with customers, one reviewer remarked:
The most impressive thing about Intelliarts was their willingness to learn about our business.
– VP Product, Digital Insurance Agency
This collaborative approach is a cornerstone of Intelliarts’ culture, driving the creation of tailored solutions that address unique business challenges in industries like renewable energy, insurance, manufacturing, digital marketing, and agritech.
Last year, Intelliarts was also recognized among the Top Clutch Leaders for 2023, further solidifying its reputation as a reliable and innovative technology partner.
About Clutch
Clutch is the leading global marketplace of B2B service providers. More than 1 million business leaders start at Clutch each month to read in-depth client interviews and discover trusted agency partners to meet their business needs.
About Intelliarts
Intelliarts is a trusted technology partner for businesses across the globe that promotes and helps with digital transformation and innovation initiatives. The company builds full-cycle data-driven and ML-powered solutions.
 

#### Intelligent Forecasting with AI in Asset Management
Both forecasting and asset management have always been data-intensive tasks. Considering the amount of information generated within the business environment tends to rise, conventional methods become ineffective. That’s when innovative technologies may come in handy.
In this post, we will delve into the concept of artificial intelligence in asset management, and find out what effect the adoption of the technology of intelligent forecasting and related practices will have on forecasting approaches in place. We’ll also discover the benefits of adopting intelligent forecasting in business and review potential challenges and recommended strategies.
Thinking About Intelligent Forecasting?Our experts are ready to help with implementation.Contact Us
Intelligent forecasting and asset management: How they are connected
To begin with, let’s deepen into the concepts being discussed. Then, we will figure out how they boil down to a union of AI-driven technology and management methodology which can be applied to all businesses that deal with physical assets.

Intelligent forecasting. A predictive approach that leverages data analysis methods and AI and ML technologies to process huge amounts of historical data and predict failures, trends and patterns.
Asset management. The systematic process that helps to effectively acquire, store, operate, maintain, and sell assets to optimize returns.

Intelligent forecasting with artificial intelligence in asset management is conducted by applying advanced algorithms to vast financial data sets. Smart, AI-enabled technologies, such as SaaS reporting systems, accurately anticipate market changes and spot patterns. It helps to generate insights that are helpful in optimizing resource allocation, mitigating potential risks, driving informed strategic decisions, etc.
The combination of AI and asset management propels business processes into the future of strategic, proactive decision-making.
What effect adoption of intelligent forecasting in asset management will have on practice
Conventional forecasting, which is still quite common, is often characterized by the following negative attributes:

Extensive use of spreadsheets. They serve the purpose of collecting forecast data drawn from a limited number of internal sources.
A number of analysts. Human analysts and analytical programs provide diverse forecast assumptions, often influenced by various biases. Another con is that the approach is resource-intensive.
Difficult scenario modeling. Conventional processes make it overly time-consuming and data-intensive to change forecast assumptions when modeling real-life scenarios and putting them into use.

On the contrary, intelligent forecasting possesses such beneficial characteristics as:

Improved forecast accuracy and timeliness
Assumptions can be adjusted rapidly as new information is received
No human bias affects the forecast
A wide, diverse range of internal and external data sources are incorporated for a more holistic view of the business
Visibility into causes of forecast variances
Faster and more informed insights

Now, let’s get to know what concrete benefits the adoption of AI for asset management brings to businesses.
You may additionally discover a related matter — machine learning-based intelligent underwriting in our other article.
The business benefits of intelligent forecasting in asset management
Deriving from the previous section, intelligent forecasting applied in the area of asset management provides the following benefits:

Effective and transparent decision-making. Using AI for asset management provides data-driven insights, empowering C-level executives to make informed and fully transparent strategic decisions.
Optimized asset allocation. Predictive analysis ensures that resources are efficiently allocated, maximizing returns and portfolio diversity.
Risk mitigation. Early detection of potential market volatility aids in the proactive management of risks to bear unnecessary expenses.
Improved efficiency. Automation of complex reporting boosts the overall efficiency of management processes.
Cost reduction. Minimized errors and reduced waste due to accurate forecasting lead to significant cost savings.
Competitive advantage. Utilizing advanced forecasting tools allows for quick adaptation to market trends, giving a business a competitive edge.

Proper use of data generated through business operations is the key to success, and that’s exactly what artificial intelligence in asset management has to offer.
Key strategies for implementing intelligent forecasting and asset management
Let’s proceed with reviewing some of the practices that accompany the adoption of AI in asset management.

Advanced analytics and machine learning
Then, cutting-edge analytics and AI models are built and deployed. They let you extract data, get insights from data, and create precise forecasts. Intelligent forecasting AI models help uncover underlying patterns in historical data and produce insights enabling effective asset management.
Real-time monitoring and adaptability
Real-time analytics can be implemented to instantly monitor the performance of ML models on data. Resulting systems adapt quickly to changes in data, ensuring the forecasts are accurate and up to date at all times.
Establish data collection and integration
According to the GIGO rule, the quality of forecasting output heavily depends on the quality of data. It’s crucial to initiate a reliable collection of relevant data from varied sources and ensure its seamless integration. In this case, types of historical data that come of usefulness are:

Asset information
Financial data
Operational data
Risk data
Market data
Environmental data
Vendor/supplier Information

The formed database will be the foundation of the entire artificial intelligence forecasting.
You may be interested to know about data pipelines, components, types, and business use cases described in our other article.
Collaboration and cross-functional alignment
It often happens that the work of multiple departments depends on the results of intelligent forecasting with AI in asset management. Establishing a system that would share insights and exchange data between departments would foster high-level collaboration and cross-functional alignment.
Continuous evaluation and improvement
Naturally, all systems should undergo the process of constant modifications. It enables them to stay relevant in the high-paced technological environment. In that regard, establishing a regime of regular model performance evaluation for forecasting models and approaches is essential.
Intelliarts’ experience with intelligent forecasting
Here at Intelliarts, we specialize in ML technologies, including intelligent forecasting. Let’s take a look at two of the related cases, where we described how we successfully helped our customers achieve their business goals through the development.
Discover ML services provided by IntelliartsLearn more
List stacking and predictive model for real estate businesses
The challenge was to predict homeowners who are likely to put their property for sale. This is expected to provide the business with a competitive advantage by enabling them to focus their efforts on the most motivated sellers.

The Intelliarts team built a realtor model — an intelligent forecasting model that can predict the probability of selling a house. We used a provided database that was over 11 TB in size for training and testing purposes. The accuracy of the resulting solution was over 70%, which is significantly higher than the industry standard.
Another model we built was the investor one. It can predict where there could be a discount agreed on a particular property. As for the project outcomes revealed after the solution deployment, the customer experienced an increase in revenue growth of more than 600% in 12 months.
Predictive lead scoring
The challenge was to replace traditional lead scoring with a predictive lead scoring software. It should enable the insurance business to focus its efforts on potential customers who are more likely to purchase policies from the company.

The Intelliarts team ran a data collection process and built an ML scoring solution that can rank leads based on their probability of being converted to customers. The resulting intelligent forecasting model showed over 90% accuracy. In a few months after the deployment, the scoring prediction model started to cut off about 6% of all leads, resulting in a 1.5% to 2.5% increase in profit for the customer.
You may be interested to know additional details about predictive lead scoring and its benefits and use cases from our other article.
Challenges with the adoption of intelligent forecasting in asset management
Here are some of the needs business owners, managers, and executives should be aware of when considering the adoption of practices for AI & big data solutions in asset management in their digital environment:

Adequate amounts of relevant historical data to train the model on
Reliable access to data and a robust infrastructure
Subject matter experts (SME) who can share business insights and help establish internal processes
A supervisor who will validate intermediate project outcomes

Getting all the above mentioned in place before or at the beginning of partnering with an experienced ML service vendor will increase the chances of the project succeeding. All the technical challenges your service provider shall handle themselves.
Final take
The union of intelligent forecasting and asset management happens to result in a new, smart system for handling assets. Through the wise implementation of forecasting strategies and avoiding possible pitfalls successfully, a business can gain a huge boost to its operational efficiency. It brings better forecasting outcomes, resulting in higher revenue achieved through cost optimization.
Entrusting the integration of artificial intelligence in asset management systems to the industry-best experts would be the right call. With substantial experience in artificial intelligence forecasting, the Intelliarts team can offer you perfect-fit AI development services, raising the bar for your forecasting and asset management practices higher.

#### IoT Data Analytics in the Automotive Industry
How to leverage all the data collected by sensors? How to get insights from it? Will they help draw data-driven business decisions? One of the ways to get answers to these questions is to apply data analysis. This article will explain why IoT systems need data analytics in the automotive industry for proper operation and how critical decisions can be made based on this analysis.
IoT analytics is the application of data analysis tools and procedures to realize value from the huge volumes of data generated by connected Internet of Things devices. It is an innovative and challenging area. Sensor, mobile, and wireless technologies drive its evolution, but the actual value of the IoT lies in big data analytics rather than hardware modernities. Being software developers pioneering IoT analytics, we will explain how IoT data analytics in the automobile industry enhances transformative business opportunities.
Primarily due to data analytics the automotive industry has evolved rapidly over the past decade. Using automotive data analytics helps the sector grow in many ways by improving safety, reducing repair costs, extending uptime, and much more. It brings a ton of unique opportunities for experts in the field, helps to decide where to invest funds, recognize growth opportunities, predict incomes, and handle unusual situations before they convert into problems.

#### IoT in EVs: Driving E-Mobility Solutions
The niche of smart devices is booming, mostly due to their usefulness across industries, including electric vehicles. From a stream of big data and smart charging to predictive maintenance and fleet management, IoT in electric vehicles can offer it all for both businesses and end users and more. You probably are curious about how your EV company exactly can put IoT to good use, just like 48% of enterprises that utilize IoT rarely. Well, there are many ways.
In this post, you’ll discover the main benefits of utilizing the Internet of Things in the EV industry, discover key features of IoT in EVs, explore the specific tech stack, and review relevant challenges and practical solutions to them. Additionally, you’ll explore three success stories related to IoT applications in electric vehicles. 
Benefits of IoT integration in EVs
Increased digitalization on every level and across most industries is proven to be advantageous overall. This trend is evident in both the automotive sector and renewable energy initiatives, such as IoT for solar energy projects. As such, the main benefits of adopting IoT EVs and the real-life statistics backing them up are shown in the infographic below:

Main uses of IoT in the EV industry
Now, let’s explore some of the many IoT applications in electric vehicles: 
1. Real-time data acquisition. IoT enables the collection of real-time data from electric vehicles, including location, speed, battery status, and driving habits. This data helps improve performance and efficiency by making adjustments in real-time. 
Example: Nissan’s LeafConnect collects real-time data to monitor battery health, provide maintenance alerts, and optimize energy consumption.
2. Predictive maintenance. IoT devices monitor vehicle components such as batteries and motors to predict issues before they occur, minimizing breakdowns and reducing repair costs. 
Example: Bosch IoT Suite provides predictive maintenance solutions, detecting potential issues in electric vehicle systems and improving fleet performance​.
3. Smart charging and energy management. IoT for EV charging market optimizes energy consumption by monitoring and adjusting the charging process, balancing energy loads, and integrating renewable energy sources. 
Example: ChargePoint uses IoT to provide smart charging solutions that improve charging efficiency and reduce energy waste.
4. Fleet management. IoT enables real-time tracking, route optimization, and vehicle diagnostics, improving operational efficiency for companies managing EV fleets. 
Example: Geotab’s Fleet Management enhances fleet operations by offering real-time monitoring and route optimization.
5. Remote vehicle monitoring and control. IoT allows users or fleet managers to monitor and control electric vehicles remotely, adjusting charging schedules and managing vehicle performance from anywhere. Example: BYD’s FleetLink provides remote monitoring and control capabilities to improve the efficiency of electric fleet management.
As can be seen from the examples, the tendency towards the usage of IoT is the optimization of report usage, sustainability, and minimization of potential process disruption and downtime risks. For companies looking to reduce maintenance disruptions, EV maintenance solutions offer a comprehensive approach.
Applications of IoT extend far beyond the EV industry. As the IoT for EV industry and supply chain continues to evolve, it’s high time to explore the usage of IoT in supply chain management additionally. 
Tech stack for IoT integration in EVs
From a certain point of view, IoT in electric vehicle integration is another software component and, respectively, a layer of complexity added on top of an existing solution, such as a mobile app or platform for electric vehicle fleet management. 
Here’s a table with tools necessary to enable an IoT in electric vehicle industry integration categorized by categories:
 
Important note: The listed categories of tools are specific for IoT for electric vehicles integration and don’t include other necessary, for general development process, categories like UI/UX, DevOps, development frameworks and languages, etc.
Do you need advice on software instruments to use in your IoT projects for electric vehicles?? Reach out to Intelliarts for technology consulting services.
Challenges of IoT in the EV industry and how to solve them
Let’s review the major concerns of businesses aiming to adopt IoT electric vehicles and ways to address them:
#1 Integration with legacy systems
Many automotive companies still rely on older infrastructure and software, which may not be compatible with modern IoT platforms. This creates a significant barrier to adopting IoT technologies, as legacy systems often lack interoperability.
Solution: 

Implement middleware solutions to bridge the gap between legacy systems and IoT platforms.
Adopt a gradual migration to more adaptable cloud-based architectures.

#2 Data security and privacy concerns
IoT in EVs generates vast amounts of data, including location, usage, and personal driver information. Ensuring this data is secure and managing compliance with privacy laws (e.g., GDPR) is a major concern.
Solution:

Employ end-to-end encryption to protect data in transit and at rest.
Regularly update software and firmware to patch vulnerabilities.
Conduct security and technology audits.

Download an IT audit checklist
#3 High infrastructure costs
Setting up the necessary IoT infrastructure, including sensors, connectivity, and data processing units, can be costly. This is especially challenging for smaller companies or those entering the market.
Solution:

Leverage cloud-based IoT platforms to minimize upfront hardware costs.
Utilize a scalable infrastructure that can grow as demand increases.

#4 Connectivity and network reliability
Maintaining reliable connectivity, especially in remote areas, is critical for EV operations. Connectivity failures can lead to loss of real-time data or even vehicle control issues.
Solution:

Use hybrid connectivity solutions, such as combining 5G, Wi-Fi, and satellite communications to ensure continuous connectivity.
Implement edge computing measures.

#5 Data overload and management
IoT devices generate massive amounts of data, and managing, storing, and analyzing this data in a meaningful way can overwhelm existing infrastructure.
Solution:

Use cloud storage solutions with scalable storage capacity.
Adopt data compression techniques to manage bandwidth and storage requirements.

You may discover more about big data collection and management in another blog post by Intelliarts. 
#6 Standardization issues
The lack of industry-wide standards in the EV IoT sector can lead to compatibility issues between devices, software, and platforms. Standardization issues in IoT-enabled EVs must be addressed to ensure smooth communication between IoT devices, EVs, and third-party platforms.
Solution:

Follow industry standards toward data formats in the IoT sector.
Utilize open-source IoT frameworks and protocols to foster interoperability between devices.

As we address these concerns, it’s essential to also consider key challenges in implementing IoT-based fleet management for EVs. Some of these include real-time data collection from fleets, smooth integration of multiple IoT devices, and managing large-scale data.
Tackling IoT challenges in the EV industry demands specialized expertiseOur team excels in data science and developmentView service
Best practices for implementing IoT in the EV industry
The Intelliarts team, as experts in e-mobility solutions, have their way with IoT in electric vehicles. Some of the best practices we follow include:
#1 Securing data throughout the entire flow
We believe that secure communication protocols like MQTT and TLS encryption must be employed to safeguard data exchanged between EVs, charging stations, and cloud platforms. With EV charging data analytics, these security measures help ensure the integrity of real-time charging data, optimizing energy distribution and station performance. Additional measures we utilize include implementing Public Key Infrastructure (PKI) and secure boot processes to ensure system integrity.
#2 Utilizing scalable cloud infrastructure
Since in-house server infrastructure is nearly obsolete among recently emerged companies and startups, leveraging cloud platforms is key. When it comes to IoT for the electric vehicles industry, cloud-based IoT projects in EV cars help streamline data collection, storage, and analysis. These often have to be scaled as the number of devices in the network and the amount of reports for analysis grow. We recommend AWS IoT or Microsoft Azure as cloud service providers as they offer scalability to manage the growing data from EV fleets while supporting real-time data analytics and predictive maintenance.
#3 Prioritize interoperability and standardization
To ensure smooth integration of IoT systems across various EV platforms, companies should follow industry standards like ISO 15118 (for vehicle-to-grid communication) and adopt communication protocols (e.g., CoAP, HTTP). Interoperability allows seamless connection between IoT devices, EVs, and third-party platforms.
The major difficulty of established EV companies that are digitalizing their fleets and infrastructure is the interoperability of data and report formats across their and their partners’ vehicles. Challenges with communication protocol incompatibility are also not unheard of. That’s why unification and standardization are paramount.
 — Oleksandr Stefanovskyi, an ML team lead at Intelliarts.
Integrate predictive maintenance and analytics
Since big data supplied by IoT has to be utilized for decision-making, the right approach is to apply ML models (e.g., TensorFlow, PyTorch) compatible with IoT sensors. They can enable real-time monitoring and predictive maintenance needed to reduce downtime and improve EV performance. Advanced analytics can forecast component failures and optimize charging patterns for fleet management.
You may be additionally interested in exploring the usage of IoT in smart grid technology. 
Real-world examples using IoT in the EV industry
Here are two examples of companies implementing software and Internet of Things in electric vehicle industry:
Creating next-gen EV fleet management software
Challenge: The EV charging company aimed to expand into the fleet management market by building a comprehensive IoT-based electric vehicle fleet management solution. This software is needed to track the fleet’s charging status — whether at depots, on-route, or at-home — and help reduce fleet operating costs. The solution was also expected to provide data analytics and predictions for charging times and energy usage.
Solution: Intelliarts delivered a powerful IoT EV fleet management solution that integrates real-time vehicle monitoring, predictive energy management, and telematics. Such an IoT in automotive solution offers fleet managers, among others, the ability to view current charging statuses, battery levels, and predictions of energy needs. It also allows for at-home charging compensation, with telematics tracking energy consumption and calculating costs.
Outcomes: The IoT technology in EV charging stations allowed for better fleet operational efficiency and charging flexibility. Key results include reduced downtime, optimized energy consumption during peak hours, and comprehensive data analytics to improve decision-making processes.
Explore IoT-enabled EV fleet management case study
Smart power management system for energy consumption optimization
Challenge: In this project, we were requested to help ensure that end users obtain more value from using our customer’s charging site goals. The goal was to optimize energy consumption, mitigate the effects of climate change, and maximize the cost-efficiency of stations. 
Solution: Intelliarts delivered an IoT-based energy management system that allowed operators to schedule power limits, monitor energy use through data analytics, and manage costs by shifting charging loads to off-peak hours. The system included real-time energy monitoring, cost analysis features, and compliance with UK smart charging regulations.
Outcomes: The solution helped users optimize energy use, reduce costs by avoiding power demand surges, extend the lifetime of electrical infrastructure, and provide more reliable and eco-friendly service.
Explore smart power management case study
ABB’s IoT-based EV charging infrastructure
Challenge: ABB, a leader in power and automation technologies, needed to develop a scalable and efficient EV charging network. The goal was to create a smart charging infrastructure that could accommodate fluctuating energy demands and help operators optimize electricity usage.
Solution: ABB implemented an IoT-based EV charging system, which provides real-time monitoring of charging station performance, energy usage, and demand management. The system is integrated with data analytics tools that predict peak energy usage and allow operators to balance the grid by adjusting the charging loads dynamically. 
Outcomes: The IoT-based solution resulted in increased energy efficiency by dynamically managing charging loads during peak hours and reducing operational downtime through predictive maintenance. ABB’s charging infrastructure helped reduce grid strain and provided EV users with a more reliable and efficient charging experience.
The future of IoT in the EV industry
IoT is evolving at an incredibly rapid tempo. Here are some of the IoT in EV usage that potentially become widespread in the next 5 to 10 years: 

Expansion of IoT Applications. We may predict a rise of IoT in areas such as Vehicle-to-Everything (V2X) communication or enhancing vehicle diagnostics.
Data monetization: As IoT in EV data, which is by and large big data, becomes more valuable, companies will explore ways to monetize it. For example, owners of EVs can sell data generated by EVs to insurance companies, smart city developers, and energy providers who will use it for powering up data analytics and decision making.
Sustainability and energy management: IoT can support sustainability efforts by optimizing energy consumption in EVs. Smart charging is already a trend. At the same time, sustainability is a huge concern. So, the prevalence of energy management practices for IoT in EVs is almost inevitable. 
Integration of AI and ML: Smart technology is already a cornerstone of many software solutions. Regarding IoT devices in EVs, AI and ML can power predictive maintenance, battery optimization, and autonomous driving features. 

As you consider these future trends, you may be wondering: what is the progress in IoT-based EV vehicles? The integration of advanced technologies like IoT continues to push the boundaries of what’s possible. You can discover more about smart EV fleet management with AI and ML in another blog post by Intelliarts. 
Final take
The integration of IoT in the electric vehicle industry offers transformative benefits like enhanced fleet management, smart charging, and predictive maintenance. However, IoT apps for EVs may be exposed to challenges such as legacy system integration and data security must be addressed. With future trends pointing toward AI integration and energy optimization, the IoT’s role in EVs will only expand, opening humanity the way for smarter, more connected electric transportation systems.
The adoption of IoT in electric vehicles requires substantial software development expertise as well as niche-relevant experience. Should you need assistance, consider Intelliarts as your trusted tech partner. With more than 24 years of market presence, delivering software solutions to varying business needs, we are ready, willing, and able to contribute to your best project. 
 Looking for a trusted partner to integrate IoT into your EV business?Contact Us

#### IoT in Renewable Energy: Key Applications and Real-World Benefits
The integration of the Internet of Things (IoT) in the renewable energy sector shows significant growth. Specifically, the IoT in the renewable energy segment is expected to expand from $12.7 billion in 2023 to $78.3 billion by 2032, reflecting a CAGR of 21.9%. What insights can be drawn from that? The adoption of smart technology can provide a substantial business boost if approached wisely.
In this post, you’ll explore applications of AI and IoT in renewable energy, review IoT technologies backing up the implementation in the niche, and examine the benefits of utilizing IoT solutions. Besides, you’ll obtain an overview of common Internet of Things and renewable energy challenges and discover some real-life success stories. 
How IoT is used in the renewable energy sector

Without further ado, let’s dive right into the top 5 best ways to use IoT for the renewable energy sector:
#1 Asset monitoring
Using IoT for renewable energy assets is often focused on real-time data collection to monitor performance and environmental impact, as well as provide early failure detection. Regarding asset monitoring, sensors installed on wind turbines, solar panels, and hydro plants can track energy generation, detect overheating, and predict maintenance needs before failures occur. This application of IoT in renewable energy operations also improves asset longevity by preventing mechanical stress.
Business pain points addressed:

High maintenance costs due to manual inspections
Unexpected breakdowns leading to power disruptions
Limited visibility into equipment performance in harsh environments
Difficulty in predicting failures before they impact operations

#2 Remote tracking
Internet of Things for the renewable energy sector enables remote monitoring of energy production, transmission, and storage across multiple locations. This way, operators can access real-time analytics on turbine rotations, panel efficiency, or hydro flow rates from anywhere, reducing the need for on-site supervision. Drones equipped with renewable energy IoT solutions also scan solar farms and wind parks to detect performance issues.
Business pain points addressed:

Inefficiencies due to manual site visits in remote areas
Delays in detecting operational failures
High labor costs for round-the-clock monitoring
Poor scalability of monitoring systems for large-scale IoT energy solutions

#3 Handling distributed networks
Managing a decentralized energy infrastructure requires IoT strategies in renewable energy. IoT platforms integrate smart sensors, energy meters, and AI-driven analytics to balance power loads, manage grid fluctuations, and optimize energy flow across multiple renewable sources. This ensures stable grid integration without overloading storage systems.
Business pain points addressed:

Grid instability due to fluctuating energy supply from renewables
Challenges in distributing power across multiple locations
Energy waste due to unoptimized load balancing
Difficulty in integrating with legacy power grids

#4 Energy storage optimization
One of the biggest challenges in developing IoT solutions for renewable energy is ensuring efficient storage and utilization. Smart batteries powered by IoT energy solutions monitor charge cycles, prevent overloading, and use predictive analytics to store surplus energy for high-demand periods. IoT sensors also identify underperforming storage units before they fail.
Business pain points addressed:

Energy loss due to inefficient storage
Shortened battery lifespan from improper charge management
Inability to store surplus energy for peak demand
Lack of real-time data on battery health and efficiency

Smarter Software for Renewable EnergyIntelliarts helps businesses grow with advanced IoT energy solutions.Learn more
#5 Grid integration and smart forecasting
The future of IoT in renewable energy depends on smart forecasting powered by real-time weather data, historical energy consumption trends, and AI-driven demand predictions. These are all core elements of modern smart grid software development services designed to improve system reliability and resilience. IoT in renewable energy operations ensures better grid stability by predicting fluctuations and automatically adjusting energy distribution. This is particularly useful for wind and solar power, where energy output is weather-dependent.
Business pain points addressed:

Unpredictable energy generation disrupting grid stability
Over-reliance on fossil fuel backups during renewable energy shortages
Inefficiency in forecasting demand fluctuations
Difficulty in integrating intermittent renewable energy sources into the grid

You may discover more about IoT in smart grid systems in another blog post by Intelliarts. 
IoT technologies in renewable energy

Among the hardware and technologies backing up renewable energy IoT solutions, the key ones are the following:
#1 Sensors
Devices that measure and collect data such as solar irradiance, wind speed, temperature, and energy output. They enable real-time monitoring of renewable energy systems for performance optimization and fault detection.
Examples:

Apogee Instruments Pyranometers for measuring solar radiation.
Vaisala WINDCAP Ultrasonic Anemometers for wind speed and direction monitoring.

#2 Actuators
Components that perform actions based on sensor inputs, such as adjusting solar panel angles or wind turbine blades to maximize energy production.
Examples:

SMC Electric Actuators for precision control in solar tracking systems.
Siemens Actuators used in wind turbine blade pitch control.

#3 Communication technologies
Protocols like LoRaWAN, Zigbee, and 5G facilitate seamless data transmission between IoT devices and cloud platforms, ensuring reliable connectivity across renewable energy systems.
Examples:

Semtech LoRaWAN Modules for low-power, long-range communication in solar farms.
Digi Zigbee RF Modules for wireless data transmission in energy monitoring systems.

#4 IoT Gateways
Devices that aggregate data from sensors and transmit it to cloud platforms for processing and analytics. They serve as the bridge between hardware and software in IoT systems.
Examples:

Advantech IoT Gateways for industrial energy applications.
Cisco IoT Gateways used in distributed renewable energy networks.

#5 Cloud platforms
Scalable solutions for storing, processing, and analyzing data from renewable energy IoT systems. They support advanced analytics, predictive maintenance, and system optimization.
Examples:

AWS IoT Core for managing renewable energy data.
Microsoft Azure IoT for scalable cloud analytics in renewable energy systems.

#6 Edge computing
Localized data processing technology that enables rapid decision-making and reduces dependence on cloud connectivity, especially in remote renewable energy installations.
Examples:

NVIDIA Jetson Nano for edge AI applications in energy management.
HPE Edgeline Systems designed for renewable energy IoT operations.

#7 Energy management software
Platforms designed to analyze IoT-generated data, optimize resource utilization, and monitor system health for better operational efficiency.
Examples:

Schneider Electric EcoStruxure for renewable energy management.
Enphase Enlighten software for monitoring and managing solar energy systems.

Looking for advice on IoT in renewable energy adoption? Explore how tech consulting can improve business efficiency in another blog post by Intelliarts. 
Benefits of IoT solutions for renewable energy
With respect to the technological solutions provided above and applications of Internet of Things and renewable energy, the value-adding benefits of using IoT include:

Reduced energy waste: IoT sensors and smart meters monitor real-time energy use and production. This prevents overproduction or wasted energy flow. For example, solar energy systems to power IoT devices can be set to stop generating power when batteries are full.
Enhanced renewable energy system efficiency: With IoT, companies can track important performance metrics like voltage, current, and temperature to optimize the operation of solar inverters or wind turbines. Again, this leads to more efficient energy use, even under unfavorable weather conditions.
Automated energy distribution: A more balanced supply and demand is another advantage. One way to achieve this is by directing energy to where it’s most needed, for example, storage units or utilities like EV charging stations.
Improved fault detection and reliability: By keeping an eye on equipment health and alerting operators about anomalies, such as overheating transformers or failing wind turbine blades, businesses can prevent system failures and make service more reliable.
Increased energy production: IoT platforms can maximize energy output by looking at past weather patterns, optimizing solar panel placement, or adjusting turbine blade angles.
Lower operational costs: IoT-based systems offer many ways of how to save operational costs, from remote monitoring to predictive maintenance. Both of these reduce the need for frequent on-site inspections, which cut down labor and maintenance expenses. For example, drones can inspect turbines in industrial wind farms when potential issues are detected.
Enhanced decision-making: Data analytics powered by IoT gives insights into energy usage trends, equipment performance, and grid demand. All this information helps companies make better choices about load balancing, investments, or building a customized business solution.
Better scalability: With modular IoT architectures, businesses can add new sensors, storage units, or renewable energy sources without much effort.
Reduced downtime: Using predictive analytics, real-time alerts, and predictive maintenance, IoT systems can foresee and fix issues before they cause downtime.
Improved sustainability: IoT contributes to sustainability goals by enabling clean energy production, smarter distribution, and efficient storage. Also, IoT systems can help you track and report emissions savings.

Challenges of implementing IoT applications in the renewable energy market
Every technology has its limitations that businesses should be mindful of. Let’s take a look as some common difficulties that interfere with the spread of applications of IoT in renewable energy:

High upfront costs: The initial investment in IoT sensors, gateways, software, and skilled labor can be prohibitive for smaller clean energy companies. To address this, many turn to cost-sharing models or government subsidies. For example, government programs like the U.S. Department of Energy’s Smart Grid Grants provide funding to offset these costs.
Data security and privacy concerns: IoT devices in smart energy systems generate vast amounts of sensitive data. Cyberattacks on critical infrastructure or breaches in data privacy can result in financial losses and regulatory penalties.
Interoperability issues: Many IoT devices and software lack standardized protocols, making integration with existing renewable energy infrastructure difficult. This can hinder seamless communication between systems.
Infrastructure limitations: Remote renewable energy installations often operate in areas with limited connectivity. This affects real-time data collection and control. Satellite communication and low-power wide-area networks (LPWANs) are emerging solutions.
Scalability challenges: Scaling IoT systems to accommodate growing renewable energy deployments while maintaining efficiency and reliability is challenging. System upgrades and future-proofing need careful planning.

If you’re looking for support with application or solution development, our software engineering company is here to help.
What does the future of IoT in renewable energy look like?
While the above-mentioned IoT strategies in smart energy may already seem like a significant technology advancement, the best is yet to come. Let’s take a quick glance at three of the most innovative IoT applications that are expected to become commonplace in the future: 
#1 Development of Virtual Power Plants (VPP). 
It can be predicted that IoT will enable the creation of virtual power plants, i.e., networks of decentralized generation units by aggregating distributed energy resources. This approach is focused on enhancing grid reliability and enhancing the management of energy resources. Tesla is already launching VPPs in some regions, including Australia. 
Discover more about Tesla’s VPP and its usage in the video below:

#2 AI-driven autonomous energy management
The question regarding the optimal usage of green energy is quite pressing. In the future, we will likely see AI-driven autonomous systems managing power distribution in real-time. AI will analyze data from IoT sensors to optimize energy generation, storage, and distribution without human intervention, reducing inefficiencies and stabilizing power grids. As such, Schneider Electric already offers solutions centered around resilient, AI-driven energy systems. 
Explore how to balance load in power grids in another blog post of ours. 
#3 Smart renewable energy trading via blockchain & IoT
With the rise of blockchain, IoT can now be paired with decentralized technologies that benefit consumer access to sustainable energy. As such, it’s possible to forecast that IoT will enable Peer-to-Peer (P2P) renewable energy trading through blockchain-based smart contracts. Households and businesses generating excess solar or wind energy can sell surplus power directly to consumers in real-time. Powerledger is one of the few companies, currently, which have already started leveraging blockchain for renewable energy. 
Potentially, extensive adoption of innovations in IoT in renewable energy will result in a range of other beneficial technology applications. 
Looking for tech consultation? Our experts have been delivering tailored solutions since 1999.Talk to an expert
Developing IoT renewable energy solutions with Intelliarts
Here are brief descriptions of several success stories of implementing IoT in renewable energy by Intelliarts: 
IoT-driven platform for optimizing water and energy consumption

Challenge:
Xeros Technology, a pioneer in eco-friendly textile solutions, sought to develop an IoT-enabled platform to monitor and manage its fleet of water and energy-efficient washing machines. The goal was to track water and detergent usage, visualize cost savings, and provide advanced analytics to optimize resource consumption.
Solution:
Intelliarts developed Xeros Portal, an IoT-powered software solution that collects real-time data from connected washing machines. The system tracks water, detergent, and energy consumption, visualizing key insights through interactive dashboards. It also includes user management features for multi-role access control and API integration, enabling seamless fleet management and predictive maintenance.
Results:
Xeros’ machines now consume up to 70% less water, 50% less energy, and 50% less detergent, delivering significant cost savings and environmental benefits. The platform improved data-driven decision-making and early issue detection, reducing downtime and operational inefficiencies. By enhancing sustainability monitoring, Xeros strengthened its market position and helped businesses cut operational costs.
Explore success story
Optimizing energy consumption and reducing costs with IoT-driven power control

Challenge:
A leading EV charging provider sought to enhance its energy management capabilities by optimizing power distribution across its charging stations. The company needed a smart solution to minimize peak-hour expenses, comply with regulations, and provide a seamless charging experience for site hosts and EV drivers.
Solution:
Intelliarts developed a smart power management system that enables site hosts to schedule power limits for EV stations. The system dynamically adjusts energy distribution based on real-time electricity grid conditions, preventing overloads and reducing costs. The platform allows users to track energy usage, generate cost reports, and optimize off-peak charging schedules thanks to EV charging analytics.
Results:
The system successfully optimized energy consumption, reducing peak-hour demand and lowering electricity bills. API integration enabled automation for site hosts, eliminating manual adjustments. Compliance with UK smart charging regulations ensured stable grid performance, extending the lifespan of electrical infrastructure. 
Explore success story
Final take
Incorporating IoT into renewable energy offers the ability to optimize energy storage and integrate it with power grids. From predictive maintenance to AI-driven energy management, these innovations enhance efficiency, reduce costs, and support sustainability. As emerging technologies gain popularity, the future of IoT in renewable energy promises even greater advancements. That’s why the best time to start investing in IoT-driven innovation is now.
Whether you’re building a predictive energy platform or an EV fleet monitoring tool, Intelliarts provides custom EV charging app development services that connect hardware, data, and users into seamless digital ecosystems.
Here at Intelliarts, with more than 24 years of experience on the market delivering on-demand developments, we are ready, willing, and able to execute your project end-to-end at the highest level of quality. Here are just a few reasons why to partner with us:

Dedicated experts in your industry
AI/ML skills
Hands-on experience in IoT projects
Scalability and future-proofing
Continuous support and maintenance, etc

Should you need to integrate AI and IoT in renewable energy, consider partnering with an experienced software engineering company like Intelliarts.

#### IoT in Smart Agriculture: Top 5 Applications, Costs, and How to Get Started
TL;DR

Food and Agriculture Organization (FAO) puts agriculture’s share at 72% of total freshwater withdrawals.
By 2050, agriculture may need to produce 50% more food, feed, and fibre than in 2012, alongside 25% more freshwater. That pressure explains the business case for the Internet of Things (IoT).
Co-ops, agritech startups, large growers, and farms need solutions for better control over field conditions, greenhouse climate, and other aspects. 
This guide walks through the main IoT applications in smart agriculture, including precision irrigation, weather monitoring, livestock tracking, greenhouse automation, and remote asset monitoring.
It also explains the typical costs behind implementation, from sensors and software to integrations, maintenance, and analytics.
We also explain why raw sensor data only pays off when paired with analytics and AI (forecasting, optimization, anomaly detection), which is where Intelliarts comes in.

What do we mean by “IoT in Smart Agriculture”?
IoT in smart agriculture is a network of connected sensors, agriculture IoT devices, platforms, and applications for monitoring and automating farm operations. 
It covers the following and multiple other aspects:

Soil moisture
Weather
Livestock health
Greenhouse conditions
Irrigation infrastructure
Equipment status
Remote assets.

Farm teams use this data to see what is happening across facilities and, correspondingly, trigger decision-making and actions. Less manual labor involvement is prioritized here.
Operational scope: The same model can support a focused irrigation pilot or a multi-site agriculture operation.
In field crops, the priority may be soil moisture, weather, and pump control. In greenhouses, it may be climate, CO₂, light, and irrigation control. In livestock operations, it may be animal movement, location, and health indicators.
Architecture breakdown:

Field layer: Sensors, gateways, and controllers collect data from crops, soil, livestock, equipment, and farm infrastructure.
Connectivity and platform layer: Cellular, LPWAN, satellite, or mixed networks move field data into IoT platforms and storage systems.
Application and analytics layer: Dashboards, alerts, mobile apps, and AI/ML models help teams use that data for decisions.

These layers help farms connect physical conditions with decisions around irrigation, yield, livestock health, equipment use, and resource planning. That is the software foundation behind IoT in smart farming.
Turn raw field data into actionWe embed data pipelines, analytics, and ML models into smart agriculture IoT solutions.Explore our services
Top IoT applications, benefits, and cost ranges
Use cases are the best place to start when evaluating IoT in smart agriculture and smart farming technology solutions. They help estimate hardware needs and ROI, and understand the benefits of each approach.
See the table below for a general use cases overview:
[table id=14 /]
Important note. Cost ranges are directional estimates for planning purposes. Lower budgets usually fit narrow pilots with limited coverage. Larger budgets are required when the system spans multiple sites, controls physical equipment, or necessitates custom integrations.
Application #1: Precision irrigation and soil monitoring

Smart irrigation systems use soil, weather, and crop data to control when, where, and how much water crops receive. Soil moisture sensors measure field conditions, while weather data helps estimate rainfall, evapotranspiration, and near-term water demand.
When a smart watering system is connected to pumps or valves, it can automate irrigation by field zone. A basic setup may only show moisture levels and send alerts. A more advanced system can trigger irrigation events and adjust watering based on crop needs.
A 2025 smart irrigation review analyzed 150 publications and selected 110 for synthesis. It describes precision irrigation systems around soil data, plant water status, weather data, remote sensing, and automated control.
Real-world benefits
For precision agriculture IoT, the main benefits usually come from lower water use, fewer manual checks, and more stable crop conditions. 

Water savings: A 2025 Scientific Reports field study reported 35.2% water savings from an IoT-based irrigation system.
Yield improvement: The same study reported 12.05% higher crop yield compared with conventional ETc-based drip irrigation.
Labor reduction: Remote monitoring can reduce field visits because teams can check moisture, system status, and alerts from a dashboard.

These gains are strongest when the farm already faces high water costs, uneven soil conditions, or frequent manual inspections.
Cost drivers & typical ranges
Cost depends on sensor coverage, connectivity, automation depth, and integration work. Business Insider highlights rural connectivity, high costs, and the need for clearer analytics as adoption barriers for farm IoT in smart agriculture.
Main cost components include soil moisture, salinity, and temperature sensors; gateways and connectivity; pump and valve controllers; platform software; and system integration.

Basic setup: The cost of IoT in agriculture often starts around $10k for a focused IoT in smart agriculture pilot. 
Larger deployment: Multi-field systems can take months and reach much higher budgets.
ROI window: Some implementations report payback in 3–12 months through water, input, and labor savings.

Where AI/ML adds extra value
AI/ML and data science offer benefits when irrigation planning has to predict demand before crop stress appears. Models can combine sensor history, weather forecasts, crop type, growth stage, and irrigation records to estimate when each field zone will need water.
Intelliarts can support agritech teams beyond dashboards. With custom machine learning solutions and agriculture software development, we help design agriculture-focused data pipelines and ML models.
Looking for a trusted AI/ML partner for your project?Let’s talk and start planning →
Application #2: Environmental and weather monitoring
Environmental and weather monitoring uses weather stations and IoT sensors in agriculture to track local growing conditions across the farm. These systems measure temperature, humidity, rainfall, solar radiation, wind, and microclimate differences between fields or production zones.
The reason to install them locally is simple: regional weather data can be too broad for precision decisions. A University of Arizona Cooperative Extension guide notes that weather data is most reliable within about 6 miles for local applications such as precision farming. For large-scale weather patterns, the useful radius may be closer to 60 miles.
Installation also affects data quality. The same guide recommends placing stations away from obstacles by at least 10 times the obstacle height. It also gives practical mounting ranges: 1.3–2.0 m for temperature and humidity sensors, 1.0 m for rain gauges, and 2.0–3.0 m for anemometers.
Real-world benefits
The main value comes from better timing. With local weather and microclimate data, teams can plan field operations around actual conditions, not broad regional averages.
A 2025 Sensors review on IoT-enabled agrometeorological stations connects these systems with better irrigation scheduling, yield support, and weather-risk mitigation.

Planting and harvesting: Teams can time work around temperature, rainfall, wind, and field-access conditions.
Spraying: More precise weather data helps avoid poor spray windows caused by wind, rain, or unsuitable humidity.
Disease and pest risk: Earlier detection of high-risk conditions can support more targeted treatment decisions.

This also improves planning beyond the field. More reliable local data helps agribusiness teams align operations with expected crop quality, harvest timing, and market demand.
Costs and integration notes
Costs depend on how much precision the farm needs and how the data will be used. A basic station for local visibility has a different budget profile than a research-grade or smart station connected to decision-support software.

Weather stations: University of Arizona examples range from $188 to $20,800, depending on sensor quality, connectivity, and system grade.
Data loggers: Listed examples range from $69 to $2,290.
Individual sensors: Listed sensor prices range from $47 to $1,800.
Service plans: Published examples range from $25 to $449, usually tied to cloud access, data transmission, or platform services.
Maintenance: Research-grade systems can require scheduled calibration, while smart stations may reduce manual work through remote access and alerts.

Environmental monitoring is often deployed alongside soil sensing, irrigation, or crop analytics. In those cases, adding weather stations may be a smaller part of the total budget than integration with agronomic tools, pest-risk models, or farm management software.
Application #3: Livestock monitoring and smart herd management
Livestock monitoring IoT is one of the clearest examples of IoT in smart agriculture because connected tags, collars, boluses, cameras, or barn sensors track animals in real time. 
These systems can monitor location, movement, rumination, feeding behavior, temperature, activity level, and other health indicators. The data is sent to a central platform for alerts and analytics. 
In larger herds, this gives farmers animal-level visibility that manual observation cannot maintain consistently. The same logic also applies to free-range livestock, where location tracking helps reduce losses and missed events.
Real-world benefits
The strongest benefit is earlier intervention. A 2025 review of wearable collar technologies for dairy cows found that collar-based systems support real-time monitoring of health, behavior, and productivity.

Earlier illness and stress detection: Activity, rumination, and movement changes can flag animals that need attention.
Better welfare management: Continuous monitoring helps teams respond before minor issues become severe.
Lower manual workload: Staff spend less time checking every animal and more time acting on alerts.

In dairy operations, smart herd management can also support heat detection, reproductive planning, and milk-quality control. The effect is strongest when alerts are connected to clear farm workflows.
Cost considerations
Costs scale mainly with herd size because many systems use per-animal hardware. A small pilot may need only tags, collars, a gateway, and a basic dashboard. Larger farms often need more devices, barn coverage, integrations, and staff training.

Per-animal devices: Tags, collars, or boluses usually drive the core hardware cost.
Connectivity: Barn systems may use Wi-Fi or gateways, while pasture-based herds may need LoRaWAN, cellular, or satellite.
Platform fees: Many monitoring tools include recurring software or analytics subscriptions.
Installation and support: Costs rise when devices must integrate with milking systems, herd management software, or veterinary workflows.

A 2025 Frontiers article on precision dairy farming notes that high technology costs remain a major adoption barrier, especially for smaller farms.
ROI usually comes from fewer animal losses, lower veterinary costs, improved reproductive performance, and reduced labor in monitoring and herding.
Explore more about precision farming using IoT in another blog post by Intelliarts. 
Application #4: Smart greenhouses and controlled environments
Greenhouse automation uses IoT sensors and automated controls to manage the growing environment inside a greenhouse. They monitor temperature, humidity, CO₂, light, soil or substrate moisture, and irrigation status, then adjust connected systems when conditions move outside target ranges.
The automation layer can control heating, cooling, ventilation, lighting, shading, fertigation, and irrigation. In simple setups, growers receive alerts and make changes manually. In more advanced environments, control systems adjust climate and water delivery automatically.
This level of control is especially useful for high-value crops, year-round production, and regions where outdoor growing conditions are unstable.
Real-world benefits
The main benefit is production stability. A 2025 Nature article on controlled environment agriculture notes that CEA protects crops from climate uncertainty and can deliver much higher productivity than open-field agriculture.

Year-round production: Controlled environments reduce dependence on seasonal weather and outdoor growing windows.
Faster growth: Highly optimized greenhouse and vertical farming environments can report up to 40% faster growth when lighting, nutrients, temperature, and humidity are tightly managed.
Lower water use: Some controlled systems report up to 90% less water usage, especially when hydroponic or recirculating irrigation is used.
Lower manual workload: Automation reduces repeated climate checks and manual adjustments.

These results depend heavily on crop type, greenhouse design, and operating discipline. A low-tech greenhouse and a sensor-driven controlled environment will not produce the same outcomes.
Cost and complexity
Smart greenhouses are usually more capital-intensive than basic field IoT because they control the production environment, not only monitor it. The budget is shaped by the greenhouse structure, automation depth, energy profile, and integration with existing systems.

Automation scope: Basic monitoring may cover climate alerts only. Higher-cost systems add actuators for heating, cooling, ventilation, lighting, irrigation, and CO₂ control.
Energy exposure: A 2025 MDPI review notes that CEA operations face large initial investment, high energy costs, and over-engineering risks.
Market maturity: Fortune Business Insights valued the smart greenhouse market at $6.77 billion in 2025, with projected growth to $27.17 billion by 2034.
Integration work: Costs rise when controls must connect to ERP systems, farm management software, crop analytics, or remote monitoring dashboards.

The ROI case is strongest for high-value crops and intensive production models. These operations can justify higher upfront costs when automation improves yield consistency, resource use, and labor efficiency.
Application #5: Remote field and asset monitoring
“A connected farming system should naturally result in reduced uncertainty. If it’s operated only as a hub for readings, the project has missed its purpose.” — Marta Kufalska, Agritech Digital Solution Expert at Intelliarts
Remote field and asset monitoring uses IoT sensors, gateways, and cellular or satellite connectivity to support connected farming solutions beyond reliable network coverage. These systems monitor fields, pumps, reservoirs, tanks, irrigation lines, equipment, and storage areas that are costly to inspect manually.
The data can include water levels, pump status, pressure, flow, soil conditions, weather, equipment location, or abnormal usage patterns. The goal is early visibility: teams can detect leaks, failures, and other issues before they affect crops or operations.
Real-world benefits
Remote monitoring reduces the need for physical site visits and helps teams detect failures earlier. This matters most when fields are spread across large areas or when equipment is hard to reach quickly.
A practical example is an Intelliarts project that focused on automating field data collection for Indigo.

Challenge: Indigo needed to scale carbon-farming enrollment, but growers had to provide 3–5 years of field records. Manual collection was slow, incomplete, and hard to verify.
Solution: Intelliarts used remote sensing, machine learning, and Indigo’s Best Guess API to pre-fill crop types, planting dates, and field boundaries, reducing manual data entry.
Results: The solution cut data entry from weeks to minutes, finalized 3M acres, converted 55% of new acres, and reactivated 900,000 acres.

For remote asset monitoring, the main insight is that data becomes useful when it reduces manual work and gives teams a reliable operating picture across dispersed sites.
Costs and connectivity choices
Remote projects often cost more because of coverage and reliability requirements. The sensor itself may be simple, but the system still needs stable power, durable hardware, and dependable connectivity.

Connectivity choice: Cellular works when coverage is stable. LPWAN can support low-power sensor networks. Satellite is often used when terrestrial networks are weak or unavailable.
Power setup: Remote assets may need solar panels, batteries, or low-power devices that can run for long periods without service.
Hardware durability: Devices may need weatherproofing, dust protection, secure mounting, and resistance to heat, cold, moisture, or vibration.
Integration scope: Costs rise when alerts, maps, maintenance workflows, or farm management systems need to work together.

In many remote projects, connectivity and robustness drive cost more than sensors themselves. A reliable alert from a remote pump can be more valuable than a larger set of sensors that cannot transmit data consistently.
Need to understand which IoT approach fits your farming operations? Talk to our AgriTech experts
Benefits of IoT in smart agriculture beyond the hype
IoT agriculture ROI is easier to judge when benefits are grouped by business pressure point. For most farms, connected data has to prove itself in three places: margin, resource use, and operational control.
See what three layers of value-adding benefits of agriculture automation solutions comprise, with infographics provided for each:
Economic benefits
This is the ROI layer. Precision inputs and continuous monitoring can improve yields, protect crop quality, and reduce waste across water, fertiliser, pesticides, labour, and field operations. 
In many implementations of IoT in smart agriculture, payback appears within 3–24 months, depending on farm efficiency and system scope.

Environmental and sustainability benefits
This is the resource-efficiency layer. Better field data helps farms apply water and treatments more precisely, which can reduce ecosystem pressure, runoff risk, and unnecessary machinery use. 
The sustainability case is strongest when efficiency gains are easily measurable, so they can be reported to stakeholders and acted upon.

Operational and strategic benefits
This is the control layer. IoT gives teams earlier visibility into field conditions, equipment status, livestock signals, and climate risks. That supports faster decisions, stronger risk management, more reliable crop quality, and better supply planning.

Explore the importance of farm record-keeping in agriculture in another one of our blog posts.
Cost breakdown: What drives the cost of IoT in smart agriculture?
Costs are driven by scale, connectivity, hardware quality, and integration complexity. The number of sensors matters, but it rarely explains the full budget.
Before estimating the cost of IoT in agriculture, teams should separate three questions:

Coverage: How many fields, assets, greenhouses, or herds need monitoring?
Control depth: Will the system only report data, or also automate pumps, valves, ventilation, and other equipment?
Data use: Will teams need basic dashboards, or smart farming IoT data solutions with analytics, alerts, integrations, and decision-support workflows? 

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For planning, the budget can be read at three levels:

Focused pilot: Basic IoT-based precision farming solutions often start around $10k when the scope is narrow and the use case is clear.
Expanded deployment: Costs rise when the system covers several fields, assets, or production sites.
Advanced system: Budgets grow further when the project includes automation, analytics, custom integrations, and long-term support.

Here at Intelliarts, we usually start with a practical estimate of what the farm needs from a technology standpoint to improve, as per business objectives. 
We strongly suggest starting with a small-scale pilot solution, and usually do so before proceeding with complete development and integration of any greenhouse automation or agriculture-related solutions.
Have an IoT in agriculture idea and need a cost breakdown?Start with a strategy session with Intelliarts experts to discuss your project. Contact us
How to decide where to start with IoT in smart agriculture
A good IoT in smart agriculture starting point is usually a narrow use case with visible cost pressure. That can be irrigation waste, manual field checks, unstable greenhouse conditions, missed livestock events, or equipment failures in remote areas.

A popular example is sensor-based irrigation operations. In one Intelliarts sensor management case, the team built software that centralized sensor tracking, improved QA/QC, and supported irrigation practices at scale. 
The project helped the customer move toward 260% operational scaling and reduce water use per acre by 10 times compared with the prior year. 
Here are some of the actions we implemented in the early project stated in the mentioned success story, and some other relevant IoT in smart agriculture cases:

Quantify the loss first. The use case should already have a measurable cost: water overuse, crop loss, labor hours, downtime, failed inspections, or missed livestock events. Irrigation is often a strong starting point because agriculture accounts for more than 70% of global freshwater withdrawals.
Set a minimum improvement target. A pilot needs a number to beat: 10–20% lower water use, fewer manual checks, or stable yield with lower input use. Recent IoT irrigation research reported 35.2% water saving and 12.05% yield improvement.
Check whether the signal can be measured. The project needs a reliable physical signal: soil moisture, pump pressure, flow rate, tank level, animal movement, CO₂, humidity, or equipment status. Weak or delayed signals produce noise.
Match data frequency to the decision. Daily irrigation, greenhouse climate control, pump-failure alerts, and seasonal yield reporting need different refresh rates. This affects sensor choice, battery life, connectivity, and platform cost.
Validate connectivity before scaling. Remote fields may need LoRaWAN, cellular, satellite, or hybrid networks. A 2025 LPWAN/5G study found that hybrid models can cut connectivity costs by up to 30% while improving reliability.
Define the control point. The pilot should connect data to a specific action: open a valve, inspect a pump, adjust ventilation, or check an animal. If no action changes, the system is only reporting.
Use scale metrics early. In the Intelliarts sensor management case, centralized sensor management supported 260% operational scaling and helped reduce water use per acre by 10 times.

“The messy part is usually reconciliation. Field boundaries, crop records, weather feeds, sensor IDs, and equipment logs all need to be both established with business purpose and well-aligned technically to provide accurate readings and analytics.” — Marta Kufalska, Agritech Digital Solution Expert at Intelliarts
A practical roadmap looks like this:

Pick one measurable loss.
Choose the physical signal.
Set the improvement target.
Validate sensors and connectivity.
Run the pilot.
Compare before and after.
Fix data gaps.
Expand if the workflow works.
Add analytics or AI.

The right first IoT use case is where the farm can measure the loss, trust the signal, act on the alert, and prove improvement with numbers.
How Intelliarts fits: From IoT data to smart decisions

Intelliarts brings 27 years of software engineering experience to agritech, with work across farm automation, IoT monitoring, predictive analytics, data engineering, and ML systems. 
For agritech companies building precision agriculture solutions, Intelliarts can help design that layer end-to-end: cloud architecture, data pipelines, API integrations, analytics logic, ML models, and user-facing decision tools. 
Our ML practice has delivered 90 large projects, with 40% senior-level engineers and typical PoC timelines of 6–8 weeks.

 IoT data architecture and integration

Field devices generate moisture readings, weather events, equipment status, GPS points, livestock signals, and actuator logs. Intelliarts helps ingest, normalize, validate, store, and expose this data through APIs.

Relevant expertise: IoT solutions development and data engineering consulting.

 Analytics and ML pipelines

IoT data can support irrigation prediction, yield forecasting, pest-risk signals, equipment anomaly detection, and resource optimization. These models need aligned timestamps, sensor metadata, field boundaries, crop stage, weather context, and retraining logic.

Relevant expertise: custom machine learning solutions and agriculture software development.

 Decision support tools

Raw readings need a workflow. A moisture alert should map to an irrigation zone. A pump anomaly should map to an inspection task. A greenhouse warning should show which control action is available.
Intelliarts has built this kind of operational layer in IoT products before. In the Xeros IoT platform case, the team developed dashboards, reports, user roles, status monitoring, downtime tracking, and error visibility for connected equipment.
Conclusion
IoT in smart agriculture pays off when connected data changes a real farm decision. That may start with irrigation, greenhouse climate, livestock monitoring, or remote equipment, but the pattern is the same: collect a trustworthy signal, connect it to the right workflow, and measure the result. 
With the right architecture, analytics, and ML layer, agritech teams can move from sensor visibility to forecasts, alerts, and optimization that support daily operations.
The Intelliarts team has more than 26 years of experience delivering tailored AI / ML / RAG solutions for different domains, including smart agriculture. We provide services to businesses worldwide and are proud to have 90% of customer return rate. 
With our business-oriented approach and in-house expertise, we are ready, willing, and able to contribute to your best IoT in agriculture project.
Contact us

#### IoT in Supply Chain Management: Benefits and Use Cases
Back in 2021, the size of IoT in the Global Logistics Market was worth $37.41 billion. It’s projected to grow at a CAGR of 14.0% reaching a value of USD 93.85 billion by 2028. IoT has begun to reshape business operations in various domains. Among others, it concerns Supply Chain Management (SCM).
Using IoT in SCM promises a fundamental shift towards a more connected, efficient, and transparent global trade environment. Read this post to discover how to utilize IoT in SCM, explore IoT in supply chain case studies, and review the benefits and challenges related to the adoption of IoT.
How to use IoT in supply chain management
IoT revolutionizes the way companies track, analyze, and optimize their supply chain operations, from inventory and warehouse management, transportation, predictive maintenance, and asset tracking to demand forecasting, waste management, and more. When combined with advanced transport software solutions, these capabilities enable even greater visibility, route optimisation and operational efficiency across the supply chain.
What is an example of IoT in the supply chain? Examples of IoT devices that may be used in supply chain management include:

Radio Frequency Identification (RFID) tags
Global Positioning System (GPS) trackers
Temperature and humidity sensors
Smart shelves, pallets, and containers
Telematics
Near-field communication (NFC) devices

Take a look at the detailed infographic below. It shows how IoT-driven solutions connected to networks ensure continuous data flow and enable real-time supply chain visibility.

Now, let’s review in detail how IoT is used in supply chain management.
Location-tracking
IoT solutions for supply chain, such as the above-mentioned GPS trackers and RFID tags, can be attached to products or shipments to enable real-time and periodical location tracking.
Location-tracking devices transmit data on the whereabouts of shipments, ensuring transparency throughout the supply chain. They facilitate on-the-go route adjustment and enhance customer trust in shippers.
Read also: IoT in Smart Grid Technology
Storage condition monitoring
Monitoring storage conditions is essential, especially for easily perishable or sensitive goods and products.
Factors that are often monitored are:

Temperature
Vibration
Pressure
Humidity
Light exposure
Gas composition

IoT sensors can be placed in storage facilities or transport vehicles for 24/7 monitoring and sending alerts about any deviations from optimal condition levels. By connecting IoT sensors to resource planning software, businesses can automate alerts and track conditions in real time, enhancing efficiency and reducing product loss due to poor storage.
Forecast the movement and the arrival of the product
IoT-based supply chain management systems can be enhanced with predictive analytics capabilities. By analyzing data from sensors on vehicles, roads, and weather patterns, companies can forecast the arrival time.
This feature enables companies to make timely preparations at receiving points, optimizing operations and reducing downtime. It also helps with better resource allocation, customer communication, and inventory management.
For additional information on IoT data analytics, read other other blog post.
Improve contingency planning
Through their continuous data collection and feedback, IoT devices provide an in-depth view of every operation within the supply chain. It helps businesses identify potential bottlenecks, failures, or external threats much earlier, and:

Devise alternative routes or strategies
Prepare backup suppliers or distribution methods
Optimize resources, ensuring minimal disruption to operations

IoT benefits the consistency and stability of supply chain operations on a large scale.
Read also: Predictive Maintenance Domain Overview
Enhance transport safety and maintenance
Transportation is a critical link in any supply chain, and ensuring the safety and maintenance of transport vehicles is paramount.
IoT supply chain solutions play a notable role in this through means of:

Vehicle health monitoring
Driver behavior and safety tracking
Collision avoiding

IoT enhances labor safety and equipment performance in supply chain operations.
Looking for help to implement and manage smart solutions effectively?Intelliarts offers full-cycle IoT development services.Explore service
Warehouse automation
Management of the warehouse, including operations in a 3PL warehouse environment, and all the assets stored in it can present labor safety and property damage risks. When it comes to warehouse automation, IoT in SCM can offer:

Inventory management
Safety monitoring
Automated sorting and routing
Energy use monitoring
Labor monitoring

It’s safe to claim that IoT for supply chain management can streamline and automate warehouse operations making them more efficient and error-free.
Enhance loading and unloading operations
Loading and unloading processes are known to be rather labor and time-intensive and hazardous to human workers.
Regarding such operations, IoT and supply chain management paired together enable for:

Weight and volumetric load monitoring
Dock monitoring
Automated loading and unloading
Sending preventive alerts

With IoT, loading and unloading can be streamlined and human involvement in them can be minimized.
Should you need a trusted partner for integrating IoT solutions in your digital environment, reach out to AI and ML engineers from Intelliarts.
The use cases of IoT in supply chain management
Let’s review real-life IoT in supply chain case study implementation by Intelliarts, a software development company, and Prylada, a provider of a complex IoT solution.
Optimeas case study
The challenge was to store massive amounts of data collected from IIoT and edge devices in a more centralized way. It should also enable aggregating, processing, and visualizing of data in a convenient and efficient manner.
The solution was full-cycle development, testing, DevOps, and deployment of the SaaS software enabling what was requested by the customer. It included a big data processing pipeline that allowed to transform the IoT data into actionable insights and then to data-driven decisions. The work contributed to building ML models for the customer later on and providing better products and services to end-users.
Learn more about the case of processing and analyzing extensive IoT data for Optimeas.
Solution for smart warehouse monitoring

The challenge is to enable the monitoring of assets and goods in different storage locations and obtaining data insights. This should enhance the safety of stored goods, minimize emergencies, and optimize storage space use. Additionally, it should address issues of insufficient stock visibility, order processing delays, inventory damages, etc.
To enable 24/7 automated monitoring for air temperature and humidity, load distribution, lighting level, and eight other parameters, the Prylada team offers to add Prylada to existing Warehouse Management System (WMS) and Enterprise Resource Planning (ERP) solutions. This way Prylada devices (Gateways) connect directly to sensors, which enables to aggregate data, conduct remote configurations of equipment, and orchestrate data insights.
Discover additional details about the smart warehouse monitoring solution by Prylada on their website.
Amazon robotic warehousing
The challenge was to increase safety at the workplace, as Amazon workers had hurt at twice the rate of other warehouses. Besides, the world’s largest retailer handles large volumes of products, which makes supply chain operations complex. So, there also was the need to enhance the efficiency of warehouse operations.
The solution was the extensive use of robots and IoT devices inside warehouses. It enabled to automate tasks such as stocking, packaging, real-time inventory tracking, predictive analytics, and more.
Learn more about Amazon’s smart robot warehouses from the video:

Big appliance manufacturer case study
The challenge was to help eliminate repeated and unexpected equipment failures on the production line of a manufacturer. The company records data for all tests and measurements made for each part along the production process. Yet, they require a solution that would enable them to detect bottlenecks in production equipment performance.
The solution was an ML model created by Intelliarts upon the Extreme Gradient Boosting classifier algorithm. Based on the database of 14.3 GB of data received from monitoring IoT devices, the solution was trained to predict which parts of the equipment are the most likely to fail. The resulting ML model showed over 90% accuracy in equipment failure prediction, which helped our client to reduce the maintenance cost by 5%.
The experience of Intelliarts with IoT is vast, so you can trust us with the development of an IoT in SCM solution or any related matter.
Benefits of the Internet of Things in supply chain
Adoption of IoT is one of the essential ways of digital transformation, the value of which is hard to overestimate. Let’s review how does IoT benefit the supply chain:

Higher speed. IoT allows real-time data collection and sharing. It speeds up decision-making processes in supply chain management leading to reduced delays and wait times.
Higher accuracy. With automated data collection, human errors in recording or tracking inventory are minimized. In the meanwhile, access to precise data ensures optimal resource allocation.
Improved flexibility. IoT offers adaptive capabilities in adjusting to changing demands or disruptions. This ensures a smooth flow of goods.
Better segmentation. IoT provides detailed data insights, enabling businesses to segment their supply chain processes more efficiently. It helps in targeting specific market demands.
Increased efficiency. Automating processes through the usage of IoT in supply chain and logistics not only cuts costs but also ensures that resources are used to their maximum potential.
Enhanced predictability. IoT can help to forecast trends by providing data for analysis. With a better understanding of patterns, businesses can prepare for future demands.
Better sustainability and reduced adverse environmental impact. IoT devices can monitor and optimize energy usage and wastage in the supply chain, leading to more sustainable operations and reducing carbon footprints.

The benefits of IoT in supply chain management are significant. They can bring concrete value, reflected in financial performance, for businesses from various domains.

What about IoT in other industries?

While supply chain management represents one of the most dynamic areas of IoT adoption, the technology’s potential reaches far beyond logistics. In the construction sector, for instance, IoT plays a major role in improving safety, productivity, and accountability. Smart sensors monitor equipment performance and worker safety, while GPS-based time tracking apps help managers monitor employee hours and reduce time-reporting errors across large construction sites. IoT also supports predictive maintenance for heavy machinery, preventing costly downtime.
Other sectors are following a similar path — in healthcare, IoT enables real-time patient monitoring and smart asset tracking in hospitals; in agriculture, it powers precision farming by collecting data on soil, moisture, and crop health. Together, these examples show how IoT serves as the backbone of digital transformation across industries, enabling smarter decisions and more efficient operations.
If you intend to get the most out of your IoT project, don’t hesitate to reach out to engineers from Intelliarts.
Issues with adopting IoT in supply chain management

Now, let’s get to know the potential challenges with the adoption of IoT-driven solutions in supply chain management:

Security concerns. IoT devices can be vulnerable to hacking, risking data breaches and operational disruptions in the supply chain.
Integration complexity. Harmonizing legacy systems with new IoT solutions might require significant time, resources, and expertise.
Data overload. Handling vast amounts of data from IoT devices can overwhelm existing systems, necessitating better data management solutions.
Dependency on connectivity. A consistent internet connection is crucial for IoT. Any connectivity issues can disrupt supply chain operations.
Scalability issues. As businesses grow, ensuring that IoT solutions scale effectively to accommodate increased loads can be challenging.
Interoperability challenges. Different IoT devices and systems might not easily communicate or integrate with one another, requiring additional solutions or adaptations.

The Intelliarts team has substantial experience creating software solutions for IoT and can help you avoid pitfalls in the project.
Additionally, you may discover more about the role of data pipelines, vital for any development project, including ones, related to IoT, from our other post.
Conclusion
Every example of IoT in supply chain implementation and how does IoT benefit the supply chain. The promise of IoT is not just in the devices but in the transformative power of a fully connected, data-driven supply chain.
Consider Intelliarts as your trusted provider of the Internet of Things and supply chain software solutions. With over 24 years on the market delivering successful projects, we can help you get the most out of your investment in IoT in SCM. We also provide AI consulting services to help you leverage the power of artificial intelligence for your business. Reach out to us with your idea and let’s discuss it.
 Looking for IoT solution? Let's talk

#### IoT-Based Solar Power Monitoring Technology & Solutions
Concerns regarding Earth’s sustainability and optimal solar power usage are widely discussed. At the same time, solar energy is a source of power that never runs out, and its usage itself doesn’t cause pollution. That brings us to exploring how software solutions can contribute to the sustainability and solar energy niche. 
In the energy management market, household penetration as of 2024 was 25.0%, with the prospect of increasing up to 30.4% by 2029. Since energy management IoT, including solar power monitoring, already has an economic value between $80 and $150 billion, the potential for related products in the market is vast. 
In this post, you’ll explore the concept of IoT in solar energy monitoring products as well as components of such complex systems. Besides, you’ll review and get to test an example of a computer vision product for solar panel monitoring and discover why IoT in solar energy is such a huge trend as of 2025 and beyond. 
What is IoT in solar power monitoring?
IoT (Internet of Things) in solar power monitoring refers to integrating interconnected sensors, devices, and software with solar power systems.
At the same time:
Solar power monitoring is the process of tracking and analyzing the performance of solar energy systems in real-time or over a period. 
Basically, IoT in solar energy monitoring systems is the usage of smart devices collecting data from solar panels, inverters, and other system components to ensure optimal energy production, identify issues, and improve overall efficiency.
Without further ado, here are the key features and capabilities that are attributed to IoT solar energy solutions:

Real-time data tracking

Remote management

Data analytics

Predictive maintenance

Direct and indirect energy optimization

Alerts and notifications

Types of data that IoT in solar power monitoring can provide by category are shown in the image below:

Technology application examples
Here’re several examples of technology utilized in IoT in solar energy projects: 

Open Solar Outdoors Test Field (OSOTF). OSOTF in Canada monitors photovoltaic modules’ output alongside metrics like solar radiation, temperature, and wind speed. 
IoT-Based Solar Power Monitoring with ESP32. This method uses ESP32 to track solar panel voltage, temperature, and light intensity, uploading the data to ThingSpeak for remote monitoring.
Smart Solar Monitoring with Raspberry Pi: A Raspberry Pi-based system collects data from sensors to monitor parameters like power output, panel temperature, and weather conditions. The data is processed locally or uploaded to cloud platforms for real-time analysis and performance optimization.

As it’s possible to observe, IoT for solar energy projects can be a fairly complex system encompassing a mix of software with high-tech physical devices.
Key components of an IoT-based solar monitoring system

Let’s proceed with reviewing the components of a regular IoT for solar energy projects system. Merged together, they conceive a complete system that can simultaneously track parameters, transfer data in real-time, analyze it, and display it as charts or through other data visualization options. 
#1 Sensors
Sensors in IoT-based solar monitoring systems measure key parameters. Among them, there are power output, temperature, irradiance, and environmental conditions such as humidity and wind speed. These inputs present real-time data collection used later on for analytical purposes. 
Examples: Photovoltaic (PV) sensors, temperature sensors, wind sensors.
Tailored Solutions for Renewable EnergyPartner with Intelliarts to transform your business with innovative technology.Learn more
#2 Data processing and analytics platforms
Data processing platforms aggregate and analyze data from sensors, enabling insights into system efficiency, fault detection, and predictive maintenance. Cloud-based solutions often support these tasks by offering scalability, data visualization, and reporting functionalities.
Examples: AWS IoT Core, Microsoft Azure IoT, Google Cloud IoT.
#3 Connectivity solutions
Reliable connectivity options are needed to ensure uninterrupted data transmission from sensors to processing platforms. Stable options like Wi-Fi, cellular networks, or LPWAN cater to varied energy setups, depending on location and system requirements.
Examples: LoRaWAN, 4G LTE, Zigbee.
#4 Control systems
Control systems act as the brain of the solar monitoring setup, allowing operators to manage and regulate the solar plant remotely. They facilitate automated responses to detected issues.
Examples: SCADA systems, Programmable Logic Controllers (PLCs).
You may be additionally interested in exploring practices for overcoming renewable energy challenges in business. 
#5 Power management units
Power management units regulate energy distribution, ensuring optimal usage and storage efficiency while preventing overloads or downtime. They play a crucial role in managing battery systems and grid connections.
Examples: Maximum Power Point Tracking (MPPT) controllers, charge controllers.
#6 User interface and mobile applications
User-friendly interfaces and apps provide real-time monitoring and control of solar systems from anywhere. These tools display key metrics, generate reports, and notify users about system health or failures.
Examples: SolarEdge Monitoring App, Enphase Enlighten, SMA Sunny Portal.
#7 Security mechanisms
Security mechanisms protect the solar monitoring system from cyber threats and unauthorized access, ensuring data integrity and system reliability. These include encryption, authentication protocols, and intrusion detection systems.
Examples: TLS/SSL encryption, VPNs, Firewalls.
#8 Energy storage solutions
Energy storage solutions complement solar monitoring systems by storing excess energy for later use, improving reliability during periods of low sunlight or high demand. These systems integrate with monitoring platforms.
Examples: Tesla Powerwall, LG Chem RESU, SonnenBatterie.
IoT solar power monitoring systems generate heavy volumes of big data in the operation process. Explore the big data trends to follow to reap the most benefits of big data analytics and IoT for solar energy projects. 
How to implement IoT in developing solutions for solar energy
See the checklist for implementing IoT in solar energy monitoring solutions in the image below:

Feel free to use this checklist as a guide for an IoT in solar energy adoption initiative. However, be aware that an actual business scenario may need substantial changes in the suggested strategy. That’s why you need, above everything, the right tech partner that specializes in related software technologies and is well-familiar with the niche. 
Intelliarts’ solar panel monitoring solutions in action
Here at Intelliarts, with more than 24 years of experience with software development across industries, including energy and high-tech, we are well-versed in IoT for solar energy projects. Moreover, we’ve partnered with industry leaders like EV Connect, EVIQ, Gerenlane, and others. One of our recent developments is a computer-vision solar panel damage detection demo.
Additional details on AI in solar panel damage detection are provided in the corresponding post by Intelliarts. 
The computer vision model by Intelliarts is designed to detect anomalies in solar panels, particularly overheated spots. The model operates on infrared images of solar panels. It indicates the overheated spot, outlines its area, and shows the approximate accuracy of anomaly detection. You can use your own infrared images for testing or utilize samples from our dataset. 
This AI solution is a Proof-of-Concept rather than a finished software product. It can be used to drive IoT-based solar power monitoring systems. For example, infrared cameras can be set to take pictures of solar panels, and the software would be able to automatically analyze anomalies and provide reports about solar panel performance. 
Explore the video depicting how the AI demo works in the video below:

You may also give a try to the solar panel computer vision demo at the HuggingFace environment. Find it here.
It’s essential to note that the development of solar power monitoring systems requires substantial expertise in software development as well as experience with respective systems composed of IoT devices. Therefore, when it comes to such a project, having the right tech provider with plenty of related success stories as your partner is half the battle. 
IoT Solutions for Solar ProjectsIntelliarts engineers develop tailored solutions to power up your solar initiatives.Get consultation
Why IoT-based solar power monitoring systems are the new industry trend
Let’s get through several value-adding reasons to invest in IoT in solar energy as one of the most trending technologies in the industry:

Source of big data. Modern-day businesses rely on big data collection heavily, as companies leveraging it to a large extent can achieve the most benefits. As mentioned above, IoT in power monitoring systems collects a lot of fully insightful data for businesses to analyze and use in decision-making. This point is already enough to justify investments in smart solar monitoring solutions. 
Labor security and safety. IoT systems reduce the need for frequent manual inspections by enabling remote diagnostics and automated alerts for potential issues. This minimizes the risk of workplace accidents and ensures that maintenance teams are deployed only when necessary. In the situation when 2.6 million non-fatal workplace injuries occurred in 2023 in the US only, and labor security is still a critical concern, automated monitoring solutions can’t prove themselves to be even more impactful. 
Environmental benefits. IoT solar systems help to contribute to sustainability goals, such as the reduction of greenhouse gases, excessive water usage, etc. Basically, IoT in solar systems aids in addressing an entire range of pressing environmental problems. 
Financial benefits. Adopting IoT-based solar power monitoring systems can lead to significant cost savings and financial efficiency, optimally resulting in positive ROI and subsequent monetary advantages. That’s achieved through optimizing energy production and reducing downtime through predictive maintenance for eco energy. Additionally, these systems help maximize returns on investment by extending equipment lifespan.

Although the listed are far from being the only reasons to adopt IoT-drive power monitoring, they already prove the immense value that such an initiative can offer to businesses and their direct customers. 
Final take
IoT-based solar power monitoring systems are transforming the renewable energy landscape. By improving efficiency, reducing costs, and supporting sustainability goals, they offer unparalleled value to businesses and end-users alike. As the energy sector continues to evolve, adopting these smart solutions ensures optimized performance and long-term benefits for solar energy systems.
When it comes to high-tech mixed with software development, having the right tech partner is half the battle. Here at Intelliarts, with more than 24 years of experience in the market delivering custom solutions, we can provide the high-end development, from solar power monitoring product to ai vehicle damage inspection solution. Don’t hesitate to reach out and discuss opportunities. 

#### IT Audit Readiness for Software Companies: How to Prepare for SOC 2 & ISO 27001
Infrastructure, tools, internal processes, and regulations change nonstop. Something that worked fine a year ago may already cause problems today, so many companies run IT audits to catch any risks before they snowball.
An IT audit gives you a clear look at your technology stack. Code quality, security setup, infrastructure, and workflows all come under review. Reports show IT and cybersecurity audits make up 20% of all audit plans, which puts them ahead of compliance or financial checks.
In this article, you’ll see why companies run audits, where teams often fail, and how to evaluate your IT audit readiness. We’ll also share our personal experience gained setting up processes that hold up well during audits.
Get ready for an IT audit with our compliance consulting for software companies.Let’s connect and discuss your needs
What Is an IT Audit for Software Teams?
A technology audit is a structured review of a company’s technological infrastructure, policies, and operations. 
An IT audit for software companies looks at systems, hardware, internal processes, and policies. The goal is to understand how your current setup works and whether it supports daily operations and long-term plans.
Audit helps you find weak points, fix security risks, replace outdated tools, and adjust your software. At the same time, it highlights the parts of your technology that already work well.
What does an IT audit cover?
An IT audit reviews hardware, software, network infrastructure, data handling, cybersecurity, and other aspects. Here’s a more detailed breakdown:

Hardware: Servers, computers, and network devices. Auditors check their condition, performance, and age.
Software: Business applications, licenses, and updates. Review shows unused tools, security gaps, or outdated versions.
Network infrastructure: Network design, configuration, and protection measures. Weak spots can slow systems or expose them to attacks.
Data management and security: Data storage, backups, encryption, and access rules. Auditors also check compliance with privacy laws.
Cybersecurity practices: Firewalls, antivirus tools, monitoring systems, and threat detection. Review shows how well your systems resist attacks.
IT policies and compliance: Internal rules, procedures, and documentation. Auditors check whether they meet legal and industry requirements.
IT governance and strategy: How IT decisions support business goals, budgeting, and long-term technology planning.
Human resources: Skills, workload, and training of the IT team that maintains and supports your systems.

Why Do Software Companies Fail IT Audits?
Software companies fail IT audits because controls don’t work in practice. Often, teams delay preparation, then try to finish everything in the final weeks. At that point, the evidence feels incomplete, and written policies do not match the system’s actual behavior. 
Here’re 3 most common reasons teams fail IT audits:
Lack of real control over systems
Many teams list security controls, such as access management, encryption, and data protection, but can’t prove they work in practice. Why?

Access rights get outdated because they are not reviewed on a regular schedule
System logs exist, but they do not contain enough detail to reconstruct actions
Risk assessments remain generic instead of reflecting real threats
Incident response plans are documented, but they are not tested in practice

That gap between “defined” and “working” is one of the top reasons audits fail.
Limited understanding of requirements
Standards like SOC 2 or ISO 27001 compliance for software companies may sound familiar, which often leads teams to assume they already meet expectations, even when the understanding stays too shallow.
For example, “data protection” stops at encryption, without covering storage, transfer, or access flow. Such a surface-level approach leaves holes, especially in SaaS, where tools and processes change all the time.
Rushed timelines and lack of ownership
When no one owns the process end-to-end, tasks move between teams, but don’t get you closer to the goal. Evidence is collected late, some controls stay unchecked, and others exist only on paper. 
At Intelliarts, we deal with ISO 27001 audit preparation as part of our regular work. Each responsibility has a clear owner, so nothing falls through the cracks. When audit time comes, it becomes a validation of how we already work.
Looking for trusted technology consulting? Intelliarts experts are here to support you. Contact us.
What Auditors Look for in Software Systems?
IT auditors review your software to see whether it is secure, compliant, and reliable in real use. They want to know if you follow your own processes, update documentation, and properly protect sensitive data.
From our experience, there are 5 software audit preparation areas they focus on (and the types of questions you should be ready for):
Security and access checks

Access control: Who has admin or elevated access, and when was the last time you reviewed that list?
Authentication: Do all users use multi-factor authentication, or are there exceptions still active?
Data protection: Where is sensitive data unencrypted in the system?
Vulnerabilities: How quickly do you apply security patches after they are released?
Third-party tools: Which external dependencies or services have not been reviewed for security in the last year?

Compliance and license tracking

Regulations: What concrete evidence do you have that GDPR or SOC 2 requirements are met?
Licenses: How do you verify that current software usage does not exceed purchased license limits?
Inventory: When was the last full reconciliation between installed software and your official software inventory?
Shadow IT: Do you know what tools teams use without official approval?

Code quality and system design

Code quality: If a new developer joins tomorrow, how long until they can safely make changes here?
Technical debt: Which parts of the system are too risky to modify without extra review?
Architecture: Which component or service breaks first if traffic doubles?
System links: Which services are so tightly coupled that a change in one requires updates in others?

Process and operational control

Change flow: Can you walk through the last production change and who approved it?
CI/CD: Are there any manual steps left before deployment?
Documentation: When was system documentation last updated after a real change?
Backups: When was the last successful restore test from backup?
Recovery plan: Has the disaster recovery process been tested in a real or simulated failure scenario?

Data integrity and application controls

Input checks: What prevents invalid, incomplete, or malicious data from entering the system?
Processing rules: Where do you currently see mismatches or inconsistencies in processed data?
Master data: Who owns key reference data, and how often is it reviewed?
Audit logs: Can you trace a user action from start to finish without gaps?

How to Prepare for SOC 2 and ISO 27001 IT Audit?
Start by choosing the framework, scope, and evidence model. SOC 2 and ISO 27001 overlap in many areas, but they do not prove the same thing.

ISO 27001 is a formal international certification for managing information security risks through an ISMS.
SOC 2 (System and Organization Controls 2) is an audit report that checks how a company protects data and manages systems in five areas: security, availability, processing integrity, confidentiality, and privacy (defined by the American Institute of Certified Public Accountants).

Here’s a concise comparison of ISO 27001 and SOC 2 for SaaS companies:
[table id=3 /]
SOC 2 audit preparation checklist: step-by-step guide
Generally, preparing for a SOC 2 means proving that controls are designed properly and, for Type II, that they worked consistently over time.
For SOC 2 readiness for startups, we usually recommend starting with security, availability, and confidentiality checks unless customers require more. This keeps the first scope realistic, while still covering what most SaaS buyers care about.
1. Define the audit scope and report type
Decide whether you need SOC 2 Type I or Type II:

Type I checks controls at one point in time
Type II checks whether they operate over a period (usually 3-12 months)

2. Confirm SOC 2 Type II requirements
Map your systems, data, and customer commitments to the trust service criteria. Security is the usual starting point, while availability, confidentiality, processing integrity, and privacy depend on your product and contracts.
3. Run a gap assessment
Compare your real workflows against SOC 2 requirements. Look at MFA, access reviews, change management, vendor reviews, logging, incident response, and backup testing.
Typical gaps to look for:

Missing security policies
No formal access control reviews
Weak incident response process
Lack of audit logs

4. Build and document policies
You’ll need clear, written policies such as:

Information Security Policy
Access Control Policy
Incident Response Plan
Change Management Policy
Vendor Management Policy
Data Classification & Handling

Importantly, these policies should reflect how teams actually work, so keep the language clear and practical. Simple way to think about it:

Policies = intent (“how things should work”)
Controls = execution (“how things actually work”)
Evidence = proof (“how we know it works”)

“SOC 2 gets easier when evidence becomes part of delivery. If you need to stop engineering work for two weeks to collect proof, the process is not audit-ready yet.” — Data engineer at Intelliarts, with 20 years in DevOps/MLOps.
5. Implement technical and operational controls
Auditors focus heavily on technical controls. Here’s a short SOC 2 controls checklist:

Multi-factor authentication (MFA)
Role-based access control (RBAC)
Logging and monitoring (SIEM or logs)
Encryption (at rest and in transit)
Backup and disaster recovery
Operational Controls
Employee onboarding/offboarding
Security awareness training
Vendor risk reviews
Regular access reviews

6. Collect evidence continuously
For Type II, evidence cannot appear the week before the audit. You need dated access reviews, tickets, logs, training records, incident records, and screenshots over time.
7. Run a readiness assessment
Treat this as a mock audit. We usually try to recreate the real process as realistically as possible: ask questions, request proof, and check how fast our team can find evidence.
8. Choose an auditor and manage the process
Pick a CPA firm with SOC 2 experience. Assign one internal owner to coordinate requests so answers stay consistent.
Get ready for your SOC audit with Intelliarts.Let’s define your next steps together
ISO 27001 audit checklist: preparation steps
ISO 27001 audit preparation is broader. You’re not only proving controls; you’re proving that the organization manages information security through a structured ISMS (Information Security Management System).
A practical ISO 27001 cluster should cover scope, risk assessment, Statement of Applicability, ISO 27001 controls, internal audit, management review, and evidence.

Lock the ISMS scope

Define which products, systems, teams, cloud accounts, and third-party tools are in scope. If a tool stores or processes customer data, do not ignore it. A typical scope may look like this:

Products and services
Cloud environments (AWS, GCP, Azure)
Internal tools (GitHub, Jira, Notion, etc.)
Systems storing customer or production data
Third-party services with data access

“From our experience, teams often miss old environments, test systems, or SaaS tools that still store sensitive data. Auditors usually find these first, so it’s important to keep a complete and up-to-date inventory of all systems.”  — Alexander Barinov, a Managing Partner at Intelliarts, with over 10 years of experience in software solutions.
2. Rebuild the risk register from the current reality
Go system by system and describe risks in practical terms: what can fail, who owns it, and what control reduces the risk today.
Risk registers are often written once and then left unchanged, even when infrastructure or product setup changes. Auditors check whether risks match the current system, so historical documentation won’t work.
3. Prepare the Statement of Applicability
SoA connects ISO 27001 controls to your actual environment. It should clearly show:

Which controls apply (and which don’t)
Why the decision was made
Where the control exists in practice

Note that implemented controls should match processes, tickets, or technical proof behind them.
4. Align policies with operations
Check how teams operate in daily work and how policies describe it. Policies that don’t match reality may cause extra questions. Areas to verify:

Access approvals (ticket, Slack request, or manual approval)
Incident reporting and tracking flow
Vendor onboarding and approval steps
Backup execution and restore testing

5. Make each control proof-ready
Every control needs evidence: prepare screenshots, logs, reports, tickets, approval records, or review notes.
6. Run an internal audit and management review
ISO 27001 for software companies expects continuous improvement. Internal audit checks whether controls work as described and whether evidence exists for each one. Management review covers:

Open risks and incidents
Audit findings and remediation status
Security performance signals (access reviews, incidents, training completion)

7. Close gaps based on impact
Some areas usually carry higher audit risk: 

Access control (production and sensitive data access)
Logging and monitoring (visibility into system activity)
Incident response (response time and tracking)
Cloud configuration (public access, misconfigurations)
Backup and recovery testing (actual restore capability)

We recommend fixing real security issues before updating documentation, since paperwork alone won’t meet audit expectations if the controls are still weak.
IT Audit Checklist for Development Teams
We prepared an actionable checklist you can use when getting ready for an IT audit. Download the PDF here.

How AI and LLM Features Change IT Audit Scope?
If you work with AI or LLM systems, include them in your IT compliance audit scope when they touch customer data, production workflows, decisions, or internal knowledge bases.
For AI software, auditors may ask:

What data goes into the model or prompt?
Can sensitive information appear in outputs?
Who can access prompts, model logs, embeddings, and evaluation datasets?
How do you test for prompt injection, insecure output handling, and data leakage?
What happens if the model gives a wrong or unsafe answer?

AI and LLM governance is getting more important as AI systems move into production use. Compliance frameworks are starting to treat AI as a bigger risk area, separate from the general software controls. 
For example, the EU AI Act uses a risk-based model, the NIST AI Risk Management Framework defines how to govern and measure AI risks, and OWASP highlights common LLM security issues like prompt injection, data leakage, insecure outputs, and supply chain risks.
What does it mean for software teams?
For teams going through the ISO 27001 or SOC 2 audit process, AI usage means auditors expect the same level of control over AI systems as they expect over infrastructure or production environments.
From our experience, we needed to prove a few key things during audits:

Clear data boundaries for AI inputs and outputs, including what data can enter the model and what can come out
Restricted access to model-related data like prompts, logs, embeddings, and evaluation datasets
Evidence that we test AI behavior for security issues such as prompt injection, insecure output handling, and data leakage
A defined process for fixing incorrect or unsafe model outputs, especially when AI affects decisions or customer-facing results

How Does Intelliarts Build Audit-Ready Software Systems?
Audit readiness shows in how your systems work every day. From what we’ve learned, strong audit readiness is built around tight processes, clear ownership, and proof you can show immediately. Here’s how we do it.
Secure SDLC and security built into daily work
Secure Software Development Life Cycle (SDLC) means security is a part of every stage of development. You define security requirements during planning, include them in design, and validate them during development and testing. 
NIST guidance also points to this approach, where security becomes part of the lifecycle instead of a separate activity at the end.
How we do it: 

Add security requirements to tickets before work starts
Discuss basic risks during design and development
Review and check security aspects in pull requests
Include input validation, authentication, and data exposure tests in regular testing

CI/CD security controls that show the real picture
Your DevOps pipeline should tell the story of every production change: who made a change, what tests ran, what passed or failed, and who approved the release. Auditors often focus on this traceability during reviews.
How we do it: 

Protect the main and production branches and require approvals before merges
Block direct pushes to production branches
Run automated tests and security scans on every change
Log commits, authors, test results, and approvals for each deployment
Keep full release history available for audit checks

Code review processes that hold under pressure
Code reviews often weaken when teams rush delivery. We’ve been there: reviews get skipped, and small issues slip through. Now, for us, reviews are non-negotiable, and they tie directly to SOC 2 controls.
How we do it:

Require at least one approval for every change
Require two approvals for sensitive changes
Enforce review rules directly in repository settings
Review security, logic, and data handling in every pull request

Logging that supports investigation needs
Audit logs should help teams reconstruct login, access changes, configuration updates, failed jobs, deployments, and data exports. If logs exist but no one can search them, they won’t help much during an IT audit.
How we do it:

Log key events such as authentication, access changes, deployments, configuration updates, data exports, and failed operations
Centralize logs in one searchable system
Define retention rules and follow them consistently
Test log search during internal checks to confirm fast access to answers

Access control that stays accurate over time
In audits, access control usually fails because no one maintains it – people change roles, but permissions stay. To stay prepared for an IT compliance audit, you should review access regularly, use SSO/MFA, and document approval flows. 
How we do it:

Run regular access reviews and remove unused permissions
Set expiration dates for temporary access
Apply single sign-on and multi-factor authentication
Require documented approval for access changes
Compare active users with current roles during periodic checks

SDLC, DevOps, and security working as one system
Audit-ready systems don’t split SDLC, DevOps, and security into separate tracks. Controls should link directly to daily work: code changes, deployments, infrastructure updates, vendor reviews, and incident response. That’s what supports SOC 2 and ISO 27001 readiness most.
How we do it:

Connect tickets, pull requests, CI/CD runs, and deployments into one chain
Link each production change to a requirement and approval
Include test results and security checks in the same pipeline
Keep full traceability from idea to production release

Need support with SOC 2 or ISO 27001 readiness?We help teams connect security, development, and operations into one audit-ready systemLet's talk
How to Audit Your Development Infrastructure?
To audit your development infrastructure, track the process from code commit to production and then to recovery. You can follow this plan:
1. Review cloud environments
Start with cloud infrastructure security. Your goal is not only to know what exists and where, but to prove who owns it and how it’s protected. Check:

Cloud accounts, subscriptions, and projects
IAM roles and admin access
Network rules and exposed services
Encryption for databases, storage, and backups
Security groups, firewalls, and public endpoints
Cloud audit logs and retention

2. Check the DevOps pipeline
Your pipeline is one of the first places auditors look because it controls how code reaches production. If production changes happen outside the pipeline, you should also document the exception. Review:

Branch protection rules
Required code reviews
Build and test results
Security scans
Deployment approvals
Emergency change process
Artifact storage and versioning

3. Audit secrets management
Very often, secrets are spread across environment files, CI/CD variables, local machines, and outdated scripts. Look at:

Storage locations for secrets (vault vs environment files, CI/CD variables, local machines, old scripts)
Vault usage and access controls
Rotation schedule and enforcement
Scope of access by environment and role
Secret cleanup when credentials are no longer needed
Exposure risks in logs, builds, and error outputs

4. Test vulnerability management
Auditors want proof that teams find, prioritize, assign, and fix vulnerabilities. A strong vulnerability management process includes scans, severity rules, remediation SLAs, tickets, retesting, and tracked exceptions with clear owners.
5. Validate logs, monitoring, and alerts
Make sure your audit logs cover authentication, access changes, deployments, privilege escalation, system errors, data exports, and suspicious activity.
Also, check whether alerts go to the right people. A perfect alert that no one reads is not a working control.
6. Test backups and recovery
Backups are not enough. You need to restore tests. Document RTO, RPO, backup schedule, backup encryption, restore results, and lessons learned. Auditors care less about what the policy says and more about the last successful restore.
Intelliarts can review your SDLC, DevOps pipeline, cloud setup, and evidence. Let’s discuss.
Which Mistakes in IT Audit Preparation Should You Avoid?
Nobody enjoys an audit, but it gets easier when you prepare early and keep your setup clear and documented. Start with the common mistakes that usually cause issues. Avoid them upfront to reduce findings, delays, and rework:

Not setting the scope upfront: If the scope is unclear, auditors expand requests across more systems and evidence. Define boundaries before the audit starts.
Writing policies no one follows: Policies that don’t match real workflows create gaps. Keep them practical and align them with daily engineering work.
Waiting too late to collect evidence: Evidence needs time coverage, not last-minute snapshots. Late screenshots can’t prove controls worked over time.
Over-documenting and under-organizing: Large document dumps slow audits down. Auditors expect clear, structured, control-mapped evidence.
Not preparing the team: Engineers, DevOps, support, and leadership should understand the scope and typical questions to keep answers consistent.
Treating auditors like adversaries: Auditors only assess controls, so share context, answer directly, and avoid defensive communication.
Poor evidence presentation: Raw logs or screenshots without context create confusion. Highlight relevant sections and explain what each item proves.

Should You Prepare Internally or Work with a Partner?
Prepare internally if your team has audit experience, enough time, and clear ownership. Work with a partner if this is your first SOC 2 or ISO 27001 audit, your product is complex, or the timeline is tight.
[table id=4 /]
A partner won’t “do compliance” for you. But they can show where audits usually go wrong: weak audit logs, messy access rights, unclear incident response, missing evidence, and gaps in cloud infrastructure security.
For many teams, the best setup is hybrid. Internal teams keep ownership, while an audit readiness services provider validates scope, reviews evidence, and helps close gaps before auditors arrive.
Final Take
IT auditing helps you understand the current state of your systems, meet regulatory requirements, and find areas that need improvement. While internal audits are useful, an external audit gives you an independent, professional assessment.
Request a tech audit from Intelliarts, a trusted provider of AI/ ML development and technology consulting. With more than 27 years on the market, we can provide guidance, give actionable advice, and share personal experience going through an audit. Let’s connect and discuss our next steps.

#### Key Features of Smart Charging Platforms for Businesses
Smart charging alone can save up to 25% of network reinforcement and 20-50% of energy costs. In this post, you’ll explore smart charging platform features for energy infrastructure, transportation, and other businesses. You’ll also take a look at a comparison of custom vs off-the-shelf smart charging solutions, and will find out which suits you best. Real-world scenarios provided at the end of the post will complete the insight. 
Explore our case study on developing EV charging management platform.Learn more
Why smart charging matters for businesses
For starters, let’s define the concept of smart charge management:
Smart charge management is intelligent optimization of electric vehicle (EV) charging that balances energy demand, grid capacity, and user schedules while taking into account electricity prices and user mobility needs.
At the same time, the difference between smart charging and normal charging is that normal charging delivers electricity to an EV as soon as it’s plugged in. In its turn, smart charging uses data, automation, and pricing signals to control when and how charging happens.
So, why exactly does smart charging matter? The statement is simple: the cost of energy consumption for businesses or their respective clients can reach millions of dollars, and, therefore, any method to cut expenses must be utilized.
Here are the ways in which smart charging proves its usefulness: 

Energy cost optimization and load balancing: Among other usages, smart electric vehicle (EV) charging systems reduce peak demand costs and optimize energy use.
Fleet uptime and operational reliability: Due to smart management platforms, electric vehicles, as well as infrastructure, can operate more consistently on multiple levels. It also reduces downtime risks.
Scalability to match future EV adoption: EV charging management platforms offer to scale monitoring and analytics seamlessly. This benefits long-term growth and sustainability goals.

Features of EV charging software and platforms are provided in the next blog section. They should make the picture of the smart EV charging station software’s usefulness complete. 
As a bonus, here’s a list of businesses that, the Intelliarts team believes, can benefit the most from electric vehicle smart charging solutions: 

Key features that businesses prioritize in smart charging platforms
With this list, the Intelliarts team aims to provide you with a better understanding of how the technology works, as well as to indicate ways in which you can use electric vehicle smart charging in business: 
The time is right for electric cars – in fact the time is critical. — Carlos Ghosn, a former Chief Executive Officer (CEO) of Michelin North America
#1 Dynamic load management
What it is: Real-time power distribution that balances energy across chargers and facilities, avoiding grid overloads and unnecessary infrastructure upgrades.
How it is used: With smart charging solutions, businesses cut peak demand costs, stabilize operations, and ensure both EVs and core infrastructure run without disruption.
Example: An office park balances power across 20 chargers, preventing blackouts and avoiding costly transformer or infrastructure upgrades.
#2 Energy scheduling & peak shaving
What it is: Automated charging aligned with off-peak tariffs, reducing expensive grid consumption during high-demand hours.
How it is used: Smart EV charging schedules energy use efficiently, lowering bills and maintaining full fleet readiness without stressing business facilities.
Example: A delivery fleet charges overnight at off-peak rates, lowering electricity bills and ensuring vehicles are ready by morning.
#3 Integration with renewable energy sources
What it is: Platforms connect with solar, wind, or storage to maximize clean energy usage and reduce grid reliance.
How it is used: By linking smart EV charging stations with renewables, businesses cut emissions, reach ESG targets, and boost resilience.
Example: A supermarket powers EV chargers with rooftop solar, cutting grid reliance and supporting sustainability goals.
Examine IoT in EV charging solutions from our blog post. 
#4 Fleet & asset management tools
What it is: Dashboards track charging, usage patterns, and predictive maintenance for vehicles and energy assets.
How it is used: An EV smart charge solution keeps fleets operational, lowers maintenance costs, and improves energy efficiency across transport and facilities.
Example: A logistics firm predicts battery replacements early, reducing downtime and lowering fleet maintenance costs.
#5 Access control & user management
What it is: Role-based access via RFID, apps, or backend systems to secure and manage the use of smart electric vehicle charging systems. 
How it is used: A smart EV charging station ensures only authorized users charge, supports pricing models, and prevents misuse of the infrastructure.
Example: Employees charge with RFID cards, ensuring only authorized staff use stations and supporting flexible pricing.
#6 Data analytics & reporting
What it is: EV charging data analytics tools collect and analyze usage, cost, and performance data for informed energy decisions.
How it is used: With smart charging of electric vehicles, businesses optimize expenses, monitor ESG metrics, and prove sustainability compliance.
Example: A campus tracks CO₂ savings and expenses, optimizing charging while proving ESG compliance.
Here at Intelliarts, we have extensive experience developing and integrating custom analytics and reporting solutions across domains on demand. Don’t hesitate to explore our portfolio to discover more. 
#7 Scalability & interoperability
What it is: Ability to expand charging points and integrate with diverse hardware, IT, and energy systems.
How it is used: An electric vehicle charging management system future-proofs investments, ensuring growth in EV adoption and energy infrastructure without costly replacements.
Example: A city grows from 10 to 100 chargers, integrating multi-vendor hardware on one platform without disruptions.
Custom-built vs off-the-shelf platform smart charging solutions
Let’s quickly compare custom-tailored solutions that have smart charging platform features with smart charging platforms available on the market: 

From Intelliarts’ experience, an off-the-shelf platform, despite having a seemingly better price, may not be easily integrated with your software and working processes. They also tend to offer unnecessary features, thus increasing the load on employees, and lack essential options. However, off-the-shelf platforms still may prove themselves suitable. Here are our tips to help you make the best choice:
When to consider custom-built development:

You need deep integration with existing ERP, fleet management, or renewable systems.
Scalability is critical, with plans to expand charging infrastructure rapidly.
Unique features or tailored analytics are required for competitive differentiation.
Long-term ROI and flexibility matter more than initial launch speed.

When to consider off-the-shelf solutions:

You want a faster time-to-market with lower upfront costs.
Custom integrations are limited, and vendor APIs meet current requirements.
Budget constraints favor predictable licensing and subscription models.

Software for Renewable Energy & E-MobilityIntelliarts’ experts are here to build smart, sustainable solutions for your business.Explore our service
Real-world impact of smart charging solutions: Top 3 success stories by Intelliarts
#1 EV charging management software platform for EVIQ
Challenge: EVIQ needed a custom EV charging management platform with smart charging platform features to scale its Saudi network. The solution had to support monitoring, lifecycle management, and local payment compliance.
Solution: Intelliarts built an EV charging management software with AWS serverless architecture, local payment, and ZATCA e-invoicing integration, plus a multilingual mobile app. 
Results: The key components of the platform were delivered in record time. It enabled efficient station monitoring, streamlined charger onboarding, ensured compliance, and provided a seamless driver experience — supporting EVIQ’s rapid market expansion.
#2 Smart power management system development

Challenge: The EV provider needed smart power management system to control loads and costs. Site hosts lacked tools and visibility at peak hours.
Solution: Intelliarts built a flexible energy management system with scheduling, analytics, APIs, and cost reports. It enforced off-peak charging and UK smart charging compliance.
Results: The platform balanced loads and lowered electricity bills. It extended infrastructure life and improved charging reliability.
EV adoption rates are rising nationally, but we’re seeing considerable differences in readiness levels among utilities. — Doug Black, a lead at Lawrence Berkeley National Lab
#3 Creating next-gen EV fleet management software

Challenge: The EV provider wanted to expand into fleet management. They needed software to track vehicles, optimize charging, and cut operating costs.
Solution: Intelliarts built EV fleet management software with telematics, energy management, analytics, and at-home charging compensation. 
Results: The platform improved efficiency, reduced downtime, lowered energy costs, and gave fleet operators flexible depot, on-route, and at-home charging control.
Final take
Smart charging has become essential for cost savings and resilience. By adopting a solution with smart charging platform features early, businesses secure faster ROI and long-term infrastructure efficiency. 
Here at Intelliarts, we are proud to have more than 25 years of dev experience, over 80 large projects under our belt, and a 90% customer return rate. We are ready, willing, and able to handle your EV smart charging project. 
Ready to build your next EV software with a reliable partner?Contact Us

#### Large Language Model Examples: 30+ LLMs Compared & How to Choose (2026)
TL;DR

By 2030, the global Large Language Model (LLM) market is projected to reach $36.1 billion, highlighting how LLMs have emerged today as a core driver of business innovation.
The 5 most commonly deployed LLMs in 2026: GPT-5.5 (OpenAI), Claude Opus 4.7 (Anthropic), Gemini 3.1 Pro (Google), Grok 4.3 (xAI), and Llama 4 (Meta)
Open-weight models (Llama 4, DeepSeek V4 Pro, Mistral) offer lower TCO and on-prem deployment; proprietary models deliver stronger out-of-the-box performance and managed APIs
Leading enterprises now route different tasks to different models instead of relying on a single provider
Start with our evaluation framework  or decision matrix, or jump to your use case

The 2026 LLM Comparison Table: 30 Models at a Glance
The table below covers 30 LLMs across the metrics that matter most for enterprise evaluation. Prices reflect verified API list rates as of July 2026.
[table id=5 /]
Find Out the Real Cost of LLM AdoptionExplore the hidden costs, ROI challenges, and optimization strategies behind enterprise AI adoption.Download our white paper
How to Choose an LLM for Enterprise: 5 Evaluation Criteria
Selecting the right large language model examples for your business requires careful evaluation to ensure they meet your operational and scalability needs. Here’s a five-step guide that our engineering team uses when scoping LLM projects for enterprise clients.
1. Deployment options (API vs. self-hosted vs. fine-tuned)
The right deployment model shapes your total cost, data privacy posture, and long-term flexibility. API access offers the fastest time to value; self-hosted models give you full data control; fine-tuning narrows a model to your specific domain.
[table id=6 /]
2. Data privacy, GDPR, and compliance requirements
EU AI Act obligations, GDPR Article 22 on automated decision-making, and CCPA restrictions for California deployments are live concerns. 
When evaluating an LLM for enterprise, ask: 

Does the provider offer EU data residency? 
Is on-premises deployment available? 
Are audit logs and data processing agreements (DPAs) part of the offering?

Models with EU data residency or full on-premises options: 

Claude Opus 4.7 (Anthropic, AWS EU regions + Microsoft Foundry)
Azure OpenAI (EU regions, BAA available)
Llama 4 (self-hosted, full control)
Mistral AI (EU-based provider, GDPR-native)
Command R+ (Cohere, EU deployment available)

3. Cost-performance benchmarks for enterprise use cases
API pricing is only one line in the true total cost of ownership (TCO). A complete TCO calculation must include: 

Token cost (input + output)
Fine-tuning compute
GPU infrastructure for self-hosted models
Maintenance and retraining overhead
Integration engineering time

Note that Gemini 3.1 Pro pricing doubles above 200K tokens, which is a meaningful consideration for long-document workloads.
[table id=7 /]
4. Fine-tuning vs. RAG trade-offs by LLM

Fine-tune when: the task is narrow and repetitive, the output format must be tightly controlled, and you have labelled training data available
Use RAG when: the task is knowledge-intensive, the information needs to be current or changes frequently, or you want to avoid retraining costs as your data evolves

[table id=8 /]
5. Latency and throughput benchmarks
Latency is an operational filter as much as an accuracy metric. The figures below are directional benchmarks based on publicly available data and our own project observations as of May 2026. Actual performance varies by prompt length, infrastructure, and load.
[table id=9 /]
Large Language Model Examples by Enterprise Use Case: Profiles of 30 Models
Rather than listing models alphabetically, we have organised these large language model examples by the enterprise use cases they serve best.
Reasoning & analysis
Choosing the right reasoning model depends on how accurate you need it to be and what costs you can accept, not just where it ranks in benchmarks. Here’s a comparison of the top options.
GPT-5.5 (OpenAI)
GPT-5.5 is OpenAI’s current flagship, released in April 2026. Compared to other large language model examples, it is designed to handle more work itself: writing and debugging code, researching online, analysing data, creating documents and spreadsheets, and operating software autonomously.

Context window: 1M tokens
Licence: Proprietary
Pricing tier: $5/$30 per M tokens
Best for: Agentic enterprise workflows, coding, research, data analysis
Real-world example: Morgan Stanley uses GPT-4 (now migrating to GPT-5.x) to power its AI @ Morgan Stanley Assistant, which searches over 100,000 research reports and gives financial advisors instant, cited answers, increasing document retrieval efficiency from 20% to 80%. Over 98% of advisor teams actively use the tool.

Claude Opus 4.7 (Anthropic)
Claude Opus 4.7 is Anthropic’s latest flagship, released April 16, 2026. It scores 87.6% on SWE-bench Verified and 94.2% on GPQA Diamond.

Context window: 1M tokens
Licence: Proprietary
Pricing tier: $5/$25 per M tokens
Best for: Long-horizon agentic coding, safety-critical workflows, software engineering
Real-world example: Cursor, the AI-powered code editor, uses Claude Opus 4.7 at its highest tier for complex multi-file refactors and full-codebase reasoning. Cursor reports that Opus-class models handle architecture-level decisions that smaller models fail on, enabling developers to delegate entire feature implementations.

Learn more about the difference between Claude vs. ChatGPT.
Gemini 3.1 Pro (Google DeepMind)
Gemini 3.1 Pro is Google DeepMind’s frontier reasoning model, released in February 2026. It leads on ARC-AGI-2 abstract reasoning (77.1%) and GPQA Diamond (94.3%), and is the only major frontier model with native text, image, audio, and video input in a single model.

Context window: 1M tokens
Licence: Proprietary
Pricing tier: $2/$12 per M tokens (<200K); $4/$18 (>200K)
Best for: Multimodal tasks, long-context document analysis, cost-sensitive frontier workloads
Real-world example: Google integrated Gemini 3.1 Pro into NotebookLM, allowing users to upload full research corpora such as PDFs, audio recordings, and video transcripts. They can then query all of them simultaneously. Enterprise teams at Deloitte use NotebookLM for document-heavy due diligence workflows.

Grok 4.3 (xAI)
Grok 4.3 is xAI’s current flagship, offering a 1M token context window at $1.25 or $2.50 per million tokens, the most affordable pricing among frontier-tier models as of May 2026. 

Context window: 1M tokens
Licence: Proprietary
Pricing tier: $1.25/$2.50 per M tokens
Best for: Real-time data tasks, enterprise automation, business intelligence, cost-optimised reasoning
Real-world example: xAI integrated Grok 4.3 into X Premium, where it provides real-time analysis of trending topics and market sentiment. Financial services firms use the API to monitor real-time social signals for trading intelligence workflows.

“For reasoning tasks, we find model choice matters less than people expect. When accuracy on structured extraction is non-negotiable, Claude Opus 4.7 is our default.” — Our engineering team.
Code generation & software engineering
Frontier models and lightweight open-weights options serve very different needs. Here’s which model from the large language model examples fits which coding workflow.
Claude Opus 4.7 (Anthropic)
Claude Opus 4.7 holds the highest reported score on SWE-bench Verified at 87.6% and leads Terminal-Bench 2.0 at 69.4%.

Context window: 1M tokens
Licence: Proprietary
Pricing tier: $5/$25 per M tokens
Best for: Complex multi-file refactors, agentic coding pipelines, codebase-level reasoning
Real-world example: Windsurf (formerly Codeium) routes its highest-complexity agentic coding tasks to Claude Opus 4.7. Rakuten Group, an early-access partner, reported that Opus 4.7 resolved 3x more production tasks end-to-end compared to Opus 4.6, particularly on tasks requiring reasoning across multiple files and test suites simultaneously.

GPT-5.5 (OpenAI)
GPT-5.5 is designed for autonomous software work: writing and debugging code, operating software, and completing tasks end-to-end across tools. 

Context window: 1M tokens
Licence: Proprietary
Pricing tier: $5/$30 per M tokens
Best for: Autonomous multi-step coding, GitHub Copilot agent mode, Microsoft ecosystem workflows
Real-world example: GitHub Copilot’s highest-capability tier uses the GPT-5.x model family for its agent mode, which can autonomously open pull requests, write tests, and fix CI failures. GitHub reported that teams using Copilot agent mode complete feature-level tasks up to 55% faster than those using autocomplete-only assistance.

Get to know more about ChatGPT by checking out ChatGPT business stats.
Mistral Large 2 & Mistral 7B (Mistral AI)
Mistral Large 2 is a strong multilingual code model at a significantly lower price than the frontier tier. Its Apache-licensed sibling Mistral 7B is the reference open-weights model for cost-sensitive code generation.

Context window: 128K tokens (Large 2) / 32K tokens (7B)
Licence: Proprietary API (Large 2) / Apache 2.0 (7B)
Pricing tier: $$ (Large 2) / Free self-hosted (7B)
Best for: Multilingual code generation, edge deployment, high-volume batch inference, cost-sensitive tasks
Real-world example: Mistral AI’s partnership with BNP Paribas, renewed for three years in 2026, covers deploying Mistral models across the bank’s European operations. This includes a customer-facing virtual assistant, Hello bank!’s HelloïZ chatbot, which has served over one million clients since January 2026, and an internal employee AI assistant for summarisation, translation, and document generation. BNP Paribas cited Mistral’s EU-based infrastructure and GDPR-native design as key reasons for choosing it over US-based providers.

“For code generation, the frontier models are genuinely differentiated from smaller ones on complex, multi-file tasks. — Our engineering team.”
Long-context document processing
Longest context window ≠ best reasoning quality. Use a large-context model for ingestion, a more precise one for structured extraction.
Llama 4 Scout (Meta AI)
Llama 4 Scout ships with a 10M token context window, the longest of any publicly available model.

Context window: 10M tokens
Licence: Open (Llama licence)
Pricing tier: Free (self-hosted)
Best for: On-premises long-document RAG, privacy-first knowledge pipelines, high-throughput batch inference
Real-world example: A European legal-services firm deployed Llama 4 Scout self-hosted on-premises to process entire case file archives, including thousands of pages of precedents, contracts, and court filings, in a single inference pass. The 10M context window removed their chunking infrastructure and cut document-processing latency by over 60%.

Gemini 3.1 Pro (Google DeepMind)
Gemini 3.1 Pro supports a 1M token context window with native text, image, audio, and video input. It is the best commercial choice for workloads combining long documents with non-text content, such as annotated contract review or multimedia research synthesis.

Context window: 1M tokens
Licence: Proprietary
Pricing tier: $2/$12 per M tokens (<200K); $4/$18 (>200K)
Best for: Multimodal long-context analysis, video + document workflows, annotated contract review
Real-world example: Google integrated Gemini 3.1 Pro into NotebookLM Enterprise, used by teams at KPMG and Accenture for multi-source document synthesis. Analysts upload entire project document sets, including PDFs, slide decks, and meeting recordings. They query across all sources simultaneously, replacing manual cross-referencing workflows that took days.

Interested in using a large language model in your project?Our experts will guide you to successContact us
Claude Opus 4.7 (Anthropic)
While Llama 4 Scout leads on raw context length and Gemini 3.1 Pro on multimodal input, Claude Opus 4.7 is widely preferred for long-context tasks where reasoning quality and instruction-following accuracy matter most.

Context window: 1M tokens
Licence: Proprietary
Pricing tier: $5/$25 per M tokens
Best for: Structured clause extraction, regulatory document analysis, long-context reasoning with precision
Real-world example: Harvey, the AI legal platform used by law firms including Allen & Overy, uses Claude as its primary model for contract analysis and due diligence. Lawyers report that Claude’s ability to reason over full contracts, tracking defined terms and cross-references across hundreds of pages, significantly reduces review time on complex M&A transactions.

Yi-34B (01.AI)
Yi-34B supports up to 200K tokens on an Apache 2.0 licence, making it one of the most capable open-weights options for organisations requiring long-context processing with full data residency.

Context window: 200K tokens
Licence: Apache 2.0
Pricing tier: Free (self-hosted)
Best for: Long-context multilingual document processing, on-premises deployment, Chinese-English bilingual workflows
Real-world example: Several Chinese enterprise clients have deployed Yi-34B on-premises for bilingual contract analysis and regulatory compliance review, processing lengthy government procurement documents and financial disclosures that require simultaneous understanding of Chinese regulatory language and English technical specifications.

“Context window size and reasoning quality are different. We typically use the largest-context model available for ingestion and retrieval. Then we use a more precise model for structured extraction and Q&A over the retrieved chunks. — Our engineering team.”
RAG pipelines & knowledge retrieval
Model choice is only part of the RAG equation. Here are the models best suited to enterprise retrieval pipelines.
Cohere Command R+
Cohere Command R+ is purpose-built for enterprise RAG applications. It delivers contextually relevant and highly accurate responses in document retrieval workflows.

Context window: 128K tokens
Licence: Proprietary
Pricing tier: $$
Best for: Enterprise RAG pipelines, knowledge management, customer support automation, content moderation
Real-world example: Cohere deployed its Rerank model inside Notion’s workspace search, applying re-ranking before the generative model processes each query to ensure users receive the most accurate and contextually relevant results. The deployment reduced operational costs linked to embedding, storage, and system complexity, while delivering more contextually relevant results than Notion’s previous keyword-based approach.

  White PaperElevate Your LLM Performance at least by 40% with RAG Systems Explore our RAG for LLMs white paper
Llama 4 Scout (Meta AI)
Llama 4 Scout’s 10M token context window makes conventional chunking largely optional for most enterprise document sets. For organisations building RAG pipelines where full data residency and zero per-token cost are requirements, it is the strongest open-weights choice in 2026.

Context window: 10M tokens
Licence: Open (Llama licence)
Pricing tier: Free (self-hosted)
Best for: On-premises RAG, privacy-first knowledge pipelines, high-volume document retrieval
Real-world example: An insurance carrier deployed Llama 4 Scout self-hosted to power a claims knowledge assistant querying their full policy library, claims history, and regulatory guidance. These were previously three separate search systems. Consolidating onto a single 10M-context model eliminated the inter-system retrieval layer and cut query latency by 45%.

DeepSeek V4 Pro
DeepSeek V4 Pro is a 1.6T parameter MoE open-weights model with a 1M token context window under an MIT licence.

Context window: 1M tokens
Licence: MIT (open-weights)
Pricing tier: $1.74/$3.48 per M tokens (or free self-hosted)
Best for: Agentic coding, long-context RAG, cost-sensitive enterprise knowledge workflows
Real-world example: Several European fintech firms adopted DeepSeek V4 Pro for self-hosted regulatory document analysis, citing the MIT licence for full customisation rights. One client reported a 70% reduction in inference cost compared to their previous GPT-4-based pipeline at equivalent accuracy on their compliance classification task.

“The retrieval layer is where most RAG projects succeed or fail, not the generation layer. We have seen more improvement from switching to hybrid retrieval than from switching LLMs. — Our engineering team.”
Learn more about RAG development services at Intelliarts.
Multilingual & global operations
Multilingual benchmark scores can obscure significant per-language variance. Here’s how the leading large language model examples compare across global use cases.
Qwen2.5-72B (Alibaba)
Qwen2.5-72B is an enterprise-focused LLM with strong multilingual processing, particularly across Asian language markets alongside English.

Context window: 128K tokens
Licence: Apache 2.0
Pricing tier: Free (self-hosted)
Best for: Multilingual ops, e-commerce, document automation, customer service across Asian and European languages
Real-world example: AliExpress (Alibaba’s international e-commerce platform) uses Qwen models across its e-commerce ecosystem to power product recommendation engines and customer service workflows, leveraging Qwen’s strength across Chinese, English, and other Asian languages to adapt content for cultural context rather than performing word-for-word translation.

Mistral Large 2 (Mistral AI)
Built by a European AI company and GDPR-native by design, Mistral Large 2 offers competitive multilingual performance across European languages with strong data residency guarantees.

Context window: 128K tokens
Licence: Proprietary (API)
Pricing tier: $$
Best for: European multilingual enterprise, GDPR-sensitive deployments, EU regulatory compliance workflows
Real-world example: Mistral AI’s longest-standing financial services partner BNP Paribas renewed their three-year partnership in 2026, deploying Mistral models for customer-facing virtual assistants and internal employee tools across the bank’s EU operations. BNP Paribas explicitly cited Mistral’s EU-based infrastructure and GDPR-native design as key reasons for choosing it over US-based providers.

Bloom (BigScience)
Bloom is a community-driven, open-weights model supporting a wide array of languages and dialects. It is optimised for academic research, large-scale multilingual content generation, and machine translation and localisation.

Context window: 2K tokens
Licence: RAIL licence
Pricing tier: Free (self-hosted)
Best for: Academic multilingual NLP research, ethical AI projects, low-resource language tasks
Real-world example: Academic and research teams use BLOOM for low-resource NLP tasks in African languages where commercial models lack coverage. BLOOM’s training across 46 languages, including several African languages absent from GPT or Gemini, makes it the reference open-weighted option for multilingual research in underserved language communities. It has been used in university- and NGO-led initiatives focused on the preservation and digitisation of sub-Saharan African languages.

“Aggregate multilingual benchmark scores are almost always misleading for real deployments. A model that leads overall can underperform significantly on specific language pairs. We benchmark on the exact languages in scope before recommending anything. — Our engineering team.”

Open-source & on-premises deployment
Self-hosting changes the cost and control calculus significantly. Here are the strongest open-weights options for on-premises deployment.
Llama 4 (Meta AI)
The Llama 4 family is the strongest open-weights option for both RAG pipelines and reasoning tasks in 2026.

Context window: Up to 10M tokens (Scout) / 1M tokens (Maverick)
Licence: Open (Llama licence)
Pricing tier: Free (self-hosted)
Best for: On-premises reasoning, high-throughput RAG, privacy-first deployments
Real-world example: AirAsia deployed its AVA virtual assistant providing customer service in 11 languages across Southeast Asian routes. By self-hosting, AirAsia eliminated per-query API costs at scale, handling millions of interactions monthly. This also kept all customer data within their own infrastructure, a regulatory requirement in several ASEAN markets.

DeepSeek V4 Pro
DeepSeek V4 Pro is a 1.6T parameter MoE model under an MIT licence, self-hostable at zero marginal cost. It scores 80.6% on SWE-bench and supports three reasoning modes for agentic coding and knowledge work.

Context window: 1M tokens
Licence: MIT (open-weights)
Pricing tier: $1.74/$3.48 per M tokens (or free self-hosted)
Best for: Agentic coding, knowledge work, cost-sensitive enterprise reasoning
Real-world example: Several European fintech firms self-host DeepSeek V4 Pro for regulatory document analysis, citing the MIT licence for full customisation rights and 1.6T MoE architecture for frontier-grade reasoning. One client reported a 70% reduction in inference cost versus their previous GPT-4-based pipeline at equivalent accuracy.

DeepSeek R1
DeepSeek R1 delivers reasoning performance at roughly 27 times lower cost than comparable GPT-5.x tier models under an MIT licence. It has become the default option for cost-sensitive analytical workloads.

Context window: 128K tokens
Licence: MIT
Pricing tier: $0.55/$2.19 per M tokens (or free self-hosted)
Best for: Reasoning, math, scientific analysis, cost-sensitive enterprise inference
Real-world example: A US-based quantitative research firm replaced their OpenAI o1-based financial modelling pipeline with DeepSeek R1, reporting equivalent accuracy on structured financial reasoning tasks at about 25x lower API cost, reducing their monthly AI inference spend from six figures to five figures.

Learn more about document data extraction using OpenAI GPT.
Mistral 7B (Mistral AI)
With just 7 billion parameters, Mistral 7B balances computational efficiency with strong language understanding. It is well-suited for code generation, automated content generation, and chatbots where reducing computational costs matters.

Context window: 32K tokens
Licence: Apache 2.0
Pricing tier: Free (self-hosted)
Best for: Edge deployment, high-volume batch inference, cost-sensitive code generation and classification
Real-world example: A European e-commerce company deployed Mistral 7B on a single A10G GPU per region to power real-time product description generation for 2 million listings monthly. The self-hosted deployment costs about $0.30/hr per region, a fraction of the equivalent API cost, with sub-second generation latency that meets their page-load time requirements.

Falcon (Technology Innovation Institute)
Developed by the Technology Innovation Institute (TII) in Abu Dhabi, Falcon is a high-performance open-weights LLM built for optimised training and deployment.

Context window: 2K tokens (Falcon 40B)
Licence: Apache 2.0
Pricing tier: Free (self-hosted)
Best for: Customer support automation, document summarisation, data extraction, enterprise NLP
Real-world example: The Abu Dhabi government deployed Falcon within its digital government initiative to power Arabic-language citizen services chatbots across multiple departments. The open-weights model allowed full customisation for Gulf Arabic dialect, integration with government data systems, and complete data sovereignty — all requirements that ruled out third-party API providers.

“The self-host-vs-API decision comes down to two things: data sensitivity and token volume. If the data can leave your perimeter and volumes are modest, managed API wins on simplicity and total cost. If data residency is required, or you are running hundreds of millions of tokens per month on a task a smaller model handles well, self-hosted open-weights models make economic sense. — Our engineering team.”
Which LLM for Which Enterprise Scenario: Decision Matrix
The table below maps the most common enterprise use cases to recommended models with May 2026 pricing in mind.
[table id=10 /]
API vs. Self-Hosted vs. Fine-Tuned: Deployment Trade-Off Guide
How you deploy large language model examples is often as consequential as which model you choose. The three primary deployment modes each carry distinct trade-offs across cost, data control, latency, and engineering overhead.
[table id=11 /]
We treat deployment mode selection as a discovery step. Before evaluating any model, we map three variables with every client: data sensitivity, expected token volume, and task specificity. These three answers almost always determine the right deployment architecture before model comparison.
If you are scoping an LLM deployment and are not yet sure which direction makes sense, talk to our LLM engineers, and we will give you a clear architecture recommendation based on your use case.
LLM Use Cases by Business Function
LLMs are core tools for business growth and efficiency. The large language model examples below cover the most impactful applications, each illustrated with a named real-world example and an outcome metric.

Operational Efficiency
LLMs enable businesses to automate complex workflows, like content generation, data analysis, and document processing, to increase efficiency and productivity. GPT-4 powers automated customer service agents for Duolingo Max, handling thousands of queries in different languages without human intervention.
Cost Reduction: 20–50% Operational Savings at Scale
Implementing AI automation with large language model examples reduces operational costs by minimising human intervention. Cohere helped Notion deploy their embedding model into workspace search, cutting costs related to embedding, storage, and system complexity. 
DeepSeek R1 delivers reasoning performance at about 27 times lower cost than GPT-5.x tier models for similar tasks, greatly expanding the range of economically viable workloads.
Better Customer Experience: Faster, More Personalised Interactions
LLMs improve customer experience by enabling faster, more natural AI-driven interactions across multiple touchpoints. AirAsia’s virtual assistant, built on Meta’s LLaMA, offers customer service in over 15 languages, a scalability win that would otherwise require large multilingual support teams.
Scalability for Global Expansion
LLMs with multilingual capabilities are essential for businesses expanding globally without a full local workforce. AirAsia’s chatbot supports over 15 languages. 
Google Translate, powered by large language model examples, supports more than 100, enabling seamless communication for e-commerce platforms, travel agencies, and global customer operations.
Improved Decision-Making: Predictive Insights at Scale
LLMs assist decision-makers across industries by summarising research papers, extracting key findings, and offering predictive insights. Pharmaceutical companies like Pfizer use ChatGPT to accelerate drug discovery, specifically for safety-related table summarisation in clinical study reports. 
Tools like Brandwatch use LLM-powered sentiment analysis to help brands understand public opinion in real time.
Language modelling for writing assistance
LLMs enable grammar correction, sentence completion, and fluency improvement in machine-generated text. Writing assistance tools such as Grammarly apply this capability to ensure written content across education, marketing, and corporate communications is clear, professional, and error-free.
Ready to Build an LLM for Your Business?Intelliarts provides expert support at every step of the development journeyExplore more
Final take
As LLM platforms continue to expand in capability, they offer numerous solutions tailored to various requirements. In 2025, selecting the right LLM model and an experienced tech partner is crucial to ensure your AI strategy remains competitive. With rapidly evolving technology, leveraging the best language model can optimize processes, reduce costs, and improve decision-making.
Choosing the right model is key to staying ahead, as it directly influences how effectively your business can use AI for specific tasks and scale over time. Use the provided list of LLMs to guide your choice of an option that would benefit your business the most.
At Intelliarts, we bring over 24 years of experience in delivering cutting-edge AI/ML solutions. Our team of experts is dedicated to ensuring that your integration of LLMs drives measurable results and keeps you at the forefront of your industry. Let the Intelliarts team make an impact on your project.
Get an Architecture Recommendation for Your Use CaseTalk to our LLM engineers

#### Machine Failure Prediction using Machine Learning [Case Study]
The faster your product passes the stage of the production pipeline and gets shipped out the factory door, the sooner it will fall into the hands of happy customers. That’s your major goal in manufacturing — to maximize the production line performance and deliver finished goods to customers in a timely manner.
This goal is though difficult to reach with repeated manufacturing failures, which result in increasing maintenance costs and delays in manufacturing. Both expected and unexpected equipment failures can wreak havoc on your production line performance and company profits.
In this article, we describe how a machine learning (ML) approach can bring value to your manufacturing company and reduce industrial equipment failures with machine failure prediction. To explain things better, we discuss an appliance manufacturer’s case and tell how we helped this company to reduce machine failure that occurred on the factory floor using machine learning.

#### Machine Learning for Risk Assessment: The Ultimate Guide
It takes a lot of time and effort to perform an assessment for a risk analyst. The process involves researching and analyzing numerous factors to evaluate the risks a potential customer might undergo. Moreover, because of the subjectivity of this process, various risk analysts frequently provide diverse assessments for the same policy.
However, machine learning can help to evolve the risk assessment process by creating risk models that process the information stored about previous policies to learn from previous mistakes and successes, which results in evaluating newly signed ones more quickly and reliably.
In this way, machine learning for risk analysis benefits both sides: businesses and customers. ML solutions allow insurance companies to reduce the time of evaluation and encourage employees to spend free time on other important tasks, like improving customer service. From the perspective of customers, they receive better services and more suitable products as a consequence.
With machine learning technology in insurance, companies are freed from numerous repeatable and time-consuming tasks, as after the initial ML model is trained and a necessary ML pipeline is set up, it continues to educate itself, finding ways to make better predictions over time.

#### Machine Learning in Business: 10 Use Cases, Benefits & Implementation Guide
TL;DR 
Machine learning in business involves using data‑trained models to improve key decisions, automate pattern‑heavy work, and predict outcomes such as churn, fraud, demand, downtime, or price sensitivity.
When combined with the right engineering practices, ML increases efficiency, protects revenue, and reduces risk across industries, from lending and insurance to manufacturing and real estate.
What is machine learning in business?
Machine learning in business is the use of algorithms and statistical models that learn from historical and live data to support decisions, detect patterns, and automate tasks. 
Instead of relying solely on fixed rules, businesses use ML to let systems adapt to changing conditions and improve over time.
In practice, ML applications range from recommendation engines and underwriting models to predictive maintenance, fraud detection, and intelligent customer support. As data volumes grow, ML helps teams move from static reporting to proactive, data‑driven action.
Looking for a provider that will help you take advantage of ML in business?Contact us
What are the most important machine learning use cases in business?
Machine learning for business has many applications, but ten use cases stand out as both practical and widely adopted across industries. Each use case can be tailored to sectors such as machine learning in insurance, lending, equipment leasing, manufacturing, and retail.

1. Intelligent customer support & conversational AI
How does ML improve customer support?
Machine learning enhances support by classifying inquiries, suggesting responses, routing tickets to the right team, and providing intelligent self‑service options.
Modern systems often combine ML with large language models (LLMs) so that businesses can automate both structured decisions (like routing and escalation) and conversational flows (like answering questions or summarizing cases). 
Where do we see this?

AI‑powered chatbots that resolve FAQs and simple issues.
Intelligent routing based on topic, urgency, or customer value.
Suggested replies and knowledge‑base retrieval for agents.

The result is faster response times, better scalability, and a smoother hand‑off from automation to human agents for complex issues.
2. Enhanced decision support systems (DSS)
How does machine learning support managerial decisions?
Decision support systems use ML to turn raw data into actionable insights for planning, monitoring, and resource allocation. Examples include:

Sales and forecasting analysis dashboards.
Executive scorecards tracking KPIs across departments.
Resource allocation tools that predict demand and recommend staffing or inventory levels.

These systems help leaders move from hindsight (reports) to foresight (predictions), improving overall decision quality.
You can explore related forecasting approaches in Intelliarts’ article on intelligent forecasting.
3. Customer recommendation engines
How do recommendation engines create business value?
Recommendation engines analyze user behavior and preferences to suggest relevant products, content, or services. They increase engagement, conversion, and retention by reducing search friction and surfacing what customers are most likely to want.
Typical applications include:

Product recommendations in e‑commerce.
Content suggestions on media platforms.
Offer or package recommendations in lending, insurance, and subscription services.

These systems help businesses personalize at scale, turning generic catalogs into tailored experiences.
4. Predictive customer churn analysis
Why is churn prediction critical for profitability?
Predictive churn models estimate which customers are likely to cancel or stop using a product or service. This allows businesses to focus retention efforts where they matter most.
Common applications are:

Identifying at‑risk customers in SaaS, telecom, and financial services.
Designing targeted retention campaigns.
Flagging “willing‑to‑buy” customers who might respond to upsell or cross‑sell.

Churn analysis is crucial in subscription and long‑term contract models where customer retention directly determines revenue and profitability.
See how an ML approach to customer churn analysis can work in the image below:

5. Dynamic pricing models
How does ML power dynamic pricing?
Dynamic pricing models adjust prices in real time based on demand, competition, customer behavior, and market conditions. This is common in sectors where price sensitivity varies strongly over time, such as travel, energy, and online commerce.
Use cases include:

Time‑based pricing for flights and hotel rooms.
Market‑responsive pricing for retail and marketplaces.
Tariff optimization in energy and utilities.

The business outcome is improved margin, better utilization of capacity, and more competitive pricing tailored to real‑time conditions.
6. Data‑driven market segmentation
How does ML improve market segmentation?
Machine learning can segment customers into data‑driven groups based on demographics, behavior, value, risk, and preferences. This is more precise than manual segmentation and can update as patterns change.
Applications include:

Targeted marketing campaigns.
Product and offer design for specific segments.
Prioritization of accounts based on potential value or risk.

Accurate segmentation helps businesses focus resources on the right groups and craft messaging that resonates with each segment.
7. Fraud detection systems
How does ML detect fraud more effectively than rules alone?
Fraud detection models analyze transaction, behavioral, and contextual data to identify suspicious activity that may indicate fraud. They are widely used in banking, payments, insurance, lending, and e‑commerce.
Use cases include:

Identifying unusual transaction patterns.
Detecting application or identity fraud.
Adapting to new fraud tactics and reducing false positives.

Because unchecked fraud can eat into yearly revenue, ML‑based detection has become a critical part of risk and compliance strategies.

See how Visa’s AI solution is used in fraud detection in the video below:

For more on sector‑specific applications, see Intelliarts’ article on machine learning in insurance and ML applications across the insurance value chain.
8. Supply chain optimization
How can machine learning optimize supply chains?
Supply chain optimization models balance demand and supply, automate planning, and improve logistics performance. They use historical and live data to anticipate stock needs, optimize routes, and prevent overstocking or stockouts.
Applications include:

Autonomous demand and supply correlation.
Route planning for delivery fleets.
Revenue and cost management for inventory decisions.

This use case is especially valuable for consumer packaged goods, manufacturing, and distribution networks where small improvements in accuracy can produce large cost savings.
9. Optimization of operational processes
Where does ML fit when it comes to process optimization?
ML supports process optimization by identifying bottlenecks, predicting workload, and automating decisions in workflows such as invoice processing, scheduling, and inventory management.
This can be seen in:

Robotic Process Automation (RPA) enriched with ML to handle exceptions.
AI‑driven improvements in back‑office and administrative workflows.
Forecast‑based scheduling for appointments, production slots, or service windows.

The RPA industry is expected to keep growing as more businesses look to streamline operations and reduce manual intervention, and ML plays a key role in making these automations more flexible.
10. Predictive maintenance & industrial analytics
How does predictive maintenance reduce downtime and cost?
Predictive maintenance models forecast which assets are likely to fail, allowing teams to service equipment before breakdowns occur. This is a core ML use case in manufacturing, energy, fleet management, and equipment leasing, where unplanned downtime is very expensive.
Business outcomes are as follows:

Reduced emergency repairs.
Better maintenance scheduling and spare parts planning.
Longer asset life and higher reliability.

You can explore this topic further in the Intelliarts guide on predictive maintenance in manufacturing.
How should businesses choose the right machine learning use case?
Not every ML use case will yield the same ROI in every organization. The right starting point depends on business goals, data maturity, and operational readiness.
A simple decision matrix helps prioritize things:
[table id=28 /]
Before committing to an ML initiative, it is useful to ask:

Is the business problem clearly defined?
Do we have sufficient historical data?
Can success be measured with clear KPIs?
Is there executive sponsorship?
Can the solution be integrated into existing workflows?

Need help with an ML project?Schedule a meeting with our team to discuss your needs.Let's talk
What real‑world cases show ML impact?
As can be observed, the applications of ML in business are vast. Let’s get down to several case studies that shed light on different ways smart technology was used in a real-life environment:  
Home and car insurance company
The challenge was to help an insurance company replace its inefficient system for communicating with leads with a custom predictive lead-scoring solution. 
The solution by Intelliarts was a predictive lead-scoring model capable of forecasting how likely the lead would buy an insurance policy. The end model enabled the customer to cut off approximately 6% of inefficient leads, which resulted in a 1.5% increase in profit within a few months.

Big appliance manufacturer case study
The manufacturer experienced an issue with frequent and unexpected breakdowns in their production line. To solve this issue, Intelliarts developed a machine learning (ML) model using the Extreme Gradient Boosting algorithm. 
The model, trained with 14.3 GB of data from IoT devices, could predict which parts were most likely to break down and, with 90% accuracy, predicted failures to help the company cut maintenance costs by 5%.
Bring extensive ML experience to the table. You can trust Intelliarts with the development of a smart solution to meet your business’s needs.
PayPal
One of the companies that uses machine learning, PayPal, faced challenges in identifying and preventing fraudulent transactions. To tackle this, they implemented fraud detection ML algorithms that scrutinize various aspects of each transaction, including the transaction location, the device being utilized, and the user’s historical behavior. 
This approach has significantly enhanced PayPal’s ability to safeguard its users’ transactions and maintain the integrity of its payment platform.
YouTube
YouTube, a long-time user of machine learning in its operations, employs recommendation algorithms to suggest videos to its viewers. This method is grounded in analyzing extensive historical data. 
As of now, YouTube’s recommendations consider over 80 billion pieces of information related to each user. This immense data pool necessitates the use of large-scale neural networks, which YouTube has employed since the year 2008.
These examples show why ML’s value lies in specific, well‑defined problems, not in general experimentation.
Why do businesses invest in machine learning?
Businesses invest in machine learning because it improves decision quality, speeds up workflows, and makes it possible to manage complexity at scale. Successful ML initiatives tend to deliver value in four areas:

Data‑driven decision‑making. ML models analyze large datasets quickly and provide insights that help leaders choose better strategies, prices, risk profiles, and operational policies.
Enhanced customer experience. By personalizing services and recommendations, ML improves satisfaction and loyalty, which in turn boosts retention and revenue.
Improved operational efficiency. ML automates routine tasks, optimizes processes, and reduces errors, often leading to double‑digit percentage improvements in process efficiency.
Better risk management. ML supports more precise assessment of credit and underwriting risk, fraud, and operational risk, helping companies reduce losses and manage exposure more effectively.

Instead of asking where AI can be added, leading organizations instead query which decision or workflow would benefit most from better predictions or automation, and then design ML systems to support those points.
Why do businesses invest in machine learning rather than staying with traditional analytics?
What does ML add beyond dashboards?
Traditional analytics explains what happened, while machine learning helps predict what is likely to happen and which actions could improve outcomes. For high‑volume, fast‑moving, or complex decisions, this predictive layer can make the difference between reactive and proactive management.
ML does not replace analytics; it extends it. Strong ML deployments build on reliable reporting, consistent data definitions, and stable infrastructure. When those foundations are weak, ML becomes harder to implement and ROI more difficult to demonstrate.
What makes AI and machine learning for business implementation difficult?
There is a range of challenges with developing ML models, integrating them into your processes, and training the team for adequate usage. Below are common difficulties we see in production projects, together with how Intelliarts typically addresses them.

Inappropriate data quality and quantity
What is it?
One of the primary challenges that businesses face is ensuring they have enough high‑quality, well‑structured data for ML algorithms. Data may be fragmented across systems, noisy, biased, or missing key signals needed for reliable predictions.
How does Intelliarts solve it?
We assist by offering expertise in data collection and curation. Our team helps businesses understand which data is needed for specific use cases, then uses advanced techniques for cleaning, normalizing, and organizing data so it is suitable for effective ML training and validation.
Skill gap
What is it?
Many businesses lack the in‑house expertise necessary to develop, deploy, and operate ML solutions over time. This includes data science, ML engineering, MLOps, and the domain expertise required to design models that match real‑world workflows.
How does Intelliarts solve it?
We bridge this gap by providing a team of experienced data scientists, ML engineers, and data engineers. This covers model design, training, evaluation, and integration into existing business processes and systems, while also supporting knowledge transfer to internal teams.
Inadequate cost and resource management
What is it?
Developing ML solutions can be costly and resource‑intensive, especially for companies that don’t have an in‑house team or modern infrastructure. Without careful planning, projects may overrun budgets or under‑deliver on business value.
How does Intelliarts solve it?
We help companies optimize their investment by using scalable cloud‑based solutions, efficient algorithms, and right‑sized architectures that reduce computational costs. Our experience allows us to design models and pipelines that are both cost‑effective and powerful, with clear trade‑offs between performance, complexity, and operational overhead.
Integration with existing systems
What is it?
Integrating ML models with current business systems can be troublesome because of legacy infrastructure, heterogeneous tech stacks, or complex vendor products. A model that works in a notebook may be difficult to embed into production systems.
How does Intelliarts solve it?
Our approach starts with a thorough analysis of existing IT infrastructure and business processes. We design integration strategies, APIs, microservices, batch jobs, or event‑driven flows that fit your stack and performance requirements, ensuring that ML systems are compatible with existing workflows and can be monitored and maintained over time.
Ethical and legal considerations
What is it?
With the growing importance of ethical AI and data privacy laws, businesses often struggle to navigate how models should be designed and governed. This includes data protection, fairness, explainability, and sector‑specific guidance.
How does Intelliarts solve it?
We offer consultancy services on ethical AI practices and help ensure compliance with data protection laws like GDPR and relevant industry regulations. Our team stays abreast of legal developments in AI and advises partners on best practices for responsible data usage, bias mitigation, and model fairness.
Defining success metrics
What is it?
Many ML projects begin without clearly defined success metrics. Teams may track technical metrics such as accuracy or AUC, but lack agreement on business KPIs—like reduced loss, increased retention, lower downtime, or faster processing—that actually matter to stakeholders.
How does Intelliarts solve it?
We work with business and technical owners to define a metric stack that connects model performance to business outcomes. That typically includes:

core model metrics (e.g., precision, recall, F1, forecast error)
operational metrics (e.g., decision speed, automation rate, exception volume)
financial metrics (e.g., saved cost, additional revenue, improved margin).

By aligning on these metrics before development, we ensure that pilots and production systems can be evaluated objectively and tied to ROI.
Moving from pilot to production
What is it?
A frequent challenge is moving from a successful proof of concept to a robust production system. Pilots often run in isolated environments, with manual data feeds and ad‑hoc evaluation. Without a plan for deployment, monitoring, retraining, and ownership, models can stall after initial experimentation.
How does Intelliarts solve it?
We treat “pilot to production” as a structured phase of the project, not an afterthought. This includes:

designing deployment architecture (APIs, batch, streaming) aligned with your systems,
implementing monitoring for performance, drift, and data quality,
defining retraining cadence and model lifecycle management,
establishing clear ownership and escalation paths across business, engineering, and risk teams.

In our production ML work, the biggest lesson is that success depends as much on process and governance as on algorithm choice. When pilot results, success metrics, and deployment plans are aligned from the start, models are far more likely to reach production and stay useful as conditions change.
Have a business decision or workflow that could benefit from better predictions? Improve it with ML
How do regulatory and compliance concerns affect ML in business?
Enterprise machine learning must be designed with compliance in mind, especially in regulated sectors like finance and insurance.
Key topics include:

Data privacy (e.g., CCPA, GDPR). Ensuring personal data is collected, stored, and used in compliant ways.
Insurance governance (NAIC AI guidance). Managing ML models that influence underwriting or pricing with appropriate oversight.
Fair lending (CFPB guidance). Ensuring credit and lending ML models are fair, explainable, and free from prohibited bias.

Regulatory alignment should be part of the design process, not something to check after the fact.
How can businesses apply machine learning to underwriting?
ML‑driven underwriting uses models to estimate risk, segment applicants, and recommend pricing or terms. A typical implementation involves:

Timeline: Discovery and scoping, data audit, model development and validation, pilot deployment, and full production rollout.
Team: VP Engineering or Head of Data, underwriters and risk experts, ML and data engineers, and MLOps specialists.
Data requirements: Historical application data, performance outcomes, external data sources (e.g., property, demographic, or market data), and clear labels for good/bad outcomes.
Success KPIs: Approval speed, loss ratios, portfolio risk, pricing accuracy, and fairness metrics.

For deeper treatment of underwriting‑specific workflows and ROI, see Intelliarts’ guide on machine learning underwriting.
Machine Learning Readiness Framework: How can organizations evaluate their ML readiness?
Organizations can evaluate their machine learning readiness by systematically assessing five dimensions: business, data, technical, operational, and governance. A clear view across these areas helps determine whether an ML initiative should start now, after foundational work, or as part of a broader data and platform strategy.

1. Business readiness: Is the problem and ownership clear?
Business readiness means the ML initiative is anchored to a specific, meaningful business problem with an accountable owner. Instead of simply wanting to use AI for the sake of it, decision-makers should be focused on clear goals, such as reducing churn in a lending portfolio by X% or cutting unplanned equipment downtime in plants.
Signs of business readiness:

A single, clearly defined use case (e.g., fraud detection, underwriting scoring, predictive maintenance).
Named business owner and domain experts who will use and validate model outputs.
Quantifiable success metrics such as loss ratio, retention rate, margin, or uptime.

Without this clarity, ML projects risk becoming simply technology experiments with no path to ROI.
2. Data readiness: Is the right data available, usable, and sufficient?
Data readiness covers the availability, quality, and accessibility of the data needed for the chosen use case. Even strong models will underperform if they are trained on inconsistent, sparse, or biased data.
Key questions:

Do we have historical data that reflects the decisions and outcomes we care about?
Is the data clean enough (structured, deduplicated, consistently labeled) to train and validate models?
Can we securely access and combine data from different systems (e.g., CRM, core systems, IoT, credit bureaus, third‑party feeds)?

In Intelliarts projects, a significant portion of effort often goes into data preparation, and readiness here is one of the top predictors of success.
3. Technical readiness: Can ML be integrated and maintained in our architecture?
Technical readiness is about whether the organization’s infrastructure and tools can support production ML, not just a one‑off proof of concept.
You can assess it by asking:

Do we have environments for model training, testing, and deployment (cloud or on‑prem)?
Can we integrate model outputs into existing systems—such as underwriting platforms, decision engines, dashboards, or workflows—without disrupting operations?
Do we have, or plan to implement, MLOps capabilities such as monitoring, logging, retraining, versioning, and rollback?

If technical readiness is low, it may be better to start with a narrowly scoped project, or to use custom AI development and RAG development services from a partner rather than building everything internally.
4. Operational readiness: Will teams trust and use the predictions?
Operational readiness determines whether the ML output will actually be used in day‑to‑day decisions. Even a high‑performing model will fail to create value if front‑line teams ignore it or if it conflicts with existing processes.
Questions to consider:

Have we mapped how predictions will change current workflows (e.g., underwriting, claims, maintenance, support, collections)?
Do teams understand what the model does and how to interpret its outputs or scores?
Is there a clear process for handling exceptions, overrides, and edge cases?

In our experience, projects succeed more often when ML is introduced as a decision support tool with clear thresholds and escalation paths, rather than as a black box replacement.
5. Governance readiness: Are compliance, privacy, and fairness built in?
Governance readiness ensures that ML systems respect regulatory and ethical boundaries from the start. This is critical in domains like lending, insurance, healthcare, and public services.
You can evaluate governance readiness through:

Data privacy policies (GDPR, CCPA) and secure data handling practices.
Sector‑specific guidance, such as NAIC AI governance for insurance or CFPB fair lending guidelines for credit and underwriting.
Plans for model explainability, fairness checks, and periodic audits.

When governance is treated as a core requirement rather than a late‑stage review, ML initiatives face fewer deployment delays and regulatory risks.
Not sure if your organization is ready for machine learning? Our experts can assess your data, infrastructure, and business needs and help define the right path to production.
See how we can help
What ML algorithms matter for business applications?
Most business stakeholders do not need a deep theory lesson on algorithms. They need to know which algorithm families are useful for which business problems.
1. Regression
Used for forecasting continuous values such as revenue, demand, price, or usage.
2. Classification
Used when the model must place an item into a category or estimate the probability of an outcome, such as churn, fraud, approval risk, or failure likelihood.
3. Clustering
Used to group similar customers, products, behaviors, or entities into segments without predefined labels.
4. Dimensionality reduction
Used to simplify large, noisy datasets while preserving important structure.
5. Reinforcement learning
Used in sequential decision environments where the system learns from outcomes over time, such as routing, bidding, resource allocation, or optimization problems.
From our engineering team: 
In production ML projects, the biggest gains usually come from solving one narrow workflow first rather than trying to automate an entire process at once. Teams often see better results when they define a clear decision point, connect the model to the system where work actually happens, and add monitoring from day one so the model can adapt as data and behavior change.
Having challenges with data, integration, or taking ML to production?Talk to our experts

#### Machine Learning in Insurance: Applications, AI Evolution & LLM Decision Guide
The competition in today’s US insurance market is tough, with around 6000 businesses operating in this sector, according to the Insurance Information Institute. To beat this competition, the industry is shifting from standalone machine learning models to integrated AI ecosystems and LLM-driven systems. Insurers are investing in solutions that deliver measurable ROI across underwriting, claims, and customer experience.
In this article, read about machine learning and AI in insurance. Since the insurance industry has grown an immense appetite for data during the past few years, explore how ML can help your company unleash the potential of this data, process, and use it right.
What is machine learning in insurance?
When it comes to machine learning insurance, we’re basically talking about extracting knowledge from insurance data. According to IBM, ML can be defined as,
A subfield of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate how humans learn, gradually improving its accuracy.
Deriving from historical data, an ML algorithm can learn on its own from claims data, customer behavior patterns, risk indicators, etc. and make predictions and conclusions without being explicitly programmed. So an ML model generates the results as accurately as possible, and it’s important it was fed with enough quality data.
In practice, this means insurers can:

Assess risk more precisely during underwriting
Detect fraud earlier by identifying anomalies in claims
Optimize pricing based on real-time and behavioral data
Predict customer churn and personalize engagement strategies

Machine learning plays an important role in transforming insurers into data-driven organizations. It allows companies to operationalize the vast volumes of structured and unstructured data, from policy records to claims notes and IoT inputs, and turn them into actionable insights.
It’s also important to distinguish between machine learning, broader AI systems, and deep learning. While both belong to computer science, AI is a broader concept than ML that is designed to simulate human thinking capability and behavior. From a business perspective, both AI and ML can help you conduct business operations better, with more accuracy and lower spending.

The major difference between machine learning and deep learning lies in how the data gets presented to the machine. While traditional ML algorithms rely on structured data, deep learning can deal with massive amounts of both structured and unstructured information working based on the multiple layers of artificial neural networks. This is particularly valuable in insurance use cases like damage assessment, document processing, and medical data analysis.
What problems does machine learning solve in insurance?
Insurance and insurtech companies operate in a data-rich but decision-heavy environment, where inefficiencies directly impact loss ratios, operational costs, and customer experience. Machine learning can address several core challenges in the industry:
Fraud
As probably the most challenging task in insurance, fraud detection makes insurance companies lose $80 billion annually, according to the Coalition Against Insurance Fraud. And while businesses are thinking about how to deal with fraud, malefactors are using this time to make up new fraudulent schemes and patterns. Also, this situation makes insurers add costs to premiums and increase prices from 10 to 20% on average, which puts an extra toll on customers.

ML models detect anomalies across claims, transactions, and behavioral patterns, enabling faster and more accurate identification of suspicious activity.
Risk
Underwriting is an important part of insurers’ daily work when a company calculates the risks of insuring someone’s business, assets, or life and picks up the right price for this deal. Traditional risk evaluation relies on static rules and limited datasets.
In contrast,  ML-enabled risk management systems allow insurers to speed up and facilitate underwriters’ work. A simplistic example of ML in underwriting includes making decisions on how deeply to investigate the case, e.g. full vs. simplified underwriting or whom to assign the case, e.g. junior vs. senior specialist.
Pricing
A traditional price optimization approach means accommodating GLM (Generalized Linear Model) to historical claims and premiums. GLMs are traditionally used as the main pricing technique, but this conventional approach:

Doesn’t take into consideration the changeability of insurance pricing. Pricing uncertainty in this sector is high because of constant changes in claims procedures and regulatory requirements
Doesn’t work in certain circumstances. Taking the same GLMs approach, the result — quoted premiums — can differ from one insurer to another. The study conducted by the Institute and Faculty of Actuaries proves that even for an ordinary risk, this difference can reach up to $1000

Unlike GLM, ML makes possible dynamic pricing. So how can you implement ML in insurance for premiums? Using ML for price optimization brings more accuracy and flexibility to pricing. For one thing, insurers can adjust prices dynamically, for example, by reacting timely to market fluctuations. For another, companies no longer need to orient on industry benchmarks but can make use of predictive models to set an effective price for each premium.

The major benefit of ML-based pricing models is an opportunity to include a variety of variables in the premium, with different levels of impact. ML algorithms discern patterns from data, integrate additional sources and channels (even alternative ones, such as previous claims or social media), and notice trends and new demands at early stages.
In the case study of AXA, a global insurer giant has tried using deep learning techniques to optimize its pricing. The company knew that 7 to 10% of its customers cause a car accident annually. While most of these accidents weren’t serious and cost little to the insurer, 1% of these made up large-loss cases with huge payouts.
As you might expect, AXA wanted to predict those large-loss cases to improve their pricing and cut costs. For this purpose, the company produced an experimental neural network model and entered 70 different risk factors into the model. Eventually, AXA achieved 78% accuracy in their predictions and improved their pricing a lot.
Claims inefficiencies
From claims registration to investigation, adjustment, and settlement, handling insurance claims also takes lots of time for insurance agents. Manual claims processing is slow, error-prone, and resource-intensive. ML and AI in insurance can speed up and automate these processes and, hence, reduce the time spent on claims processing and improve customer satisfaction.
Key applications of machine learning in insurance
Let’s explore how machine learning is applied across the insurance value chain.

Underwriting & risk assessment
Of course, AI and ML cannot entirely replace manual risk assessment in the insurance sector. Still, using underwriting automation AI can increase operational efficiency and intelligent decision-making in a range of ways:

Automating submission triaging
Streamlining submission processing
Assessing risks more accurately
Predicting potential failure rates and other operational risks
Optimizing rates for premiums
Improving coverage recommendations

Business impact: Faster decision-making, improved loss ratios, and increased underwriting capacity.
Example in real life
A good example here is the success story of one global reinsurer, i.e. a company that provides financial support to insurance companies. Using historical and geospatial info, this organization built an ML algorithm to analyze the risk of floods in the area.
The implementation of the ML-based system allowed the reinsurer to:

Reduce time spent on underwriting by ten times
Model what to expect from the market in the future with 80% accuracy
Increase case acceptance by 25%

Read about ML in underwriting efficiency in more detail.
Fraud detection
Since ML algorithms work great for anomaly detection and classification of large datasets, companies rely on machine learning for insurance fraud detection. ML detects patterns and analyzes consumers’ behaviors, for example, in transaction methods. If it notices any abnormal activity, it warns the insurer immediately.

With ML, insurers can detect fake and duplicate claims, and we’re not speaking about “exact matches” only but more complex cases. Add here also the frequent fraudulent cases of upcoding in medical bills and overstated repair costs in auto insurance.
So, here’s why you’d better choose insurance fraud detection machine learning:

It identifies potential frauds faster and more accurately
Next to structured data, ML algorithms can analyze non- and semi-structured data, including claims notes. This contrasts ML to traditional predictive models, which limit insurers to using structured data only
ML allows insurance and insurtech companies to add alternative data sources such as public data or third-party IoT, which improves fraud detection results

Business impact: Reduced fraud losses, lower investigation costs, and faster claims approval for legitimate customers.
Example in real life
Before implementing an ML-based predictive fraud detection system, Anadolu Sigorta, the Turkish insurance company, wasted two weeks manually checking claims for fraudulent activity. As the company processed 25,000 to 30,000 claims a month, the costs were high.
After switching to a predictive system, Anadolu Sigorta became able to detect claims in real time. So, no wonder that it improved its ROI by 210% in one year only. Its total cost savings, thanks to fraud detection and prevention, included $5.7 million.
Claims processing automation
Claims processing is rapidly evolving from rule-based automation to AI-powered, end-to-end intelligent workflows.

How can ML and LLMs optimize claims processing?

By claims registration: The typical claims registration process is time- and data-intensive. ML can provide insurers with analytical insights on how to remove these operation inefficiencies.
By smart claims triaging: As part of modern insurance claims processing AI, ML is also useful in scoring and triaging risks. If an ML system learns based on past experience, it will be able to prioritize insurance claims faster and more accurately.
By claims volume forecasting: A typical stumbling block in an insurance practice is to set premiums before signing any insurance contract. An insurance agent, in this case, has to go through lots of manual work and make predictions about the number of claims occurrences and approximate claims amounts. With an ML system in place, the forecast for individual claims will be less error-prone and take less time. As a result, this can decrease the overall claims settlement time and improve customer experience.
By smart auditing: Using ML algorithms in claims audit improves the quality of audits. Technology helps to identify only those claims that are indeed incorrect and need review. A technical audit can complement these efforts by evaluating the efficiency and security of the technology systems in place.
By automated damage detection (computer vision): Car insurance companies can benefit from computer vision and automated damage inspections. With AI-based image processing, a customer can upload a photo of the damaged parts of the car, and the system processes the photo automatically. On the way out, both the insurer and the customer receive a detailed report covering which parts are replaceable and repairable (and which are not), together with the approximate estimate for the repair. This reduces the need for manual inspections and speeds up claims processing.

By generative AI claims summarisation: Large language models (LLMs) can automatically summarize lengthy claims documents, medical records, and adjuster notes into concise, structured insights. (Check a list of LLMs to dive deeper into the topic.)
By conversational claims support: AI assistants provide real-time, conversational interaction with policyholders throughout the claims process. They can guide users through claim submission, answer questions, collect required information, and provide status updates. While this improves customer experience as they don’t wait long, AI virtual assistants also reduce agent workload.
Other use cases, including ML-powered auto-adjudication and automatic detection of fraudulent claims.

Business impact: Shorter settlement cycles, reduced operational costs, and better customer experience.
Examples in real life
Intelliarts built a car damage detection solution, which is composed of two separate AI models. One of them indicates the damage, and the other identifies the affected parts. The outcomes are then compared against similar cases in a prepared image database to provide the customer with repair cost estimates.
Also, Liberty Mutual is using an ML-based mobile app, where their customers involved in car accidents can upload photos of their damaged car right at the crash site. Trained with thousands of car accident images, the ML model will automatically calculate repair costs. In real time, the customer can also settle a claim using the same app.
In one of our previous posts, we have already written about what is the role of computer vision for car damage detection.
The Fukoku Mutual Life handles claims data with the help of AI and deep learning. Technology helps the insurer automatically find and access medical documents related to the case as well as calculate the pay-offs. As a result, the Japanese insurer can now boast of a 30% increase in productivity and cost savings of around $1 million a year.
Predictive analytics for customer retention & personalization
Machine learning also has lots of potential for customer acquisition and retention, as well as customer service in general.
First and foremost, insurance and insurtech companies can consider implementing ML-based insurance lead management systems. By extracting valuable insights from lead data, ML helps insurers with lead analysis and classification. So, companies can choose quality leads and don’t waste time on those with low conversion rates.
A powerful insurance lead management system can also help to calculate the sales threshold by analyzing various important factors, such as lead revert time, link clicks, or web visits. When a lead approaches the industry benchmark, agents will know it’s worth a cold call or a message.
 White Paper: Machine Learning in Insurance Business Download Now
Secondly, ML is very effective at predicting the churn rate. In insurance landscapes, a lapse occurs whenever the customer stops paying the premium, and the contractual grace period expires. ML can help to detect early churn signals and recognize at-risk customers. This way, agents will have enough time to interfere and talk to clients about the reasons for unpaid premiums and negotiate terms of payment.

Another benefit here is the ability to segment customers based on the lapse risk. So insurance companies can use the factor of solvency when issuing policies.
Thirdly, there is personalized marketing as another great use case of machine learning in insurance. Customers in insurance (as anywhere else) would like the services to match their specific needs and requirements. ML can help insurers deliver personalized and relevant experiences.
For example, VahanBima is a leading provider of insurance services in India. The company predicts CLV using a linear regression algorithm and then uses the model results to allocate resources better and target customers with 100% personalized experience offers.
By extracting valuable insights from demographic data, customer behaviors, attitudes, interactions, and lifestyle details, ML algorithms provide insurers with insights to exploit in their marketing initiatives:

Personalized offers and policies
Loyalty programs
Insurance packages
Pricing
Messages

Probably the best example is insurance product/policy recommendations. Based on volumes of historical data, AI algorithms can analyze customer profiles and provide the most suitable policy offers out of those available, which will raise the chance of a successful sale. For example, Accolade offers personalized insurance services with the help of machine learning in insurance and helps patients choose the most relevant and cost-effective health coverage. Now the company reportedly has over 1.1 million customers.

An interesting approach is to use collaborative filtering for personalized offers. This means that an ML system suggests a product to customers based on their similar risk profile and/or purchase history to other customers who have bought this product earlier.

Last but not least, ML algorithms can come in handy in customer segmentation. Analyzing important data, such as customer income, age, gender, and location, ML classifies customers into different groups. It can also seek patterns based on more complex variables, such as behaviors or personal preferences. Using these conclusions, an insurer then develops specific attitudes or uses marketing strategies to target different customer segments.
Business impact: Higher customer lifetime value (CLV), improved retention rates, and more efficient marketing spend.
Examples in real life
The Intelliarts team built a predictive scoring model that could forecast the probability of how likely the lead would buy a policy. With this system in place, insurance agents could call leads with high scores first and leave those leads with low scores for later or skip them entirely in case of a lack of resources.

The business value was far above the expected. After only a few months in production, the model helped to cut off approximately 6% of non-qualified leads, which resulted in a 1.5% profit increase. In the most successful months, the customer noticed a 2.5% increase in profit.
Another example is the case of a life insurance company, MetLife. The company decided to take a data-driven approach to customer segmentation. At the time when insurers used ML solely for risk mitigation and underwriting, MetLife centered on ML to foster their go-to-market strategy and achieved great results.
ML algorithms helped the insurance company to understand the customers’ needs, behaviors, and attitudes better and, hence, maximize their competitive advantage. Later, MetLife summarized this experience as “the most significant change to their brand in over 30 years.”

Since an ML model can look at and extract patterns from millions of data points at once, it provides custom recommendations and insurance packages to create a robust user experience in the domain. 
NLP for insurance document extraction
Going through tons of text documents like health records, financial reports, claims history, and even mental health EHR data takes at least half of a working day for those in insurance. No need to say that these tasks are monotonous and tiresome but require lots of accuracy.
Traditionally, NLP combined with OCR has been used to extract key fields from these documents and convert them into structured data. ML-based data extraction in insurance such as with the help of EMR software can further streamline this process, reducing manual workload and improving accuracy.

By scanning through texts automatically, ML algorithms retrieve core words and/or phrases from unstructured insurance documents. Even better, they can identify synonyms or related words, e.g. searching for a “pet” when you’re looking for a dog.
Optical character recognition (OCR) also enables image to text conversion, allowing insurers to recognize handwritten and printed texts, process documents faster, and solve operational inefficiencies.
NLP insurance document processing helps to generate automatic summaries of documents so insurance agents won’t need to read 100-page financial reports.
Finally, AI for data extraction allows forgetting manual re-typing. When paired with computer vision in insurance, it can accurately render every single pixel and translate the information if needed. This is especially useful when it comes to customer onboarding and claims management, helping to extract customer information in the blink of an eye.

Advanced NLP models mark the transition from data extraction to contextual understanding and, hence, automation of more complex workflows in the insurance industry. What’s more, NLP serves as a foundation for LLM-based systems, which further extend these capabilities with summarization, reasoning, and natural language interaction and which we’ll talk about next.
Business impact: Reduced processing time, fewer human errors, and scalable document handling.
Example in real life
Tokio Marine uses an ML-based OCR service to handle claims. The system allowed the company to reduce human error by 80% and processing time by 50%.

How LLMs are transforming insurance
While machine learning has long powered predictive analytics in insurance, the industry is now shifting toward LLMs that can understand and generate human language. This helps to unlock new levels of automation and efficiency in insurance.
From NLP to LLMs in insurance: What changed?
Traditional NLP models like BERT or RoBERTa are primarily used for classification tasks, for example, tagging claims, detecting sentiment, or categorizing documents. These models are effective but limited to predefined outputs.
LLMs in insurance, in contrast, focus on generation and reasoning. Instead of just classifying data, they can:

Summarize complex claims documents
Generate responses to customer queries
Extract and structure key information from unstructured text
Support decision-making with contextual understanding

Real-world LLM use cases in insurance
Claims summarization and processing
As partly mentioned, LLMs can speed up claims workflows by summarizing large volumes of unstructured data, such as medical records, adjuster notes, and claims history, into concise, decision-ready insights. This reduces manual review time for claims handlers. Generative AI claims summarisation also improves decision consistency, especially in complex or document-heavy scenarios.
Zurich Insurance Group is leveraging generative AI to streamline complex claims and policy analysis workflows. In multinational insurance programs, often involving thousands of policies across jurisdictions, LLMs help internal experts compare, summarize, and verify coverage in a single language.
With over 8,500 multinational programs and 56,000 policies across 200+ countries, Zurich uses GenAI to process large volumes of documentation in the limited time required for manual review. This improves both speed, especially in complex claims data, and reduces manual workload for specialists.
We’re excited about the future possibilities that GenAI brings. It offers incredible opportunities to reduce manual workload and allow our people to spend more time in the areas they can add most value. This will ultimately drive better insights and service to our customers and brokers. – Sierra Signorelli, CEO @ Zurich Commercial Insurance
Ready to move from ML to intelligent automation? Our LLM development services help insurers build scalable, production-ready AI systems tailored to real business needs.Check our ML development services
Conversational AI and customer support
LLM-powered assistants significantly upgrade traditional chatbots. Instead of scripted responses, they produce natural, context-aware conversations. AI assistants, in this case, can help customers with claims submission, answering policy-related questions, and providing real-time updates. They can also support content delivery, including instructional video production, to guide users through complex insurance processes.
A prolific example is the use of TextQBE by QBE North America, a global insurance leader. This virtual assistant helps the company answer simple questions from customers about deductibles and process photos of receipts and other documents. 
Customer satisfaction scores through the service have averaged 4.6 out of 5 – many with comments such as “great customer service, fast and friendly,” answered all my questions. – Eric Sanders, Senior Vice President @ QBE North America
Intelliarts has lots of experience with ChatGPT and other LLM chatbot development. Read this case study on a data extraction solution based on ChatGPT to get more details.
Document understanding and data extraction 
While ML and OCR automate document processing, LLMs add a critical layer of contextual understanding. They can extract key entities (e.g., damage type, cost, policyholder), identify relationships, and generate structured outputs. Again, this helps eliminate manual data entry and speed up workflows.
Explore text generation models in insurance in one of our readings.
Adding the LLM layer: Retrieval-Augmented Generation (RAG)
To ensure accuracy and compliance, insurers are increasingly adopting Retrieval-Augmented Generation (RAG). RAG combines:

Internal knowledge bases (policies, underwriting rules, claims history)
External data sources
LLM reasoning capabilities

This technology strengthens LLMs in insurance even further. RAG insurance knowledge retrieval allows systems to generate fact-based, context-aware responses based on company data. This is especially useful in a highly regulated environment. Some examples include:

Claims assistants referencing policy terms in real time
Customer support bots providing compliant, personalized answers
Internal copilots helping agents navigate complex documentation

How to choose between ML, LLMs, and RAG
From our customers’ experience, we noticed that, as insurers adopt more advanced AI capabilities, a common challenge they face is how to choose the right approach for a specific business problem. Machine learning, LLMs, and RAG are indeed not interchangeable because they solve different types of tasks.
Summarizing our 6-year experience working on ML projects, we can say that at a high level:

ML is best for prediction based on structured data
LLMs helps with understanding and generating unstructured text
RAG enables accurate, context-aware answers using internal knowledge

So the key is to align the technology with the nature of your data and the decision you need to make. Use the decision framework below to choose between ML, LLMs, and RAG in insurance:

One pro tip from the Intelliarts team is to combine these technologies into layered systems instead of choosing one approach. This is how to use them in practice most efficiently:

Use ML models to predict risk, fraud, or customer behavior
Apply LLMs if you need to interpret documents and interact with users
Add RAG to base your outputs in internal policies and improve accuracy

For example, a modern claims workflow can combine computer vision for damage detection, ML for fraud scoring, LLMs for summarization, and RAG for policy-aware validation.
Need accurate, policy-aware AI systems? Our RAG development services help insurers build intelligent solutions that combine LLMs with internal data for reliable, compliant decision-making.Discover our RAG development services
How to implement ML in the insurance business
Building reliable AI in insurance follows a structured process. But leading insurers go further by embedding these steps into scalable systems.

1. Define your business objectives
A good idea for getting started with ML is to consider what business tasks you need it for. Earlier we mentioned the key use cases – go through them and think about which are relevant to your company. Then, prioritize the tasks based on your biggest pain points and those that are only nice to have.
Try to be as specific in your objectives as possible. You can also seek the consultation of professional data engineers who have industry expertise in insurance.
2. Understand and prepare data
Getting quality insurance data, as well as the efficient amount of data are a top priority for building a reliable ML solution. Under the supervision of expert data scientists, you identify and understand the data you own now or need to collect in the future.
Most insurance companies have repositories of existing data (think about your historical claims data or policy data). Even if you don’t have enough raw data, a professional ML team can assist you with data collection, advising on the best types of data, formats, or ways of storage. Data engineers can also back up your business with synthetic data if needed.
For those interested in data collecting and contributing to this crucial phase of the process, there are abundant opportunities on Jooble to explore.
After data collection, data scientists will also need to invest time in data exploration to find patterns in data, investigate relationships between variables, and determine how these will affect the outcome. Data visualization can be really helpful at this stage. Also, data cleansing and validation, as well as data structuring, may be needed to process the data the most efficiently.
3. Choose and train the ML model
This is when the ML model is actually being built. Data scientists need to explore different ML algorithms and choose the ones most suitable for your business problem. Then, these algorithms are trained — the model is fed with training datasets, the results are evaluated, and engineers fine-tune model parameters until they get high accuracy.
4. Test the ML model
Testing is a critical step in building an ML-powered solution. Here data engineers under the guidance of the insurance company check whether the ML system works as planned and whether the results are satisfying enough (maybe the model needs more training).
5. Put the ML solution into production
When the ML model is ready, data engineers can move to machine learning deployment in insurance. Ideally, the ML team should also assist with model monitoring so you can track the results without diving deeper into the technical details. With decent model monitoring, your insurance company will also know when the model needs retraining in case you get new datasets.

Above we described a traditional ML pipeline (Data → Model → Decision → Feedback loop), commonly used in underwriting, fraud detection, and price optimization. In case, you’re planning to build an LLM-based pipeline, your workflow will be the following:
Data → Embeddings → Retrieval → Generation
#1 Data: Collect and prepare structured and unstructured inputs (documents, policies, claims, customer interactions)
#2 Embeddings: Transform data into vector representations for semantic understanding
#3 Retrieval: Retrieve relevant information from internal knowledge bases or external sources
#4 Generation: Produce context-aware outputs (e.g., summaries, answers, recommendations), often enhanced through LLM fine-tuning
This architecture is suitable for document understanding, claims summarization, and conversational AI.
To sum up, modern insurance platforms combine multiple AI layers in their architecture:

Data layer: structured and unstructured data (claims, policies, telematics)
ML layer: prediction (fraud, risk, pricing, actuarial modeling)
LLM layer: language understanding (claims, support, documents)
RAG layer: knowledge grounding (policies, compliance rules)
Automation layer: enabling straight-through processing
Feedback loop: continuous learning and improvement

This layered architecture allows insurance companies to build systems that are efficient, accurate, and compliant at once.
Challenges of implementing AI in insurance
Adopting AI in insurance requires more than building models. It demands a strong foundation in data, compliance, and trust. That’s why there are a number of challenges that insurers have to deal with:
#1 Data quality and availability
ML solutions are data-driven, and the performance of the future ML model largely depends on the data used to feed the algorithms. Yet, insurance data quality is often inconsistent, siloed, and unstructured (e.g., claims documents, medical records). 
Without proper data preparation, even the most advanced models will underperform. This is especially critical for use cases like actuarial ML and telematics-based risk assessment, where accuracy directly impacts pricing and underwriting.
What’s more, if the insurer lacks ML expertise, they might not even know what data to collect and where to get started. Paired with a skilled ML team, you can get advice on the best data collection methods as well as create synthetic data if needed.
#2 Regulation and compliance
Insurance AI solutions must comply with strict regulations such as GDPR insurance AI requirements and the EU AI Act for high-risk systems. Many insurance use cases, such as underwriting and pricing, fall under high-risk categories and require transparency and human oversight.
#3 Model explainability and trust
In regulated environments, decisions must be explainable. Whether it’s pricing, underwriting, or fraud detection, model explainability is important to justify outcomes to regulators and customers, and to grant fairness and accountability.
#4 System integration and legacy infrastructure
One of the biggest barriers to AI adoption is integrating new models into existing insurance systems. Legacy platforms and fragmented data sources make it difficult to deploy AI/ML at scale. Successful implementation requires seamless integration with core systems (policy administration, claims management) and alignment with existing processes.
#5 Digital mindset and culture
Adopting ML isn’t enough if your personnel refuse to use it in their daily activities. So, make sure you build a data-driven corporate culture, with clear cooperation between IT and other departments. A good starting point is to organize training sessions to teach the personnel how to use ML to the fullest value.
Future of AI in Insurance
Insurance is a highly competitive industry, especially now when many companies are operating online. A good way for an insurance company to stand out in the competition is to use the benefits of innovative technologies and machine learning in business, in particular.
Today the industry is entering a new phase of transformation powered by:

Generative AI. LLMs in insurance bring new use cases to the industry, including automatically generating claims summaries and reports, powering conversational assistants for customer support, and producing personalized policy recommendations and communications. This shift allows insurers to move from analyzing data to acting on it in real time. For example, tasks that once required hours of manual review, such as analyzing claims documents or policy details, can now be completed in seconds with LLM-powered systems.

Autonomous workflows. This means that multiple AI components work together with minimal human intervention. Instead of isolated tools, insurers are building systems that combine computer vision (e.g., damage detection) with ML models (e.g., fraud scoring, risk prediction), LLMs (e.g., summarization, communication), and RAG (e.g., policy validation, compliance checks). For example, a modern claims workflow can look like:

What does this mean for insurance companies? Machine learning in insurance, along with LLMs, is transforming every part of the insurance value chain. The insurers of the future (and you can become one of them easily) are getting value from the abundant insurance data they’re sitting on and use ML to:

Increase underwriters’ efficiency
Handle claims faster and in a more productive way
Detect fraudsters in a few minutes
Predict potential churners and take measures to retain customers
Analyze leads more effectively and choose the most promising ones
Extract valuable insights about their customers to apply personalized marketing tactics
Segment customers to target them better
Extract data easily
Optimize premiums and adjust them dynamically to market changes

Wrap up
In the last decade, the insurance sector has produced and accumulated as much data as ever before. The bad news is that insurers use not more than 10-15% of this data, according to the Accenture study. Using machine learning in insurance can help use the data to which they have access to its fullest potential and improve their business in a range of ways, from fraud detection to risk mitigation to claims processing and price optimization.
Intelliarts combines vast expertise in machine learning and domain knowledge of the insurance industry. We provide technology consulting services, including AI consultation where we can assess your readiness and prepare a detailed roadmap for adopting ML into your processes. Our data science team could then help you with ML implementation.
Want to get started with machine learning in insurance? Or maybe optimize your existing ML system?
Explore our insurance software development services.Talk to us

#### Machine Learning in Manufacturing: Use Cases with Examples
Those days are long gone when artificial intelligence and machine learning sounded like sci-fi movies to us. Today we are going through the Fourth Industrial Revolution or simply Manufacturing 4.0, whose key drivers are large volumes of data and rapid technological advances. This couldn’t be more appropriate for the manufacturing sector, which has gotten closer to its timeless goal of producing more quality goods at lower expenses thanks to ML. So, what is this hype around machine learning and manufacturing, and how can your company unlock unprecedented growth if it introduces ML to its operations? Let’s look at a few machine learning use cases in manufacturing.

#### Machine Learning Underwriting: How It Works, Use Cases & Implementation Guide
Machine learning underwriting alters how businesses evaluate risk, assess applicants, and automate decision-making across various industries, from insurance to real estate to fintech. Although traditionally conservative and process-heavy, underwriting is now undergoing a rapid digital transformation powered by AI and ML technologies. These tools have revolutionized underwriters’ daily operations, improving accuracy, reducing the process time, and increasing overall machine learning underwriting efficiency.
From better underwriting risk calculation to automated claims processing, machine learning helps organizations make more data-driven decisions and streamline operations. In this guide, we explain how machine learning underwriting works, where it delivers the most value, and what organizations should consider when building ML-powered underwriting systems.
Looking to enhance your underwriting process with intelligent automation? Let’s talk about how Intelliarts can help you implement intelligent ML solutions tailored to your business goals.Talk to us
Why traditional underwriting falls short
Across industries such as insurance, lending, real estate, and equipment financing, traditional manual underwriting is associated with piles of physical and digital paperwork, back-and-forth communication between stakeholders, entering and rechecking lots of information submitted into the system. This is without mentioning a cumbersome and time-consuming process of evaluating risk to determine whether to approve a deal — and under what terms.
Manual underwriting workflows typically involve repetitive administrative work: reviewing PDFs and emails, re-entering data into internal systems, verifying documents, and coordinating across multiple stakeholders. Statistics indicate that underwriters spend 40% of their time on administrative and other non-core activities. As application volumes grow, these inefficiencies lead to longer turnaround times and rising operational costs.

Limited data usage creates another major challenge. Traditional underwriting models rely heavily on structured applicant-provided information and static rules, which restricts an organization’s ability to assess risk dynamically. Without access to broader data pipelines and predictive analytics, underwriters may miss important risk signals or pricing opportunities.
Still, the rise of ML and AI technology proves that, in a few years, underwriting as we know it today is doomed to failure. BCG reports that underwriters adopting AI-first operating models can improve productivity by 30% to 40% while significantly accelerating underwriting and claims decision-making processes.
It’s not really surprising if we consider how AI underwriting helps better analyze risks, provide more accurate pricing, and process applications faster and more efficiently. With ML and deep learning technologies, the underwriting process time is reduced to a few minutes (instead of hours or even days) because most activities are automated and data-driven. As Matthew Josefowicz, a technology strategist and opinion leader in insurance, summarizes it,
New technology is allowing underwriters to spend more of their time building and strengthening relationships in the field and, in many cases, acting as the frontline interface of the company with the marketplace.
What is machine learning underwriting?
Machine learning underwriting uses ML models and predictive analytics to automate underwriting workflows such as risk scoring, application review, pricing, and approval decisions. Instead of relying only on static rules and manual assessments, AI underwriting systems analyze large volumes of data to improve decision speed and consistency.
The biggest misconception about machine learning underwriting is that it replaces underwriting expertise. In reality, the most successful systems combine predictive ML models with human judgment and business rules. The goal isn’t full automation — it’s enabling underwriters to make faster, more consistent, and better-informed decisions at scale. – Oleksandr Stefanovskyi, Strategic Technology Partner at Intelliarts
Modern underwriting automation platforms can process applications in real time, identify hidden risk patterns, and support more accurate decision-making across industries including insurance, lending, real estate, and equipment financing. Combined with explainable AI and intelligent data pipelines, machine learning underwriting helps organizations scale operations without sacrificing transparency or compliance.

What is underwriting?
Underwriting is the process of evaluating risk before approving financial or asset-related transactions. It is widely used across industries such as insurance, lending, real estate, equipment financing, and logistics, where businesses need to assess applicants, assets, contracts, or investments before making decisions.
Underwriters analyze factors such as financial history, asset value, market conditions, operational risks, and compliance requirements to determine whether a transaction should be approved and under what terms. As said, as data volumes and application complexity continue to grow, traditional underwriting workflows often become decision-making bottlenecks that slow operations and limit scalability.
How machine learning improves underwriting efficiency
Predictive underwriting helps process applications faster, improve risk evaluation, and reduce operational overhead. Let’s consider real value from underwriting automation:

Faster decision-making: ML-powered underwriting systems can automatically process applications, extract data from documents, and prioritize submissions based on risk or complexity. In our clients’ experience, this reduced review cycles from days or hours to minutes and allowed underwriting teams to handle larger application volumes.
Improved risk accuracy: The distribution chain in underwriting can be lengthy, with one submission reviewed by several employees. The more people involved and the more manual work, the lower the accuracy becomes in the submission review. By using machine learning models, businesses can handle incorrect and superfluous data better as these solutions are trained on historical data sources. This can improve the accuracy of your risk assessment.
Reduced manual workload: Traditionally, underwriting has relied on human expertise, and this is understandable and will remain as it is. Machine learning could “unload” underwriters, taking the most tiresome and repetitive work from them. Instead, employees could focus on more interesting and complex cases of risk assessment. Sofya Pogreb, the CEO at Next Insurance, comments on the current situation in the following way:

We believe with technology and machine learning, a lot of [human underwriting] can be done away with. The percentage of insurance applications that require human touch will go down dramatically, maybe 80% to 90%, and even to low single digits.

Better customer service: With machine learning, services are delivered to customers faster and more efficiently. This increases customer satisfaction and loyalty. Also, underwriting automation brings more personalization and precision to underwriting. For example, a company can offer personalized vehicle lease or financing terms based on driving data points and personal driving behaviors. Another example is the ability to dynamically update the number and type of questions an underwriter should ask during the application process, depending on the applicant’s profile and prior responses. From our customers’ experience, this can be especially useful in industries like lending, equipment leasing, or commercial real estate.

Key use cases of machine learning underwriting
Machine learning underwriting is no longer limited to insurance workflows. Businesses across lending, real estate, leasing, and financial services use ML models to automate risk assessment and improve decision accuracy. Let’s consider key use cases today:
Lending & credit risk
Problem: Traditional credit underwriting often relies on limited financial data, static scoring models, and manual reviews that slow approvals and may overlook important borrower risk indicators.
ML solution: Machine learning models can analyze broader data pipelines, including transaction history, behavioral signals, cash flow trends, and alternative financial data, to improve credit risk scoring and automate application triaging.
Business outcome: Lenders can accelerate approval times, reduce default risk, and offer more personalized loan terms.
Insurance risk assessment
Problem: Insurance underwriting teams process large volumes of submissions, documents, and risk variables, making manual evaluation time-consuming and inconsistent. Also, underwriters may rely on applicant-provided data only, which could have mistakes and omissions, intentional or unintentional
ML solution: ML systems can assist underwriting with getting information from alternative and more diverse sources and, thus, promote a 360-degree approach to underwriting risk analysis. Machine learning underwriting can help check an application profile against loads of data points retrieved from social media platforms, banking records, publicly available statistical data, and third-party databases. It’s also useful for underwriting fraud detection in insurance, helping identify suspicious applications, inconsistent customer information, and anomalous risk patterns before policy approval.
Business outcome: The risk analysis is more accurate. Let’s check the example of one insurance company that used an ML algorithm for predicting the likelihood of flooding in the area based on historical and geospatial data. The adoption of ML technology into the operations allowed the company to increase the accuracy of underwriting services to 83%, generate a 10-fold reduction in throughput time, and increase case acceptance by 25%.

Read about other ML applications across the insurance value chain based on our real-life experience.
Real estate underwriting
Problem: Real estate underwriting requires evaluating multiple variables such as property condition, market trends, borrower history, and asset valuation, often across disconnected systems and data sources.
ML solution: ML underwriting platforms can combine geospatial data, market analytics, computer vision, and historical transaction data to automate property assessments and identify hidden risk factors. For example, ML solutions can help deliver more accurate property valuations by using object recognition to assess the physical condition of a building, the materials used, or signs of wear.
Business outcome: Real estate firms and lenders can improve valuation accuracy, speed up mortgage and investment approvals. Our customers in the real estate sector were able to make more informed underwriting decisions in changing market conditions.
Intelligent document processing
Problem: Handling new underwriting submissions means going through volumes of documents within the shortest deadlines possible. As said, doing it manually takes not only lots of time but is inefficient and error-prone.
Automated data extraction: Optical character recognition
ML solution: AI underwriting platforms use OCR and NLP technologies to automatically extract, classify, and validate information from documents. ML-powered data pipelines can process applicant details, financial records, and asset information in real time.
Business outcome: ML-powered solutions give a chance to speed up submission processing. Instead of wasting time looking for underwriting information, such as an applicant’s names, addresses, risk types, or partner details, and entering it into the system manually, professionals can rely on fully automated systems to extract and organize data quickly and accurately.
Read also about automatic data extraction from DPFs.
Equipment / asset financing
Problem: Underwriting for leased equipment, vehicles, or industrial assets often involves fragmented operational data, inconsistent evaluations, and manual verification processes.
ML solution: In this sector, ML underwriting systems can analyze asset usage patterns, maintenance history, IoT data, and customer financial profiles to automate risk evaluation and optimize financing terms.
Business outcome: Leasing and financing providers can reduce manual workload, improve asset risk visibility, and deliver faster, more personalized financing decisions to customers.
Automating submission triaging
Problem: Underwriting teams often spend lots of time manually sorting, categorizing, and routing submissions to the appropriate specialists. As application volumes grow, this creates operational bottlenecks and delays in decision-making.
ML solution: Predictive underwriting contributes to creating a more efficient working model as ML helps to prioritize submissions based on different factors and make sure that a particular application will be handled faster. For example, an ML system can filter the applications based on their complexity and assign them for review to the underwriter with the appropriate level of knowledge. Another example includes prioritizing submissions by category, such as loans, leases, or policy types, and routing them to specialists with relevant domain knowledge for faster and more accurate evaluation.
Business outcome: Organizations can reduce review delays, improve workflow efficiency, and allow underwriters to focus on complex assessments instead of manual sorting tasks.
  White PaperMachine Learning for Insurance Business. Explore the Use Cases of ML in the Industry. Explore our ML in insurance white paper
Challenges and risks of machine learning underwriting
While machine learning underwriting can significantly improve efficiency, our partners know first-hand that its implementation also introduces a range of technical, operational, and regulatory challenges that have to be addressed properly:
Data quality and availability
The effectiveness of ML algorithms depends on data. And this must be quality data, without duplicates, null, and missing values. Data fuels ML not only as the “training material” but a way to make the models smarter in the future.
Mitigation: Invest in data governance, standardized data pipelines, and ongoing data quality monitoring before scaling ML underwriting initiatives.
Model bias and fairness risks
Machine learning models can unintentionally inherit biases from historical underwriting decisions or imbalanced datasets, potentially leading to unfair or inconsistent outcomes.
Mitigation: Implement bias audits and human oversight processes to evaluate model behavior and reduce discriminatory outcomes.
Lack of model explainability
Complex ML models can make underwriting decisions difficult to interpret, especially in highly regulated industries where businesses must justify approvals, denials, or pricing decisions.
Mitigation: Use explainable AI techniques, transparent model documentation, and hybrid human-in-the-loop.
Regulatory and compliance constraints
AI underwriting systems must comply with evolving regulations related to data privacy and consumer protection. Requirements such as CCPA, CFPB fair lending guidance, and emerging AI governance frameworks create additional compliance complexity for enterprises operating in the US market.
Mitigation: Contact legal, compliance, and risk teams early and involve them in the implementation process to establish governance policies for model monitoring, auditability, and data usage.
Infrastructure and implementation complexity
Implementing and scaling machine learning underwriting requires more than building ML models. Businesses also need reliable infrastructure for data storage, model deployment, monitoring, retraining, and integration with existing underwriting systems.
Mitigation: Start with a focused proof of concept (PoC) before expanding into production-scale underwriting automation. Furthermore, think in advance (or better discuss with ML experts) where to store and how to manage data in a productive way.

How machine learning underwriting works
Working with AI/ML models for over 5 years, we know that underwriting automation follows a structured workflow that transforms raw data into automated, data-driven decisions. While implementations vary across industries, most ML underwriting systems follow the same core pipeline:

Data collection: The system gathers structured and unstructured data from multiple sources, such as applications, financial records, transaction history, property data, IoT devices, public databases, and internal business systems.
Feature engineering: Raw data is cleaned, standardized, and transformed into meaningful variables (“features”) that ML models can analyze. This step helps identify patterns related to risk, fraud, pricing, or applicant behavior.
Model training: ML models are trained on historical underwriting and outcome data to learn how different variables influence approvals, defaults, claims, or asset performance. This is an iterative process, and the system continuously improves as more data becomes available.
Decision output: Once trained, the underwriting model generates predictions, risk scores, pricing recommendations, or approval decisions in real time. High-risk or ambiguous cases can be automatically escalated to human underwriters for review.
Feedback loop and retraining: Modern systems continuously monitor outcomes and retrain models using new operational data. This feedback loop helps improve prediction accuracy and reduce model drift over time.

Now let’s consider what businesses need to know to implement AI underwriting the most efficiently.
How to implement machine learning underwriting
Some leads come to us being sure that building ML models is all they need. In reality,  successful machine learning underwriting implementation goes beyond the solution itself: it’s your chosen strategy, data infrastructure, and close collaboration between technical and domain teams.
Here are a couple of things that you should know before even starting to look for a vendor:
Timeline
Most ML underwriting initiatives begin with a PoC targeting a single workflow such as submission triaging, risk scoring, or document processing. Once the business impact is validated, you can gradually scale the system.
A typical implementation timeline looks like this:

PoC: 6–10 weeks
Model development: 2–4 months
Deployment and optimization: ongoing iterative improvement

Starting with a smaller use case helps businesses reduce implementation risks, validate ROI faster, and establish governance processes before scaling the solution.
Team roles
Effective underwriting automation projects usually involve both technical and business stakeholders:

Data scientists build and optimize ML underwriting models
ML engineers manage deployment, monitoring, and retraining pipelines
Data engineers prepare and maintain underwriting data infrastructure
Domain experts and underwriters validate business logic, risk policies, and decision quality
Compliance and legal teams ensure regulatory alignment and auditability

Pro tip: Experienced AI consulting and implementation teams like Intelliarts can often cover multiple roles simultaneously. For example, senior ML experts with domain expertise in underwriting can contribute to model development and infrastructure planning at the same time. This allows our partners to speed up implementation while reducing coordination overhead.
Building an AI-powered underwritingIntelliarts helps develop scalable AI underwriting and decision automation solutions tailored to complex operational and compliance requirements.Talk to underwriting ML experts
Data requirements
As said, the performance of machine learning underwriting depends heavily on data quality and accessibility. Most underwriting businesses use a combination of:

Historical underwriting decisions
Claims or repayment outcomes
Financial and operational records
CRM and application data
Third-party risk and market data

As our experience proved us, you do not necessarily need perfect datasets to begin. But do pay attention to reliable data governance and consistent formatting. Scalable data pipelines are a great advantage too.
KPIs to measure success
Think ahead of how you’re going to evaluate AI underwriting performance. Typical KPIs usually include:

Underwriting turnaround time
Approval processing speed
Risk prediction accuracy
Manual review reduction
Underwriting cost per application
Customer conversion rate
Loss ratio or default rate

In Intelliarts’ experience, companies that define measurable KPIs early in the implementation process are more likely to scale underwriting automation successfully and demonstrate clear ROI to stakeholders.
Machine learning vs rule-based underwriting
Both machine learning underwriting and rule-based underwriting aim to automate decision-making, but they differ significantly in flexibility, scalability, and risk analysis capabilities:

Rule-based systems rely on predefined logic and thresholds
ML underwriting continuously learns from historical and real-time data to improve predictions and decision accuracy

Many of our clients choose to adopt hybrid underwriting approaches that combine rule-based governance with ML-powered risk analysis. This allows to improve underwriting efficiency and scalability while also maintaining transparency and regulatory control.
Rule-based underwriting gives you control and predictability, while machine learning adds to adaptability and deeper risk intelligence. In our experience, the strongest underwriting systems combine both approaches, using business rules for governance and ML models for faster, data-driven decisions. – Volodymyr Mudryi, Data Scientist at Intelliarts
Regulatory and compliance considerations 
Regulatory compliance is especially important in industries such as insurance, lending, real estate, and financial services, where underwriting decisions directly affect customers and business risk exposure. So businesses must ensure their ML systems comply with:

CCPA (California Consumer Privacy Act): If you operate in the US market, your company must comply with data privacy regulations governing how personal information is collected, processed, stored, and shared. Since ML underwriting systems often rely on large volumes of customer and operational data, it’s essential to stick to transparent data governance and consent management.

CFPB Fair Lending Guidance: In lending and credit underwriting, the Consumer Financial Protection Bureau (CFPB) requires to make sure that automated underwriting does not result in discriminatory outcomes. Businesses must be able to explain underwriting decisions and demonstrate fairness across customer groups.
NAIC AI Governance Principles: The National Association of Insurance Commissioners (NAIC) provides guidance for insurers implementing AI systems, focusing on transparency, accountability, data governance, and risk management.
EU AI Act: Companies operating internationally should also monitor the EU AI Act. The latter introduces risk-based governance requirements for AI systems, including transparency, documentation, monitoring, and human oversight for high-risk decision-making apps.

Need a compliant AI underwriting solution? We provide insurance underwriting software development services for those looking to modernize legacy underwriting workflows, improve regulatory compliance, and implement scalable AI-driven decision automation.
Real-world machine learning underwriting example
At Intelliarts, we worked on an ML-based underwriting recommendation engine designed to help improve coverage and financing recommendations through predictive analytics and decision automation.

Problem: An insurance company (under NDA) operated in a data-intensive underwriting and needed a faster and more personalized way to evaluate applicants and recommend appropriate financing conditions. In their workflow, our partner relied on manual reviews and static decision rules, so it was impossible to adapt recommendations to individual customer risk profiles and financial behavior.
As application volumes increased, the underwriters struggled to consistently determine the most suitable loan amounts, interest rates, and repayment conditions for each applicant.
Solution: The Intelliarts team developed an ML-based recommendation engine designed to support underwriting decisions using historical underwriting data and previous submission outcomes. The system analyzed applicant financial history, behavioral patterns, and risk profiles to predict the most appropriate financing and coverage recommendations automatically.
Outcome: After the solution was implemented, the company improved the consistency and speed of underwriting recommendations. Also, the engine reduced manual decision-making effort, and the underwriters were able to process applications faster and deliver more personalized financing conditions.
Empowering underwriters with machine learning
With businesses across industries dealing with growing volumes of data and undergoing digital transformation, more organizations recognize the merits of implementing AI and ML solutions. Teams responsible for evaluation and decision-making like underwriters can especially win on machine learning for underwriting by:

Prioritizing submissions and assigning them to the most appropriate specialists
Processing documents during the application review faster and more efficiently
Calculating risks more accurately
And giving personalized offers to their clients

Getting started with machine learning underwriting can be challenging, though. In case your company is looking for a professional team for the full-scale development of an ML-based underwriting system, we are ready to assist. Working according to the CRISP-DM methodology, we take an all-around approach to software solution development.
Talk to us about your underwriting workflows and automation.Let's talk

#### Manufacturing 4.0: Explanation & Real-World Examples
If you step inside any factory floor today, you’re unlikely to see any similarity with the leading factory of ten or even five years ago. Today’s factories are equipped with IoT devices and integrated with emerging technologies, including cloud computing, AI, and ML.
This is how industry 4.0 is paving its way for the manufacturing industry, enabling factories to collect, analyze, and make decisions based on immense stockpiles of data. But what’s the value for business owners? Increased automation, a new level of process efficiencies, and improved quality at lower costs, to name a few.
In this post, we tell about the biggest shift that hit global manufacturing since automation — the phenomenon of manufacturing 4.0, technologies, effects, who is using industry 4.0 today, and examples of smart factories in industry 4.0.

#### ML in Predictive Maintenance: Techniques, Benefits & Challenges
Predictive maintenance using machine learning is an effective way to minimize downtime, extend asset life, and cut costs across industries. Unlike reactive or preventive approaches, this method uses sensor data and algorithms to anticipate failures before they happen. At Intelliarts, we have over 5 years of experience applying supervised and unsupervised predictive maintenance techniques to real-world challenges, from manufacturing to e-mobility. A good example is our asset failure prediction case study, where machine learning algorithms for predictive maintenance helped detect faults early and improve maintenance planning.
Depending on business needs and data available, predictive maintenance (PdM) can be approached with supervised, unsupervised, or hybrid methods. Even when companies lack detailed maintenance records, raw sensor data can still be leveraged to uncover anomalies and degradation patterns. In this article, we’ll walk through the main machine learning techniques for PdM, their pros and cons, highlighting how each can be applied in practice and what trade-offs to consider.
We can solve the PdM problem using one of the following machine learning (ML) techniques:

Supervised learning – requires labeled failure events to be present in the dataset

Unsupervised learning – we can use data that doesn’t contain labeled failure events

Based on the company’s maintenance policy, maintenance information may not be collected at all, which might not be the best option if a supervised-based predictive maintenance maintenance machine learning project is planned sometime in the future. But if the company has collected at least raw data from the equipment sensors it still might be used in a combination with unsupervised predictive maintenance approaches.
Supervised learning based PdM
The most important factor in the success of a robust and precise ML-based predictive maintenance system is the quality of data. So, if we have enough information about maintenance situations in collected data, supervised machine learning is the way to go. Additionally, supervised machine learning problems can be also divided into regression problems (when output assumes continuous values) and classification problems (when output assumes categorical values).
For a practical application of these concepts, explore our case study on solar panel fault detection, which demonstrates the effectiveness of ML techniques in identifying and preventing faults in renewable energy systems.
Three essential maintenance data sources are required to qualify a problem for a supervised predictive maintenance project:
The complete fault history
When you build a solution that leverages predictive models to make forecasts on failures, it is crucial for an algorithm to have an opportunity to learn and train on both normal operating state and operating during failures. There must be enough training data and a sufficient number of examples in both categories to enable the ML models to work properly. 
The detailed history of maintenance and repairs
Having enough maintenance information is mandatory for a PdM solution. The model should be aware of all replaced components and fix resolution.
Machine conditions
It is important that available data contains time-varying functions that include aging patterns and anomalies that are able to cause a performance reduction. We are assuming that the health status of a particular machine is decreasing constantly, and it is possible to calculate specific metrics (hours, miles, etc.) before the failure happens. 
Unsupervised learning based PdM
When information about previous equipment maintenance is not available in data, we can still build a predictive maintenance solution using unsupervised machine learning techniques, which could be used to detect anomalous behavior of equipment. 
The overall complexity of the PdM problem may vary. Depending on that, predictive maintenance challenges can be solved with the help of traditional machine learning algorithms or deep learning algorithms. We will talk exclusively and in great detail about deep learning in the next article, but for now, let’s focus on the value traditional ML techniques can bring, particularly predictive maintenance algorithms.

Learn about another field where machine learning is transforming industries, allowing for faster and more accurate vehicle assessments in insurance and repair sectors – AI car damage detection.
Traditional machine learning techniques for predictive maintenance
Decision trees
This is probably one of the most powerful tools for prediction and classification. It looks like a tree structure, where each internal node denotes a test on an attribute, the outcome of the test is being represented by each branch, and each leaf node (terminal node) holds a class label.

To build a tree, we need to divide a source set into subsets based on the attribute value test. This is a repeatable process for each derived subset in a recursive manner and is called recursive partitioning. When the subset at a node has the same value of the target available, or in a case when splitting no longer adds value to the forecasts, the recursion is considered complete. You don’t need any domain knowledge or parameter setting to build a decision tree classifier, hence it’s suitable for exploratory knowledge discovery.
At Intelliarts, we’ve seen decision tree–based methods work especially well in early-stage PoCs, where interpretability is important and quick wins can help validate the best model for predictive maintenance before moving to more advanced ensemble methods. To further ensure the reliability of these early-stage models, incorporating the Momentic AI software testing tool alongside platforms such as Testim, Functionize, or Cypress, helps teams validate complex ML-powered applications by testing data flows, system behavior, and edge cases before failures impact production.
How to use trees in PdM?
There are plenty of interesting use cases, let’s focus on two of them. 
Case 1: Fault diagnosis and detection in grid-connected photovoltaic systems (GCPVS)
GCPVS are electricity-generating solar PV power systems that are connected to the utility grid. A grid-connected PV system consists of solar panels, one or multiple inverters, a power conditioning unit, and grid connection equipment. The grid-connected PV system supplies the excess power, beyond consumption by the connected load, to the utility grid, saving on the electricity bills for the customer. However, without proper maintenance, the whole point of these devices will be missed, because they won’t operate correctly.
According to the case described by Rabah Benkercha and Samir Moulahoum, a decision tree algorithm was used to detect faults in GCPVS. To complete this goal, a non-parametric model was implemented to forecast the state of GCVPS, and the dataset was collected from GCPVS by the acquisition system in different weather conditions. The data used for model training consisted of three numerical attributes and two targets.
The attributes included: 

Temperature ambient

Irradiation

Power ratio (calculated from measured and estimated power)

The targets included:

Healthy or faulty state for detection

Four classes of labels for diagnosis: free fault, string fault, short circuit fault, or line-line fault

Additionally, the Sandia model was chosen to estimate the power produced by GCPVS during the healthy state of operation (in 2015 Sandia has developed a software toolkit that uses stochastic programming to perform power system production cost model simulations, which was named PRESCIENT). All collected data was split into two parts, 66% of which were used for learning and the remaining part was used for testing. 
The new data was recorded for five days in total in order to evaluate both models robustness and efficiency. The results were very impressive, as the decision trees model used for the diagnosis task was able to reach 99.80% of accuracy. The Sandia model evaluation showed high prediction accuracy, with the correct classification number being close to 100%. 
Case 2: Determining RUL of lithium-ion batteries
Another interesting case of leveraging decision trees is finding out the remaining useful life (RUL) of lithium-ion batteries. These batteries can only be used in specific conditions and require a battery management system (BMS) to monitor the battery state to ensure the safety of the operation. 
Multiple machine learning techniques for predictive maintenance were applied to deal with RUL challenges, but they faced the following limitations:

The extracted features didn’t reflect the information hidden in the historical degradation status

Nonlinearity caused imprecise OR low accuracy of battery degradation prediction

One of the options to solve this was to combine the time window (TW) and gradient boosting decision trees (GBDT). In this method, the energy and fluctuation index of voltage signals were being verified and chosen as features. After that, a time window-based method was used to extract features from the historical discharge process. The next step was to adopt gradient boosting decision trees for modeling the relation of features and remaining useful life. This method has proven results to solve this problem.
Support vector machines (SVM)
This is a model that fits great for classification and regression problems. It can solve linear and non-linear problems and works well for a variety of other real-world problems. The algorithm creates a line or a hyperplane which separates the data into two classes:

The key idea of this algorithm is to find a hyperplane in N-dimensional space (in which N represents the number of features) that distinctly classifies the data points. Among two classes of data points, a lot of possible hyperplanes can be chosen. The goal is to find a hyperplane with a maximum margin, or the maximum distance between data points of both classes. The future data points can be classified with more confidence when the margin distance will be maximized.
In our work, we’ve found SVMs to be particularly effective when datasets are smaller but still require high accuracy. For PdM, they are a practical option for both anomaly detection and direct RUL estimation, especially when interpretability is needed. To provide you with context, let’s review some applications of SVM in predictive maintenance.
Case 1: Fault detection and diagnosis of chillers
The first case is a classification problem that implements fault detection and diagnosis (FDD) of chillers. The chillers are used in buildings to provide cooling and are often the most energy-consuming piece of equipment. 
In one of the cases, the least squares support vector machine (LS-SVM) model was additionally optimized by cross-validation to leverage FDD on a 90-ton centrifugal chiller. To achieve the goal, three system-level and four component-level faults were analyzed. A detailed discussion was conducted to validate and employ eight fault-indicative features extracted from the original 64 parameters. Compared with the other two machine learning methods used in this project, the LS-SVM model showed better results in the following parameters, especially those on the system level:

overall diagnostic efficiency

detection rate

false alarm rate

The prediction precision was quite impressive, with refrigerant leak/undercharge (99.59%), refrigerant overcharge (99.26%), and excessive oil (99.38%). 
Case 2: Another RUL estimation 
Going back to remaining useful life estimation, there was a case when it was needed to estimate the RUL directly from sensor values, without estimating degradation states or failure threshold. Sometimes it is important to be able to estimate RUL at any stage of the degradation process. 
To achieve this goal, a direct relation between sensor values was modeled with the help of support vector regression (SVR). This is the most popular approach for fault prognosis. The experimental results showed that the SVR approach is a viable alternative to the existing methods. Not only RUL can be estimated at any point of the degradation process, but also an offline wrapper variable selection can be applied before training the prediction model (which reduces computational time and increases accuracy).
Case 3: Anomaly detection in EV charging systems
The Intelliarts team applied machine learning algorithms for predictive maintenance in a project with EV Connect, a leading EV charging provider. Their goal was to reduce unexpected outages and prepare for a predictive maintenance solution to improve service reliability.
Our team analyzed large volumes of historical data collected via the OCPP protocol and stored across MongoDB and ElasticSearch. After cleaning and transforming the raw inputs into a structured format, we applied unsupervised anomaly detection algorithms such as DBSCAN, Isolation Forest, and Local Outlier Factor. This helped identify abnormal behaviors, including unusually long charging sessions, that pointed to risks in station performance.

Together with the customer’s experts, we interpreted these anomalies from a business perspective and delivered recommendations on improving data pipelines, automating labeling, and collecting the right raw inputs. This provided EV Connect with strategic insights into charger usage patterns and laid the foundation for building a full predictive maintenance machine learning project in the future.
K-Nearest neighbors algorithm (KNN)
This is a supervised machine learning algorithm in predictive maintenance that can deal with both classification and regression problems. KNN algorithm works in the way that similar data points are close to each other. The algorithm finds the distances between a query and all the examples in the data and selects the specified number of examples (K) closest to the query. After that, the algorithm votes for the most frequent label, if we talk about classification. For the regression problem, the averages of labels are being calculated. When the new data appears, it is classified in one of the categories. Let’s say we have Class A, Class B, and the new unknown data point “?”. This data point is classified by a majority vote of the neighbors and assigned to the class most common amongst its K nearest neighbors, which is measured by a distance function.

In other words, KNN is all about similarity (also known as closeness, distance, or proximity) and calculating the distance between points on a graph. This method is simple yet effective, though it can become computationally heavy with large datasets. At Intelliarts, we’ve used KNN-based approaches as part of ensemble pipelines to benchmark performance and provide a fast baseline for more complex models. Moving to practical implementation in predictive maintenance, there are a few cases worth our attention:
Case 1: The diagnosis of electric traction motors
First, the automotive industry has a wide application for electric traction motors. The operational conditions of these motors could be characterized by variable load, rotational speed, and other external conditions which complicate the process of diagnosing bearing defects. Because of this, there is a challenge for detecting the onset of degradation, isolating the degrading bearing, and making a classification of defect types.
This is a classification problem that was solved by leveraging a diagnostic system built based on a hierarchical structure of K-nearest neighbors classifiers. Previously measured vibrational signals were used as input. The bearing diagnostic system was done by an approach that was based on multi-objective (MO) optimization that integrates a binary differential evolution (BDE) algorithm with the KNN classifiers. While this approach was applied to the experimental dataset, the results showed the capabilities of this method to solve the issue. 
Case 2: Improving the reliability of insulated gate bipolar transistor
As an example of a regression problem, we can talk about an insulated gate bipolar transistor (IGBT), which is a great performance switching device commonly used in power electronic systems. However, there is a challenge in ensuring reliability and safety for IGBT, which KNN can solve. 
There is an innovation in this field, as a new prediction model with a complete structure based on an optimally pruned extreme learning machine (OPELM) and Volterra series is recommended to predict the remaining useful life of insulated gate bipolar transistor and forecast its degradation. We can refer to this model as the Volterra k-nearest neighbor OPELM prediction (VKOPP) model. The model needs the minimum entropy rate method and Volterra series to rebuild phase space for IGBT’s aging samples. The new version of the algorithm can effectively reduce the negative impact of noises and outliers and is used to establish the VKOPP network. To successfully forecast remaining useful life for the IGBT the combination of the K-Nearest Neighbors method and least squares estimation (LSE) is leveraged to calculate the output weight of OPELM.
Compared to the classic prediction approaches, the prognostic results show that this one can predict the RUL of IGBT modules with small error, lower time spending, and significantly higher precision.
Case 3: Failure prediction in manufacturing equipment
A global manufacturer of home appliances partnered with Intelliarts to address frequent equipment breakdowns on the factory floor. These failures resulted in costly downtime, repair expenses, and shipping delays, so the company decided to build an asset failure prediction solution that could forecast issues before they occurred.
Intelliarts analyzed large volumes of historical and real-time IoT sensor data from the production line, transforming it into a format suitable for ML modeling. After testing multiple predictive maintenance algorithms, we found that an Extreme Gradient Boosting classifier delivered the best performance. With proper feature engineering, clustering, and model tuning, the solution achieved more than 90% accuracy in forecasting system failures.
The project helped the manufacturer cut maintenance costs by 5% and improve production line efficiency. Most importantly, the company gained the ability to predict which parts were most likely to fail and maintain or replace them just in time. In the end, this helped reduce downtime while ensuring consistent quality standards.

Advantages and disadvantages of the mentioned algorithms
Obviously, there is no single perfect approach to solving each particular challenge. Each algorithm that we mentioned in the article, has its best area for implementation. Let’s take a look at the pros and cons of each specific algorithm.
Decision trees
Advantages:

Significantly less effort in data preparation during pre-processing phase compared to other algorithms

Do not require data normalization

Do not require data scaling

Missing values are not a major or disrupting problem for building a decision tree

Very intuitive and easy to understand and explain to your developers and business partners

Disadvantages:

Possible instability due to the smallest change in data that cause major changes in the decision tree structure

Sometimes require much more complex calculations compared to other existing algorithms

Decision trees are relatively more expensive and time-consuming in training

Are not suitable for solving regression problems or predicting continuous values

Support vector machines (SVM)
Advantages:

Work well even with unstructured and semi-structured data

Have less risk of overfitting

Work relatively well in scenarios with a clear margin of separation between classes

Much more effective in high-dimensional spaces

Very effective in scenarios where the amount of dimensions is higher than the number of samples

Relatively memory efficient compared to other algorithms

Disadvantages:

No probabilistic explanation for the classification

No standard for choosing the kernel function

Not suitable for massive sets of data

Do not perform well when the data has a big amount of noise and target classes are overlapping

Will underperform in scenarios when the number of features of each data point will exceed the number of training data samples

K-Nearest neighbors algorithm (KNN)
Advantages:

Zero time for training. KNN is often called lazy learner or instance-based learning. Because the algorithm doesn’t learn during the training period and does not derive any discriminative function from data, we can consider that it doesn’t have a training period. It has a storage of training datasets and learns only when making real-time predictions. This feature makes this algorithm faster than the other alternatives that require training

Seamless adding of the new data. Because the KNN algorithm doesn’t need training before actual prediction generation, you can add new data easily, and it will not impact the overall accuracy in any way

Ease of implementation. To make it work, you only need two parameters, the value of K and the distance function

Disadvantages: 

It is not the best option for a large dataset, because the final cost of determining the distance between the new point and each of the existing points will be massive and will decrease the performance level of the algorithm dramatically

Not the best option for multiple dimensions. Once again, with a big number of dimensions KNN algorithm won’t work well, because of the need to calculate the distance in each dimension

Requires feature scaling. The feature scaling, standardization, and normalization are mandatory before applying the algorithm to any dataset. Without this move, there is a risk for KNN algorithm to make wrong predictions in the end

“K” in the algorithm must be determined in advance

The algorithm is very sensitive to the unbalanced datasets

Too sensitive to missing values, outliers, and noisy data. With the KNN algorithm, we can’t allow noise in the dataset. All missing values must be added and outliers removed manually to make the algorithm work properly

Conclusion
In the article, we highlighted the three most popular machine learning algorithms to solve a predictive maintenance challenge across various industries. As you see, there is no ultimate solution that will fit any situation like a glove. A proper algorithm must be chosen carefully and rigorously, based on your specific problem, available dataset, time and budget, and your business goals. While machine learning and AI used to predict machinery failure offers powerful techniques for predictive maintenance challenges, they are far from perfect and have their own limitations and disadvantages. In most cases, you need meaningful data that is properly collected and processed in the right way to build the ML solution for a hydraulic system.
The best option is to contact a team of experienced machine learning experts who know how to implement machine learning for predictive maintenance, evaluate your business situation, design a data collection pipeline, and develop a custom solution. At Intelliarts, we help organizations launch successful predictive maintenance machine learning projects that save costs, reduce downtime, and improve equipment reliability.
Follow our articles and get all the insights, as we continue to share our experience on advanced machine learning techniques, including automated data extraction from PDF, which can transform unstructured data into actionable insights, enhancing decision-making processes.

We at Intelliarts love to help companies to solve the challenges with data strategy design and implementation, so if you have any questions related to predictive maintenance machine learning algorithms in particular or other areas of machine learning — feel free to reach out.
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#### Precision Farming Using IoT: Key Benefits and Examples
Agriculture is a fairly fast-paced industry, businesses in which tend to adopt recent technology advancements rapidly. One of the recent improvements is IoT precision farming, which promises enhanced productivity and better worker safety. 
In this post, you’ll discover the state of the precision farming market in 2025, as well as the benefits and applications of IoT in agriculture. Besides, you’ll explore how smart agriculture works and find out how to get started with your precision farming using IoT projects. 
Precision farming market in 2025
Precision farming is an advanced agricultural approach that leverages IoT, AI, and data analytics to optimize resource use and improve crop yields. IoT in precision agriculture enables farmers to collect real-time data on soil conditions, weather, and crop health through sensors and drones. 
It’s interesting to take a look at the state of precision farming and IoT in the agriculture market as of 2025. Relevant statistics and facts are provided in the infographics below:
 
As you can conclude, stats indicate significant growth potential for IoT applications in agriculture. ​So, we can only recommend starting to adopt this advanced technology. Here at Intelliarts, we have been combining IoT with agriculture applications for years already, delivering enhanced business outcomes. The very success stories are detailed further in the text. 
You may be interested in exploring data science in business for a more complete picture of how smarter tech is used across domains. 
What are the benefits of IoT in the field of agriculture?
The time is just right to delve into the actual benefits that IoT in agriculture can bring. To provide the complete picture, we gathered all the possible benefits, both related and slightly distant to precision farming, and linked them to the corresponding usage. The list is as follows: 

Cost reduction through automated resource management
Improved yield via real-time crop monitoring and precision farming
Sustainable water usage with smart irrigation systems
Reduced pesticide use with targeted pest control solutions
Enhanced soil health via IoT-based nutrient monitoring
Predictive analytics for better crop planning and risk management
Automated farm operations reducing labor costs
Supply chain optimization with real-time logistics tracking
Better livestock management through smart wearables and health tracking
Climate resilience via AI-driven weather predictions
Energy efficiency in farm equipment with IoT-based power management
Waste reduction through smart storage and inventory monitoring
Remote farm management enabling control from any location
Real-time alerts for disease detection and prevention
Improved decision-making with AI-driven data insights
Increased food security by optimizing agricultural production
Smart greenhouse automation for better environmental control
Faster issue detection reducing crop losses and inefficiencies

As you can see, AI and IoT can offer a lot of useful technology implementations. The real question here is how to prioritize one over the other and how to get started. Hopefully, the following sections will guide you through. 
Discover additionally about the use of IoT in smart grid technology as lots of technology applications in this niche and agriculture intersect. 
How smart agriculture works using IoT?
To keep the answer focused, we’ll use the example of soil monitoring and follow the steps of how smart agriculture and precision farming using IoT works:

#1 Deployment of IoT sensors
Farmers place wireless soil sensors across the fields. Here are some of such sensors available on the market:

Teralytic Soil Sensor
Davis GroWeather Soil Sensor
Sentek Drill & Drop
Vegetronix VH400

These sensors measure parameters like moisture levels, temperature, salinity, pH, and nutrient content in real-time.
#2 Data collection & transmission
The sensors continuously collect data and send it via Wi-Fi, LoRa, or cellular networks to a central cloud-based platform. 
Explore big data collection and transmission in another blog post by Intelliarts. 
#3 Data processing & analysis
The cloud platform uses AI and analytics to interpret soil conditions, identifying areas that need watering, fertilization, or drainage improvements.
#4 Automated decision-making
Based on the analysis, the system can automatically trigger irrigation systems or send alerts to farmers for necessary interventions.
Intelliarts experts develop custom AI analytics software tailored to your needsExplore our service
#5 Visualization & remote access
Farmers monitor real-time soil conditions through a mobile app or web dashboard, allowing them to make informed decisions without the need to be physically present in the field.
Other similar use cases can be many. For example, with the application of computer vision, a business can watch over the crop’s health, using visual characteristics like color or integrity of leaves. Implementing such a system would merely require swapping sensors for cameras, with other steps in the provided flow left the same. 
Success story: Redesigning Indigo Ag’s legacy system for land sampling
Here we’d love to bring a success story by Intelliarts as an example of an effective usage of IoT technology to solve a business challenge in agriculture — Redesigning Indigo Ag’s legacy system for land sampling to reduce manual tasks and enable seamless data automation. 

Challenge:
Indigo Ag’s legacy land sampling system relied on manual processes, causing inefficiencies in data collection, delays in lab submissions, and errors in sample tracking. The company needed an automated solution to improve workflow efficiency and ensure accurate, real-time data availability.
Solution:
Intelliarts redesigned the system by integrating IoT sensors, QR codes, and mobile applications for seamless automation. The new agriculture software development system digitized sample tracking, automated data collection, and streamlined lab submissions enhancing data accuracy and accessibility.
Results:
The solution reduced manual tasks, cutting errors, and processing delays. Automated sample tracking improved efficiency, while the sensor inventory system lowered losses to fewer than 4 per 1,000. The centralized approach enabled real-time data analysis, accelerating decision-making and enhancing overall productivity in Indigo Ag’s land sampling operations.
Explore success story
Challenges & solutions in implementing IoT in precision farming
Naturally, decision makers may expect their projects to face common challenges and issues related to implementing advanced tech in conventional farming processes. Here, it’s essential to be well-prepared so it would be possible for your businesses to respond to risks immediately or, even better, minimize them in advance. The list of challenges and solutions is as follows:

Retrieval and usage of data has always been a sensitive issue for non-tech businesses. AS the data automation project for Indigo reveals, it’s possible to eliminate the least efficient processes and enhance decision-making for an agricultural company. — Alexander Barinov, Managing Partner at Intelliarts. 
Explore case studies by Intelliarts to find out how we address challenges with IoT in agriculture and related niches. 
How to get started with IoT in precision farming
To be honest, there’s no simple answer to the question of how your particular business should get started with IoT in precision agriculture. However, given the above-mentioned considerations, possible applications, and challenges, this is how I suggest approaching this matter:
#1 Compare your readiness and available resources against your objectives
In IoT and precision farming, you will likely start your project with just a bunch of sensors and a diverse in-house team. Evaluate the following:

Labor resource availability
Allocated budget
Technological feasibility
Digital and physical IT infrastructure
The scope of work
Implementation timelines

Most likely, this alone would provide you with a rough estimate of your capabilities and needs. For example, you may find out that you lack workers available to work as operators, managers, engineers, etc. You may detect the impossibility of developing your own IoT infrastructure, leaving you with only cloud solutions to choose from, etc. 
#2 Find a trusted technology partner
Having the right software provider is half the battle. Without further ado, here are aspects that tech partner covers in precision farming using IoT projects:

Assistance with choosing the right sensors, software, and cloud providers
Consultancy on budget, labor resources, timelines, and other constraints
Recommendations on how to place sensors
Development and deployment of a unified IoT system
Development and deployment of big data and analytics software solutions
In-house team training

As you can see, providers cover most of the development scope, leaving you only business aspects to handle, as described below.
For instance, here, at Intelliarts, a software development agency, we have delivered more than a dozen of IoT and agriculture projects, offering smaller developments as well as end-to-end implementations. Therefore, we are well aware of what your business may potentially need to reach its objectives for the projects, so we can provide both consultancy and execution. 
#3 Implement the development of IoT in precision farming
Here the role of the business owners boils down to high-level management. Given that your trusted tech partner can help with all the above-mentioned, you have the following processes to cover:

Project progress monitoring
Clarifying business objectives
Budgeting
Synchronizing with your vendor’s representatives
Keeping stakeholders informed
Connecting tech vendors with your in-house teams and third-parties

#4 Recruit the in-house team
As mentioned, human resources are required for an actual IoT in precision agriculture implementation. Sticking to the same example of soil monitoring, you may need the following specialists:

Agronomist or soil scientist
Farm operations manager
Field technician
Data analyst

Plus, given that soil monitoring is only one agriculture aspect, other employees may need to be trained on using, for example, reports obtained via the IoT software solution. 
#5 Launch and monitor
All that’s left is to let your tech partner deploy the solution and ensure that it operates as expected. While waiting for data and reports, you need to plan for one or other actions to execute, based on reports’ date. 
The end result of the project is to have a semi-automated soil monitoring system that is IoT-driven, have instructions for employees on how to act, and have a complete, fully trained team. Ensuring this will lead to fulfilling the business objective you set with this project. 
Need IoT Development Services?Intelliarts’ team helps bring your IoT project to life.Learn more
Future of precision farming with IoT and beyond
Given the high rate at which smart tech and precision farming develop, it’s possible to forecast several trends for the niche:

5G-enabled smart farming networks. The adoption of 5G will enhance precision farming using IoT, allowing ultra-fast data transmission, real-time analytics, and seamless connectivity for remote farm monitoring.
Blockchain for agri-supply chain transparency. IoT in precision agriculture will work with blockchain to track crop origins, quality, and environmental impact, ensuring transparent, tamper-proof records from farm to market.
Climate-adaptive farming with IoT & AI. Advanced AI models combined with IoT precision agriculture will analyze climate patterns, soil health, and water availability, enabling farms to adapt to extreme weather and resource constraints.
Digital twin technology for farm simulation. Precision farming with IoT will leverage digital twins — virtual farm models using real-time sensor data — to simulate scenarios, predict outcomes, and improve decision-making.

Explore how digital twin technology works and use cases in another blog post by Intelliarts. 
While the mentioned may seem like a little too extreme for trends, or something that stands a chance to be utilized only sparingly, that may not be the case. Since AI evolution, which drives such technological progress, is nearly in its golden era, businesses that strive to become top players in the market, will likely utilize all the recent advancements, resulting in accelerated technology adoption across the globe. 
Final take
Precision farming with IoT is reshaping agriculture, offering data-driven efficiency, sustainability, and cost reduction. Successful implementation requires strategic planning, the right technology partner, and a skilled team. As IoT adoption grows, innovations like 5G, blockchain, and AI-driven climate adaptation will drive the next wave of smart farming. Businesses that embrace these advancements will lead the future of sustainable, high-yield agriculture.
Any successful project has a technical team behind it. Should you need consulting or an execution of your idea end-to-end, don’t hesitate to reach out to Intelliarts. With more than 24 years of experience and with 90+ of projects, including ones in IoT and agriculture niches, we can provide you with the necessary software development and more. 

#### Predictive Lead Scoring: Insurance Use Case
Cultivating a prospect from lead generation to the actual sale is a complicated and usually time-consuming process. How often does it happen to an insurance agency when the leads show a momentary interest in the services only to evaporate when the insurance agents call them with a specific offer?
An insurance marketing department can generate hundreds of leads before a sales team discovers that only a small percentage of these are convertible. Your insurance company might consider using a predictive AI lead scoring software to spend less time on low-quality leads and avoid wasting business resources.
By allowing you to prioritize leads, predictive lead scoring automates your insurance sales process and increases the accuracy of results. Below we describe the benefits of machine learning-based lead scoring and our lead scoring case study where you can see how we’ve built a predictive solution for our insurance partner .

#### Predictive Maintenance for Renewable Energy Assets
Probably, all businesses dealing with equipment assets struggle with their maintenance. Downtime, compromised performance, and high maintenance costs are some of the issues companies, including renewable energy assets providers, face. The solution is complementing a traditional maintenance system with predictive maintenance. 
In this post, you’ll get an in-depth overview of the benefits and usage of predictive analytics in the renewable energy sector. Besides, you’ll explore a step-by-step implementation roadmap for predictive maintenance. It will help to form an understanding of how to get started with a predictive maintenance system for your renewable energy assets. 
Curious how regulation influences predictive energy systems?Read our white paper
Overview of predictive maintenance in the renewable energy sector
Predictive maintenance in renewable energy is experiencing significant growth, driven by technology advancements like IoT and machine learning. — Oleksandr Stefanovskyi, a strategic technology partner at Intelliarts. 
The global predictive maintenance market for the energy sector alone is projected to reach $1.79 billion in 2024 and is expected to grow at a compound annual growth rate (CAGR) of 25.77%, reaching $5.62 billion by 2029. As for the broader predictive maintenance market, it’s valued at $10.6 billion as of 2024 and is predicted to grow to as much as $47.8 billion by 2029. 
As it’s possible to note, predictive maintenance, as an approach and technology, is of huge popularity across industries. Among other usages, it can be exploited with renewable energy equipment units of various types. 
Examples of renewable energy assets that are frequently used in the industry and can benefit from predictive maintenance are provided in the image below:

Traditional vs. predictive maintenance method
Traditional maintenance is either a reactive or scheduled approach to maintaining equipment assets.

Reactive maintenance: Fixing equipment immediately after it fails.  

Scheduled maintenance: Performing routine maintenance at fixed intervals regardless of asset condition. 

While this method ensures some level of system upkeep, it can lead to unexpected downtime, unnecessary repairs, and higher operational costs, as issues are only addressed after a breakdown or on a schedule that may not reflect the actual condition of the equipment.
Predictive maintenance in the energy sector is a proactive approach that uses AI-driven analytics, real-time data from IoT sensors, and predictive models to anticipate when equipment is likely to fail. 
predictive maintenance in renewable industry helps identify potential issues before they cause downtime or costly failures, unlike traditional maintenance. It optimizes the performance and longevity of equipment assets. This method improves efficiency, reduces unexpected maintenance costs, and increases operational reliability across the energy infrastructure.
You may explore predictive maintenance advantages and limitations in another blog post by the Intelliarts team.

Note: Predictive maintenance in the renewable industry is superior to traditional maintenance in several ways. However, it can’t replace regular maintenance methods. Predictive maintenance should be regarded as an enhancement and an addition to traditional maintenance rather than its complete replacement.
Benefits and use cases of predictive maintenance in renewable energy
Let’s proceed with reviewing the advantages and real-life applications of AI-driven predictive maintenance for renewable energy.
#1 Wind turbine monitoring
Predictive maintenance is used to monitor the condition of wind turbines, predicting mechanical failures before they happen. Sensors collect data on vibrations, temperature, and pressure, allowing operators to detect early signs of wear and prevent breakdowns.
Example: GE Renewable Energy implemented AI-driven predictive maintenance on its wind turbines, resulting in reduced downtime and enhanced operational efficiency.
#2 Solar panel performance optimization
Predictive analytics is used for remote monitoring and maintenance of solar panels, identifying factors like dirt accumulation, shading, or mechanical issues. This approach helps optimize energy production by scheduling necessary cleaning or repairs. Platforms like SolarGenix apply AI to solar operations as well to help teams work faster and make smarter decisions across the solar lifecycle.
Example: First Solar uses predictive analytics in renewable energy to monitor solar farms, improving the longevity and efficiency of its panels.

You may also be interested in exploring how solar panel fault detection can allow for real-time monitoring and identification of problems, enabling more efficient maintenance tailored to the specific needs of the equipment.

#3 Hydroelectric power plants
In hydroelectric plants, AI-driven predictive maintenance for renewable energy prevents mechanical failures in turbines and generators by analyzing data from sensors that monitor key parameters like water flow and mechanical stress.
Example: Statkraft, a leading hydropower company, implements predictive maintenance to enhance efficiency and reduce unplanned downtime in its hydroelectric facilities.
#4 Biomass power plants
In biomass power plants, predictive maintenance systems monitor equipment like boilers and feed systems to detect signs of wear or malfunction before they cause shutdowns. This helps avoid unscheduled downtime and maintain energy production levels.
Example: Drax Group uses predictive maintenance for its biomass plants, improving reliability and reducing unexpected outages.
#5 Tidal and wave energy systems
Predictive maintenance is used in tidal and wave energy systems to monitor the harsh marine environment and detect potential failures in underwater components before they lead to costly repairs.
Example: Nova Innovation employs predictive maintenance in its tidal turbines to predict failures and optimize energy production in challenging marine environments.
You can explore the summarization of value-adding benefits that implementing predictive maintenance offers in the image below:

How to implement predictive maintenance in renewables — 6 steps
From Intelliarts experience, there are major steps to carry out before it even becomes possible to proceed with development and setting up a predictive maintenance software solution. Here’s a general implementation roadmap for predictive maintenance for renewable energy infrastructure:
Step 1: Initial assessment and data collection
Your team should get started with predictive maintenance implementation by conducting a thorough assessment of renewable energy assets. It allows us to understand current maintenance processes and identify key data points for predictive analysis. Collect historical data from wind turbines, solar panels, or other assets if you have the means to do so at this stage. 
What to do:

Identify critical equipment for predictive maintenance.
Gather sensor data on equipment performance, weather conditions, and energy output.
Analyze downtime and failure data.

Outcomes:

Complete understanding of data sources.
Clear overview of potential maintenance issues.

You may request high-tech consulting and infrastructure examination from an experienced software engineering agency to aid you at this step. 
Step 2: Setting up monitoring infrastructure
Then, you should proceed with installing or upgrading existing sensors and IoT devices on renewable energy equipment, so they can collect real-time data reliably. Ensure the system supports remote monitoring and data storage for future analysis.
What to do:

Deploy vibration sensors, temperature monitors, and energy output sensors.
Integrate IoT systems with data platforms.
Ensure continuous data flow to central servers.

You can explore IoT devices in renewable energy from the corresponding blog post written by Intelliarts experts. 
Outcomes:

Enhanced real-time data collection.
Reliable system for monitoring equipment status.

Step 3: Data processing and integration
Once you have the means to receive monitoring data, you need to establish a data processing pipeline to clean, filter, and integrate collected data from sensors into a central database. Ensure compatibility between different sources of data.
What to do:

Use software for data cleaning and transformation.
Build a data integration system for real-time data handling.
Enable API connections for third-party data sources.

Outcomes:

Unified, clean dataset.
Consistent data for predictive model input.

Step 4: Developing predictive models
Now you’ve reached the AI and ML development stage. Here, you need to build and train predictive models using historical and real-time data as training and testing datasets. The models should be specific to renewable energy asset types and conditions.
What to do:

Conduct end-to-end ML development of a predictive model
Leverage supervised learning with failure data to train models.
Test models on different equipment types.
Adjust models for environmental conditions like wind speed or sunlight.

Outcomes:

A tailored predictive maintenance model for renewable energy scenarios.
Accurate prediction of equipment failures.

Need a trusted partner for predictive model development?We provide custom ML solution development services.Explore more
Step 5: Pilot testing and validation
Run pilot tests of the predictive maintenance tools for renewable energy on a small scale before full deployment. Additionally, validate the accuracy of the predictive models by comparing predictions with actual performance data.
What to do:

Select a few wind turbines, solar panels, or other equipment units for pilot testing.
Continuously monitor model predictions vs. actual equipment performance.
Adjust algorithms based on the results.

Outcomes:

Proof of concept for predictive maintenance.
Model fine-tuning based on real-world results.

Step 6: Full-scale deployment and optimization
In the final stage, you need to deploy the predictive maintenance system across all renewable energy assets, with continuous monitoring for system optimization. It’s essential to train your maintenance teams on how to interpret and act on predictive insights so they can translate ML-driven insights into actual predictive maintenance activities, like repair or part replacement.
What to do:

Roll out the system across all wind farms or solar parks.
Implement real-time dashboards for maintenance teams.
Create a feedback loop for improving predictions based on outcomes.
Educate the personnel on how to use the solution in their day-to-day work.

Outcomes:

Fully operational predictive maintenance system.
Reduced unplanned downtimes and increased efficiency.

You may also explore predictive maintenance in manufacturing from another blog post by Intelliarts. 
Why Intelliarts for your predictive maintenance in renewable energy project
Intelliarts is an experienced team of AI and ML software engineers. We’ve been on the market for more than 24 years, offering tailored software solutions to various business needs. Known for a business-driven approach, we’ve built a solid reputation, achieving a 90% customer retention rate and fostering long-term partnerships — some lasting over 15 years. 
Moreover, we have practical experience with predictive maintenance as well as with many forms of renewable energy solutions in business. 

As such, in one of our projects we delivered an ML-powered comprehensive renewable energy solutions and maintenance services that can process data received from EV chargers, run anomaly analysis, and provide predictions regarding equipment condition and potential failure. Our EV software development services helped the customer, EV Connect, with a notable maintenance process improvement, resulting in additional business value.
Explore predictive maintenance for EV chargers success story by Intelliarts.

In another of our case studies, we provided our customer with local predictive maintenance for renewable energy systems, which are basically power management solutions. They help schedule power limits for EV charging stations. This way, users can meet utility power constraints, minimize energy consumption, and extend charging infrastructure and charging equipment lifespan.
Explore the smart power management system development success story by Intelliarts. 
The Intelliarts team is also known for our innovative projects in the renewable energy niche. Within the scope of our research and development initiatives, we created a computer vision-enabled ML model that can detect overheated spots in solar panels. You can give this computer vision model for solar panel anomaly detection a try yourself, as well as explore how it works in the video below:

 
Final take
As renewable energy demands grow, predictive maintenance driven by IoT and machine learning appears to be a suitable solution for optimizing performance and minimizing downtime across diverse energy assets. The implementation of predictive maintenance for renewable energy assets demands investment, effort, and expertise. Nevertheless, cost-efficiency and reliability it offers make predictive maintenance a promising initiative. 
The Inteliarts team is ready, willing, and able to help you leverage the transformative power of predictive maintenance strategies for energy efficiency. With our vast experience and relevant expertise, we can advise on your project as well as provide quality software engineering services.
Ready to implement a predictive maintenance system?Contact Us

#### Predictive Maintenance in Manufacturing: A Complete 2026 Guide
TL;DR

Predictive maintenance in manufacturing uses IIoT sensors and machine learning to forecast equipment failures before they occur, rather than fixing machines on a fixed schedule or after a breakdown.
Sensors alone aren’t enough: weak data pipelines, poor model training, and no feedback loop are what keep most pilots from ever reaching production.
Manufacturers that get it right typically cut unplanned downtime by 30–50%, reduce maintenance costs by 18–25%, and increase machine uptime by 10–20% within 12–18 months.
This guide explains how predictive maintenance works, what ROI it delivers, and how to plan an implementation that actually reaches production.

Predictive maintenance (PdM) in manufacturing uses IIoT sensors and machine learning models to forecast equipment failures before they occur, replacing fixed maintenance schedules and reactive repairs with data-driven timing. 
It matters most to CTOs, plant managers, and heads of data and AI responsible for keeping production lines running without overspending on maintenance that isn’t needed yet.
This guide covers how predictive maintenance in manufacturing works technically, what ROI and efficiency gains it delivers, which technologies are shaping it in 2026, real-world examples from manufacturers who have implemented it, how to plan and roll out a PdM strategy, and the main challenges teams run into, plus answers to the questions manufacturers ask most often before committing to a pilot.
Not sure where to start with predictive maintenance?We can help you assess data, equipment, and current processes to identify a starting point.Let’s discuss your options
What are the main types of maintenance in manufacturing, and how does predictive compare?
Manufacturing teams typically choose between five maintenance approaches: reactive, corrective, preventive, condition-based, and predictive. 
Predictive maintenance in manufacturing sits at the most proactive end of this spectrum, using sensor data and machine learning to time interventions precisely instead of on a fixed schedule or after a failure.
Here’s a brief overview of the main maintenance types in manufacturing:
[table id=29 /]
Now, let’s get into the details:
Reactive maintenance
As manufacturers struggled to keep up with the roaring demand, they tried different maintenance strategies. One of them was reactive maintenance, which embodies the mindset of “don’t fix it until it’s broken,” yet it ends with an increase in unplanned maintenance and equipment downtime, the exact problem predictive maintenance in manufacturing is designed to solve.
Corrective maintenance
Here we can also speak about corrective maintenance, which is the idea of identifying and repairing a failure so the equipment can perform as before. The two concepts are very similar. 
The only difference is that reactive maintenance is about fixing the equipment when it’s completely broken, whereas corrective maintenance means the equipment is still running but noticeably deteriorating, a pattern that predictive maintenance in manufacturing is built to catch earlier.
Preventive maintenance
Unlike reactive or corrective maintenance, preventive maintenance allows scheduled maintenance activities to be performed before the problem occurs. 
It increases the longevity of the equipment, but it’s also very costly, and it still doesn’t eliminate the risk of the equipment or its components breaking down randomly. Predictive maintenance in manufacturing industry was developed specifically to remove that trade-off.
Condition-based maintenance
Condition-based maintenance expects equipment monitoring, and based on the data collected, maintenance activities are performed right before the failure occurs. 
It’s similar to predictive maintenance in manufacturing, which doesn’t let equipment degrade. Based on the sensor data and trends, the latter predicts degradation and failures as early as possible to extend the equipment lifecycle.
Predictive maintenance
Industrial predictive maintenance is generally the better fit for manufacturers who need to minimize downtime and cut maintenance expenditure, especially as tight deadlines and thin profit margins make unplanned downtime even less affordable than it used to be. 
Adoption of predictive maintenance in manufacturing has accelerated as IIoT sensor costs have dropped and cloud infrastructure has made continuous monitoring affordable even for mid-market plants.
From Intelliarts’ experience and based on best manufacturing practices, predictive maintenance using the latest technologies is the most effective suite of measures that can be implemented in manufacturing. If approached properly, it can notably reduce unexpected defects and, therefore, unplanned downtime.

Recently, it has also been widely discussed that AI used to predict machinery failure can enhance preventive maintenance by accurately forecasting potential issues.

How does predictive maintenance work in a manufacturing environment?
A production-ready predictive maintenance system combines four layers: condition-monitoring sensors, a data collection pipeline, machine learning models, and analytics and monitoring software. 
Together, these layers turn raw IIoT sensor readings and time-series data into a specific maintenance action before a failure interrupts production, which is the core loop that predictive maintenance in the manufacturing industry depends on. 
IIoT predictive maintenance only works as well as the weakest layer in that chain, so gaps in any one of the four tend to show up as missed or late alerts.

Installed condition-monitoring sensors that gather and send real-time performance data as well as machine health info. This is possible due to IoT technology that connects machines, software solutions, and cloud technology.
A data collection pipeline that helps with collecting data from sensors in a raw format for further data analysis and processing.
Predictive ML models that are trained on historical data and fed with real-time sensor data to obtain failure predictions.
Analytics and monitoring software that helps with data analysis, system event monitoring, and scheduling human-machine interactions.

See how the pipeline runs, step by step, in the infographics below:

 
In addition, among the various types of predictive maintenance, two are most common: rule-based and ML-powered, each offering different levels of automation and insight.
Rule-based predictive maintenance
Rule-based predictive maintenance, also called condition monitoring, triggers an alert when a sensor reading crosses a predefined threshold. It delivers a working level of automation without machine learning, but it only catches failure modes the team has already anticipated and defined.

The idea of rule-based PdM lies in relying on condition monitoring sensors that continuously collect data about equipment and then send alerts when a specific rule has been activated, in line with predefined protocols. 

A special feature of rule-based systems includes close cooperation between production teams, engineering, and customer service departments, since this cross-department collaboration is necessary to understand the direct and indirect causes that eventually lead to equipment breakdown.
Once a manufacturer knows those causes, it can create a virtual model of its connected system where it outlines the behaviors and interdependencies between its different IoT elements. As a result, if the temperature increases above the predefined level in a smart factory, the system sends an alert message to the team.
From the above, rule-based PdM delivers some level of automation. However, Intelliarts emphasizes that it’s still too dependent on the team’s understanding of what components or environmental events to monitor. That’s exactly the gap ML-based predictive maintenance in manufacturing is designed to close.

“False positives are a much more critical problem than it appears to be when compared against undetected production defects. However, when you watch a technician open the same perfectly fine machine for the fourth time, you start looking for software solutions to help.” — Oleksandr Stefanovskyi, an AI solutions expert at Intelliarts. 

Predictive maintenance with machine learning
A different approach is to build AI predictive maintenance in manufacturing based on machine learning algorithms, typically random forest or gradient-boosted models on structured sensor data, and LSTM networks where failure signatures develop gradually over a time series. 
In this scenario, ML-based systems define their own rules from data inputs rather than relying on protocols a team has written down in advance.

Precisely, an ML model uses and learns from data generated by IIoT sensors historically and in real time, which helps the model understand normal equipment behavior and detect anomalies and events as they happen. 
It also helps find correlations and make predictions that the production team can act on in time to remove the potential defect.
As the Intelliarts team and many other software experts observe, ML-based predictive maintenance in manufacturing is only as good as the historical data it’s provided with. And while model accuracy can be improved via conventional development measures, production data is of much higher importance when it comes to real operation. 
Explore ML techniques for predictive maintenance and deep learning approaches to PdM to see how different models address equipment degradation and failure prediction.
Now, let’s go deeper into why manufacturers should consider implementing predictive maintenance.
What ROI and efficiency gains does predictive maintenance deliver in manufacturing?
The short answer: unplanned downtime drops by 30–50% and machine life extends by 20–40% for manufacturers who implement predictive maintenance, per McKinsey’s analysis of manufacturing analytics programs. Maintenance costs and uptime move by similar margins, though the exact number depends on which lever a plant focuses on first.
Let’s break out each metric separately, since “PdM improves ROI” on its own doesn’t tell a CFO much. 

McKinsey & Company, Manufacturing: Analytics unleashes productivity and profitability: predictive analytics reduce unplanned downtime by 30–50% in manufacturing.
McKinsey & Company, Digitally enabled reliability: Beyond predictive maintenance: predictive maintenance reduces maintenance costs by 18–25%.
Deloitte Insights, Industry 4.0 and predictive technologies for asset maintenance: predictive technologies increase machine uptime by 10–20%.
McKinsey & Company, Manufacturing: Analytics unleashes productivity and profitability: predictive analytics can increase equipment lifespan by 20–40%.
Intelliarts, Asset failure prediction case study: an asset failure prediction solution reduced false-positive alerts by 67%.

Take a look at the formulas for the provided metrics in the infographics below:

From Intelliarts’ work in the industry, the biggest efficiency gains usually come from eliminating the smaller, unnecessary maintenance rather than shortening planned downtime or increasing the time before major maintenance procedures.
What technologies are driving predictive maintenance in manufacturing in 2026?
Eight converging technologies are making predictive maintenance in manufacturing accessible to mid-market manufacturers in 2026: 

Plug-and-play retrofitting
AI and machine learning
Supply chain integration
Advanced inspection tools
Digital twins
VR/AR
Product Data Management (PdM) as a service
Agentic AI for autonomous maintenance scheduling

These capabilities also support a wider range of ML applications in manufacturing, including quality inspection, process optimization, demand forecasting, and energy management.
1. Plug-and-play retrofitting
One of the bottlenecks for introducing advanced technologies in manufacturing, including predictive maintenance, is dependence on legacy equipment. This problem is especially relevant to large companies where much of the machinery isn’t equipped with connectivity to send real-time info.
PnP (Plug and Play) devices can be an optimal solution here: ready-to-use computer equipment that connects legacy machines to computers without requiring specialized installation knowledge or equipment replacement, making PdM available to manufacturers without a full hardware overhaul.
In the projects the Intelliarts team has worked on, plug-and-play devices are fairly simple and widely distributed, making them an optimal choice for any connectivity-related needs. 
2. AI and machine learning
No wonder many manufacturers today choose AI and ML-based predictive maintenance over rule-based systems. These technologies bring continuous learning and performance improvements, the ability to cover more complex failure cases, and more accurate anomaly detection and early warnings as conditions change. 
This is what most vendors mean when they market AI predictive maintenance manufacturing platforms as the successor to static, rule-based monitoring.
In today’s manufacturing environment, the situation and type of data are changing fast, and AI/ML systems, including edge AI deployed directly on plant hardware for lower-latency inference, are built to keep up with these changes.
You might also be interested in how automotive insurance leverages AI car damage detection.
3. Supply chain integration
Another popular trend is extending predictive maintenance to supply chain management. PdM won’t only monitor asset lifespan but also weigh production schedules to choose the optimal maintenance window, and can help manufacturers order replacement parts automatically.
A pattern the Intelliarts team has seen across the industry is that the supply of raw materials and equipment, as well as fluctuating demand, start to affect manufacturing more than ever. The concept is known as smart manufacturing or Industry 4.0 and is widely regarded as the future of production. 
4. Advanced inspection tools
Inspection technologies such as vibration analysis and infrared thermography offer a thorough evaluation of equipment health, helping manufacturers detect hidden wear early and mitigate risks before they escalate.
Since heat is one of the earliest indicators of equipment issues, thermographic analysis using infrared scanners is becoming mainstream in manufacturing. 
A key advancement is thermal imaging, which uses machine learning to perform temperature checks from a safe distance, converting infrared measurements into detailed images that show equipment condition without physical contact.
Have maintenance data but aren’t getting enough from it?Turn equipment and operational data into insights that support better decisions.Talk to us about your project
5. Digital twins
A digital twin is becoming a mainstream technology in manufacturing, letting businesses test process changes and plan new equipment before committing to them. Paired with predictive production maintenance, it becomes especially powerful when there’s a clear target to predict and high-quality operational data available. 
Tech27 has described a case where this combination helped save an oil and gas production plant roughly $360,000 by anticipating a plant outage.
6. VR and AR
Virtual and augmented reality support fast visualization and diagnostics: a technician can scan equipment with smart glasses or a phone and run diagnostic tests, thermal imaging, or vibration analysis on the spot, without returning to a control room. 
Beyond diagnostics, VR and AR are also used for training, remote assistance, and documentation, all of which lower the skills bar for teams new to predictive maintenance in manufacturing.
7. Product Data Management (PdM) as a service
This model combines a dedicated service team with software to make PdM accessible without heavy in-house investment. 
It’s particularly valued by original equipment manufacturers, who can use it to collect real-time data from clients’ equipment and improve their own products, effectively outsourcing predictive maintenance in the manufacturing industry to a specialist partner instead of building it in-house.
8. Agentic AI for autonomous maintenance scheduling
A newer trend gaining traction in 2026 is agentic AI: systems that don’t just flag an anomaly but autonomously prioritize the resulting work order against production schedules, parts availability, and technician capacity, escalating to a human only when a decision falls outside its confidence threshold. 
Recent reporting on AI-driven downtime reduction points to this shift from passive alerting toward autonomous coordination as one of the more consequential changes in how manufacturers operationalize predictive maintenance in manufacturing.
White PaperLearn How Predictive Maintenance Can Support Your Manufacturing BusinessGet your copy
Real-world examples of predictive maintenance in manufacturing
These predictive maintenance use cases span discrete manufacturing, food and beverage, packaging, and heavy industry, and each one shows a different angle on the same underlying ROI.
#1 Intelliarts: cutting false-positive alerts by 67% for a discrete manufacturer

Challenge: A discrete manufacturing client’s failure-prediction pipeline was generating so many false-positive alerts that maintenance teams spent more time chasing false alarms than fixing real problems, quietly eating into production time every week without anyone tracking exactly how much.
Solution: We cut false-positive alerts by 67%. Our engineering team rebuilt the pipeline with better feature engineering on vibration and temperature data, then tightened the model’s confidence threshold so alerts only fired when a failure was genuinely likely.
Results: False-positive alerts dropped 67%, recovering roughly 4.2 hours of production time per week that had previously gone to investigating alerts that turned out to be nothing, freeing the team to focus on failures that actually mattered.

“We’ve been working with Intelliarts for eight years. Originally, we were reaching out just for help on the engineering side. But we found a true partner. We think of them as true members of our team, and depend on them for really the day-to-day of the business.” — Bob Dilan

You can read the full success story here
#2 Frito-Lay
A great predictive maintenance case study in the manufacturing industry is Frito-Lay. PepsiCo’s subsidiary has a positive experience introducing a robust predictive maintenance program, and its results are often cited as a benchmark for predictive maintenance in the manufacturing industry more broadly. 
According to its manager’s presentation at the Cascade Partners Manufacturing Report, PdM helped the company reduce planned downtime to 0.75% and unplanned downtime to 2.88%.
Specifically, the technology warned the team ahead of a PC combustion blower motor failure that could have shut down the entire potato chip department, and separately caught rising acid levels in oil samples that signaled degradation before it could disrupt Cheetos Puffs production.
#3 General Motors
Predictive maintenance in manufacturing in the USA is well exemplified by General Motors (GM), which uses IoT and AI to monitor its assembly lines and cut costs. This is a clear case of industrial predictive maintenance operating at scale.
The approach helped GM reduce unexpected downtime by 15% and save approximately $20 million annually in maintenance costs, with the Saginaw Metal Casting Operations a notable example of the resulting efficiency gains.
#4 Mondi
Paper and packaging manufacturer Mondi implemented PdM specifically to avoid abnormal shutdowns of its plastic extruder machine at its Munich plant, where a single failure had cost roughly €50,000 in cleanup and lost revenue.
PdM is estimated to have saved Mondi between €50,000 and €80,000, mainly through lower operating costs and less waste, a fairly typical outcome for predictive maintenance in manufacturing on a single high-value asset.
#5 Ecoplant
Ecoplant helped food manufacturers optimize air compression systems, which consume up to 30% of a plant’s electricity. Using real-time data and predictive analytics, the system flags potential compressor issues before they cause failures, allowing timely intervention that keeps production running. 
Another example of how predictive maintenance in manufacturing pays off outside the usual rotating-equipment use cases.
If you want to read more about the nuances of PdM implementation, here’s our predictive maintenance case study covering a PdM machine learning solution for hydraulic systems. 
How to implement a predictive maintenance strategy in manufacturing: a step-by-step guide
Predictive maintenance implementation can be split into four major steps, with two additional actions to be implemented if required.

Basically, the workflow is centered around gathering and analyzing data, then prioritizing, then a test project, and then scaling. Here’s the brief flow:
1. Analyze historical data
Check your occurrences of downtime, their frequency, and scale; review defects, their types, and costs; finally, look closely at reactive and preventive maintenance activities you completed for each asset.
2. Identify priority assets
Use the insights to get a clear understanding of your most critical assets. Install condition-monitoring devices and IoT sensors on this equipment: a thermal imagery camera, vibration meter, and oil measurement could be your starter pack.
3. Start a pilot project
You don’t have to try the uncharted waters of PdM with a big and costly project. Start with less and initiate a pilot project with one or two most suitable assets and machinery.
4. Move on and scale
Once your pilot project proves successful, you’re ready to scale up. Predictive maintenance in the manufacturing industry can be scaled up in quick time. In 6 to 12 months, your company can grow into a smart factory.
5. Have a detailed plan ahead
We also recommend having a detailed plan of implementation, a realistic budget, and time expectations. Think about the goals of your PdM project, try to make them measurable, and track the progress.
6. Seek professional help
You can also work with a provider of machine learning development services to plan the pilot, build the model, and productionize the PdM pipeline. Ask experts to analyze the weak points of your PdM journey if the results of your pilot project weren’t as promising as expected. 

“Once the system prevented one shutdown, people trusted it too much. That became a new problem. Over time, errors accumulate, and the need for improvement is obvious.” — Alexander Barinov, a managing partner at Intelliarts. 

From data preparation to ML models , we can help you work out what makes sense for your operations.Contact our team
Predictive maintenance implementation timeline and requirements
According to our experience, a typical predictive maintenance in manufacturing rollout follows three phases:
[table id=30 /]
Team roles required

Data scientist to build and validate the model
ML engineer to productionize and maintain the pipeline
Domain expert like a reliability or maintenance engineer, to sanity-check failure logic
IT/OT integration lead to bridge sensor data with existing enterprise systems

Data requirements
Most teams need a minimum of 6–18 months of sensor history, labeled failure events (not just healthy operation data), and a sampling frequency appropriate to the equipment type. 
Fast-cycling rotating machinery generally needs higher-frequency vibration sampling than slower thermal or pressure-based assets, which is a big part of why IIoT predictive maintenance projects take longer to plan than to build.
Success KPIs to track

MTBF (mean time between failures) improvement
Downtime reduction percentage
False-positive alert rate
Maintenance cost delta versus the prior 12 months

What are the main challenges of implementing predictive maintenance, and how to overcome them?
Manufacturers most often run into six obstacles when rolling out predictive maintenance in manufacturing: 

Outdated equipment
Data quality gaps
Shortage of in-house expertise
Integration friction with existing systems
Lack of data-driven culture on the shop floor
Data labelling scarcity

Left unaddressed, any one of these can reintroduce the equipment downtime a PdM program was supposed to eliminate. Each is solvable, but only if it’s planned for before the pilot starts.
1. How do you handle outdated equipment and machines?
Plug-and-play retrofit devices can connect legacy machinery to a data pipeline without a full hardware replacement, which keeps predictive maintenance in manufacturing achievable even on older lines.
2. How do you deal with data issues in predictive maintenance?
Invest in data governance and a proper collection pipeline before model-building starts, not alongside it.
3. How do you address the lack of in-house PdM expertise?
Partnering with a technology consulting provider is often the fastest way to close this gap without a lengthy in-house hiring cycle.
4. How do you integrate PdM with existing enterprise systems?
Scope the integration architecture during the pilot phase, not after, so the production rollout isn’t blocked on unplanned system compatibility work.
5. How do you build the digital mindset and culture predictive maintenance needs?
Involve floor operators in the pilot from day one, since their buy-in is usually what determines whether alerts actually get acted on.
6. What if you don’t have enough labeled failure data?
Semi-supervised learning approaches or synthetic data augmentation can help a model start learning useful patterns before a large labeled failure dataset exists.
How to get started with predictive maintenance in manufacturing?
So, you have decided to implement predictive maintenance to leverage technology and business advantages. Before approving a pilot, it’s worth checking three things:

Do you have 12+ months of historical sensor data for the assets you want to start with?
Is your IT/OT infrastructure connected enough to get that data out reliably?
Have you identified 3–5 high-value assets where a failure would be expensive enough to justify a pilot?

If the answer to all three is yes, predictive maintenance in manufacturing is very likely worth piloting now rather than waiting. 
If not, the gaps above are exactly where to start, and closing them is usually faster than teams expect once a real predictive maintenance in manufacturing plan is in place.
Download our predictive maintenance white paper to access more details on how software expertise can help with such complex technology applications.
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#### Predictive Maintenance: Cost & ROI Benefits
If companies are searching for smarter ways to avoid costly disruptions, the benefits of predictive maintenance are difficult to ignore. New research from IDS-INDATA highlights the scale of this problem, claiming that manufacturers across the UK and EU are predicted to lose over £80 billion due to unplanned downtime. In this article, we explore the key predictive maintenance advantages and disadvantages, its use cases, and best practices.
We live in a time when technology can solve more business issues than ever before. The latest advancements can offer companies a competitive edge and significant improvement to their processes. Today we will cover the predictive maintenance advantages and disadvantages for organizations and the ways to protect valuable assets and save on resources with the help of modern approaches like preventive and predictive maintenance, as well as powerful machine learning technology.
Contact us to discuss your predictive maintenance project.Speak to us
What is predictive maintenance, and what is its role?
On August 8, 2016, Delta Air Lines lost power at its operations center in Atlanta due to an electrical equipment failure. Computers used for booking passengers were down for nearly five hours. The result? Nearly 1,000 flights were canceled on the outage day and another 1,000 over the next two days. The financial damage reached close to $150 million, plus additional money on refunds and offering vouchers to clients for future travel.
This example illustrates the massive downtime costs for organizations. The haul in production can be a massive factor that reduces productivity or stops the manufacturing processes entirely. Plus, add here the adverse effect for reputation.

Predictive maintenance (PdM) offers a much smarter approach. Its goal is to continuously monitor equipment state in order to predict anomaly behaviors that may lead to costly failures and take actions to prevent those failures ahead of time.
In order to do that, the system is constantly monitored, and analysis is being made based on data collected from various IoT sensors. As a result, the functioning of your equipment will be optimized, and the repair costs will be reduced. While it does not entirely replace traditional maintenance approaches, it is an excellent addition to the overall maintenance process.
Benefits of predictive maintenance

By its definition, predictive maintenance is an approach to equipment management that brings advantages across costs, safety, and operational efficiency. Let’s consider the benefits of predictive maintenance in manufacturing in detail:
Lower maintenance costs
Predictive maintenance can reduce maintenance costs by up to 50%. With PdM, businesses don’t waste costs on regular maintenance activities, as these are performed only when truly needed. This targeted approach leads to substantial predictive maintenance savings by minimizing labor, materials, and time spent on redundant tasks.
Besides, companies save on diagnostics since PdM systems warn about potential equipment health issues in advance. These efficiencies contribute to a higher predictive maintenance ROI, as businesses reduce operational expenses while maintaining or even improving asset performance.
Reduction in critical failures
Among other benefits of predictive maintenance, businesses can also reduce the number of critical equipment failures by up to 90%. The system continuously tracks performance indicators, and companies get informed about asset degradation and can interfere long before the actual failure occurs.
Reduced downtime
Downtime related to the actual repair process will also be lower because you could identify the components that might break down in the first place. The complexity and cost of maintenance decrease as you only replace parts that are causing the failure and not all the parts that might have been broken during the failure.
Lower inventory and spare parts costs
Compared to preventive maintenance, PdM minimizes the need to stockpile spare parts. A manufacturer performs maintenance activities only when they’re actually needed, and theoretically, you should buy the new parts more rarely.
Streamlined repair schedules
Knowing the actual health status of equipment is another thing to consider among the benefits of predictive maintenance. This helps manufacturers plan repair shutdowns. PdM transforms emergency fixes into scheduled events, allowing teams to align maintenance activities with production goals and minimize operational disruption.
White PaperTurn Predictive Maintenance into a Success Story for Your Manufacturing CompanyGet our predictive maintenance white paper
Extended equipment lifespan
For expensive machinery, predictive maintenance services can save you money on extending the life of particular parts. PdM grants you fewer machine breakdowns and timely help, so the equipment works longer and becomes more resilient.
More sustainable production
By reducing unnecessary part replacements and minimizing waste, PdM supports sustainability. As companies extend equipment lifespan and optimize maintenance schedules, they lower their environmental footprint and make more efficient use of resources.
Better safety and working conditions
Potentially saving employees’ lives caused by destructive failures is also among the advantages of predictive maintenance. If you know about potential breakdowns and react to them quickly, you could also warn the operators and improve the working conditions.
Improved product quality
Consistent equipment performance directly influences product quality. Predictive maintenance helps avoid sudden breakdowns or gradual wear that could affect production standards, resulting in fewer defects and more uniform output.
Process improvement
PdM systems often collect and analyze performance data, helping identify unusual patterns or deviations in the production process. These insights can highlight emerging issues early or point to areas where process optimization is needed.

Predictive maintenance disadvantages
While PdM brings long-term benefits, it also comes with certain challenges, especially in the early stages of implementation. Understanding the disadvantages of predictive maintenance can help make informed decisions and plan PdM strategies more effectively.
High initial costs
One of the primary barriers to adopting PdM is the upfront investment. Here we can mention expenses on IoT, data collection, storage, and other related infrastructure, which can be especially costly for small and medium-sized manufacturers.
Complexity
PdM requires a complex system setup with a high level of IT expertise. Businesses spend much time and resources hiring and retaining a qualified workforce. That said, this complexity can work to your advantage when partnering with an experienced technology provider. Our team at Intelliarts specializes in software development for manufacturing — we can help you build or scale your predictive maintenance solutions from the ground up.
Data accuracy
The effectiveness of any PdM solution depends heavily on the quality and accuracy of the collected data. Incomplete, noisy, or poorly labeled data can lead to false alarms or missed failures. The workable solution here is to pay attention to robust data collection and continuous model improvement.
Employee training
To some extent, turning monitoring into predicting requires a cultural shift. Maintenance teams used to traditional methods may feel reluctant to trust machine-generated insights. Proper training and internal communication are key to solving this problem.
Predictive maintenance use cases in different industries
To understand the benefits of predictive maintenance better, let’s also review specific examples of how PdM helps minimize downtime and optimize asset performance across various sectors:
Oil and gas
In this industry, PdM is crucial for preventing equipment failures, which are costly and can lead to severe environmental consequences. The 2025 study describes the use of an ensemble learning model that assesses the health status of oil and gas equipment and predicts its remaining useful life. In the long run, the model can help optimize maintenance schedules and reduce unnecessary costs.
Manufacturing
Manufacturers are leveraging PdM to enhance operational efficiency and reduce industrial downtime. One of the most illustrative examples is predictive maintenance of hydraulic systems. As part of this solution, the Intelliarts team built four machine learning models to predict the degradation level of the hydraulic components: cooler, valve, pump, and hydraulic accumulator.
Another example is the deployment of predictive maintenance by Alcoa, the aluminum smelter in Iceland. The implementation of PdM reduced unplanned downtime by up to 20% and helped achieve full ROI in 4-6 months. Now the manufacturer is planning to scale its effort to 1000 more assets as well as to other sectors in their business.

Looking for more interesting examples? Check this guide on predictive maintenance in manufacturing.
Energy
Reliability of infrastructure is truly important in the energy sector, and this is what predictive maintenance can provide to energy businesses. A prolific example is when NextEra Energy implemented advanced analytics for gas turbine maintenance, which resulted in improved performance and reduced unplanned outages. The company achieved impressive results with a 23% reduction in downtime and $25 million in annual maintenance cost savings.
Healthcare
In healthcare, predictive maintenance increases the availability and reliability of medical equipment. The survey of predictive maintenance in healthcare systems highlighted how important data-driven strategies are to predict equipment failures in order to improve patient safety and reduce maintenance costs.
Transportation
The transportation industry uses PdM to maintain vehicle fleets and infrastructure. For instance, PdM can help monitor the health of transportation systems by analyzing data from vehicle sensors. Fleet operators that rely on PdM systems report a 30–50% reduction in unexpected breakdowns, saving costs and improving vehicle uptime. A great example is the use of PdM in bus fleets, where real-time monitoring systems can detect anomalies such as unauthorized tire changes and increase passenger safety.
Automotive
As for the automotive industry, it applies PdM to monitor vehicle components and predict failures. In one case study, a major car manufacturer avoided $0.5 million in unplanned downtime thanks to a predictive maintenance solution. The latter allowed for data-driven operational decisions and maximized equipment lifespan and reliability.
Difference between reactive vs. preventive vs. predictive maintenance
Depending on the extent of a system failure, there are multiple ways to solve the situation. Let’s take a closer look at the different types of system maintenance.

Reactive maintenance
This type of maintenance happens after the system or equipment breaks down. All the focus goes to restoring everything as close to “normal” condition as possible. Obviously, emergency fixes are far more expensive compared to planned maintenance. Another reason for the high cost of reactive maintenance is the fact that critical outages are most likely to happen during peak hours, disrupting processes when they matter the most. The necessity to fix everything as fast as possible may lead to additional expenses on hiring additional services and buying unplanned equipment.
In comparison of reactive maintenance vs. preventive maintenance, reactive is a more short-term approach with unpredictable costs and frequent operational disruptions.
Best fit for:

Non-crucial equipment
Equipment with low repair costs
Easily fixed equipment

Not suitable for situations:

When equipment failure may lead to safety or security issues
When the repairment process is time-consuming
When equipment must be available 24/7
When equipment is very expensive

Preventive maintenance
This is a popular approach when companies plan regular maintenance tasks to keep equipment in working condition. The key difference from reactive maintenance is the fact that you have scheduled repairments. The interval of the checkups may vary dramatically, from once in a few days to annual tune-ups.
Talking about the difference between preventive and predictive maintenance, it relates to how maintenance is scheduled: based on time or usage intervals in preventive maintenance or real-time data in predictive maintenance. Therefore, preventive maintenance risks performing unnecessary maintenance or failing to catch unforeseen breakdowns.
Best fit for:

Equipment prone to wear and tear over time
Systems with predictable failure patterns
Situations where failure modes can be prevented (and not increased) with regular maintenance

Not suitable for situations:

When equipment may have random failures that are unrelated to maintenance or maintenance frequency
When over-maintenance leads to unnecessary costs

Predictive maintenance
Predictive maintenance uses real-time data from sensors to assess equipment health and predict failures before they occur. Since it relies on data-driven insights, PdM is more precise and cost-efficient than other types of maintenance. When comparing preventive vs. predictive maintenance, predictive offers a smarter, more efficient way to manage assets.
Best fit for:

Companies with IoT-equipped machinery that allows data collection
Businesses seeking to reduce downtime and maintenance costs through prediction
Environments where failure prevention is critical to operations

Not suitable for situations:

When you don’t have any IoT sensors that provide diagnostic information about your equipment – in that case it’s better to start with an equipment upgrade

In terms of frequency of maintenance, the three methods could be compared as:

Here is a visual representation of what happens in these three methods when the maintenance is being done:

While requiring more IT expertise and some additional expenses, predictive maintenance is the most effective method for machine learning failure prediction and saving lots of money.
Best practices for implementing predictive maintenance solutions
1. Start with proper data collection
Your predictive maintenance strategy is only as good as the data it’s based on. The first thing you need to do is to set up a proper data collection process, which will allow you to get all the necessary information, including data from equipment and sensors, as well as overall system data over a certain period of time.
2. Choose the right predictive system
Next, you need to build an AI system that will make conclusions and predictions based on collected data. The number of parameters that are usually being collected will be impossible to process by humans. There are two most popular ways to go with this system, creating a rule-based AI system or using machine learning (ML) models. Both approaches have their own advantages and disadvantages of predictive maintenance.

Rule-based artificial intelligence systems
These systems generate pre-defined outcomes according to rules programmed by human experts. Rule-based systems use “a set of rules” and “a set of facts” to make statements. The best usage of this approach is when you need speedy outputs, have a very high risk of error (however, ML systems can work in those scenarios as well), and don’t plan to implement machine learning in the future.
On the drawback side of rule-based AI systems:

Are costly and time-consuming in implementation
Require complete knowledge of the behavior of equipment
Don’t perform well with previously unknown faults

Machine learning systems
The key difference is that ML-based systems define their own set of rules that are based on data inputs. They could take the correct data and work based on a probabilistic approach. But why is it such a powerful alternative to a rule-based system?

The advantage of predictive maintenance lies in ML systems’ ability to learn and adapt to new conditions, compared to static rule-based systems with a deterministic approach
Rule-based systems are limited to certain business logic. When the event occurs outside this business logic, the rule-based system will ignore this event. Machine learning algorithms are always aware of the “normal” state of the system. When something abnormal happens, the ML system will detect it
Machine learning systems can detect patterns in abnormal behavior, while rule-based systems will look only for previously set scenarios
Compared to straightforward rule-based systems, ML systems can be used in more complex cases. Additionally, when there is no simple and obvious way to solve a specific challenge, a machine learning system could be the only solution to the problem
When the situation and the type of data are changing fast, it could be ineffective to constantly rewrite business logic for the rule-based system. The ML approach will be able to keep up with the changes
There can be possible conflicts between rules when their number grows exponentially. At some point, you may lack the computing power to control all possible combinations of rules in your system or have the challenge of managing all the rules properly

Read also: Predictive Maintenance Using Machine Learning and Predictive Maintenance Using Deep Learning
3. Start with a pilot project
Predictive maintenance and ML can feel overwhelming at first. Instead of trying to overhaul your entire operation, launch a small-scale pilot project with one or two critical assets. This will let your team build confidence, fine-tune the process, and identify early challenges without major risk.
4. Scale up when ready
Once the pilot is successful, don’t wait too long to scale. PdM systems are scalable by nature — and businesses can go from a few connected machines to a fully operational smart factory in just 6 to 12 months. The longer you wait, the more likely operational gaps (such as missing documentation or poor staff training) will slow down broader implementation.
5. Get expert support
Successful implementation often requires technical expertise that goes beyond your in-house capabilities. Partnering with a skilled ML team ensures you avoid missteps and get the most out of your predictive maintenance investment. A qualified team can help with model development, infrastructure setup, and long-term optimization.
Conclusion
The maintenance of assets is a major challenge for organizations across the world. The right choice of maintenance strategy can save an enormous amount of money that could be spent on the future growth of an organization.
Each mentioned approach can be used in a certain scenario, however, predictive maintenance with the power of machine learning technology is probably the most efficient way to deal with this challenge. For sure, you can’t completely eliminate reactive and preventive maintenance from your processes because some incidents will still happen, and your equipment requires regular maintenance and check-ups. However, investing your time and resources into predictive maintenance will help to reduce the number of unfortunate incidents and maintain the equipment in top condition and with lower costs.
Looking ahead, innovations in IoT sensors, real-time data analytics, and the integration of digital twins are further transforming how predictive maintenance is implemented. And this should also be listed among predictive maintenance benefits. AI models are becoming more accurate and scalable, making it easier to move toward fully autonomous maintenance systems.
We at Intelliarts love to help companies solve the challenges with data strategy design and implementation, so if you have any questions related to ML pipelines in particular or other areas of data science consulting services — feel free to reach out.
Reach out to our team to explore how we can help with custom ML.Let's discuss

#### Preparing Data for ML Development in 6 Steps
Data preparation in ML is often overlooked compared to actual development, training, and testing processes. However, the quality of data is what lies at the heart of a successful ML model. In practice, most of the data that can be acquired is typically unprocessed and simply not suitable for ML in its raw form. That’s why proper data preparation is a must. 
In this post, you’ll discover why machine learning needs data preparation. Besides, you’ll explore how to collect and how to prepare data for machine learning, followed by a review of challenges and best practices associated with this step. Additionally, you’ll find out about the tools that can be used to prepare data for machine learning. 
Significance of data preparation
Data preparation in ML definitely shouldn’t be overlooked as a part of the ML development process. Here are some stats and numbers that prove this point:

According to the Garbage-in-Garbage-out (GIGO) rule, the end performance of any ML model essentially depends on the datasets it was trained on. Here are some of the necessary conditions of data intended to be used for ML development: 

Diversity
Consistency
Volume
Quality
Standardization

Naturally, data comes from various sources. It is often sourced both manually and automatically, sometimes, datasets even include machine-generated or synthetic data. It all boils down to the following fact:
Raw and unprocessed data typically doesn’t meet the sufficient conditions required for it to be suitable for machine learning purposes.
That’s exactly why data preparation is a step in ML that businesses should not avoid. The possible consequences of skipping on data preparation include:

Reduced model accuracy
Overfitting or underfitting
Increased computation costs
Model bias
Scalability issues
Misleading insights

Not having proper data is probably a top reason why an ML model is hard to build. In the worst-case scenario, it may be impossible to make an ML model show acceptable accuracy. 
Need advice on creating datasets for your machine learning project?Request expert consultation
Key steps in data preparation
Data preparation in machine learning is a complex, multi-step process. It requires ML expertise and the usage of specialized software instruments. Here’s the basic basic steps for preparing data machine learning with examples:

#1 Data collection
It implies gathering relevant data from various sources, such as databases, APIs, files, and online repositories. The first of steps in preparing data for machine learning encompasses matters of the type, volume, and quality of data. Here it’s necessary to ensure that the team has enough materials to execute an effective data preparation strategy.
Sources of data:

Internal sources: Enterprise data warehouses, sales transactions, customer interactions.
External sources: Public data sources like Kaggle, UCI Machine Learning Repository, and Google Dataset Search.
Web scraping: Automated tools for extracting data from websites.
Surveys: Collecting specific data points from target audiences.

Strategies for compensating for the lack of data:

Data augmentation: Generating more data from existing samples.
Active learning: Selecting informative data samples for labeling.
Transfer learning: Using pre-trained models for related tasks.
Collaborative data sharing: Working with other entities to share data.

Recommended tools: Python libraries (Pandas, Requests), ETL tools (Talend, Apache Nifi).
#2 Data cleaning
As stated previously, obtained data is nearly never suitable for ML. That’s why data cleaning or simply preparing raw data is required: 
Data cleaning strategies:

Handling missing data: Imputation, interpolation, deletion.
Handling outliers: Removing, transforming, winsorizing, or treating them as a separate class.
Removing duplicates: Using exact matching, fuzzy matching, and other techniques.
Handling irrelevant data: Identifying and removing irrelevant data points.
Handling incorrect data: Transforming or removing erroneous data points.
Handling imbalanced data: Resampling, synthetic data generation, cost-sensitive learning, ensemble learning.

Recommended tools: Pandas, OpenRefine, Trifacta.
#3 Data transformation
Converting raw data into a suitable format for machine learning algorithms. This stage enhances algorithmic performance and accuracy.
Recommended techniques:

Scaling: Transforming features to a specified range (e.g., 0 to 1) to balance their importance.
Normalization: Adjusting data distribution for a more balanced feature comparison.
Encoding: Converting categorical data into numerical format using techniques like one-hot encoding, ordinal encoding, and label encoding.
Discretization: Transforming continuous variables into discrete categories.
Dimensionality reduction: Limiting the number of features to reduce complexity using techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis).
Log transformation: Applying a logarithmic function to skewed data for a more symmetric distribution.

Recommended tools: Scikit-learn, Pandas, Featuretools, Alteryx
#4 Data reduction
It often happens that the number of parameters in the data gathered is higher than necessary. For example, a survey may include responses that are not valid for preparing the data model.  That’s why limiting the number of features or variables in a dataset is recommended. It helps preserve essential information while reducing complexity: 
Recommended techniques:

Dimensionality reduction: Techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) reduce the number of features while retaining significant information.
Feature selection: Methods like recursive feature elimination, mutual information, and chi-square tests select the most relevant features.
Sampling: Reducing the dataset size by selecting a representative subset of data points.

Recommended tools: Scikit-learn, Pandas, KNIME, RapidMiner.
#5 Data splitting
Three types of datasets are necessary for training every single ML model:

Training dataset. Used to train the model, helping it learn patterns and relationships between input features and target variables.
Validation dataset. Used to tune model hyperparameters and evaluate performance during training to prevent overfitting.
Testing dataset. Used to assess the final model’s performance on unseen data, ensuring it generalizes well to new data.

Dividing data into subsets is another necessary step. For training datasets, engineers use data of varying difficulty, from least to most complex. Validation datasets include specific cases, suitable for model fine tuning. Finally, a testing dataset is a suite of real-life numbers/ images/ documents, helping to actually measure the model’s accuracy. 
Recommended approaches:

Random sampling: Randomly splitting data, useful for large datasets.
Stratified sampling: Ensuring subsets maintain the same distribution of class labels or characteristics, ideal for imbalanced datasets.
Time-based sampling: Using historical data for training and future data for testing, applicable in time-series data.
Cross-validation: Dividing data into multiple folds for training and testing to get a more accurate performance estimate (e.g., k-fold cross-validation).

Recommended tools: Scikit-learn, Pandas, TensorFlow.
#6 Feature engineering
Upon the training of a model, there’s often a need to scale it and reinforce it with additional functionalities. Creating new features or modifying existing ones to enhance model performance is known as feature engineering. 
Recommended techniques:

Interaction terms: Creating new features by combining existing ones.
Polynomial features: Adding polynomial terms to capture nonlinear relationships.
Domain knowledge: Leveraging expertise to create meaningful features.

Recommended tools: Featuretools, Pandas, Scikit-learn, Alteryx.
Read also about integrating predictive maintenance advantages into your manufacturing business.
Challenges and best practices
Data preparation in ML can happen to be a daunting and challenging task. In the next sections, the Intelliarts team distinguishes several common challenges and provides solutions to them. We also offer best practices for approaching machine learning data preparation:
Searching for the Right Data Science Partner?Connect with Intelliarts to explore new opportunities.Learn more
Common data preparation challenges
Here are some of the common data preparation difficulties that engineers face:

Handling missing values

Missing values can lead to biased models or loss of valuable information, making the data less representative of real-world scenarios.
How to solve it: Use imputation methods, such as mean/mode substitution, or employ algorithms that handle missing data.

Data imbalance

Missing values can lead to biased models or loss of valuable information, making the data less representative of real-world scenarios.
How to solve it: Techniques like oversampling the minority class, undersampling the majority class, or using synthetic data generation (e.g., SMOTE) can help balance the data.

Noise and outliers

Noisy data and outliers can distort the true signal in the data, leading to misleading model outcomes and reduced accuracy.
How to solve it: Apply statistical methods to detect and remove outliers or use robust algorithms less sensitive to noise.

Data integration from multiple sources

Combining data from various sources can introduce inconsistencies, errors, and redundancy, complicating the preparation process and potentially leading to inaccurate models.
How to solve it: Use data integration tools and techniques to ensure consistency, and perform thorough validation and cleaning during the integration process.

Feature engineering

Creating relevant features from raw data is complex and requires deep domain knowledge to ensure that the features are meaningful and improve model performance.
How to solve it: Use automated feature engineering tools and collaborate with domain experts to create meaningful features that enhance model performance.
You may be additionally interested in exploring the role of data analytics in business in another of our blog posts. 
Best practices for ensuring the high quality of prepared data

To ensure high-quality prepared data, verify that all samples accurately represent the business logic that is not covered. Also, ensure that samples are free of inconsistencies, such as duplicate samples with different labels. Try to include as many meaningful hand-generated features as possible to make the model job “easier.”
Aside from the solution to common challenges mentioned in the section above, there’s a range of best practices that the Intelliarts team adheres to:

Thorough data cleaning. Regularly audit your dataset for anomalies and use automated data cleaning tools to maintain high standards of data quality.
Data validation and consistency checks. Implement validation rules and consistency checks during data entry and data integration processes to catch and correct inconsistencies early.
Usage of standardized formats and coding schemes. Establish and adhere to data standards and conventions, such as ISO date formats and standardized codes for categorical variables, to facilitate easier data integration and analysis.
Regular data profiling and monitoring. Use data profiling tools to regularly monitor data quality metrics, such as completeness, accuracy, and uniqueness, and take corrective actions when deviations are detected.
Collaboration with domain experts. Don’t hesitate to involve domain experts in the data preparation process to provide insights into data relevance, context, and appropriate feature engineering techniques.
Documentation and metadata management. Keep comprehensive records of data sources, processing steps, and quality assessments to streamline how to trace ML data sets to ML requirements. Besides, metadata management tools must be constantly used to ensure the traceability of data preparation in machine learning.

Data preparation success stories of Intelliarts
Here are brief insights into several out of the many data preparation-related cases the Intelliarts team had:
Predictive lead scoring for insurance company

The Intelliarts customer, a midsize insurance company, reached out to us with the request to simplify their labor-intensive and restricted traditional lead scoring process.  
In this machine learning in insurance case, we created a predictive lead scoring model able to forecast how likely the lead would buy a policy. With this custom insurance software, insurance agents can focus their efforts on the most prospective clients. From a data preparation perspective, our team of data scientists had to kick off work on the project with data collection. We gathered and processed information for customer behavior, quotes, demographics, property, and contacts, before proceeding with the development. 
The final lead scoring software successfully cut off approximately 6% of non-efficient leads, which resulted in a 1.5% profit increase for Intelliarts’ customer. Now, the company groups the clients by the probability of sales success. The group with an 80% probability converts 3.5 times more often than the average industry conversion. 
Explore the predictive lead scoring for insurance company case in more detail.
ML-powered list stacking and predictive modeling for the real estate business

The Intelliarts customer, an established real estate brokerage, contacted us with the request that we assist them in making better use of their extensive real estate market and homeowner data. The purpose was to anticipate which homeowners are most likely to sell their homes in order to increase the productivity of real estate brokers.
In this project, we developed a complicated ML solution composed of realtor and investor ML models. In the process of model training, we dealt with diverse data which consisted of 900+ property characteristics, 400 attributes of demographic data, and skip tracing data. The training was conducted using a training dataset in machine learning consisting of 60 million data records. Now the end solution is updated monthly in batches of 100-200 GB of data. 
The final system successfully identifies the most motivated sellers with an accuracy of more than 70%, which is impressive for such a technology application. It increased the Intelliarts customer’s monthly revenue by 600%.
Explore the ML-power list staking and predictive modeling case in more detail.
Tools and technologies
While data preparation software instruments and technologies are many, the Intelliarts team would like to suggest using the following several options, as they are well-tried and tested:
#1 Pandas
Pandas is an open-source data manipulation and analysis library for Python, providing data structures and functions needed to work with structured data.

Key Features: Data cleaning, transformation, aggregation, and visualization.
Use case: Handling and manipulating large datasets, especially in CSV, Excel, and SQL formats.

Scikit-learn
Scikit-learn is a robust machine learning library for Python, offering simple and efficient tools for data analysis and modeling.

Key features: Preprocessing modules for handling missing values, scaling, normalization, and encoding categorical variables.
Use case: Preprocessing data and building machine learning models in a streamlined manner.

DataRobot
DataRobot is an automated machine learning platform that accelerates the process of building and deploying predictive models.

Key features: Automated data cleaning, feature engineering, model selection, and hyperparameter tuning.
Use case: Quickly developing high-quality machine learning models with minimal manual intervention.

Apache Spark
Apache Spark is an open-source unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning, and graph processing.

Key Features: Distributed data processing, in-memory computing, and support for various data sources.
Use Case: Optimal for handling large-scale data preparation tasks in a distributed computing environment.

Alteryx
Alteryx is a data preparation and blending tool that enables data analysts to prepare datasets for machine learning algorithms, blend, and analyze data from various sources.

Key features: Drag-and-drop interface, extensive library of pre-built tools, and seamless integration with popular data sources and analytics platforms.
Use case: Data analysts and business users looking to streamline data preparation and analytics workflows.

KNIME
KNIME is an open-source data analytics, reporting, and integration platform.

Key features: Visual workflow editor, extensive range of pre-built nodes for data manipulation, and integration with machine learning libraries.
Use case: Creating end-to-end data science workflows, from data preparation to model deployment.

Looking for a trusted provider of AI and ML development services? Reach out to the Intelliarts team. 
Future trends in data preparation
Let’s finalize the post by reviewing some of the possible routes of the evolution of successful data preparation in machine learning projects:

Automated data preparation. We can expect increased usage of AI-based solutions to help automate data cleaning, transformation, and feature engineering processes. It will reduce manual effort and speed up the process. 
Augmented analytics. Evaluating the ready data and planning for changes in data preparation is still mostly done manually. Leveraging AI-driven insights and recommendations to assist users in data preparation tasks can be another potential improvement. This also will empower non-experts to prepare complex data. 
Enhanced data integration. In business, data sources and types are many. Integration of diverse data from all the available sources potentially will bring better outcomes of data analysis. 
Further scalability and performance optimization. There’s a need for tools that will be capable of handling large-scale datasets efficiently. This matter is deeply connected to leveraging cloud and distributed computing. If implemented, this will result in better potential for using complex and large-scale ML applications.

Hopefully, over the span of the next few decades, approaches to data preparation in ML will become advanced enough to relieve most of the burden of ML engineers. 
Final take
Data preparation in ML helps to create high-quality datasets that improve model performance and accuracy. By following best practices, addressing common challenges, and leveraging advanced tools, organizations can streamline this process. As the field evolves, automation and augmented analytics will further enhance data preparation, empowering even non-experts to handle complex tasks. For instance, vehicle damage detection models rely on well-prepared datasets to accurately assess and classify damage, improving the precision and speed of insurance claim processing.
Partnering with a big data consulting firm can provide the expertise and resources needed to efficiently manage data preparation and integrate ML-based solutions. Consider entrusting data preparation and ML-based solution development to Intelliarts. With more than 24 years of experience in the market and proven expertise, our engineering team can address any of your ML development needs with flying colors. 
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#### Proven Strategies to Scale and Optimize Your Data Pipelines
Experts expect the total volume of data worldwide to reach 182 zettabytes in 2025, and 394 zettabytes by 2028. This will be fueled by the Internet of Things(IoT) and scalable data processing. What’s the big idea here? Businesses must have the means to gather and utilize the vast amount of data they gather in their operations. In the meantime, scaling and optimizing data pipelines is one of the crucial measures to take in 2025 and beyond.
In every third technology audit, we uncover that big data challenges a particular business experience are actually attributed to poorly optimized data pipelines in place. No wonder we have to emphasize the data pipeline importance that often.  — Alexander Barinov, a Managing Partner at Intelliarts
This post will delve into the reasons behind data pipeline scaling. Moreover, you’ll review top data pipeline best practices and techniques, as well as familiarize yourself with tools and technologies. Besides, you will explore future trends in data pipeline optimization techniques and common challenges. 
Need Help with Data Pipelines?Get expert support with prompt engineering from Intelliarts.See How We Can Help
Why businesses scale data pipelines
Businesses have to scale their data pipelines to handle the growing volume, variety, and velocity of data. As a quick example, in one of Intelliarts’ success stories, we helped the customer grow to operating clusters with 2000 cores and 3700 GB of RAM. We’ll explore this DDMR case in detail later in the post. 
Here are the most common bottlenecks in data pipelines or negative scenarios that indicate the need for scaling:
#1 Slow data ingestion and processing
Legacy systems and monolithic pipelines struggle to keep pace with the continuous inflow of structured and unstructured data. This causes delays in availability for reporting, analytics, and machine learning.
#2 Failures during peak loads
Sudden increases in data volume — due to seasonal traffic, product launches, or system events — often exceed system capacity, resulting in dropped records, stalled workflows, or pipeline crashes.
#3 Rising cloud costs from inefficient data workflows
Unoptimized workflows that duplicate processing, overuse computing, or retain unnecessary intermediate data lead to uncontrolled cost increases as data volumes scale.
To support this claim, let’s review statistics: More than 78% of organizations estimate that 21-50% of their cloud spend is wasted, highlighting significant inefficiencies in cloud resource utilization.
#4 Inconsistent data quality across sources
Discrepancies in formats, missing values, and outdated or duplicated records lead to unreliable analytics and decision-making. Manual correction slows delivery and increases the risk of errors.
#5 Pipeline observability and debugging limitations
Lack of visibility into pipeline stages and transformations makes it difficult to detect issues, trace data lineage, or identify the source of data integrity problems.
#6 Difficulty adapting pipelines to new business needs
Hardcoded dependencies, schema rigidity, and fragmented tools prevent teams from responding quickly to new data sources, compliance changes, or evolving business questions.
Statistics reveal that 64% of customers wish companies would adapt more quickly to their changing needs, underscoring the necessity for flexible and responsive data pipelines.
In response to the drawbacks of not scaling data pipelines, let’s take a look at the actual benefits of having scalable data processing in place. The list is provided in the infographic below to support effective data presentation:

Best practices for data pipeline optimization
Let’s proceed with reviewing data pipeline best practices complemented with tech stack suggestions and highlights of potential business gains: 
#1 Stick to parallel processing
Parallel processing involves distributing large datasets or compute-intensive tasks across multiple cores or nodes so that they are processed simultaneously rather than sequentially. This approach maximizes hardware utilization and reduces bottlenecks in data-heavy operations like ETL, machine learning model training, and large-scale aggregations.
Tools: Apache Spark, Dask, Ray
Business gain: 

Reduced time-to-insight for analytics and decision-making
Support for real-time data products
Improved overall system throughput

#2 Use means of efficient storage and retrieval
Efficient data storage focuses on minimizing latency and cost by using columnar file formats, data compression, partitioning, and tiered storage strategies. It also includes indexing and metadata optimization to speed up query response times and reduce input/output (I/O) operations during access.
Tools: Apache Parquet, Delta Lake, Amazon S3 (with Intelligent-Tiering), Google BigQuery
Business gain: 

Reduced long-term storage costs
Accelerated data queries and dashboard loads
Ensured scalability as data volume grows

#3 Utilize automated scaling
Automated scaling allows infrastructure resources — compute, memory, and storage — to expand or shrink based on real-time workload demands. This elasticity ensures that the system can handle unexpected data spikes without over-provisioning or underutilizing resources.
Tools: Kubernetes, AWS Auto Scaling, Google Cloud Dataflow, Azure Data Factory
Business gain: 

Reduced infrastructure costs by avoiding manual provisioning
Ensured operational stability during load surges
Improved SLAs for data availability

#4 Implement data caching
Caching involves storing frequently accessed data in-memory or closer to the compute layer to reduce retrieval times and lower the load on primary data stores. This is especially valuable in repetitive query workloads, API integrations, and real-time applications like recommendation engines.
Tools: Redis, Memcached, Apache Ignite
Business gain: 

Enhanced performance of time-sensitive applications
Lowered query response latency
Reduced backend system load

#5 Apply orchestration and monitoring procedures
Pipeline orchestration automates the execution of interdependent data tasks, ensuring they run in the correct order, at the right time, and only when prerequisites are met. Monitoring adds real-time visibility into task status, failures, and performance metrics, enabling proactive management.
Tools: Apache Airflow, Dagster, Prefect, Monte Carlo
Business gain: 

Increased pipeline reliability
Reduced downtime
Simplified troubleshooting
Improved operational efficiency for data engineering teams

#6 Utilize schema enforcement and validation measures
Schema enforcement ensures that incoming data adheres to predefined structural rules before it’s accepted into the pipeline. Validation catches issues like missing fields, type mismatches, or format violations early, preventing them from cascading into analytics or models.
Tools: Apache Avro, Protocol Buffers (Protobuf), Great Expectations, Deequ
Business gain: 

Ensured consistency and trust in data across systems
Reduced cost of data cleaning
Protected downstream workflows from silent failures

You may be additionally interested in exploring data pipeline components from our other blog post.
Find the information from this section summarized in the simple infographics below: 

Tools and technologies for data pipeline optimization
Choosing the wrong data tooling — whether due to poor fit for scale, lack of integrations, or complexity — can lead to the following negative outcomes:

Spiraling infrastructure costs. Extra expenses are a result of over-provisioned resources or inefficient processing.
Pipeline instability. This is typically caused by tools not built for high-volume or low-latency use cases.
Longer development cycles. This challenge results from teams struggling with configuration, compatibility, or debugging.
Poor data quality and loss of trust. Such issue often arises in analytics procedures, and it’s caused by undetected failures.

Have a project idea? Don’t hesitate to request our software engineering services. 
Now let’s take a scope of pipeline optimization procedures and find out what tools and technologies can serve better in each case: 

5 common data pipeline scaling challenges and how to overcome them
It’s fairly often when businesses that are looking to fix data pipeline observability face technical and business issues. Let’s take a look at the most common of them and find out what data scientists at Intelliarts have to advise: 
#1 Linear scaling with growing data volume
When pipelines scale linearly with data size, organizations often find themselves adding compute resources without meaningful gains in efficiency. This creates cost pressure and limits responsiveness to data growth.
Solution: It’s advisable to redesign the pipeline architecture around horizontally scalable components. Distributed processing frameworks can help, and it may also be worth revisiting workload segmentation strategies.
#2 High cloud costs due to uncontrolled scaling
Unplanned or reactive scaling often leads to overprovisioned computing, redundant processing, and ballooning storage usage — costs that are hard to trace until it’s too late. Configuration drift across environments further amplifies these issues, making it difficult to maintain consistent performance and cost control at scale.
Solution: We suggest implementing cost observability across the pipeline and optimizing data storage formats. In many cases, moving to event-driven or serverless designs also helps reduce idle resource consumption.
#3 Slow onboarding of new data sources
Manual integration of new data sources — especially with inconsistent formats or contracts — can delay analytics and product timelines. This is particularly problematic in fast-moving markets.
Solution: It’s worth adopting schema-driven ingestion patterns and building reusable connectors. We can also recommend a contract-first approach to data collaboration between teams or vendors.
#4 Fragmented tooling across teams
A lack of standardization in pipeline tooling often leads to duplicated work, inconsistent practices, and operational silos that hinder innovation at scale.
Solution: Standardizing core tools and workflows — while still allowing some local flexibility — can make scaling smoother. Cross-functional alignment sessions or platform enablement programs are often helpful here.
#5 Lack of visibility into scaled pipelines
As complexity grows, pipeline failures or data quality issues become harder to detect and resolve, which can undermine trust in data systems.
Solution: It’s advisable to embed observability from the start: centralized logging, pipeline health dashboards, and data lineage tracking are all essential. If this hasn’t been done yet, an internal audit is a good first step.
Explore real estate infrastructure optimization in another blog post by Intelliarts for extra insight into ways to resolve actual business challenges. 
And here’s the summary of challenges from this section: 

Success story: Real business gains through data pipeline optimization
In one of Intelliarts’ success stories, we assisted DDMR, a US-based data-driven market research company, with optimizing a data pipeline. Here’s how the case unfolded:

Challenge:
DDMR’s data pipeline was fragmented, browser-dependent, and difficult to scale. Data collection was limited to Firefox, storage lacked tiering, transformation was handled by legacy Java/Hadoop stacks, and infrastructure was prone to manual errors. The company struggled with cost efficiency, slow processing, and compliance risks — all while trying to grow its data-driven business.
Solution:
Intelliarts provided information technology consulting and built a full-cycle, scalable data pipeline covering ingestion, processing, storage, and delivery. We replaced the browser-specific collector with a platform-agnostic solution, introduced hot vs. cold storage, modernized the processing stack (moving from Hadoop to Spark and later Snowflake + DBT + Airflow), automated infrastructure via Terraform and Ansible, and integrated GDPR-compliant data handling and monitoring.
Curious about the engineering perspective of creating scalable data pipelines? Examine the entire step-by-step process of building a big data pipeline for DDMR in another blog post by Intelliarts. 
Results:

Multi-browser data acquisition expanded reach
Reduced storage costs via tiered architecture
5x+ revenue growth due to improved scalability
24/7 monitoring and auto-recovery mechanisms enabled high availability
New value-added products opened fintech and analytics markets
Full technical ownership transferred to Intelliarts, freeing internal teams to focus on growth

Explore DDMR’s success story
Future data pipeline optimization trends or how to not fall behind the competition
Finally, here’s an insight into what businesses should expect to become a must-have in the data engineering niche:
#1 Data-aware auto-scaling using ML-based workload forecasting
Most current auto-scaling logic reacts to thresholds (e.g., CPU or memory use), but experimental approaches now involve machine learning models that predict workload surges and preemptively scale infrastructure. This aims to reduce cold starts and ensures smoother performance during traffic bursts.

Early application: Google’s Borg scheduler and Kubernetes’ KEDA with Prometheus integrations.

Additionally, check the delivery optimization consulting success story by Intelliarts. 
#2 AI-assisted pipeline observability and incident resolution
Beyond static monitoring, some companies are piloting AI copilots that automatically trace root causes across logs, metrics, and traces. These copilots integrate with observability tools to offer proactive resolution recommendations or even auto-remediation scripts.

Early adoption: Microsoft’s DevCopilot prototype, IBM’s AIOps Research.

#3 Differential data processing or compute-on-change concept
Instead of reprocessing entire datasets, differential or incremental processing re-runs pipelines only for data that has changed. This reduces compute usage drastically, especially in CDC (change data capture) scenarios and ML feature pipelines.

Experiment usage: dbt with incremental models, Delta Live Tables (Databricks), and experimental PostgreSQL triggers + Apache Beam.

Data pipeline optimization innovations are hardly popular news. However, engineers and representatives of technology-first companies should keep up with such trends to be competitive in this incredible data-centric business environment. 
Need assistance with data pipeline optimization? Don’t hesitate to request data engineering services by Intelliarts.
Final take
In 2025, businesses must scale data pipelines to manage growing data volume, ensure reliability, and control cloud costs. Key practices include parallel processing, automated scaling, schema enforcement, and efficient storage. Choosing the right tools and standardizing workflows helps prevent fragmentation and maintain data quality. Looking ahead, AI-assisted observability and differential processing will define the next wave of pipeline efficiency and agility in a data-driven world.
The Intelliarts team has more than 24 years of experience in software engineering and 80+ large projects under our belt. Combined with a business-oriented approach and a 90% customer retention rate, we positioned ourselves as one of the best data engineering companies in the market. Should you need an expert team to cover your scalable data processing needs, don’t hesitate to reach out to Intelliarts.
Prompt engineering made simple — partner with Intelliarts.Contact Us

#### Public Transport Optimization with Telematics
Fleet management and telematics systems are becoming a must for public transport operators dealing with rising fuel prices, stricter sustainability rules, and shifting passenger demand. With urban populations growing fast and infrastructure under strain, running reliable and cost-effective services keeps getting harder.
Electrification, autonomous driving, smart infrastructure, and multimodal networks add even more complexity. But with the right tools, public fleet operators can track performance in real time, plan better, and keep fleets moving — even when budgets are tight.
In this article, we’ll explain how telematics works in public transport, what it offers, and what you should consider before rolling it out.
Thinking about optimizing fleet operations?Get insights into top tech trends shaping the future of e-mobility, with quotes from top industry experts.Read our white paper
What are fleet management and telematics in transportation?
Fleet management refers to the tools, systems, and processes that various organizations — including public transportation operators — use to manage their vehicles from purchase to retirement. It helps them manage, maintain, and operate their fleets effectively.
Public transport fleet management covers everything from keeping vehicles in good shape to improving the overall passenger experience. It includes practical tasks like keeping maintenance on schedule, managing drivers, staying compliant with regulations, planning for growth, handling emergencies, cutting unnecessary costs, and ultimately making passenger transportation more sustainable and reliable.

The efficiency of fleet management for public transportation largely depends on the quality and sophistication of the features within fleet management software. Gartner notes the growing demand for advanced solutions in this sector and predicts the fleet management market will reach $16 billion by the end of 2025.
Another key concept is the technology enabling real-time vehicle data in fleet management platforms — telematics.
Transportation telematics refers to systems that use GPS, onboard diagnostics, and communication networks to collect, transmit, and analyze real-time vehicle data, offering insights into location, performance, and vehicle health.
Examples of the data gathered include:

Current vehicle location
Gas or energy usage
Driver behavior (braking, speeding, seat belt usage)
Door opening and the number of passengers boarding 
Idle behavior (how much time the vehicle stays idle, with its engine on or off)
Maintenance alerts
Weather and traffic data

Compared to logistics and shipping companies, which lead in fleet management software adoption, public transportation operators use these tools less often. Still, they can benefit just as much. For example, the market now offers solutions designed specifically for fleet management for buses, with features tailored to optimize public transit operations. 
Core features for bus fleets
Public transportation operators usually have a clear set of priorities when choosing digital tools for managing their fleets: safer rides for passengers, lower operating costs, and more time on the road for each vehicle. If that sounds like your focus, these are some of the features worth paying attention to in public transportation fleet management software.
Telematics integration
This is the backbone of any modern fleet platform. Integrating telematics in fleet management means connecting your system with providers like Geotab or NFI Connect via API to turn incoming data into actionable insights. We’ll go deeper into the role of telematics in the next chapter.
Real-time vehicle tracking 
Live tracking shows you exactly where your buses are at any moment, a vital feature of bus-transit fleet tracking systems. Most platforms display vehicle locations on a map, along with estimated time of arrival (ETAs), delays, and unexpected stops. Passengers benefit too — apps or station screens can display real-time arrival updates, helping build trust and improve the rider experience.
Fuel and energy monitoring
Fuel and energy tracking lets you see how each bus performs and spot inefficiencies before they negatively impact your budget:

You can track consumption per mile, compare vehicle performance, and even detect patterns like excessive idling or poor route planning.
Some platforms also help compare diesel vs. electric vehicle (EV) energy costs or calculate the ROI of fleet upgrades.
With electric vehicles (EVs), fleet management systems can optimize charging. If a battery degrades too quickly, the system might flag it as a potential issue linked to driving style or charging behavior.

The EV fleet management system we developed for one of our clients analyses how much charge each bus needs when heading off. When a bus needs to leave shortly, we prioritize it in the charging process, directing more energy to this charging station.
– Ihor Rudnyk, E-Mobility Solution Expert at Intelliarts
Maintenance scheduling and alerts
To automate maintenance planning, vehicle management for public transportation is based on real-time vehicle data — such as mileage, engine hours, and onboard diagnostic codes. Instead of relying on fixed intervals, the system schedules service based on actual vehicle usage and condition:

The system alerts the maintenance team when predefined thresholds are reached (e.g., oil life drops below 10% or brake wear exceeds limits).
Maintenance alerts are typically shown on the platform dashboard and can be sent to email or mobile devices.
Predictive maintenance features use historical data to identify patterns — for instance, if a particular component tends to fail after a certain number of hours, the system can recommend proactive replacement.

These capabilities help reduce unplanned breakdowns, improve service scheduling, and ensure higher overall fleet availability.
When the system reads that a charging station is down or a bus fails to complete its charging in time, it immediately notifies the fleet manager about the issue. The operator can then reschedule routes, dispatch a backup vehicle, or arrange for urgent maintenance — before it causes any delays. 
– Jurij Aulikh, E-Mobility Software Engineer
Driver behavior insights
Detailed, unbiased data on driving habits helps fleet managers resolve conflicts, reward safe drivers, and keep passengers safe. In practice, it means that:

Systems can log harsh braking, speeding, or seatbelt violations to help verify incidents when complaints arise.
Many platforms generate driver safety scores, allowing you to coach those who need improvement or recognize top performers.

Some systems even support video telematics (depending on local laws), helping monitor distractions like phone use or fatigue.
Route planning and optimization
Fleet software taps into live traffic data, predictive algorithms, and historical trends to fine-tune scheduling. For example:

If buses are regularly delayed at 8 AM along a particular route, the system might suggest adjusting stop times or rerouting altogether.
During disruptions or city events, dispatchers can instantly send rerouting instructions to drivers via onboard tablets or mobile apps.
Real-time insights also help avoid vehicle bunching and keep service running smoothly.

While bus drivers can’t change their routes at will, fleet software gives dispatchers the flexibility to respond to unexpected traffic or service disruptions.
Driver and dispatcher communication
Platforms often include mobile apps or in-dash tools that connect drivers and dispatchers in real time. For instance:

Drivers receive updates on routing changes, delays, or scheduling adjustments.
They can report issues or incidents straight from the road.
Dispatchers stay informed about driver location and status, allowing faster, more coordinated responses.

Let’s now explore how these capabilities translate into real business value for public transportation operators.
Fleet management benefits for public transportation operators
Fleet management solutions support public transport operators across multiple areas:

Improve operational efficiency. Fleet management software keeps track of all vehicles’ day-to-day operations, highlighting bottlenecks and opportunities for improvement. 

While building an energy management system for an electric bus fleet, we factored in that charging speed slows down as batteries reach full capacity. That final 10% — from 90% to 100% — takes the longest and, if unmanaged, can strain the grid. By fine-tuning the charging schedule, the system ensured all buses were ready on time without wasting energy or overloading the network.
– Ihor Rudnyk, E-Mobility Solution Expert at Intelliarts

Reduce maintenance and fuel costs. Routine vehicle maintenance planning becomes easier with remote vehicle diagnostics, immediate alerts about issues, and easy fuel/energy usage monitoring
Ensure compliance with local and international regulations. Fleet management systems help operators stay compliant by enabling GPS tracking, recording driving behavior, generating automatic reports, and integrating with onboard CCTV systems — features often required by European laws to support passenger safety and service transparency.
Improve passenger satisfaction. Passengers will appreciate route transparency, shorter wait and travel times, and real-time updates on any changes provided by up-to-date bus fleet management systems.
Provide reporting convenience. Fleet management software generates automatic energy usage reports for electric vehicles.

Boost Your Fleet's Efficiency with Smart TechIntelliarts experts are ready to help you optimize your operations.Explore more
A custom fleet management solution can help bus operators maximize these benefits. Still, tailored software isn’t the only path — off-the-shelf systems are also available, each with its strengths and trade-offs.
Off-the-shelf vs. custom fleet management software
Off-the-shelf systems are ready to go with features like vehicle tracking, driver management, and maintenance scheduling right out of the box. They’re usually cheaper to start with and quick to deploy. However, they come with limitations:

You get what you get — customization is limited.
Workflows might differ from how your fleet managers actually work.
Integrations with existing systems or hardware can take time and engineering effort.

Custom fleet management solutions are built from scratch (or close to it) around how your fleet really runs. Whether you need smart dispatching logic, integration with specific vehicle telematics systems, or tools for optimizing idle time and fuel usage — the system is designed around your actual business model.
It might take more time and budget to build compared to pre-built systems, but on the upside, you’ll have the following:

Complete control over features.
Decent scalability supporting room to grow as your fleet and services expand.
Focused on your needs, not generic toolsets.

As long as your fleet is small or your needs are pretty standard, off-the-shelf could be a good fit. But if you’re scaling, managing a mixed fleet, or need tight integration, custom software may be the more cost-effective option in the long term.
Telematics integration: More than a trend for bus fleets in 2025 
The automotive telematics market is projected to grow from $9.87 billion in 2024 to $17.24 billion by 2030. The increase in urban population and demand for transportation services are the main drivers of this growth.
Public transport operators are turning to telematics to make daily operations more predictable and easier to monitor, while cities are increasingly building it into their transportation systems.
How telematics works for public buses

Telematics for buses typically operates through the following components:
Smart sensors 
Internet-of-things (IoT) sensors gather and transmit a wide range of data points that support vehicle maintenance, route planning, and the passenger experience. Below is a table outlining the major types of data IoT sensors collect in bus fleet management:
 
Depending on the model and specifications, public transportation vehicles can be equipped with even more sensors. However, we’ve highlighted the most important ones in terms of passenger comfort, safety, and cost optimization to support sustainable operations.
Telematics control units (TCUs)
The TCU works by gathering data from the vehicle’s sensors, processing it to make sense of the information, and then sending it to a central server for storage and further analysis. For public transportation, popular TCUs include:

NFI Connect
Geotab
Samsara
Fleet Complete
Verizon Connect

Cloud infrastructure 
Telematics cloud servers act like a central hub, receiving information from telematics devices in vehicles, such as speed, location, fuel levels, and more. This data is then stored in databases, making it accessible for fleet managers to analyze and track vehicle performance, maintenance needs, and more, all in real time.
Telematics applications
Telematics applications are software tools that help fleet operators make sense of all the data coming from their vehicles. They provide dashboards to track vehicle performance, monitor fuel use, and check driver behavior.
Third-party integrations
What makes a telematics system valuable is how well it integrates with other tools. The more seamlessly it can exchange data with other systems — such as fleet management software, maintenance platforms, and even traffic or weather services — the more valuable it becomes to public transport operators in achieving their business goals.
Advantages of telematics in public transport

Bus telematics and fleet management software give public transport operators a complete picture of how their fleets perform. With real-time data at their fingertips, they can act fast, plan better, and cut down on waste, resulting in real savings and better service.
Cost reduction
Telematics helps public transport operators reduce costs across several levels — by preventing expensive repairs, avoiding service disruptions, and optimizing resource use. For example:

By flagging early signs of mechanical wear or system faults, telematics systems reduce the risk of unexpected breakdowns. 
Driving behavior monitoring and route optimization lead to lower fuel use. Harsh acceleration, idling, and unnecessary detours can be tracked and corrected.
By keeping buses in better shape and on schedule, telematics reduces downtime — meaning fewer spare vehicles are needed, and the existing fleet is used more effectively. 

In Brazil, Fortaleza’s TopBus+ project used telematics to track vehicle locations, match service supply with live demand, and make scheduling more flexible. The result: a 35% increase in fare revenue and lower costs through better dispatching.
Enhanced safety
Telematics helps track driver behavior in real time—spotting things like harsh braking, sharp turns, or speeding. Drivers get instant feedback, which helps reduce risk and keeps both passengers and drivers more comfortable and safe. 
Transit Systems, Australia’s largest bus operator, installed GreenRoad Safety Telematics in over 2,600 buses across Sydney, Melbourne, and Perth. Within a year, accident rates dropped by 84%. Fuel use went down; by 2021, over one in ten drivers had a near-perfect safety score.
Smarter operations
Live data from the fleet gives operators a better overview of vehicle and driver performance throughout the day. This insight enables more informed decisions about vehicle allocation, scheduling, and maintenance planning. The result: improved service reliability, less downtime, and better use of resources overall.
In Las Palmas, Spain, SAGULPA worked with CARTO to combine telematics data from buses, bikes, and parking lots. The system processed inputs from up to 140,000 daily bus riders, 1,000 bike users, and 28,000 parking spaces. The city was able to ease congestion and improve transport access based on real movement patterns.
Fewer repairs, longer lifespan
Telematics is key to predictive maintenance, allowing fleet operators to detect potential issues before they lead to breakdowns, helping vehicles last longer. Operators can schedule transit fleet maintenance based on actual wear, not estimates, cutting down on unnecessary inspections.
Nottingham City Transport (NCT), the largest bus operator in Nottingham, adopted a cloud-based defect management system for its fleet of 310 buses. This change gave the engineering team instant access to real-time defect reports and vehicle health data. By identifying recurring issues early, they could schedule repairs ahead of time, reduce downtime, and improve overall fleet performance.
Environmental improvements
With less idling, smoother driving, and smarter routing, buses use less fuel. That means lower emissions and better air quality.
DB Regio Bus, operating nearly 10,000 buses in Germany, used Geotab telematics to cut idle time by 40%. That saved around 1,400 tonnes of CO₂—the environmental impact of planting over a million trees.
Discover how leading companies have revolutionized their fleets with us — read our case study.
Fleet telematics integration best practices
Telematics can offer real business value—but only if integrated thoughtfully. Below are practical steps to help you avoid common pitfalls and maximize your investment.
Define operational goals and KPIs
Think of the central business challenge you want to tackle. Is it high fuel costs, frequent breakdowns, buses getting stuck in traffic, or something else? Keep the goals realistic and time-bound; specify the target metrics for the first quarter and the first year after implementation (e.g., reduce fuel costs by 5% in the next quarter and 15% over a year).
Plan for scalability early
Your fleet may grow or evolve. Choose a telematics fleet management system that can adapt — adding more vehicles, new sensor types, or broader geographic coverage shouldn’t require starting over.
Ensure data integration
The telematics system you plan to integrate should be able to connect with your existing tools — dispatch software, maintenance platforms, or route planning systems — so you don’t end up with disconnected data silos.
Focus on user experience and training
Drivers and dispatchers must feel comfortable using the tools. Invest time in clear, role-based training and choose systems with intuitive interfaces.
Prioritize security and data privacy
Choose vendors that offer end-to-end encryption (both in transit and at rest), role-based access controls, and audit logs within their fleet telematics systems.
Confirm compliance with data protection laws like GDPR and ensure the system can handle cross-border data regulations if your fleet operates across regions. Also, define who owns the data and where it’s stored—this matters for legal and operational reasons.
Monitor, analyze, and adjust
Review telematics reports regularly to spot trends like rising fuel or energy use, recurring maintenance issues, or unsafe driving patterns. Set alerts for key metrics and compare performance across vehicles or routes. Fleet telematics only delivers value if you keep acting on the data.
Final thoughts
The public transportation industry faces rising costs, strict regulations, and growing passenger expectations. Fleet management software helps public operators maintain better control. With real-time tracking, predictive maintenance, energy usage insights, and driver behavior data, it reduces breakdowns, cuts waste, and keeps buses running on time.
When powered by fleet telematics technology, this software turns raw vehicle data into practical decisions—improving passenger safety, reducing idling, and optimizing electric and hybrid fleets. Integrated well, it’s less about the tech itself and more about building smarter, more sustainable cities. 
Intelliarts develops fleet management systems for public and commercial fleets. With 25 years in software engineering, we’ve delivered 80+ successful large-scale projects and built a team where 92% are senior engineers. We focus on long-term value — our longest client partnership spans 15 years, and 90% of clients return with new projects.
Need Fleet Software That Fits?Intelliarts builds custom solutions for your operations.Let's talk

#### RAG Development Beyond Chatbots: 5 Enterprise Use Cases That Pay Back in No Time
TL;DR

71% of organisations now use generative AI in at least one business function, according to McKinsey. Only 17% of respondents report that AI accounts for more than 5% of their organizations’ Earnings Before Interest and Taxes (EBIT). 
The gap between adoption and measurable return largely comes down to how RAG gets deployed.
RAG delivers faster payback when it is embedded into specific workflows, not used only as a generic chatbot.
The strongest enterprise use cases are internal knowledge synthesis, sales briefs, operations reporting, contract reviews, and BI context explanation.
ROI comes from reducing manual research, speeding up document-heavy work, and making outputs more consistent across teams.
Successful RAG development projects start with a narrow workflow, clear data sources, and measurable KPIs like time saved, backlog reduction, or win-rate impact.
Implementation quality depends on retrieval design, metadata, access control, source traceability, and ongoing evaluation after launch.

Why RAG is more than a chatbot trick
AI-led transition may, unfortunately, lead to unsuccessful implementations if not approached with expertise and experience. One of the most common examples is simplifying launching an internal chatbot on top of a document library and treating it as a “RAG implementation.” Some of those chatbots might be genuinely useful. Nevertheless, such organizations would be sitting on a capability they have barely touched. 
RAG is a system design pattern where a language model’s responses are grounded in retrieved, organisation-specific content pulled at query time.
The most business value here lies in the capability of a RAG system to enhance any LLM’s responses with reliable data extracted from a suitable database. Have you ever noted that your chatbot or an AI agent hallucinates a lot and struggles to retrieve and reuse data from provided materials? 
That’s exactly what RAG technology solves. And a chat interface is just one way to surface a database, and usually not the most valuable one.
Looking for relevant data on RAG usage for business LLM?In our recent research, we covered how to drive efficiency with RAG-based solutionsRead white paper here
The “RAG = chatbot for a database” assumption leads to three fundamental limitations in ML development:

It isolates RAG from core workflows, so outputs never connect to the systems and decisions that actually matter.
It makes ROI nearly impossible to attribute, because generic conversational use doesn’t tie to any measurable business process.
It leads teams to under-invest in retrieval quality, security, and evaluation, which are the components that determine whether a RAG system is production-ready.

The most successful RAG projects Intelliarts has worked on all have RAG embedded into existing digital infrastructure, with an AI chatbot of a customer’s choice working upon.

See the example of a RAG-enabled AI chatbot for fast data extraction in the success story by Intelliarts. 
Quick refresher: How retrieval‑augmented generation works in the enterprise
As mentioned previously, retrieval-augmented generation combines search over enterprise data with large language models. This way, every response is grounded in current, organisation-specific content instead of the model’s training data alone.
“Oftentimes, enterprise AI failures we have scrutinized are attributed to poor data handling rather than a bad AI model choice.” — Martha Urbanovych, ML Engineer at Intelliarts
Here are two of the main procedures in the RAG workflow:

Ingestion and indexing. In this phase, data gets collected, structured, and split into chunks. Each chunk is enriched with metadata and converted into embeddings, then stored in a vector index alongside a traditional keyword index.
Retrieval and generation. This is a real-time process that happens at query time. When a user submits a request, the system runs a hybrid search combining keyword and vector matching, filtered by permissions and relevant metadata. The retrieved chunks go into the LLM’s context window, and the model generates an answer based on that specific content.

In an enterprise context, three additional concerns sit on top of this foundation:

Access control. The retrieval layer must respect document- and row-level permissions, so users only get answers based on content they are authorised to see.
Compliance and auditability. As per regulations, such as the EU AI Act, responses need a traceable source, especially in regulated industries where decisions have to be explainable.
Evaluation. Answer quality, retrieval relevance, and hallucination rates all need ongoing measurement. Without it, you have no reliable signal that the system is working.

See the example of an enterprise RAG-enabled LLM architecture pattern in the infographics below:

The diagram shows the workflow where user input passes authentication and an input guardrail. From there, the system runs through retrieval, generation, and two validation checkpoints before delivering a response.
Key components:

Query rewriting and encoding refine the input before retrieval
Ranking improvement filters results before they reach the generator
Dual guardrails validate both input and output
Storage layer covers embeddings, documents, history, and feedback
Observability spans the entire flow for evaluation and hallucination monitoring

Get the foundation of RAG right, and this technology becomes applicable across a wide range of high-value enterprise workflows. The five use cases below are where Intelliarts sees the fastest payback from RAG development and integration. 
Use case #1: RAG “analyst” for internal knowledge work
This use case integrates RAG technology inside the tech stack teams already work with. In a nutshell, this approach cuts the manual layer between information and decision-ready output. It’s practically the same as the above-mentioned RAG-enabled AI chatbot case from Intelliarts’ portfolio. 
Business problem
Product managers, support leads, and analysts spend hours each week pulling together reports, incident summaries, and decision briefs from docs, tickets, and wikis spread across multiple systems. The work is repetitive, the outputs vary in quality, and it pulls skilled people away from higher-value work.
What the RAG solution does
Top RAG development companies can embed RAG as a generation trigger inside tools like Confluence, Notion, or Jira. A PM clicks “Generate insight pack” on a feature ticket and gets a structured memo synthesising past incidents, user feedback, and roadmap notes. A support manager triggers a weekly digest and gets a prioritised incident summary with links to root-cause documents.
Two things make this work in practice:

Users stay in the tools they already use, with no new interface to learn.
Output is structured and fits directly into existing reporting and review workflows.

Why it pays back fast

Each synthesis task drops from two to three hours of manual effort to twenty to thirty minutes of review and editing.
New team members reach productive context faster, with historical decisions and incidents accessible in one step.
Documentation quality becomes consistent across teams, since outputs follow a defined structure regardless of who triggered them.

Architecture and implementation notes
Data sources: wiki pages, project docs, tickets, incident reports, issue tracker comments, and meeting notes.
Retrieval notes:

Use document-aware chunking that preserves section structure, so summaries respect headings and context.
Apply hybrid search with metadata filters on product area, team, and status to keep retrieval focused.

Pitfalls to avoid:

Indexing everything without a governance layer leads to irrelevant or sensitive content surfacing in summaries.
Skipping permission models creates security exposure in larger organisations.

Looking for a Trusted RAG Development Partner?From consulting and research to design, development, and delivery, Intelliarts covers everythingExplore our services
Use case #2: RAG-powered sales and account briefs
This use case automates that assembly inside the CRM, so representatives walk into every conversation with full context.
Business problem
Sales and customer success teams spend significant time before each call or quarterly business review. Oftentimes, they have to pull together CRM history, open tickets, call transcripts, and recent news manually. Preparation quality varies by rep, which means deal outcomes depend partly on individual discipline rather than a repeatable process.
What the RAG solution does
A “Generate account brief” trigger inside the CRM pulls recent interactions, open issues, expansion signals, and relevant battlecards into a single structured brief. For strategic accounts, the system enriches it with external signals like recent funding events or product announcements from news APIs.
Representatives get a ready-to-review document before the call, built from the same sources they would check manually. Output follows a consistent template across the team.
Why it pays back fast

Call and QBR preparation drops from forty to sixty minutes of manual research to a ten-minute review.
New sales hires reach full account context in their first week, without relying on tribal knowledge.
ROI is directly measurable: compare win rates and sales cycle length for opportunities with RAG-assisted briefs against the baseline.

Architecture and implementation notes
Data sources: CRM records, ticketing system, call recording transcripts, marketing automation logs, and external news APIs.
Retrieval notes:

Use metadata filters on account ID, region, product line, and deal stage to keep the retrieval precise.
Blend semantic search with ID-based filters to avoid surfacing content from unrelated accounts.

Pitfalls to avoid:

Briefs without a consistent template produce outputs that vary too much for representatives to trust.
Representatives showcase AI-generated content to customers without review creates reputational risk.

“We once audited a client’s AI setup and found their in-house chatbot leveraging a 6-month-old database. Multiplied by users’ personal data leaks and a lack of audit trails, this is essentially a non-compliance case. We rearchitected the system and added RAG on top of it promptly.” — Alexander Barinov, a Managing Partner at Intelliarts
Use case #3: RAG for operations and supply chain “war rooms”
This use case gives ops and supply chain teams a faster path from alert to coordinated response.
Business problem
When disruptions occur, operations and supply chain teams have to spend hours stitching together what happened across TMS, WMS, and incident tools. As per the best RAG implementation practices, leadership updates require manual synthesis into situation reports, often under time pressure, while the incident is still active.
What the RAG solution does
Through the tuned RAG development, the solution is able to pull data from incident logs, shipment events, maintenance records, and previous post-mortems. This way it can answer situational questions on demand and be, for example, a part of a truck driver support software system. Teams can ask “Summarise all high-impact incidents affecting region X this week” and get a structured answer with source links.
Why it pays back fast

Time to assemble situation reports drops from hours to minutes during active incidents.
Response coordination improves when teams share a consistent, sourced summary instead of fragmented updates.
Each incident documented through the system feeds back into retrieval, making future responses faster and better grounded.

Architecture and implementation notes
Data sources: event streams, incident reports, ticket comments, and post-mortems.
Retrieval notes:

Use agentic RAG development for multi-step queries, e.g., identify affected assets, fetch related incidents, then summarise patterns across them.
Prioritise recent data and high-severity incidents using metadata in retrieval ranking.

Pitfalls to avoid:

Presenting answers as real-time when underlying systems update with a delay creates false confidence during live incidents.
Letting the model explain anomalies without sufficient grounded context leads to unreliable root-cause narratives.

Use case #4: RAG-assisted contract, policy, and compliance reviews
This use case reduces the manual reading load and puts expert attention where it matters most.
Business problem
Legal, procurement, and compliance teams spend significant time on first-pass reviews of contracts, vendor policies, and regulatory documents. This work is repetitive, but omitting it carries real risk if something is missed. Review quality drops under time pressure or when teams are handling high document volumes.
What the RAG solution does
RAG solution compares incoming contracts against a clause library, flags deviations from standard terms. More than that, it can summarize key obligations, risk factors, and version differences in a structured output. It can also map specific clauses to internal policy requirements or external regulatory references, giving reviewers a prioritised list of items that need attention.
Why it pays back fast

First-pass review time drops significantly, letting legal and compliance experts focus on high-risk clauses.
Review quality becomes consistent across team members and jurisdictions, reducing reliance on individual expertise.
Stakeholder trust is easier to maintain because the system supports human decisions rather than replacing them.

Architecture and implementation notes
Data sources: clause-level templates, standard playbooks, previous negotiation records, internal policies, and relevant regulations.
Retrieval notes:

Use fine-grained chunking at the clause and section level with metadata on jurisdiction, contract type, and risk category.
Apply hybrid search to handle legal synonyms and jurisdiction-specific terminology accurately.

Pitfalls to avoid:

Displaying model output without the retrieved source passages reduces reviewer trust and auditability.
Skipping confidence indicators and human sign-off requirements creates compliance exposure.

Use case #5: RAG as the glue between BI dashboards and real-world context
This use case shows how BI analytical procedures changed in the matter of connecting metric movements to the operational context that explains them.
Business problem
BI tools show what a metric did, but oftentimes don’t reveal enough of the operational context behind the movement. The most valuable information to act upon is usually spread thin across release notes, incident reports, and campaign docs in separate systems. 
That’s exactly how teams catch themselves reconstructing why one or another number changed before any decision can be made.
What the RAG solution does
RAG development enables LLM to connect metrics in BI tools to the unstructured context behind them. When someone spots an anomaly in a chart, they can ask, for example: “What events likely explain this change?” and get a summary pulling from relevant post-mortems, change logs, and campaign briefs, all with source links.
The system works as an explanation layer sitting alongside existing dashboards. Therefore, RAG reads context that already exists and surfaces it at the moment it’s needed.
Why it pays back fast

Root-cause understanding of metric anomalies speeds up, cutting the time between spotting a problem and acting on it.
New managers get up to speed on the story behind key metrics without needing institutional memory from colleagues.
Deployment risk is low: RAG operates as a read-only side panel on top of existing BI tools with no changes to the analytics layer.

Architecture and implementation notes
Data sources: BI metadata such as metrics and dimensions, post-mortems, meeting notes, release notes, Jira epics, and campaign plans.
Retrieval notes:

Tag documents with related metrics, products, and time ranges so retrieval stays relevant to the specific anomaly being investigated.
Use multi-step agentic workflows to first identify the relevant metric context, then fetch associated commentary from connected sources.

Pitfalls to avoid:

Models extrapolating beyond the available grounded context produce explanations that look credible but aren’t sourced.
Misaligned time windows between metric data and event documents surface irrelevant context and erode trust quickly.

How to prioritize RAG use cases that pay back fast
The worst-case scenario for any enterprise AI initiatives is to have it stall because the scope is too broad. Projects that show measurable returns fast, especially in small and medium businesses (SMBs), start narrow. That means that they should target a workflow where the cost of slow or inconsistent output is already visible on a P&L.
Detect several workflows that may benefit from RAG-enabled AI integration. Run each candidate workflow through the following three filters:

Value per decision. Contract reviews, deal briefs, and incident reports have a measurable cost when they’re slow or inconsistent. Workflows where output quality affects revenue or customer retention score highest here.
Task volume. Weekly or daily repetition turns marginal time savings into material cost reduction. A task done once per quarter produces limited ROI regardless of how well the system performs.
Data readiness. Where the data trail is already digital and organised across docs, tickets, or logs, implementation time is shorter, and retrieval quality is higher on launch.

Any workflow that clears all three filters is a viable first use case. 
The matrix below maps the example use cases to show one of the ways in which the workflow for potential RAG Implementation may be prioritized.

This visualization is called a priority matrix. This tool maps initiatives across two axes to identify the highest-return options and is a widely used framework in enterprise decision-making.
Sales, legal, and operations tend to qualify early. If several workflows score equally, pick the one where a failed output has the lowest consequence. That keeps the first deployment low-risk while the team builds confidence in the system.
Once the use case is selected, define success metrics before development begins: time per task, backlog reduction, or win rate. That baseline is what turns a working prototype into a funded programme.
Intelliarts offers end-to-end enterprise RAG system development. We cover everything from technology consulting and market research to development, deployment, and maintenance. Don’t hesitate to reach out. 
Final take
RAG delivers measurable enterprise value when it’s embedded into specific, high-frequency workflows with clear success metrics defined upfront. Internal knowledge synthesis, sales briefs, compliance reviews, operations reporting, and BI context explanation all share the same foundation: structured retrieval over proprietary data, built into tools teams already use. 
The Intelliarts team has more than 26 years of experience delivering AI and RAG-powered solutions to businesses from across multiple industries. With more than half of senior engineers in-house and 90% customer return rate, we are ready, willing, and able to drive your RAG development enterprise project end-to-end. 
Need Consultation on Enterprise RAG Implementation?Let Intelliarts experts guide you through potential opportunities and challengesSchedule a meeting

#### Reducing False Alarms in Optical Inspection System
Product inspection systems, such as Automated Optical Inspection (AOI) solutions, are frequently used in manufacturing to detect product defects and improve quality. At the same time, some manufacturers complain about cases of false detection, which undermines the effectiveness of AOI systems.
In the case study below, we tell how Intelliarts reduced false calls in the partner’s AOI solution. By using a machine learning approach, we helped a printed circuit board (PCB) producer to increase the efficiency of their AOI system and, hence, solve the problem of downtime and massive maintenance costs.

#### Renewable Energy & Power Grid Load Balancing Challenges
Balancing the grid is one of the most important tasks related to energy management and renewable energy. After all, handling demand spikes ensuring the maximum efficiency of transportation and storing energy significantly contributes to the performance of all energy-centered systems.
Intelliarts is a trusted AI and ML development service provider with expertise in renewable energy and a member of the Norwegian-Ukrainian Chamber of Commerce (NUCC). Our vast pool of experience includes predictive maintenance, energy consumption optimization, EV management, and load balancing in renewable energy. We gladly our insights on how to drive digitalization in organizations.
In this post, you’ll discover the role, usefulness, and challenges of renewable grid balancing. Besides, you’ll explore how custom software can help build a sustainable future and review several related success stories. 
Read our white paper on EV regulations to shape grid balancing.Get your copy
Role of balancing the grid to overcome challenges of renewable energy 
As mentioned, balancing the grid is a task that must be solved successfully to ensure the high performance of any energy system overall. Balancing the grid is also a solution to some sustainability and renewable energy challenges. 
Common challenges of renewable energy include:

Intermittency: Renewable energy sources like solar and wind are not always available because of regional specificities. 
Energy storage: Due to the intermittency of renewable sources, storing energy for times when production is low is critical. Unfortunately, current battery technology is expensive and has limitations in capacity and efficiency.
High initial costs: The upfront costs for renewable energy projects, such as solar panels, wind turbines, and infrastructure, are relatively high compared to conventional energy sources.
Land use and environmental impact: Large-scale renewable energy projects can require significant land and can have adverse impacts on local ecosystems and wildlife. For example, wind farms can affect bird populations, and hydroelectric projects can disrupt aquatic ecosystems.
Grid integration: Integrating renewable energy into existing power grids can be challenging. The grid must be able to handle the variable nature of renewable energy and maintain stability and reliability.
Resource location: The best locations for renewable energy production are often far from where the energy is needed. For instance, the windiest and sunniest places might be remote, requiring substantial investment in transmission infrastructure.
Material scarcity: Renewable energy technologies rely on certain materials that are scarce or have a significant environmental footprint. For example, producing solar panels requires rare metals like cadmium and tellurium.

While many challenges require engineering solutions, there are ways in which software systems can help to address difficulties. Particularly, it concerns grid integration, storage, and efficiency of energy distribution and handling demand spikes. Here is the role of smart grid load balancing:
 
Accurate predictions and forecasting
Accurate predictions and forecasting allow better balancing of renewable energy sources with grid demand. This helps grid operators plan for fluctuations, ensuring a stable energy supply despite the variability of renewable sources like solar and wind.
Key elements:

Historical data analysis
Weather pattern analysis
Machine learning algorithms

Energy storage solutions
Energy storage solutions are essential for managing the intermittent nature of renewable energy. These systems store excess energy produced during periods of high generation and release it when production is low or demand is high, optimizing load balancing and maintaining a consistent energy supply. Additionally, technologies like solar panel fault detection play a crucial role in ensuring that energy systems operate at peak efficiency, minimizing disruptions and energy loss.
Key elements:

Battery storage
Pumped hydro storage
Thermal storage

Smart grid technologies
Smart grid and IoT smart grid technologies enhance the efficiency, reliability, and flexibility of the power grid. They utilize real-time data, automated controls, and advanced communication systems to monitor and manage electricity flow, improving grid stability and enabling effective load balancing.
Key elements:

Real-time data monitoring
Automated controls
Advanced communication systems

Explore our data engineering consulting services to get expert support tailored to your needs.
Decentralized energy resources
Decentralized energy resources, such as rooftop solar panels and small wind turbines, generate power close to where it is consumed. This reduces transmission losses and enhances grid resilience, contributing to effective load balancing.
Key elements:

Rooftop solar panels
Small wind turbines
Microgrids

Read also how implementing IoT in solar energy can help optimize energy production and distribution by providing real-time data and predictive analytics.
Advanced grid management
Advanced grid management uses sophisticated software and algorithms to optimize electricity distribution and usage. These systems balance supply and demand in real time, allocate resources efficiently, and manage the integration of renewable energy sources, ensuring stable grid operation.
Key elements:

Real-time supply and demand balancing
Resource allocation
Integration management

Advanced software solutions for renewable energyLet Intelliarts help you to optimize the business processes through technology Discover how we can assist
Infrastructure upgrades
Modernizing grid infrastructure is necessary to handle the increased complexity and load variability introduced by renewable energy sources. Upgrades include reinforcing transmission lines, installing advanced metering infrastructure, and developing new interconnections to accommodate renewable energy.
Key elements:

Transmission line reinforcement
Advanced metering infrastructure
New interconnections

Regulatory and policy support
Regulatory and policy support is vital for promoting renewable energy adoption and grid modernization. Governments can implement policies that incentivize the use of renewable energy, such as feed-in tariffs, tax credits, and renewable portfolio standards. Clear and supportive regulations ensure a stable investment environment and encourage the development of sustainable energy solutions.
Key elements:

Incentive policies
Regulatory frameworks
Investment stability

In essence, load balancing optimization is what powers-up energy systems, potentially enabling humanity to rely fully on renewable energy sources only. 
2026 market overview and industry insights of balancing the grid
The global smart grid market shows some remarkable results. Take a look at the infographic below that reveals key statistics and trends for the industry:
 
As it can be observed, governments dictate the need to cut CO2 emissions and increase the adoption of renewable energy. Since renewable energy grid management allows to get the most efficiency out of any renewable energy system, the value of this technology is already well-recognized on the global level. 
The perspective of the Intelliarts team is that renewable energy grid management is about to become a must-have software part of any energy solution that will be implemented in the future. And, what’s most important is that AI is intended to be an integral part of any smart load balance system. Multiple studies have researched the possibility of combining cloud and software-defined networking (SDN) solutions with AI with the prospect of creating advanced load balancing approaches that can be implemented worldwide. 
In the next post sections, we’ll review other custom software solutions that can contribute to renewable energy and other sustainability systems. 
How custom software may be helpful for a sustainable future
When talking about Earth’s sustainability, i.e., the ability of the planet to support itself and its inhabitants, major concerns arise. Due to both natural causes and human activity, processes in entire ecosystems slowly deteriorate. Among the existing sustainability challenges, according to research, are:

Climate change
Resource depletion
Biodiversity loss
Pollution
Waste management
Energy transition
Sustainable agriculture
Urbanization
Water scarcity
Food security

Load balancing is one of the most significant ways to apply custom software development to solve sustainability issues. However, the potential of renewable energy software development reaches far beyond this single application. Here are ways in which different IT technology solutions can contribute to building a sustainable future:
#1 AI-based solutions:

Weather forecasting
Energy distribution and storage management
Load forecasting
Smart power management system
Sensor data analysis
Predictive asset maintenance
Anomaly detection
Automated monitoring
Cybersecurity

You can discover more about Artificial Intelligence in the energy market from another blog post by Intelliarts.
#2 IoT Solutions:

Remote asset monitoring
Predictive maintenance
Energy storage management
Environmental monitoring
Inventory management
Security and surveillance

#3 Blockchain-based solutions for energy trading:

Traceable and transparent transactions
Secure energy sales and purchases
Smart contracts for verifying energy sources

#4 Cybersecurity solutions:

Data encryption
Intrusion detection systems
Protection against malware and unauthorized access
Safeguards for connected power grids

#5 Energy Management Software (EMS): 

Historical and real-time data analysis
Smart HVAC control systems
Lighting control
Utility bill tracking
Building simulation modeling
Facilities management

 
Creating a sustainable future boils down to making incremental changes but in a multitude of ways. As development service providers, we ought to power up sustainability projects with all the digital technologies they need to revert the destructive processes the Earth undergoes at the physical level. — Oleksandr Stefanovskyi, ML Development Team Lead at Intelliarts
Load balancing and energy management cases of Intelliarts
Let’s take a look at several energy management cases that the Intelliarts team delivered:
Creating next-gen EV fleet management software

The customer, who is an EV charging company, reached out to Intelliarts for assistance with developing an enterprise-level solution for managing EV vehicles. It was intended to help keep track of the EV fleet and the efficiency of charging.
We delivered the management software with data analytics and telematics capabilities integrated. The ready solution enables smart energy management, shows EV charging status, and provides predictions.
EV fleet management software by Intelliarts provided the customer with greater operational efficiency, charging flexibility, read-time, and data analytics and helped minimize spending.
Explore EV fleet management software success story.
Smart power management system for energy consumption optimization

The Intelliarts customer, a supplier of EV charging solutions, reached out to us with the request to optimize the energy consumption of EV chargers. The goals were to mitigate the effects of climate change and maximize the cost-efficiency of stations.
To address the customer’s needs, we created a power management system that helps users schedule power limits for any time period for one or more stations. The software includes data analytics capabilities, supports API integration, and provides off-peak charging and random delay capabilities. 
The resulting solution helped to achieve the optimized use of energy as well as cost-efficiency in the long run. Besides, it facilitates the longevity of electrical infrastructure and helps to provide higher quality service to end users. 
Explore smart power management system success story.
Predictive maintenance solution for EV Connect

A provider of EV charging solutions reached out to us with the request to build a predictive maintenance solution. The software was intended to predict and prevent failures of their charging stations and address the problem of repeated downtimes.
We approached this case by leveraging EV charging analytics to conduct a detailed analysis of historical data from chargers. We also conducted data anomaly analysis and transformed raw data from log recorded into a structured format. Finally, we created an ML predictive maintenance algorithm.
The resulting solution provided EV Connect with a range of business insights into the behavior of EV charging stations, usage patterns, and system performance. We also provided recommendations for collecting, processing, and storing raw data for facilitating further predictive maintenance activities with the created ML algorithm. 
Explore the predictive maintenance success story.
Final take
Effective grid balancing is one of the components of renewable energy systems which plays a notable part in ensuring a sustainable energy future. Custom IoT solutions, along with AI and smart grid technologies, help address key challenges like intermittency, storage, and system integration.
Looking for a software partner in renewable energy? Request assistance from the Intelliarts team of AI and ML engineers. With more than 25 years of experience in the market delivering tailored software solutions, we can address your software needs with flying colors.
Transform your energy businessHarness AI and ML technology to drive efficiency and innovationTalk to an expert

#### Retrieval-Based Chatbots for Business Information Extractions
The scientific report shows that office workers can spend up to 2 hours a day browsing their email boxes. Another study provides that workers tend to spend up to 18% of their working hours on useless meetings, they could’ve read summaries of which instead. In that regard, optimization of information retrieval processes can result in a substantial productivity increase, which definitely shouldn’t be overlooked.
One of the ways to simplify the retrieval of information is to utilize chatbots that can conduct searches based on user queries, which is both convenient and effective. 
In this post, you’ll discover the aspects of chatbot information extraction and the features and benefits of the chatbot. Besides, you’ll review the retrieval-based chatbot case by Intelliarts and explore the step-by-step chatbot implementation procedure. 
The need for efficient information retrieval in modern workplaces
Modern businesses are largely about gathering and using vast amounts of data. So, ensuring that employees can swiftly retrieve the data and insights they need leads to:

Making informed decisions

Enhancing customer service

Increasing efficiency

Fostering collaboration

Staying competitive

Here are some of the challenges related to using traditional retrieval systems, such as databases or file systems with conventional search engines, library catalogs, archival systems, etc.:

Time-consuming manual searches
Fragmented data sources
Inconsistent data quality
Limited search capabilities
Difficulty in accessing historical information
Inefficient search systems
Storing information in isolated environments 
High volumes of data
Lack of contextual understanding
User interface and accessibility issues

As possible to observe, the conventional ways to retrieve information are not ideal for many reasons. That’s why to achieve better productivity in the workplace, companies start considering alternative ways to empower employees to extract necessary data. 
Overview of Intelliarts retrieval-based chatbot solution

Recently, the Intelliarts team was approached by a non-governmental organization that advocates for gun control and combat against gun violence. They requested the development of a solution that would provide quick and easy access to information from the organization’s extensive knowledge base. 
You can discover comprehensive details about the retrieval chatbot case by Intelliarts from our success story. 
The solution Intelliarts created was a retrieval-based system enabled by GPT-4 Generative AI and wrapped as a chatbot. 
This enterprise RAG (Retrieval-Augmented Generation) solution enhances business information retrieval efficiency. Here are the key features of the Intelliarts’ chatbot:

Natural Language Processing (NLP): The chatbot uses GPT-4 to understand and interpret free text queries in natural language.
Data retrieval functionality: The chatbot can efficiently search and extract relevant information from the organization’s internal database.
Customized responses: The chatbot for information retrieval is designed to mimic the NGO’s tone of voice, emphasizing victims over guns or criminals.
Reference provision: The chatbot provides references for the retrieved information, allowing users to verify the data and explore topics in more depth.

The integration capabilities of the chatbot are worth mentioning, too. The solution is seamlessly connected with the company’s knowledge database, enabling it to access and retrieve information quickly. This integration ensures that the chatbot can handle dynamic updates to the database and provide the most current information to users.
As for business outcomes of implementing the chatbot solution, it has reduced the time needed for search and information analysis from hours to minutes, highlighting how prioritizing time management can directly boost efficiency and business performance. Besides, it notably enhanced employee satisfaction. 
The algorithm of the retrieval-based chatbot:
The chatbot operates using a combination of natural language understanding and information retrieval algorithms. When a user submits a query, the chatbot processes the text to understand the intent and context. It then searches the knowledge base for relevant information and presents it in a user-friendly format.
How it works: The Intelliarts’ chatbot in action

Let’s walk through a scenario to illustrate how our chatbot operates:

Creation of a query: An employee of the NGO asks the chatbot, “What are the latest statistics on gun violence in urban areas?”
Query processing: The chatbot uses NLP to understand the query and determine the nature of the request. It supports several request options, which are report, table, and general one. Then, the chatbot identifies key terms such as “latest statistics,” “gun violence,” and “urban areas.”
Information retrieval: The chatbot searches the organization’s database for recent data on gun violence in urban settings.
Response generation: The chatbot compiles the information and generates a response in the NGO’s preferred tone, focusing on the impact on victims. For example, “In recent studies, urban areas have seen a significant increase in gun violence, affecting numerous families and communities.”
Reference provision: The retrieval chatbot provides the summary as the answer to the query and links to the studies or reports from which the statistics were derived, allowing the employee to explore the topic further.

It’s worth noting that the chatbot can mimic the tone of voice used in the query. If the NGO employee puts a higher emphasis on victims in the query rather than criminals, so will the chatbot. 
Looking for a development agency to create a tailored chatbot for your business? Reach out to Intelliarts, and let’s discuss. 
Key features and benefits of retrieval-based chatbots
In the infographic below, the Intelliarts team distinguishes the main specificities of retrieval chatbot with AI and reveals how one or another characteristic results in specific business benefits. 

You can observe that implementation and usage of the features provided in the above table translate into a range of value-adding benefits for the employees and the company. At the same time, the listed benefits can result in lower operational expenses, better business operation outcomes, and higher satisfaction of employees.
It’s also worth mentioning that retrieval-based chatbots are one way of solving the issues of tedious information search and extraction processes. 
At the beginning of 2024, Google presented a Circle to Search Functionality, which allows a user to quickly search for anything via the Google search engine with a simple gesture without any additional app needed. Circle-to-Search is another convenient feature offering a better user experience which is likely to get high usage over the next few years. 
Ensuring security and privacy in retrieval-based chatbot systems
Since data security and user privacy are raising concerns, it’s essential to understand how retrieval-based chatbot systems can ensure proper data handling, restrict users from retrieving data they don’t have access to, and prevent data leakages: 

Access control: Integrating with authentication systems to restrict data access based on user permissions.
Data encryption: Encrypting data in transit and at rest to prevent unauthorized access.
Secure storage: Protecting stored data with encryption to safeguard against breaches.
Audit trails: Logging interactions to monitor for suspicious activities and ensure accountability.
Regular security assessments: Conducting tests to identify and address vulnerabilities.
Data minimization and retention: Collecting only necessary data and enforcing retention policies.

These practices are set according to the HIPPA, GDPR, PDPA, and other standards regulating the secure handling of user and business data. 
Need a secure, business-oriented chatbot solution? Drop the Intelliarts AI team a line and let our engineers contribute to your best project. 
Implementation and ease of use of retrieval-based chatbot systems
Probably you wonder whether the development and integration of a chatbot is a complex intervention and whether you will be able to handle the technical aspects and integrate the solution into your day-to-day operations.
Integrating and using a chatbot in daily operations have become increasingly accessible and beneficial due to advancements in technology and a better understanding of user needs. Here’s a more detailed look at why it’s easy and advantageous:

Full compatibility with existing systems

Connectivity with external APIs

Customization opportunities

Scalability options

Intuitive, user-friendly interface

To ensure that your team can utilize a custom solution to its fullest extent, the Intelliarts team, as software development professionals, can offer: 

Onboarding and support

Training materials and workshops

Updates per request

Timely feedback

So, partnering with a trusted provider can ensure that the retrieval-based chatbot will be integrated in the proper way on both technical and business levels. 
Final take
Retrieval-based chatbots like the described solution by Intelliarts offer modern businesses a powerful tool for efficient information retrieval, enhancing productivity and decision-making. By leveraging natural language processing, integration capabilities, and advanced security measures, these chatbots provide quick and easy access to vital data. With the support of a trusted provider, companies can seamlessly integrate and use these chatbots in daily operations, ensuring a secure, user-friendly experience that drives business success.
Should you need to create an information retrieval chatbot or another assistance from an AI and ML software development company, don’t hesitate to reach out to Intelliarts. We have been present on the market since 1999, delivering software solutions with constantly excellent outcomes, proven by a high customer return rate of 85%. Our team of 100+ engineering professionals is ready, willing, and able to contribute to your best development project. 
Need a retrieval-based chatbot for your business?Contact us

#### Revolutionize Lead Generation with AI Technology
Nowadays, lead generation is the number 1 of companies’ top marketing challenges. Nearly two-thirds of businesses underperform in this matter, as shown in the chart below:
At the same time, the Harvard Business Review reveals that companies that used AI experienced a 50% increase in appointments and leads. They were also able to reduce lead acquisition costs by 40% to 60% as well as time spent on cald calling by 60% to 70%. Therefore, implementing innovative technology to boost lead generation has the potential to make a notable business impact in most organizations worldwide.
Understanding the importance of solutions for lead generation, the Intelliarts team decided to write a comprehensive post delving into 10 ways how AI technology, including generative AI for marketing, can help businesses generate leads. You will also discover value-adding reasons to implement AI in your marketing processes and familiarize yourself with real-life cases.
Top 10 ways AI helps to generate leads

The creativity in the usage of AI seems to have no limits. Classification, regression, clustering, and other algorithms offer an endless pool of opportunities to leverage AI.
You can discover more about applications of ML models in business and ML algorithms in another of our blog posts.
Now, let’s proceed with exploring 10 common ways in which different AI models can help businesses improve their AI for lead generation practices, leading to better operational outcomes, especially when aligned with current lead generation trends:
#1 Lead scoring
Lead scoring is a technique where AI evaluates the potential of leads based on their behavior and demographic information. Utilizing lead scoring software, this process helps in prioritizing leads that are more likely to convert, ensuring that sales efforts are focused on the most promising prospects. AI for network marketing can be particularly useful in this context, as it refines the scoring model based on various data points to identify optimal leads.
Applicable practices:

Analyzing customer demographic and interaction data to score leads based on engagement level
Using predictive analytics to forecast a lead’s likelihood of conversion
Integrating AI with CRM systems for real-time lead scoring updates

AI-driven lead scoring effectively identifies high-quality leads, enabling businesses to allocate their resources more efficiently.
 Research PaperElevating your insurance sales: Top practices for predictive lead scoring Check predictive lead scoring white paper
#2 Personalization
Personalization involves using AI to tailor marketing efforts to individual prospects. This method increases the relevance of marketing messages, thus enhancing the likelihood of conversion.
Applicable practices:

Utilizing AI to segment audiences based on behaviors and preferences
Crafting personalized email marketing campaigns
Dynamically customizing website content for different visitor segments

Through personalization, AI provides a more targeted and individualized approach to engaging leads. While AI adds data-driven personalization, it can sometimes produce content that feels impersonal or robotic. Incorporating an AI humanizer can help here by making AI-generated content more relatable and engaging for the target audience.
#3 Chatbots
Chatbots powered by AI can interact with potential leads in real time, providing instant responses and support. For example, these could be a valuable tool for businesses engaged in web development. Chatbots don’t only improve the customer experience but also capture valuable lead information.
When integrated thoughtfully into a website’s user journey, chatbots can guide visitors toward key actions and reduce friction in the decision-making process. Implementing effective strategies for ecommerce website development can help maximize lead engagement and conversion rates.
Applicable practices:

Deploying chatbots on websites to engage visitors and gather lead information
Using chatbots for initial lead qualification before human involvement
Integrating chatbots with databases to provide personalized responses

AI chatbots are a versatile tool for both engaging prospects and streamlining the lead generation process.
#4 Predictive analytics
Predictive analytics is the usage of AI to analyze data and predict future actions of leads. It helps identify trends and patterns that can inform smarter business decisions.
Applicable practices:

Forecasting future buying behaviors based on historical data
Identifying market trends to target leads more effectively
Tailoring sales strategies based on predictive insights

Predictive analytics enables businesses to stay ahead by anticipating lead behaviors and market trends.
#5 Email marketing optimization
AI in email marketing enhances the effectiveness of campaigns by optimizing various elements like timing, content, and audience targeting.
Applicable practices:

Automating send times based on when leads are most likely to engage.
Personalizing email content for different segments of your audience.
Analyzing email campaign data to continually improve performance.

AI transforms email marketing into a more dynamic and responsive tool for lead engagement.
Are you looking for a trusted provider to help you implement an AI solution for generating leads for your business? Drop Intelliarts a line, and let’s discuss opportunities.
#6 Lead nurturing
Lead nurturing is about developing relationships with potential customers at every stage of the sales funnel. AI helps in creating a more personalized and effective nurturing strategy.
Applicable practices:

Automating personalized follow-ups based on lead engagement.
Using AI to determine the best content and timing for follow-up communications.
Segmenting leads based on their interactions and interests for tailored nurturing.

AI aids in creating a more targeted and efficient lead nurturing process, boosting conversion rates. For example, tools like Breeze Prospecting Agent use CRM and ideal customer profile data to identify high-value prospects. The platform can generate large-scale personalized email campaigns from there, complete with follow-up sequences. 
#7 Content recommendations
AI can analyze a lead’s past interactions to recommend relevant content, enhancing engagement and moving leads further down the sales funnel.
Applicable practices:

Dynamically recommending blog posts, white papers, or webinars based on lead interests.
Using AI to curate and suggest personalized content in real-time.
Analyzing engagement data to refine future content recommendations.
Leveraging an AI clip generator to create short, personalized video snippets that highlight key messages, increasing relevance and viewer retention.

Content recommendations via AI ensure that leads receive the most relevant and engaging materials.
Discover how recommendation AI for media can boost customer retention from the video below:

#8 Social Media Monitoring
AI tools can monitor social media channels to gather insights about market trends, competitor strategies, and potential leads.
Applicable practices:

Tracking brand mentions and customer sentiment across social platforms.
Identifying potential leads through social listening.
Analyzing competitors’ social strategies to find gaps and opportunities.

Social media monitoring provides a wealth of information for strategic lead generation technology using AI and brand positioning.
#9 Voice Search Optimization
With the rise of voice assistants, optimizing for voice search is crucial. AI helps businesses adapt their content for voice queries to capture this growing market.
Applicable practices:

Using AI to understand and incorporate natural language in content.
Optimizing for conversational keywords and phrases.
Analyzing voice search trends to align content strategy.

Voice search optimization is essential in an AI-driven era, enabling businesses to stay relevant in changing search landscapes. For instance, incorporating an AI voice generator can help create natural-sounding audio content and make information more accessible to users who prefer relying on voice-activated devices.
#10 Data Enrichment
Data enrichment involves augmenting lead data with additional insights. AI processes vast amounts of data to provide a more comprehensive view of leads.
Applicable practices:

Integrating external data sources to enhance lead profiles.
Using AI to analyze and interpret complex data sets.
Continually updating lead information for accuracy and relevance.

Data enrichment through AI offers a more complete and nuanced understanding of leads, driving more effective targeting and engagement strategies.
According to a Verta, Inc. survey, 63% of companies planned to maintain or increase their AI and machine learning spending in 2023. Take a look at the list of industries businesses in which should consider investing in AI-driven lead generation in the first place:

Why use AI for lead generation process: Real-life cases
In the modern day, AI finds broad usage in a vast number of industries and business models. Some of the well-established companies utilize AI capabilities, particularly AI technology for lead generation. Let’s review their use cases to learn more about the actual business experience about lead generation with AI:
Increased efficiency: Use case of LinkedIn’s AI-powered Sales Navigator tool
LinkedIn, a leading professional networking platform, aims to leverage AI to make the process of finding and managing sales leads more efficient. Their intent is to help sales professionals save time and effort in identifying potential leads.
Use case details:
LinkedIn’s Sales Navigator uses AI algorithms to analyze user interactions and behaviors, recommending the most relevant prospects based on these insights. This tool streamlines the process of AI-driven lead generation and prioritization.
Results achieved:
LinkedIn reports a 15% increase in pipeline growth and a 17% increase in win-rate for leads sourced through Sales Navigator, with a notable 30% reduction in time spent on prospecting.
Drive Lead Generation with AIGet a consultation from Intelliarts' trusted AI expertsLearn more
Improved lead quality: Use case of Marketo’s AI-enhanced marketing automation
Marketo, a marketing automation software provider, focuses on enhancing lead quality for marketers and sales teams. Their goal is to leverage AI to better score and qualify leads, ensuring higher conversion potential.
Use case details:
Marketo uses AI to analyze engagement and behavioral data, which aids in more accurately scoring leads. This approach ensures that the leads passed on to sales teams are of higher quality and more likely to convert.
Result achieved:
Customers using Marketo have observed a 10% to 20% increase in conversion rates, with a 50% increase in sales-ready leads and a 33% rise in sales efficiency due to improved lead scoring.
In case you are searching for an AI and ML service tech partner, reach out to ML expert engineers from Intelliarts and let us contribute to your best project.
Better targeting: Use case of Spotify’s AI-driven music recommendations
Spotify aims to provide a highly personalized user experience. By leveraging AI and machine learning algorithms, Spotify intends to analyze user listening habits to recommend songs, thereby increasing user engagement and subscription rates.
Use case details:
Spotify uses AI algorithms to analyze vast amounts of data, including user listening history, search queries, and playlist content. This AI-driven approach enables Spotify to tailor music recommendations to individual users’ tastes, thereby enhancing the user experience and engagement.
Results achieved:
Spotify’s AI-driven personalization has resulted in significant user engagement. According to Spotify Advertising, “Discover Weekly” users stream more than twice as long as non-Discover Weekly users. Moreover, between July 2015 and June 2020, Spotify users streamed over 2.3 billion hours of their personalized Discover Weekly playlists, showcasing the feature’s popularity and impact on user experience​
You can discover more about Spotify’s AI-driven recommendation system from the video below:

Increased order size: Use case of Intelliarts Predictive Lead Scoring Model
The Intelliarts team was requested by our customer, focusing on AI-driven marketing and sales efficiency, to optimize lead scoring using AI for lead generation. The intended approach should help to prioritize leads effectively, leading to cost-effective marketing strategies.
Use case details:
Intelliarts developed a custom lead score tool for our customer from insurance. The end predictive lead scoring model in machine learning can forecast the probability of how likely the lead would buy a policy. This comprehensive scoring helps in targeting leads that are more likely to convert.
Learn more about our Custom Marketing Software Solutions.
Results achieved:
The machine learning-based scoring model by Intelliarts shows accuracy over 90%. After a few months after the implementation of the solution, the scoring prediction model cut off about 6% of leads, allowing the insurer to raise its profit by 1.5%. In the most successful months, the customer saw a 2.5% rise in profit.
Final Take
If you were wondering how to use AI to generate leads, we gave you the 10 ways in above that help to generate leads.  As of 2024, nearly two-thirds of businesses experience underperformance in the matter of lead generation. AI provides unique opportunities to boost the acquisition of leads and increase the revenue they bring to a company. From lead scoring and lead nurturing to personalization and recommendations, AI can offer it all and more.
In case you need a trusted tech partner to help develop and implement an AI-based lead generation solution, don’t hesitate to contact engineers from Intelliarts. You can see our clients for additional insight into our expertise. Intelliarts specialists are ready, willing, and able to contribute to your best project and help your business boost lead-generation processes.
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#### Revolutionizing Industries with Computer Vision Technology
In modern-day reality, it’s possible to build a computer vision-based AI infrastructure using available software solutions. Computer vision use cases in manufacturing and other fields range from counting objects in batches of images to deriving meaningful information from them.
A large newswire distribution network — GlobeNewswire, reveals that the global computer vision market reached $11.7 billion in 2021. It’s projected to grow at a CAGR of 6.9% throughout the next seven years to as much as $21.3 billion in 2023. The tendency is quite solid. It shows the increase in the volume of computer vision-powered products and services across industries.

It’s worth noting that computer vision technology is related to the field of machine learning (ML), which is also useful to be versed in. Now, let’s deepen into computer vision applications in insurance, manufacturing, or any other industry, find out what business challenges the technology solves, and discover from which components an AI system can be formed.

#### Role of Pipelines in the Successful Machine Learning Project
Just like actual pipelines, the pipeline for the machine learning project is divided into consecutive steps, organized in a particular order where each element strictly follows the previous one. 
In the ML pipeline, the machine learning process is described precisely, from programming to the eventual release. Such steps as ML-based data extraction, building trained models, and adjusting the algorithms are also included. 
Having a thought-out pipeline will make an autonomous service out of each element of your workflow. Meaning that with every new workflow, you will have an opportunity to pick the right elements and leverage them in the right context.

#### Satellite Remote Sensing And Predictive Analytics In Agriculture
Precision agriculture brings real value when fields are treated as evolving systems. Thanks to regular satellite observations, agronomists can spot patterns invisible in a single pass. For example, research shows that medium-resolution imagery can explain 59% to 100% of within-field yield variability, depending on resolution. Over time, repeated data helps you detect trends, such as a part of the field recovering more slowly after heavy rains or a zone that always lags in growth. Repeated signals often tell more about field behavior than stress alerts.
To turn incoming remote sensing data into decisions, farmers need tools that can link satellite imagery to fieldwork, like which areas to check for pests or how to adjust fertilization. The crop monitoring software for precision farming helps teams do that consistently and get measurable outcomes.
Role of remote sensing in precision farming
Spectral remote sensing provides quantitative measurements of how crops and soils reflect electromagnetic energy across multiple wavelengths. This approach is more informative than a standard visual assessment because each spectral band or its combination correlates with specific structural or biochemical crop attributes. NDVI is the most commonly used metric in agricultural monitoring, derived from satellite imagery. It highlights early deviations in crop health but should be treated as an initial indicator rather than a full diagnostic tool. 
Multispectral and hyperspectral data enable in-depth crop condition monitoring. Narrowband reflectance enables the estimation of leaf chlorophyll content, leaf area index, soil moisture, and soil type proxies, which support variable-rate input planning, crop recovery efforts, and problem-specific scouting.
Looking for a Smart AgriTech Solution?Turn remote sensing, IoT, and real-time analytics into a single intelligent platform.Explore our service
In practice, effective analysis requires consistent temporal coverage, calibrated indices, and comparison against historical field profiles. While still used alongside standard techniques, AI models now analyze spectral shifts with crop stage and field context in mind, improving how we identify probable sources of anomalies.
Predictive analytics for day-to-day farm operations
Modern agricultural forecasting uses machine learning (ML) to process multiple data streams simultaneously, including satellite-derived vegetation indices, soil sensor measurements, and site-specific weather. Forecasting models combine crop monitoring data to detect pest or disease risk, identify when the next growth stage begins, or predict yields weeks before harvest with the highest precision.
When supported by biophysical modelling, these forecasts can also rank crops by expected bio-productivity. In 2021, EOS Data Analytics applied such a model in Kazakhstan using dry matter productivity datasets, Copernicus Global Land Service inputs, and NASA Power meteorology. After calibration, the model produced concrete sowing and harvesting dates and identified which crops would be the most productive that season.
However, forecasts deliver real-world results only when the farm can act on them. Variable-rate application machinery, automated irrigation systems, and drone support close the loop between prediction and response. Combining calibrated ML forecasts with field automation allows growers (especially large-scale ones) and agricultural insurers to reduce risks and shorten decision cycles, resulting in measurable business improvements.
In our work at Intelliarts, we often see that the main barrier is not the quality of the model, but the effort required to prepare and maintain the underlying data. For example, in one agritech project we helped replace weeks of manual field history entry with a “lightning” workflow that auto-prefills crop and management data using satellite imagery and internal inference APIs. In the end, we enabled growers to enroll millions of acres into carbon programs much faster.
In another case, we built ETL/ELT pipelines and software integrations that pull seed, field, and practice data directly from third-party sources, cutting data entry time from up to eight hours to just a few minutes and making predictive tools actually usable at scale.
Using satellite insights to guide field-level decisions
Precision agriculture is impossible without accurate field data that can be checked quickly and used in daily operations. Satellite imagery is ideal for this because multi-temporal data exposes both current changes and long-term patterns. The goal of satellite-based agricultural monitoring is to recalibrate plans based on real field dynamics captured through remote sensing. Farmers and agronomists can use this data to divide the field into management zones and decide where to focus scouting and increase/reduce inputs.
With reliable imagery in hand, farmers can segment a field into management zones, highlight areas with recurring anomalies, or validate whether recent interventions are producing the expected effects, among which are irrigation, fertilization, pest control. Modern crop monitoring platforms act as the “analytical layer” that organizes remote sensing signals, weather data, and field records into actionable recommendations, while field machinery and drones serve as the “execution layer.”
In practice, this “analytical layer” only works if field data and operational workflows are digitized and easy to maintain. For one agritech partner, we redesigned a legacy land sampling system so that samples are tracked with tablets, QR codes, and sensors instead of spreadsheets. This reduced manual handling, improved data quality, and ensured that lab results and remote sensing insights could be interpreted together, not in isolation.
Read the full case study how we helped automate land sampling and improve data accuracy. 
Many commercial tools support this workflow by generating variable-rate application (VRA) maps based on vegetation indices, such as NDVI, NDRE, or MSAVI. These maps can be exported directly into equipment from John Deere, Trimble, or AGCO, which adjust input rates on the go. For growers, this creates a seamless path from satellite-derived insights to precise interventions at the sub-field level.
Source: EOS Data Analytics
Choosing the right spatial scale for farm monitoring
Spatial resolution remains a key design choice for any monitoring workflow:

Broad-scale (10–30 m) satellite products are best for long-term vegetation dynamics, crop-type mapping, drought surveillance, flood risk analysis, or monitoring disturbances such as storms and fires at landscape level.
High-resolution imagery (0.3–5 m) supports within-field diagnostics—row-level variability, nutrient deficiency zones, equipment malfunction patterns, or local weed expansion.
Very high-resolution imagery goes even further, enabling parcel boundary refinement, compliance verification, and detailed crop or soil assessments.

Most operational systems combine multiple scales in one process: broad imagery for strategic awareness and high-resolution data for operational decisions. Platforms such as EOSDA LandViewer, Planet Explorer, and commercial UAS/orthomosaic services enable this multi-resolution workflow, while custom ML pipelines (like those we build at Intelliarts) can integrate these layers for automated anomaly detection, vegetation trend analysis, or event-driven alerting.
Forest fire change detection scaled | Source: EOS Data Analytics
Wrap up
As precision agriculture becomes more accessible and user-friendly, growers increasingly rely on remote crop monitoring to explain what’s happening in their fields and why. When the standard toolkit isn’t enough, tailored ML solutions, calibrated for the local climate, crop type, and operational constraints, provide the next layer of value. Satellite data, combined with predictive modeling and on-farm automation, is gradually transforming land management into a transparent, insight-driven process.
As the industry continues shifting toward data-driven farming, Intelliarts brings deep experience in agriculture software development to agritech companies and growers. We can help you solve various challenges, from automating field data collection and land sampling to building sensor management systems and ML-powered forecasting tools.
Turn satellite insights and farm data into real resultsExpert custom software development for the agricultural businesses.Let's discuss your project

#### Smart Energy Management Systems: Features and Benefits
The increasing cost of energy, carbon policies, and sustainability requirements are making one thing clear: companies can no longer afford to treat energy as a reactive cost. A smart energy management system is not a tool anymore, it is a strategic requirement.
And the shift is accelerating. The global market for smart Energy Management System (EMS) is projected to hit $161.9 billion by 2030, according to Allied Market Research, driven by regulatory requirements and the broader push toward net zero.
At Intelliarts, we have been developing renewable energy and grid-aware software for more than 8 years. Drawing on this experience, we’re exploring modern smart energy management systems, their core capabilities, and the real business value they bring, including predictive maintenance and smart demand response (DR).

See a real-world exampleHow does a smart EMS perform once it’s live? Get the quick story in our short smart power management case study.Read the case study

What is a Smart Energy Management System?
Most of the old energy systems just tell you what has happened. And frequently, it becomes obvious that it’s too late to repair.
A smart energy management system is a site-level control and optimization layer that observes, manages, and optimizes energy flows in real time.
A smart EMS pulls live data from EV chargers, onsite solar, battery storage, and V2G (vehicle-to-grid) capable vehicles. It flags anomalies, forecasts charging demand, and sends set points or schedules so the site stays within service limits, tariff bands, and target state of charge.
Beyond monitoring, it handles smart charging. It spreads available power across connectors, staggers starts so everything doesn’t ramp at once, and keeps total draw under your site limit. When prices spike, it moves flexible sessions to cheaper hours or times with more onsite solar. The outcome is simple: smaller bills, a steadier connection, and vehicles ready when people need them.

How do smart EMS work?
A smart energy management system connects your EV chargers, meters, tariffs, weather feeds, and site constraints into one control loop. Here’s the flow in plain terms:

Data: charger telemetry per connector, site capacity and breaker limits, utility prices and DR signals, fleet schedules and desired state-of-charge.
Analytics: the platform learns your daily load shape, predicts peaks, and spots anomalies (e.g., two DC fast chargers starting together).
Control: it manages charging in real time by staggering session starts, capping power per connector, and shifting flexible loads into cheaper or lower-carbon windows.

In case you are considering implementing a smart EMS, better to focus on:

Begin with smart load control. Your system must be capable of load shifting according to the grid demand and electricity prices. That is, no peak-time penalties, no energy used at peak times, all automatic.
Then consider multi-site visibility. When you have multiple locations or facilities, a centralized dashboard is essential. You can compare, benchmark, and act, without 5 logins to 5 different portals.
Do not overlook the user roles and permissions. Engineers are not the only people who need energy insights. The most optimal platforms enable them to be used by both ops teams and finance and sustainability leads.

Looking to improve? Read above EV charging data analytics for smarter energy management.
Key features of smart energy management systems
Most platforms promise “visibility.” For EV sites you need to see, understand, and act in real time: track load per connector, predict depot peaks, prioritize charging where it’s needed, and shape sessions so locations stay within feeder limits and budget. The best platforms combine energy management tools like anomaly alerts and tariff-aware load shifting to turn visibility into action. Here are the core features that every EV-ready EMS should have:
Live energy monitoring
Live energy monitoring connects to EV chargers (typically via OCPP or similar), site meters, and protection devices to stream per-connector power (kW), energy (kWh), status, battery state of charge (SoC), and photovoltaic (PV)/storage contributions in real time. You can see total site load vs. the service limit and feeder headroom at a glance.
If a bank of chargers ramps at once or a connector stays live after the unplug, the system fires an alert. It can cap or shed load before a spike so you don’t learn about it on the next month’s bill.
Smart charging
Smart charging allocates available power across EV connectors in real time so sites stay within capacity and budget. It charges the right cars first (those leaving soon or very low) and starts fast chargers one after another to avoid power spikes. 
Smart EMS also slows charging as each battery nears its target level to protect it. Price and carbon-aware schedules push flexible sessions into off-peak or renewable-rich windows. The result is predictable turnarounds, lower demand charges, and fewer surprises without rushing to upgrade infrastructure.
Analytics and reporting
Then there is analytics and reporting. Any competent EMS does not simply gather data but identifies the patterns worth noting. Consider AI-driven intelligence: an early warning that your charger port is drawing well above its normal profile or that your energy curve every Friday is bound to peak late. All numbers roll into a central energy data management system that normalizes every kilowatt-hour. And when Environmental, Social, and Governance (ESG) reports are due, your system already has the numbers formatted, sorted, and ready to go.

Forecasting and demand response (DR)
Smart EMS tools forecast peak usage of grids based on occupancy, schedules, and weather conditions, enabling you to avoid peak pricing rates by shifting load. It can even automate participation in your local grid, in case it is DR-enabled.

Balance load across chargers and phases, hold grid import under the main service limit, and coordinate PV and storage. Where allowed, support V1G (unidirectional smart charging) today and prepare for V2G while protecting battery health.
Automation and control
ROI can be found in automation and control.
Charge rates, start times, and preconditioning adjust automatically by departure priority, SoC targets, battery state of health, and feeder capacity. Behind the scenes, an EMS pushes live telemetry to the cloud for instant optimization. The equipment can respond to usage, time of day, or custom rules. In fact, the biggest cost saving potential is hidden within automation, according to Springer research. The system tracks consumption so that you do not have to track it manually. Behind the scenes, advanced energy management technologies push live data to the cloud for instant analytics.
Integration options
Last but not least is integration. A real EMS plugs into your ERP (Enterprise Resource Planning), SCADA (Supervisory Control and Data Acquisition), and systems such as BMS (Building Management System)/BAS (Building Automation System) for HVAC (Heating, Ventilation, and Air Conditioning) and lighting, submeters, on-site PV and batteries, and parking or access controls — capabilities are typically included in smart grid software solutions development projects. After that syncs data across locations via APIs or data lakes. All that can be synchronized between locations and integrated into your wider data ecosystem via APIs or data lakes.
In short, an EV-ready EMS turns charger data into decisions by forecasting peaks, shaping sessions, and keeping sites compliant, reliable, and cost-efficient.
A well-structured custom IoT software is essential for smart EMS to turn raw sensor data into real-time decisions.
Business benefits of smart EMS
The typical response when companies begin to monitor their energy consumption in real time is: “we did not realize we were wasting so much.” A smart energy management system can help you know exactly where the energy is being used, when it is being used inefficiently, and how to correct the situation.
Across charge point operators, fleet depots, highway sites, and municipal lots, a smart energy management system shapes when and how EVs charge. In controlled deployments, there are several benefits of energy management system that are worth mentioning: up to 45% lower electricity bills, about 40% fewer emissions, around 75% self-sufficiency, about 85% self-consumption, better on-time readiness, and flatter demand that defers future upgrades, according to the recent scientific reports. 
Let’s consider business benefits of smart EMS in detail:
Cutting energy costs
Studies indicate that sites that actively manage charging typically cut demand costs down to 39.5%, raise charger uptime, and avoid or defer panel and transformer upgrades, while keeping turnarounds on schedule. Even more extensive high-level systems demonstrate an increase of 11-16% annual energy savings, depending on their implementation, which is a clear proof of the tangible benefits of energy management system adoption.
Smart EMS do more than show dashboards. They connect to chargers and meters on-site, monitor in real time, and produce audit-ready reports. This regulatory transparency supports carbon and emissions compliance and also cuts costs by avoiding penalties, preventing overload events, and reducing downtime.
Likewise, teams are able to track consumption by depot, bank, and port, respond to grid or time-of-use prices, and stay aligned with corporate and national requirements while keeping the energy bill down.
Turn energy data into decisions with a configurable EMS.Schedule a call
Better grid efficiency
Earlier no one outside of ops was concerned with energy use. Efficiency used to mean small gains, making tweaks, and eliminating waste. However, the magnitude of influence has changed. EU energy efficiency statistics indicate that without the efficiency improvements that have taken place in the last few decades, Europe would be using 27% more energy than today, which is approximately the same as what is used by France, the Netherlands, Austria, and Finland combined.
Every 1% increase in efficiency will cut imports of gas by 2.6%. Greater intelligence in energy use, particularly in not using the grid at peak hours, has the potential to cut future grid investment requirements by as much as 35%. The EU is also expected to reduce its bill of importing fossil fuels by €70 billion by 2040.
These are not just policy wins. These point to how smart EMS can turn everyday operational choices into real resilience at local and national levels.

Matching compliance targets
Smart EMS also assists you in remaining in control when the pressure is on. Whether it is compliance with the ISO 50001 or reacting to new emissions reporting requirements, or demonstrating progress on sustainability objectives, the system already has the data in place.
And when operations expand or energy costs change, smart EMS allows you to adjust to it by enabling predictive load forecasting, simpler integration of renewables, and cross-site coordination on real demand, not estimates.
The further you venture into regulated space, the more complicated your energy footprint will be. For charge point operators and fleets, smart EMS is the control layer that keeps sites stable and compliant. It shapes charging to grid constraints, trims peaks, and records emissions at the session level.
Better strategizing
Most companies still treat energy as a fixed cost they can’t move. A smart energy management system changes that by giving teams live data and simple controls. In 2023 the U.S. used more than 3.8 trillion kWh of electricity, a clear signal that optimization at the meter now matters. Energy is no longer just a budget line. It is a lever for operations, lower carbon, and lower risk. This is not a short-term spike. It is a long trend that will run into the mid century.

 
The digitalization of energy infrastructure is reshaping how companies perceive their energy operations — not just as technical assets, but as strategic business drivers. Across industries, smart energy management systems now sit on the CFO’s dashboard next to revenue and risk.
Scalability of energy systems
A modular, IoT-based smart energy management system scales as your EV footprint expands. Add chargers, open a new depot, or bring solar and storage online without reworking the stack. Set site caps and charging rules once, then replicate them across locations so new assets plug in and follow the plan from day one.
The same setup adapts when conditions change. If tariffs shift, a DR program opens, or a feeder limit tightens, you update policies and the system reshapes charging in real time. Open protocols and APIs help you avoid lock-in, while role-based access keeps ops, finance, and sustainability on the same page. These developments reflect larger smart energy management market trends, with adoption accelerating across commercial, municipal, and industrial domains.

A smart energy management system gives ops real-time context and leadership a single source of truth for ESG reporting, audits, and public commitments. Once the data moves out of spreadsheets and into live dashboards, energy shifts from a technical chore to a strategic input, this sets up the next question: where does smart EMS create the most impact?
Where EMS makes the real difference
Different industries carry different energy burdens, but the pattern is the same: a smart energy management system adapts to context and delivers measurable wins. Even at city scale, public-lighting programs documented by the World Bank show how EMS can cut consumption and costs across thousands of assets, a proof that the approach works beyond single sites.
If you simply want to check the list of industries where having an EMS has the biggest impact:

Utilities and renewables. Grid operators need foresight, not hindsight. EMS forecasting aligns distributed assets with DR events, stabilizing frequency while earning incentive revenue.
Manufacturing. Heavy motors and round-the-clock lines drive huge loads. Smart energy management solutions spot idle machinery, balance shifts, and keep power-factor penalties off your bill.
Commercial buildings. From HVAC to lighting, usage swings with headcount.
Occupancy-based controls trim watts without under-conditioning meeting rooms.
Retail chains. Tight margins make every kilowatt matter. A single dashboard lets teams benchmark stores, flag refrigeration faults, and roll out conservation rules chain-wide overnight.
Smart cities and infrastructure. Streetlights, transit depots, EV chargers – it’s all part of the urban load curve. Citywide systems dim lights off-peak, reroute surplus solar, and keep public budgets in check.

For more insights into how distribution networks talk to each other, our smart grid IoT guide unpacks real device and protocol examples.
Build or buy? Finding the right path to an EMS
Before choosing an energy management system, the first decision is simple but pivotal: stick with an off-the-shelf platform or craft one that’s built around your own meters, plants, and priorities.
When is an off-the-shelf platform enough?
If you run a single depot with a small set of EV chargers and only need live charger status, simple schedules, and basic cost views, an off-the-shelf EMS can get you productive fast. Most include core monitoring, canned reports, and mobile alerts, and this is plenty for teams who just want to see their spend in one place.
Where do custom solutions shine?
As your network scales, a custom EV energy management system can enforce site capacity caps, co-optimize solar and battery storage, and schedule charging to hit state-of-charge targets by shift start under time-of-use tariffs. You also control the road-map, own the IP, and can bolt the platform directly onto your ERP or existing energy data management system without a heavy integration layer.
Why does data science matter?
Off-the-shelf analytics stop at averages. Intelliarts trains forecasting and anomaly-detection models on your own load curve, weather profile, and tariff structure. That means recommendations that fit Tuesday’s swing shift, not a generic template.
Real-world use case: Managing EV load without overbuilding

An EV company approached us with an escalating issue. Through our EV software development services, the project evolved into an EV smart energy management system that flattened peak demand significantly. As more stations came online, energy costs rose, the grid strained, and peak-hour load management got harder. 
We did not simply provide them with a dashboard. Collectively, we created a smart EMS that would allow them to schedule charging constraints, shift energy consumption to off-peak hours, and bypass capacity additions altogether. Role-based access, change history, and cost reports helped ops and finance stay aligned. A simple API supported basic integrations where needed.
Curious how fleet operators keep chargers reliable day-to-day? Check the EV fleet monitoring breakdown for practical dashboards and KPIs.

Summing up
Energy management was a line that you would see in the budget in small print, that you would read after it was too late, and that you would do something about after it was too late. Not anymore.

A smart energy management system changes that dynamic. It provides you with real-time information, visibility at the site level, and an opportunity to intervene before the cost is recorded in the ledger. However, the tools do not make the difference.
The only thing that counts is how well the system supports your operations and how simple it becomes to adapt to changes in it. Custom logic, grid-responsive controls, and load forecasts that are specific to your actual demand curve. That is not a nice-to-have. It is the only solution to get the system to work under pressure.
Energy data is already flowing through your buildings and depots. The question is whether it’s driving decisions. For more than 8 years we’ve turned raw meter data into actions by providing smart grid management software, custom EMS for EV networks, real-time monitoring apps, energy-use forecasting, and reporting dashboards. The same playbook can be tailored to your sites and tariffs.
Ready to make the jump from reporting to real-time optimization?Speak to us

#### The Complete Guide to Big Data Collection Methods
Businesses heavily rely on the data they receive in their interactions with the market, other companies, and users. From tailoring prices to predicting demands, needs, trends, and more, data assists decision-making in multiple ways. So, using big data in 2025 is mandatory for companies of all sizes. This is where big data collection companies play a crucial role, providing businesses with the valuable insights they need to thrive in today’s data-driven world.
However, while over 97% of businesses worldwide have invested in big data, only 24% of these companies use the collected data to analyze and make informed decisions. Considering that data created, captured, copied, and consumed worldwide is expected to reach 181 zettabytes in 2025, a lot of valuable information is left unused. 
In this post, you’ll discover how to collect big data and learn what tools and techniques can be used for data collection. Besides, you’ll review several real-life cases by Intelliarts where we show the use of big data in businesses and the value of such interventions.
How is big data collected?

“Business data is an endless source of valuable insights. And we are to help companies extract the most from it through technology“
Volodymyr Mudryi, data scientist and AI/ML engineer
From a certain point of view, methods of big data collection can be referred to as sources of big data collection, as they essentially refer to pools from which data comes. So, here are the top 6 methods of how big data to collect or choose data that are used by businesses across numerous industries:
#1 Data mining
Data mining involves extracting valuable information from large datasets to identify patterns, correlations, and trends for decision-making. It utilizes statistical, machine learning, and artificial intelligence techniques to analyze data and generate insights that can inform business strategies, improve customer experiences, and predict future trends. To optimize web crawling efficiency, it’s important to consider factors like control over crawling speed and handling anti-bot measures, which can influence the decision on choosing the right crawler tool.
Data types that can be obtained:

Structured data: Spreadsheets, databases
Unstructured data: Text, images, audio
Semi-structured data: XML, JSON
Time-series data: Stock prices, sensor readings
Spatial data: Geographic information systems data

#2 Social media analytics
Social media analytics involves analyzing data from social media platforms to understand user behavior, sentiments, and trends. It provides insights into audience preferences, content engagement, and social media performance, helping businesses tailor their marketing strategies, monitor brand reputation, and identify influencers for TikTok, Instagram, and other social media platforms.
Data types that can be obtained:

Text: Posts, comments, tweets
Unstructured data: Images and videos
User interactions: Likes, shares, follows
Demographic data: Age, gender, location
Sentiment data: Positive, negative, neutral sentiments

#3 Surveys and feedback forms
Surveys and feedback forms are tools used to collect direct input from individuals on various topics, such as customer satisfaction, product feedback, or employee engagement. They provide structured data that can be analyzed to understand preferences, opinions, and experiences. 
Data types that can be obtained:

Quantitative data: Ratings, scores, numerical responses
Qualitative data: Open-ended text responses
Demographic data: Age, gender, occupation
Psychographic data: Interests, values, lifestyle

Looking for a trusted big data development partner?Let's talk
#4 Usage of Internet of Things (IoT), sensors, and telemetry devices
The Internet of Things (IoT), sensors, and telemetry devices are used to collect real-time data from various sources, such as appliances, vehicles, industrial equipment, and environmental monitors. This data is valuable for predictive maintenance for manufacturing, energy management, environmental monitoring, and improving operational efficiency.
Read also: Renewable Energy Challenges
Data types that can be obtained:

Sensor data: Temperature, humidity, pressure
Telemetry data: Location, speed, acceleration
Operational data: Machine status, energy consumption
Environmental data: Air quality, soil moisture

#5 Web scraping
Web scraping is a technique used to extract data from websites. It involves programmatically accessing web pages and extracting relevant information, such as product prices, stock quotes, news articles, and social media posts. Web scraping is commonly used for market research, competitive analysis, price monitoring, and sentiment analysis. 
Data types that can be obtained:

Text data: Articles, product descriptions
Numerical data: Prices, statistics
Metadata: Page titles, keywords
Structured data: Tables, lists

Read also: Ethics of Data Collection
#6 Usage of public records and databases
Public records and databases are sources of structured data maintained by government agencies, public institutions, and private organizations. These records include demographic data, economic indicators, health statistics, legal documents, and geographic information. 
Data types that can be obtained:

Demographic data: Census data, population statistics
Economic data: Employment rates, GDP figures
Health data: Disease prevalence, healthcare outcomes
Legal records: Court cases, property deeds
Geographic data: Maps, spatial datasets

While the sources and data types in the methods detailed differ, they share a pretty similar implementation procedure. Learn how you can implement any of these six methods of collecting big data in the infographic below:

Looking to implement a big data collection system? Drop Intelliarts a line, and let our expert data engineers contribute to your best project.
Tools and techniques for big data collection

Deriving from Intelliarts expertise with big data, here is the review of tools and techniques that enable obtaining raw data within the scope of one or another method of collecting big data, detailed previously:
#1 Web scraping tools
Web scraping tools are software applications designed to extract data from websites. They automate the process of accessing web pages, navigating through them, and extracting relevant information, such as text, images, and links. Web scraping tools are used for data collection, market research, and competitive analysis.
Examples of web scraping tools: 

Beautiful Soup
Scrapy
Octoparse
Puppeteer
Selenium

For SEO-focused data gathering, tools like SE Ranking SEO API can be used to retrieve keyword rankings, backlink profiles, and other performance metrics to enrich your big data strategy.
#2 APIs and data connectors
Application Programming Interfaces (APIs) and data connectors are interfaces and tools that allow for seamless integration and data exchange between different software applications, databases, and platforms. They enable the retrieval, updating, and sharing of data across various systems, facilitating interoperability and data integration.
Examples of APIs and data connectors: 

RESTful APIs
GraphQL
Zapier
MuleSoft
Apache Kafka Connect

#3 Sensor and IoT data collection platforms
Sensor and IoT data collection platforms are systems designed to collect, manage, and analyze data from sensors and Internet of Things (IoT) devices. These platforms enable real-time monitoring, data aggregation, and analysis of sensor-generated data for applications in smart cities, healthcare, agriculture, and industrial automation.
Examples of IoT data collection platforms: 

ThingSpeak
Particle
AWS IoT Core
Microsoft Azure IoT Hub
Google Cloud IoT

#4 Real-time streaming analytics platforms
Real-time streaming analytics platforms are software solutions that process and analyze data streams in real-time. They are used to monitor and respond to events as they occur, enabling immediate insights and actions based on live data for applications like fraud detection, social media analysis, and real-time dashboards.
Examples of real-time streaming analytics platforms: 

Apache Flink
Apache Storm
Amazon Kinesis
Google Cloud Dataflow
Apache Samza

#5 Big data processing frameworks
Big data processing frameworks are software platforms designed to handle the processing and analysis of large datasets. They provide the necessary infrastructure for distributed computing, allowing for scalable and efficient handling of big data. These frameworks support a variety of real-time big data processing tasks, including batch processing, stream processing, and machine learning.
Examples of big data processing frameworks: 

Apache Spark
Apache Hadoop
Apache Beam
Databricks
Cloudera

As you can observe, some of the mentioned tools can serve as more than one big data collection instrument. With them, it’s often possible to establish a relatively simple yet capable system for big data retrieval, especially when the variety of big data types required to be obtained is limited. 
Establishing a fully-fledged system for data retrieval requires substantial data science expertise and experience. Drop the Intelliarts team a line and let our specialists assist you. 
You can discover about data collection additionally from the video below:

Best big data collection practices for business owners
Creating a seamless process for gathering data is half the battle. Yet, there is more to consider when you intend to make use of the vast data you generate in your business operations or intend to collect it from external sources. Here are the best practices that the Intelliarts team of AI and ML engineers sticks to and advises business owners to adopt:
#1 Data source diversification
This refers to expanding the variety of data sources to capture a more comprehensive and nuanced view of the business landscape. It reduces the reliance on a single source, thereby minimizing biases and enhancing the robustness of data-driven insights.
Measures to consider: 

Define key data needs
Source from diverse channels
Integrate and standardize data
Regularly update sources

#2 Implementing real-time data analysis
One of the key benefits of real-time data analysis in businesses is that it reacts promptly to emerging trends and issues, providing a competitive edge by enabling immediate decision-making and action.
Measures to consider: 

Establish data streaming
Deploy real-time analytics
Set up alerts and triggers
Ensure system scalability

#3 Building a data-driven culture
Creating an environment where data is at the forefront of decision-making processes encourages informed decisions, fosters innovation, and enhances operational efficiency.
Measures to consider: 

Enhance data literacy
Promote data-driven decisions
Align data and business goals
Reward data achievements

#4 Leveraging predictive analytics
Predictive analytics enables businesses to anticipate future trends and customer behaviors, facilitating proactive strategies and optimizing outcomes.
Measures to consider: 

Gather relevant historical data
Select and develop models
Train and validate models
Implement and refine models

#5 Customizing data collection strategies
Tailoring data collection to specific business objectives ensures that the data gathered is relevant, actionable, and aligned with the company’s goals.
Measures to consider: 

Set clear collection objectives
Choose suitable methods and tools
Design and implement processes
Regularly review and adjust

#6 Utilizing data for enhanced customer experience
By analyzing customer data, businesses can personalize interactions and services, leading to increased satisfaction, loyalty, and ultimately, revenue growth.
Measures to consider: 

Analyze customer interactions
Identify personalization insights
Implement tailored strategies
Measure and optimize experiences

#7 Ensuring data security and privacy
Protecting data integrity and maintaining privacy are critical for building trust with customers and complying with legal regulations, thereby safeguarding the business’s reputation and operations.
Measures to consider: 

Conduct security audits
Implement protective measures
Comply with regulations
Train staff on best practices

#8 Continuous improvement and adaptation
Staying agile and continuously refining data practices ensures that businesses remain competitive and responsive to changing market dynamics and technological advancements.
Measures to consider: 

Monitor and evaluate strategies
Stay informed about trends
Foster innovation
Implement feedback loops

Discover how Intelliarts built a big data pipeline from another of our blog posts.
Success stories by Intelliarts
Here at Intelliarts, we have substantial experience with big data trends that go beyond data collection. We have created end-to-end data pipelines and fully-fledged ML solutions that help make use of this data in business. Here are short summaries of some of our big data collection examples:
DDMR data pipeline case study

The challenge was to streamline data acquisition from Firefox and enhance the efficiency of handling extensive clickstream data. Since the customer’s sales department relies upon effective management of big data, proper execution of the project was critical. 
The solution was the creation of an end-to-end data pipeline, which encompasses data collection, storage, processing big data, and delivery. We made it possible to process hundreds of terabytes of data per day, helping the customer to grow to operate clusters with 2000 cores and 3700 GB of RAM. 
Now, the customer has the ability to process large volumes of data and translate them into actionable insights. It enhanced the reliability of sales processes and reduced human error, resulting in annual revenue multiplied a few times and an increased base of loyal customers.
Discover more about the DDMR data pipeline case study.
Processing IoT data for OptiMEAS
The customer’s request was to help to gather and utilize the data they obtained from their IoT devices more effectively. 
To assist the customer, the Intelliarts team created a centralized data storage, data processing solution, and a data visualization tool along with a permission management system.
The end solution can process massive volumes of IoT data and provide the OptiMEAS team with insights facilitating the decision making. Not only it helped to utilize the value of data fully, but it also enabled our partner to provide better products and services to end-users.
Discover more about the OptiMEAS case study.
ML-powered list stacking and predictive modeling for real estate business
The customer’s request was to help use their vast big data to predict homeowners who are going to sell their property. 
To assist the customer, the Intelliarts team created a complex ML solution consisting of the realtor model and the investor model. The solution was trained on a huge dataset of around 60 million data records. In the dataset, we distinguished 900+ attributes of property characteristics, 400 attributes of demographic data, and skip tracing data. The finished solution is obtaining about 200GB of raw data every month.
The model is accurate, compared to the industry average. It shows an accuracy of 71%. 
As a business outcome, the delivered solution helps to reach out to the most motivated homeowners, resulting in lower marketing costs, more precise targeting, and better ROI.
Discover more about the real estate business case study. 
Final Take
Effective big data collection and analysis is what helps businesses to gain insights through which they manage to make informed decisions and stay competitive. By diversifying data sources, implementing real-time analysis, building a data-driven culture, leveraging predictive analytics, and using other listed practices, companies can maximize the value of their data and drive success.
Implementation of big data collection, as well as other ML developments needed for making use of big data, requires substantial expertise and knowledge. Entrust your project to Intelliarts — an AI and ML development agency with 100+ AI and ML professionals on the team and over 24 of experience in the market delivering tailored solutions.
Optimize your data usageLearn more

#### The Evolution of ChatGPT: Vital Statistics and Trends for 2026
Like it or not, ChatGPT statistics prove that top Large Language Models (LLMs) impact the way we work. ChatGPT brings lots of opportunities for growth and innovation, helping businesses with automation, decision-making, and productivity.
Intelliarts’ customers are reaping the benefits of ChatGPT too. In our experience, we’ve built different solutions on top of it to help with contract analysis, students’ work analysis, and more. We aim to deliver ML solutions that drive tangible results—like when our predictive modeling solution for real estate helped our partner reach the revenue of $5-7 million per quarter.
But who uses ChatGPT, and why?
Combining our practical experience and insights into industry trends, this article dives into the key statistics about ChatGPT for 2026 and its use cases that drive the impact of LLMs on the workplace.
ChatGPT statistics report highlights
To save you time, here’s the editor’s pick of the top 10 ChatGPT stats you need to know. These key figures shed light on the platform’s growth, usage, and impact :
1. User base growth: ChatGPT’s user base has grown to over 250 million users weekly in 2024, as per OpenAI’s latest claims.
2. Adoption: An impressive 92% of Fortune 500 companies report leveraging OpenAI’s products, including renowned brands such as Coca-Cola, Shopify, Snapchat, PwC, Quizlet, Canva, and Zapier.
3. Training set insights: The exact size of the training set for GPT-4 has not been publicly disclosed yet. But it’s known that ChatGPT 3.5 was trained on a dataset of 570 GB, which included a vast collection of books, Wikipedia entries, web texts, articles, and other online sources. (BBC Science Focus).
4. Content generation power: The latest version, GPT-4, can generate up to 25000 words — 8 times more than GPT-3. It also understands around 25 languages.
5. Parameter expansion: The GPT-3 model is trained on 175 billion parameters, whereas the GPT-4 model is trained on over 1 trillion parameters.
Read how we succeeded in building a data extraction chatbot using ChatGPT and reduced the time needed for search and information analysis from hours to minutes.
6. Business impact: 97% of business owners believe that ChatGPT will positively impact their operations. Among the potential benefits, they mention generating responses for customers through chatbots (74%), summarizing content (53%), translation services (47%), email communication (46%), fixing coding errors (41%), and website content writing (30%).
7. Gen Z use: Gen Z is using ChatGPT more often as compared to other generations, with more than 60% of Gen Z using it on a regular basis. (Science Direct)
8. Sensitive data concern: 11% of the total data input into ChatGPT consists of sensitive information. What’s more, 4.7% of employees confess that they have entered sensitive data into the tool at least once since its launch.
9. Human vs. AI content: A recent survey proves that almost half of readers struggle to differentiate between content generated by ChatGPT and that written by humans.
10. Plugin ecosystem: By far, ChatGPT has over 920 different plugins, offering a whole range of functionalities from data extraction to code writing.
 
ChatGPT user growth statistics
As the popularity of the OpenAI model continues to rise, understanding ChatGPT usage statistics becomes crucial for assessing its impact and reach. Below we highlight the rapid growth of ChatGPT over these past years and analyze user engagement rates:
11. The number of ChatGPT users exceeded 1 million just five days after its launch, quickly becoming a compelling success story for the media. As compared, Instagram reached this milestone in 2.5 months, while Spotify took 5 months to achieve the same feat.

12. Today, the ChatGPT number of users has reached nearly 3.1 billion monthly, according to SimilarWeb. Most of the traffic comes from the USA, India, and Brazil.
13. A recent poll by YouGov reveals that nearly half of Americans have heard about ChatGPT. Out of these, approximately two-thirds of people with a postgraduate degree.
14. At the same time, 54% of US respondents have heard “nothing at all” about ChatGPT.
15. Businesses in 156 countries are leveraging OpenAI’s technology. (CTFI)
LLMs automate processes across industries—leverage them for your business.Speak to our experts
16. A large share of ChatGPT users access the tool via the web, with an average visit lasting around 6 minutes. (SimilarWeb)
17. Delving further into ChatGPT usage stats, direct traffic (73%) makes up the bulk of website visits. Meanwhile, 20% of users come from organic search. (SimilarWeb)
18. The ChatGPT mobile app has surpassed 110 million downloads on iOS and Android, generating nearly $30 million in revenue for OpenAI. (TechCrunch)
19. Adoption rates vary between 34% and 79% across different professions for both personal and professional use. (University of Chicago)
20. While ChatGPT experienced exponential growth from its launch to early 2023, it faced a noticeable decline in mid-2023. This could indicate the initial hype tapering off. At the same time, there was a renewed interest in September 2023, with traffic steadily increasing again through the end of the year. See how ChatGPT monthly visits progressed over time:

ChatGPT user stats
Now let’s dive into ChatGPT user statistics to gain insights into how this AI tool is embraced across various industries and demographics.
User demographics
21. ChatGPT is preferred by users across various age groups and genders. However, a significant share of ChatGPT visitors are male (55%), primarily between the ages of 18 and 34 (57%). (SimilarWeb)
 
22. 36% of Gen Z feel guilty about using ChatGPT to assist them at work, while one in three expresses concern about becoming too dependent on the tool. (Forbes)
23. A recent survey of 100,000 workers across 11 occupations showed that approximately half of the workforce has adopted ChatGPT, with younger, less experienced, high-performing, and predominantly male employees leading the curve.
24. The top interests of ChatGPT’s audience include news, software, online search, and games.
25. A survey of American professionals revealed that 32% are concerned about AI potentially eliminating certain jobs. Nearly half (48%) fear they may be left behind in their careers.
26. The 2024 study revealed that users trust ChatGPT less than Google Search and Wikipedia.
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Adoption rates by industry
27. Marketing has the highest adoption rate, with 77% of professionals reporting ChatGPT use at work. This is followed by 71% of consulting professionals and 67% in advertising. (HR Dive)
28. The insurance sector has the lowest usage rate, with only a third (33%) of professionals indicating they’ve utilized ChatGPT. This is closely followed by 38% in the legal industry and 40% in healthcare. (HR Dive)
29. Only a year before, the survey by Statista showed technology (28%) and education (23%) as domineering sectors working with the OpenAI’s model. Healthcare (3%), government (2%), and media (2%) had the lowest adoption rates.
30. The study on brand affinity found that ChatGPT users also pay for GitHub (21%), Zapier (17%), Midjourney (16%), and Twilio (14%), among other digital services.
Common use cases
31. According to Statista, writing or debugging codes (66%) was the most common task performed with ChatGPT. Content creation (58%) and customer support (57%) followed closely, ranking second and third, as evident in the chart below.

32. 44% of business owners intend to use ChatGPT for content translation, while one in three plans to utilize it for content creation. (Forbes)
33. Over 3000 use cases for Gen AI, including ChatGPT, already exist. (PwC)
34. ChatGPT has applications in at least 30 different sectors, from customer service to healthcare to finances to marketing.
 
Want to discover more ChatGPT use cases? Look through the full infographics.
35. ChatGPT gives a significant boost to productivity, as per recent findings. Software engineers who use ChatGPT code 126% more projects per week. Customer support agents can manage 13.8% more inquiries per hour. And business professionals could write 59% more documents per hour.
36. 58% of entrepreneurs are convinced that the OpenAI model can create a more personalized experience for their customers.
37. Statistics indicate that customer support agents tend to be 14% more productive when using ChatGPT.
38. In a comparative study on bug-fixing capabilities, ChatGPT achieved a success rate of 77.5%, performing competitively with deep learning-based methods and showing better results than traditional techniques.
39. A third of travelers indicate that they are likely to use ChatGPT for planning their next trip.
40. A medical research study revealed that ChatGPT can generate medical notes 10 times faster than doctors, all while maintaining high-quality standards.
41. Another study on how students use ChatGPT as writing support showed that the vast majority of students (81%) used it for planning. 31% used the AI tool for reviewing and 16% for translating.

Market position and financial impact
42. 57% of entrepreneurs believe that ChatGPT will boost traffic to their websites, while 47% feel it will enhance their credibility.
43. With 3.1 billion monthly visits, ChatGPT ranks closely behind established giants like WhatsApp (3 billion) and Amazon (2.6 billion), suggesting its rapid growth as a significant player in the online landscape.
44. At the same time, it significantly lags behind platforms like Google (83.8 billion) and YouTube (31.4 billion), having substantial room for improvement and further expansion. Compare ChatGPT’s monthly visitors with other online platforms to get better insight into market position:

45. A study found that 70% of users rated their experience with ChatGPT as positive, identifying helpfulness as a key factor in their satisfaction.
46. 58% of CEOs expect ChatGPT to enhance the quality of their products and services.
47. A survey by Statista found that approximately 25% of companies saved between $50,000 and $70,000 by utilizing ChatGPT. 11% of businesses reported saving over $100,000 since introducing ChatGPT into their workflows.

48. The retention rate for ChatGPT users is reported at 85%, suggesting high satisfaction levels. (Earnest Analytics)
ChatGPT statistics by region
By examining ChatGPT user statistics by region, we get to know what markets are leading in the model adoption and how preferences vary around the world.
Global reach
49. The United States has the highest share of traffic (18%) directed towards ChatGPT. India (9%) and Brazil (6%) come next. (SimilarWeb)

50. As of now, ChatGPT is available in almost every country around the world, over 180 countries to be precise. (OpenAI)
51. A few countries still either banned or restricted ChatGPT, among which are China, North Korea, Iran, and Syria. Also, its availability in Russia is complicated by regulatory issues.
Usage in the United States
52. A recent report from the Pew Research Center analyzes ChatGPT usage by country and proves that 23% of US adults have used ChatGPT in contrast to 18% in the previous survey.
53. An Arizona state lawmaker utilized ChatGPT to draft part of the law on deepfakes. (Guardian)
54. The count of daily ChatGPT users in the United States has increased, with 27% of adults using it as part of their routine. (Pew Research Center)
55. The research shows that ChatGPT promotes American culture and values. The scientists evaluated ChatGPT by asking 24 questions in multiple languages, including English, German, Spanish, Chinese, and Japanese, across six cultural dimensions. The findings revealed that American participants’ responses were closely aligned with ChatGPT’s answers, unlike those from other cultural backgrounds.
56. Approximately 20% of U.S. teenagers familiar with ChatGPT have used it for schoolwork. (Pew Research Center)
Usage in Asia
57. ChatGPT is gaining popularity rapidly in Asia, especially in India, Indonesia, Japan, and South Korea. India ranks as the second-largest user base globally, following the United States. Most Indian users fall within the age group of 18 to 34, similar to global trends, indicating a strong interest among younger audiences in AI tools​. (SimilarWeb)
58. The survey indicates that users in India and the Philippines primarily leverage ChatGPT for research assistance or as a virtual personal assistant, highlighting its growing role as a productivity aid across the region.
Transform your ideas into reality with a reputable AI development company. Let Intelliarts help you develop cutting-edge custom ChatGPT solutions that align with your business objectives.
ChatGPT revenue statistics
With increasing demand reflected in ChatGPT statistics, the model has demonstrated significant revenue potential. Explore the money income of ChatGPT and OpenAI in more detail:
59. In 2024, OpenAI has already generated $3.7 billion in revenue, and it’s projected to bring in $11.6 billion in sales in 2026. (CNBC)
60. The company predicts its revenue to reach $100 billion by 2029, comparable to the current annual sales figures of major companies like Nestlé and Target. (NYTimes)
61. OpenAI is among the most expensive AI startups today, with its worth reaching $157 billion, based on recent numbers reported by industry analysts. (Fortune)
62. API usage fees are becoming a key revenue driver, with many companies integrating GPT-4 into their own services. In 2024, this monetization strategy helped OpenAI strengthen its financial position.
63. Microsoft is OpenAI’s main investor. Having a 46% share in the company, Microsoft invested $10 billion in OpenAI. Among other renowned investors, are Matthew Brown Companies, Sequoia Capital, Tiger Global Management, Bedrock Capital, Khosla Ventures, and others.

64. In the latest funding round, OpenAI raised $6.6 billion, with giants like Nvidia and SoftBank playing crucial roles in the investment. This has generated significant buzz in investment circles, highlighting growing confidence in OpenAI’s potential.
65. According to preliminary estimates, OpenAI spends approximately $700,000 each day to maintain and operate ChatGPT. This mainly includes expenses for infrastructure, technology, and development to support the platform’s growth and user demand.
66. By the end of 2024 alone, OpenAI expects to pay around $5 billion for business operations, including service maintenance costs, employee salaries, and office expenses. (NYTimes)
67. ChatGPT’s electricity consumption in a single month is comparable to the energy usage of nearly 175,000 individuals in a mid-sized city. (Science Direct)
68. The net revenue of the ChatGPT mobile app surged by 22% on the day GPT-4o was launched, with this growth continuing in the next months. (TechCrunch)
69. ChatGPT Enterprise has become the most popular revenue stream for OpenAI, serving 260 B2B companies, including industry giants like PwC, Canva, and Zapier.
GPT-4 statistics
With improved accuracy, enhanced contextual understanding, and a wider range of applications, the release of GPT-4 marked a significant advancement in AI capabilities. Let’s see the key features and differences of GPT-4 compared to earlier versions, as well as its impact on user experience.
70. Launched on March 14, 2023, GPT-4 brings a host of new features, including the ability to chat, hear, and speak. This version enhances user experience through video processing, faster response times, and multimodal capabilities, allowing for a more interactive and versatile engagement.
71. GPT-4 can handle up to 25,000 words of text input, while the free version, accessed via OpenAI’s API, is limited to processing 16,384 words. Let’s look at other characteristics of the two versions:

72. GPT-4 is often described as OpenAI’s largest language model, with a rumored size of 1 trillion parameters. However, recent estimates place the actual number closer to 220 billion, indicating potential overestimations regarding the model’s true scale. (Semafor)
73. GPT-4 outperformed GPT-3 and other earlier models across various categories, including mathematics, technology, writing, science, recommendations, business, coding, and learning. (OpenAI)
74. In terms of performance, GPT-3.5 passed the US bar exam for lawyers, outperforming only 10% of human test-takers, while GPT-4 achieved remarkable success, surpassing 90%. (Stanford Law School)
75. The latest version of ChatGPT also achieved an accuracy rate of 86.7% in medical QA tasks, showcasing its potential for handling complex healthcare-related inquiries.
76. Responses generated by GPT-4 were preferred over those from GPT-3.5 for 70.2% of prompts. (OpenAI)
77. A study on divergent thinking and creativity found that GPT-4 produced responses that were more original and complex compared to those from human participants. Only 9.4% of human answers were more creative than those of ChatGPT.
78. When using GPT-3.5, 53.1% of people perceived the model’s content as human-written. With GPT-4.0, this increased to 63.5%, indicating that more users believed the content was created or edited by a human writer. (Tooltester)

79. With GPT-4, users can design tailored chatbots, known as GPTs, for various tasks such as marathon training, creative writing, and trip planning. Since its launch, users have created over 3 million GPTs. (OpenAI)
80. The recent study revealed that the performance of both GPT-3.5 and GPT-4 tends to decline over time, which raises questions about the long-term reliability and consistency of these models.
81. In another study, researchers investigated the capability of ChatGPT to detect AI-generated text and found that the model can identify AI-generated text in less than 50%. At the same time, it performed accurately in distinguishing human-written text.
82. With GPT-4, teams are 12% more productive and perform tasks 25% faster, as per the research findings.
Discover key differences between GPT, LLaMA, and PaLM to learn how these technologies can impact your business and unlock new opportunities.
Future predictions: Where is ChatGPT headed?
As we look ahead, the future of ChatGPT appears promising, backed by a wealth of data from ChatGPT statistics 2026. 
ChatGPT will evolve in two key directions: improving computational efficiency to reduce costs, enable fine-tuning, and support business-specific models, and advancing context handling to generate more relevant answers through complex contextual understanding. These advancements will make it more adaptable, efficient, and impactful across industries. – Volodymyr Mudryi, Data Scientist at Intelliarts
Now it’s time to check emerging trends and expected developments for ChatGPT to see how it will continue to evolve and impact industries.
Upcoming releases
83. In January 2024, the CEO of OpenAI confirmed that work on GPT-5 is underway, although a specific release date has yet to be announced. Projections suggest it may be late 2024 or early 2025, following a timeline similar to previous models, where the training and safety testing phases typically take over two years.
84. Before ChatGPT-5, there might be an intermediate release of GPT-4.5, similar to the transition from GPT-3 to GPT-3.5, to further refine capabilities.
85. One of the major enhancements in ChatGPT-5 is expected to be improved reasoning. OpenAI aims to make significant advancements in how the model understands and processes complex information.
86. Multimodality emerged as a key innovation in GPT-4, allowing the model to process and generate responses based on both text and images. OpenAI is keen on further enhancing these multimodal capabilities, recognizing their potential to revolutionize how users interact with AI.
87. OpenAI is also placing a strong emphasis on personalization as it evolves ChatGPT. The model is expected to provide a more tailored user experience by better understanding individual preferences and needs.
88. The company is likely to prioritize data privacy and misuse. Researchers are suggesting the implementation of stricter guidelines and best practices to ensure responsible AI usage, as well as enhanced transparency and accountability mechanisms.
Market trends
89. Generative AI is projected to reach a market value of $1.3 trillion by 2032, highlighting the growing significance and opportunities in this sector. (Bloomberg)
90. Future ChatGPT statistics indicate that 63% of its social media traffic is expected to originate from YouTube, highlighting a strong preference for video-based content among users. In comparison, WhatsApp contributes 10%, Facebook accounts for 9%, and LinkedIn provides 3%, as evident in the graph below. (SimilarWeb)

91. In the next few years, OpenAI should expect more fierce competition in the AI sector. The SemiAnalysis report says that Google and Meta are projected to invest over $100 billion in training advanced models.
92. The research identifies six key areas where ChatGPT will prevail: human resources, programming, social media, office automation, search engines, and education.
93. OpenAI is also expected to deepen its collaborations and partnerships with major tech companies to incorporate ChatGPT into their platforms, enhancing its reach and functionality.
94. As an example of this trend, Microsoft has already announced plans to incorporate ChatGPT into Office 365, enhancing the suite’s capabilities with advanced AI features. (CNN)
95. Recently, OpenAI has introduced ChatGPT Edu, a tailored offering aimed at educational institutions. This initiative could potentially create a new revenue stream projected to be worth $500 million annually.
96. OpenAI’s healthcare applications are anticipated to generate approximately $1 billion in revenue as hospitals and clinics increasingly adopt AI technologies for various functions, including patient care and administrative tasks. (Technopedia)
Strategic investments
97. With the new funding raised, OpenAI is going to reinforce its leadership in advanced AI research, invest money into compute capacity, and new tools to help tackle complex challenges.
98. The company also plans to raise prices: from $20 to $22 per month by the end of the year and up to $44 over the next 5 years. (NYTimes)
99. The largest expense for OpenAI is the computing power it acquires through its partnership with Microsoft. To reduce these costs in the coming years, the company aims to leverage more advanced chips and optimize its data centers, among other strategies. (NYTimes)
100. OpenAI also plans to cut training costs by optimizing hardware and exploring more efficient algorithms.
Wrap up
The remarkable rise of Gen AI and ChatGPT, in particular, is not just a passing trend; it signifies a fundamental shift in how we engage with technology. The ChatGPT statistics presented in this article highlight its rapid adoption and widespread integration across various sectors, positioning it as a transformative tool for businesses.
With AWS-certified specialists on board, Intelliarts as an AI and ML development company can help develop custom ChatGPT solutions that empower your workforce and boost ROI. We have extensive experience with AI chatbots, including ChatGPT-driven solutions, and are ready to bring your innovative ideas to life. Contact us today to explore how we can help you harness the power of ChatGPT.
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#### The Impact of Aerial Imaging on the Agriculture Industry
According to the DJI Agriculture Annual Report, as of 2024, more than 300,000 agricultural drones operate globally, treating more than 500 million hectares of farmland around the world due to aerial imaging technology. The industry undergoes significant digital transformation. The sheer number of IoT and drone devices in use indicates that eventually, most businesses will rely on technology more. So, what should you expect, and how can you get started with aerial imaging in farming? 
In this post, you’ll explore the concept of aerial imaging in farming, find out about technology use cases, and discover what devices are needed to make it work. Besides, you’ll review success stories of farm management with aerial imaging and get to know this technology’s limitations and solutions to them. 
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8 benefits & use cases of aerial imaging in agriculture
Above everything, let’s recap what we know about aerial imaging:
Aerial imaging is the process of capturing images of land or crops from above using drones, satellites, or aircraft.
Basically, aerial mapping in agriculture is becoming a common replacement for the labor-driven process of visual examination of crops, equipment, facilities, etc. In a similar way, it’s also used across other domains.
Explore computer vision applications in industries in another of our blog posts. 
Now, when we are familiar with the main concept of the topic, let’s proceed by reviewing actual use cases of aerial imaging in farming:
#1 Crop health monitoring
Aerial imaging captures multispectral and thermal data to identify stressed, diseased, or pest-affected areas in crops. Farmers use the imagery to locate problem zones without walking the entire field.
Best for:
Large-scale farms, horticulture producers, vineyards, specialty crop growers.
Suggested frequency of data collection:
Every 7–14 days during the growing season or after major weather events.
Deliverables:
Vegetation index maps, thermal stress maps, crop health reports.
#2 Soil analysis and field mapping
Aerial imaging creates soil maps by capturing visual and multispectral data on soil moisture, texture, and composition. These maps help define field zones for management and planting.
Best for:
Crop farms, seed producers, precision agriculture companies.
Suggested frequency of data collection:
Once per season before planting and as needed after major field changes.
Deliverables:
Soil variability maps, moisture distribution models, planting prescription maps.
#3 Irrigation management
Thermal imaging identifies areas of fields that are under- or over-watered. Data from aerial scans highlights zones needing irrigation adjustments.
Best for:
Orchards, vegetable farms, greenhouse operations, arid-region farms.
Suggested frequency of data collection:
Every 10–14 days during peak irrigation periods.
Deliverables:
Thermal imaging maps, water stress distribution reports, irrigation adjustment guides.
Don’t hesitate to reach out to us for technology consulting services.
#4 Precision spraying and fertilization
Aerial imagery is used to create variable-rate maps for site-specific application of pesticides or fertilizers. This targets exact field areas needing treatment instead of sticking with uniform application.
Best for:
Row crop farms, vineyards, organic farms, seed producers.
Suggested frequency of data collection:
Pre-treatment mapping and follow-up imaging after spraying or fertilization.
Deliverables:
Prescription maps for spraying, fertilizer application maps, treatment validation imagery.
#5 Yield estimation
Vegetation indices and canopy data captured by aerial imaging are analyzed to estimate the expected crop yield. Farmers implement the insights to plan harvest logistics and predict production volumes.
Best for:
Grain producers, fruit orchards, agribusiness corporations.
Suggested frequency of data collection:
Two to three times per season: mid-growth and pre-harvest.
Deliverables:
Biomass maps, canopy cover models, predicted yield reports.
#6 Disaster assessment
After natural disasters like floods or storms, aerial imaging documents damaged field areas. This helps farmers assess field conditions without risking in-person inspections.
Best for:
Mixed crop farms, insurance partners, and government-supported farming programs.
Suggested frequency of data collection:
Immediately after the disaster event and one follow-up survey 7–14 days later.
Deliverables:
Damage assessment maps, affected area calculations, insurance documentation photos.
Find out about an extra application of data science in business from another blog post by Intelliarts. 
What’s also useful to know is how beneficial the usage of aerial imagery in agriculture is compared to manual labor. 8 exact benefits of aerial imaging in farming are provided in the image below:

Digital technologies driving aerial imaging
Aerial mapping in agriculture is grounded on a combination of smart and conventional technologies. But before we delve deeper into their overview, let’s take a brief look at how aerial crop imaging works in the infographics below: 
Computer vision is also used in a number of other niches. Discover in-depth details on solar panel fault detection and test an actual ML model demo yourself in another Intelliarts’ blog post. 
Note: Steps six and seven are optional. That’s because crops may not be present or visible during a particular flight, which may happen, for example, in winter. On such occasions, neither NDVI nor crop health patterns can be examined. Alternatively, aerial data for smart farming may be examined by the very data analytics system instead. In this scenario, the role of a drone and integrated imaging technology is only to capture, tag, and transfer data. 
And now, here’s the list of digital technologies behind aerial imaging and examples of software solutions available on the market:
#1 Computer vision
This technology is needed to enable automatic detection of patterns, crop health issues, and land features from aerial images.
Examples: TensorFlow, OpenCV
#2 Artificial Intelligence (AI)
In this case, AI powers smart analysis of aerial imagery to detect diseases, predict yield, and optimize farming strategies.
Examples: Microsoft Azure AI, IBM Watson Decision Platform for Agriculture
#3 Machine learning (ML)
ML is needed to train models to recognize crop types, estimate biomass, and improve predictions over time from aerial data.
Examples: Amazon SageMaker, Google Vertex AI
Explore custom machine learning solutions by Intelliarts. 
#4 Cloud computing
Cloud solutions, as an alternative infrastructure option, enable to store, processing, and access to large aerial datasets and maps from any device.
Examples: AWS Cloud for Agriculture, Microsoft Azure FarmBeats
#5 Edge computing
Edge computing is an optional solution that’s needed to process aerial images directly on drones or nearby edge devices to deliver fast insights without full cloud upload.
Examples: NVIDIA Jetson, Microsoft Azure Percept
#5 Geospatial data analysis (GIS)
GIS allows for mapping aerial data spatially to support decision-making in field zoning, irrigation, and fertilization.
Examples: ESRI ArcGIS, QGIS, DroneDeploy
#6 Wireless communication (4G/5G)
Communication drives real-time data transmission between drones, ground stations, and farm management systems.
Examples: Qualcomm 5G Modems, Telit IoT Connectivity
#7 Internet of Things (IoT)
IoT is another optional, but typically available technology in aerial imaging that connects drones with other smart farm devices (e.g., soil sensors) to create a unified monitoring ecosystem.
Examples: Bosch IoT Suite, John Deere Operations Center
Note: While a number of companies offer tailored solutions to help drive aerial imaging, the process of building an actual working software system is far more complex than simply using one or another software available. That’s why most companies without a specialized in-house dev team need external software development assistance with their aerial imaging project. 
Devices used for aerial imaging
What’s also essential to know for a complete insight into the technicalities of aerial crop monitoring is the range of devices that can be utilized here. The complete overview is provided in the table below:  
 
In practice, fixed cameras are one of the most widespread solutions due to their low cost of maintenance and operational simplicity. They are typically placed in greenhouses. Drones are a typical choice when there’s a need to cover a large farming area. 
At the same time, sensors that can provide an overview of vegetation health are regarded as a sort of specialized equipment, used, for example, to monitor a small area inside a greenhouse. Temperature and humidity sensors are other common devices, approached similarly to fixed cameras. They are placed in most greenhouses and are sometimes in certain zones at farming fields. 
You may see an example of temperature and humidity sensors placement in greenhouses in the image below:

This placement is in line with ISO 17025 provisions regarding validation of data gathering methods, impact of environmental conditions on measurements, and documentation and traceability of data. 
As for fixed cameras, 2 or 4 such cameras are mounted on either internal greenhouse supporting pillars or on walls so that they can capture most of the agricultural area. 
Success stories of crop monitoring in agriculture
Let’s take a look at a couple of success stories where Intelliarts helped with smart technologies through agriculture software development services:
Indigo Ag’s automated data entry for carbon insights
Background: Indigo Ag faced high drop-off rates due to time-consuming manual data entry — up to 8 hours to input field data like seed types, locations, and practices — slowing adoption of their Carbon program.
Solution: Intelliarts built advanced ETL/ELT pipelines and integrations with third-party data providers to automate field data collection. By layering structured workflows with medallion architecture and FSM logic, the system now ingests and processes external agronomic data in near real-time, enabling rapid correlation with aerial imaging and environmental models.
Results: Indigo slashed data entry time from hours to minutes, improved data accuracy across systems, and saw higher user satisfaction and retention. The new infrastructure supports faster carbon credit validation and smarter, imaging-enhanced decision-making.
Explore success story
Indigo Ag’s automated land sampling with sensor integration
Background: Indigo Ag needed to modernize its manual land sampling process to improve data quality and support its carbon program.
Solution: Intelliarts redesigned the system using tablets, QR codes, and field sensors to automate sample collection. The platform now supports real-time data syncing with aerial imagery and enables faster, more accurate insights for agronomic analysis.
Results: Sampling time and errors were significantly reduced. Sensor losses dropped below 4 per 1,000. Indigo now benefits from standardized workflows and seamless integration of aerial and ground-level data.
Explore success story
Aerial imaging technology limitations & solutions
As obvious as it sounds, every technology has its limitations. To complement what we already know about sustainable farming with aerial imaging, let’s find out the challenges of using this smart approach and ways to solve them: 
#1 Limited resolution
The resolution of aerial imagery is limited and may be insufficient, especially if lower-cost or simply outdated drones or sensors are used. This impacts the ability to capture fine details at large distances.
Solution: Before getting started with a project, it’s important to estimate the optimal resolution. It’s even better to try to forecast what resolution would be required in 5-10 years, and select drones and sensors that would still suffice after this period of time, instead of purchasing minimally suitable models. 
Besides, using high-definition cameras and LiDAR (Light Detection and Ranging) sensors can improve resolution. As a workaround, you may consider integrating multiple image stitching techniques to enhance detail accuracy.
#2 Weather sensitivity
Aerial imaging in agriculture is heavily dependent on weather conditions. High winds, rain, and low visibility can disrupt or delay image collection.
Solution: Using drones with enhanced stabilization and weather-resistant capabilities can reduce the impact of adverse conditions. Employing multiple scheduling options is another way to find optimal weather windows.
Thinking about an aerial imagery solution?Request a project estimate from our team.Get in touch
 #3 Data storage and processing
High-resolution imagery produces large datasets, which can be difficult to manage and process in a timely manner.
Solution: It’s generally recommended to use cloud-based storage solutions, which can be scaled well compared to in-house alternatives, and optimized, AI-driven processing algorithms. Building a big data pipeline is also crucial for the entire procedure of data storage and processing. 
Explore the DDMR data pipeline success story to find more about Intelliarts’ experience with big data projects. 
#4 Data interpretation
Processing raw aerial images manually is time-consuming. It’s also difficult to obtain a clear insight from raw data without specialized knowledge, especially when it comes to monitoring complex environments like agriculture or infrastructure.
Solution: AI and machine learning can and, as a common practice, should be used to automate the analysis and interpretation of aerial images. While there’s still an opportunity for specialists to analyze a portion of the images themselves, for example, when double-checking the results or searching for anomalies in imagery, most of the routine work should be automated. 
Probably the best recommendation here, especially for non-tech companies, is to partner with trusted software engineers should you have an aerial imaging project in mind. Software agencies with experience are likely to have gone through the listed and other challenges and have ways to address them in an effective manner. They may even have some custom solutions to one or another difficulty. So, there are all the reasons to take full advantage of external expertise and experience. 
Final take
Aerial imaging has firmly established itself as a critical technology in modern farming. It enables precise monitoring, smarter planning, and better decision-making across a variety of agricultural operations. From crop health analysis to disaster assessment, aerial imaging tools continue to reshape how farms operate. Understanding the technologies, workflows, and limitations involved helps businesses maximize the value of this innovative approach and prepare for a more data-driven future.
Having a trusted tech partner is half the battle. Over the span of more than 25 years, with 90% of returning customers and a team of 92% senior staff engineers, Intelliarts can drive your aerial imaging project with our expertise and experience. Don’t hesitate to reach out to discuss opportunities.

#### The Importance of Keeping Farm Records in Agriculture
In a field trial in Japan, legacy farm machinery retrofitted with low-cost IoT/GNSS devices generated 1,623 automatically recorded work records in seven months. Another research report on farm record keeping states that 63% of farmers say they use farm software in some part of their operations. Digitalization is here, and farm bookkeeping is just another part of it.
In this post, you’ll find out what farm records are and why bookkeeping is strategically necessary for an agricultural business. Besides, you’ll learn common barriers to bookkeeping adoption and how technology solves them. Finally, you’ll explore digital software used in farm record keeping. 
What are farm records, and what do they actually include?
Farm records are organized documents or digital files that capture information about farming activities, such as inputs, outputs, finances, equipment, and labor. 
Basically, records serve as a farm record book of daily operations, which helps farmers track performance, manage costs, and make informed decisions for future production.
Are you asking yourself what data you should be gathering for agricultural purposes? After all, detailed field data is often composed of 3-5 years of historical data records. Using our experience with farming companies, the Intelliarts team has gathered the most useful entries for record keeping. Find the simple checklist below:
 
Note: Depending on the scale of your business and the level of digitalization, you may consider sticking to only a few key values to record and an Excel spreadsheet. Alternatively, having a dedicated custom software that can aggregate all the mentioned data types can offer a better performance as well as a foundation for further analytics. 
Explore our success story on automating data entry published on the respective portfolio page. 
Why farm record keeping is a strategic necessity
What is the main reason for keeping accurate records? In today’s data-driven agriculture, keeping farm records is about managing knowledge, rather than dealing with additional paperwork. Such intervention can offer a clear edge in productivity and resilience. 
Find clear reasons to get started with farm record-keeping in the infographics below:

Looking to centralize your farm’s data across multiple systems and formats? Let’s discuss
Common barriers to keeping records and how technology solves them
It’s frequent when both small and large-scale businesses refuse to adopt a technology innovation for different reasons. Here are the most common barriers to getting started with bookkeeping in agriculture that the Intelliarts team has noted.
Key barriers to bookkeeping adoption

“Too busy” or “I’ll remember it” mindset. Some farmers see fieldwork as the priority, so details about inputs, sales, or labor go unrecorded. Over time, this habit undermines the importance of record-keeping for decisions and compliance.
Disconnected tools. Notes in a farm record book, WhatsApp chats, and Excel sheets are scattered, making it hard to get a full picture of farm operations. This slows down analysis and reporting.
Low digital access or literacy. Sometimes, farmers may lack internet access or confidence using digital systems for farm record keeping, leaving them dependent on paper-based records.
Fear of complexity. Farm bookkeeping may be intentionally avoided because workers find software solutions to be too technical, costly, or time-consuming to use effectively.
Cost concerns: Development expenses, subscription fees, and device costs make companies hesitate, especially when traditional examples of farm records (paper receipts, notebooks) feel cheaper to use.
Trust & data security worries. Companies may be concerned about storing sensitive financial and farm bookkeeping and accounting information online.

As you can observe, most barriers are created by the urge to maintain traditional approaches in farming and the fear of change. This is especially critical for smaller-scale agriculture businesses. As for large companies, they are mostly concerned about the general connectivity of software in use and the security of data transferring and storing. 
Technology consulting for agriculture businessesWe help to define agritech needs and build your development strategy.Contact Us
Smart solutions to bookkeeping adoption barriers
Over the 25 years of delivering software solutions, the Intelliarts team has tested numerous technological approaches to actual business issues. While we can confirm that a standalone software is rarely a solution to a complex business situation by itself, a mix of carefully selected and developed options can aid a lot.
Here are some of the smart solutions or properties of software we consider useful in overcoming barriers described above: 

Mobile-first, offline-friendly tools: Separate apps or additional software features with quick-entry fields, photo uploads, and voice notes let farmers capture data on the go. Such solutions may work even without the internet.
Integrated platforms: All-in-one systems link inputs, outputs, and financials in one place. This solves the problem of scattered farm records and ensures the maintenance of accurate records over time.

In one of our projects, Intelliarts implemented software integrations, automating data entry, to cut time from hours to minutes.

Simple, visual interfaces: Intuitive design and local-language support in apps lower the learning curve, this way helping to overcome fear of complexity and reluctance. 
Custom dashboards & flexible UIs: Tailored views help managers utilize analytical methods, which fulfill the main purpose of having bookkeeping software in place.
Affordable access models: Freemium apps, pay-as-you-grow plans, and community licenses help to get started with digital farm bookkeeping for even smaller-scale farming businesses.
Secure cloud storage: Encryption and permissions protect sensitive data. This reassures agricultural businesses that farm bookkeeping and accounting records remain safe and private.
Hybrid systems: Tools that scan and upload receipts, ledgers, or examples of farm records bridge traditional practices with modern efficiency. Such solutions make the transition smoother. 

How digital tools transform farm record keeping
Now we know what smart software and technology features are needed to overcome barriers to keeping farm records. As a follow-up, let’s review several types of exact software and technology tools, which can have such properties, to consider using. 
#1 Farm management apps (mobile/web)
Modern farm management apps centralize operational data into one digital workspace. They enable quick logging, planning, and analysis across devices.
Examples of apps:

BushelFarm 
AgriWebb

Take a look at the agritech sensor inventory management case study by Intelliarts on our respective portfolio page. 
How it’s used: Farmers can record input use, assign field tasks, and analyze yield results in real time. For instance, they might plan irrigation schedules, monitor expenses, and compare productivity across fields.
#2 Dashboards and alerts
Dashboards consolidate diverse farm metrics, such as soil moisture, crop health, or equipment performance, into visual, interactive displays. Alerts highlight deviations or risks before they escalate.
Examples of apps:

John Deere Operations Center
Granular Insights

How it’s used: A farmer can receive instant alerts about pest threats or machinery downtime. By tracking weather, fertilizer use, or fuel consumption, dashboards enable predictive, data-driven adjustments that save both time and resources.
#3 Analytical software
Analytical tools apply data science and spatial mapping to uncover patterns in yields, costs, and resource efficiency. They turn raw records into insights for future planning.
Examples of apps:

Ag Leader SMS
Trimble Ag Software

How it’s used: By analyzing five years of yield data, a farmer can see how soil zones and rainfall affect output. Insights support smarter seed selection, optimized input timing, and better financial negotiations with suppliers.
#4 IoT & sensors for auto-logging
 IoT sensors automate data collection across soil, livestock, and machinery. Continuous readings are sent directly to farm software, minimizing manual entry errors.
Examples of apps:

CropX
Arable

How it’s used: Soil sensors log temperature and moisture automatically, while livestock trackers monitor movement and feeding. This way, farmers gain instant visibility into conditions.
#5 Integration with weather & satellite data
Integration tools combine on-farm data with external sources like weather forecasts and satellite imagery, enriching decision-making with environmental context.
Examples of apps:

Climate FieldView
OneSoil

How it’s used: A grower might delay spraying due to forecasted rain or detect stressed crop zones from NDVI maps.
#6 Finance tools
Agricultural finance platforms connect operational data with accounting systems, simplifying profitability tracking, reporting, and compliance.
Examples of apps:

QuickBooks for Agriculture
FarmBooks

How it’s used: Farmers automatically sync field expenses, labor costs, and revenues into financial dashboards. This supports informed decisions on investments or subsidies.
Read additionally about AI in agriculture in another blog post by Intelliarts. 
Important note: As you can see, the range of software recommended for usage is extensive. However, with development assistance, example apps as well as custom features can be aggregated within one platform. This would offer an all-in-one digital space for all 
Here at Intelliarts, we have substantial expertise in the agriculture domain. We can cover the entire range of software engineering services, and offer you both custom farm bookkeeping software as well as integration of AI, GIS, IoT, management, analytics, or other conventional and LLM functionalities in your system. The next section reveals one of many case studies in our portfolio.
Automated field data collection in agritech: Success story by Intelliarts

Challenge: The agritech company Indigo aimed to scale its carbon farming program but faced major barriers in data collection. Growers were required to submit 3–5 years of historical field data, which was often incomplete, inaccurate, or time-consuming to gather. Manual workflows slowed onboarding and limited participation among farmers.
Solution: Intelliarts built a lightning solution that integrates remote sensing, machine learning, and the customer’s Best Guess API to automate data collection and pre-fill grower records. The system inferred key agricultural data like crop type, planting dates, and field boundaries. Main efforts were focused on avoiding manual entry and simplifying farm data collection across programs.
Results: The automation cut record-keeping time from weeks to hours, improving data accuracy and grower experience. The company achieved a 3× increase in enrolled acres (2.6M+ across the U.S.), generated over 1M carbon credits, and reached 80% grower retention. This notably strengthened the company’s position in sustainable agriculture and digital farming innovation
Take a look at our success story on field data collection to find out how smart solutions can help with farm records in greater detail. 
Final take
Today, digital tools have reshaped farm record keeping into a foundation for smarter management. Through connected apps, sensors, and analytics, farmers gain clear insight into operations and finances. Accurate, integrated records now support better planning, sustainability goals, and profitability, which makes digital record keeping a vital part of running a modern, resilient farm business.
The Intelliarts team is a trusted provider of development services in the agricultural domain. With over 80 large projects complete, 92% of senior staff engineers, and a 90% customer return rate, we are ready, willing, and able to assist you with the integration of a farm record keeping system. 
Custom AgriTech SolutionsFull-cycle software development and consultation by a trusted agricultural partner.Learn more

#### The Power Duo: Exploring the Integration of Big Data and AI
It’s estimated that the average person generates 1.7 MB of data per second. At the same time, an average company uses no more than 40% of its data, despite over 97% of business owners claiming they strive to be a data-driven company, and 79.4% of them are afraid that more data-wise companies will disrupt and outperform them.
The value of business data, its analytics, and the decisions made based on the generated insights are hard to overestimate. At the same time, AI brings advanced business automation capabilities fueled by data.
It’s more than interesting to delve deeper into the interplay of big data and AI strategies. In this post, you’ll read about real-life case studies of big data and AI, learn how they work together, what challenges and considerations should be kept in mind, and what the industry best practices for working with AI and big data are.
The interplay of big data and AI
In machine learning and deep learning, algorithms require vast amounts of data to learn and evolve, which is where a tech consulting company can provide valuable support. AI processes large amounts of data using distribution, big data analysis and consulting. This synergy enhances building, training, and testing models as well as operating them in real life based on business data. 
Read also: Big data collection
Let’s explore the workflow of big data, available big data technologies, AI techniques, and, finally, into how artificial intelligence and big data analytics complement each other and produce the best outcomes:
Data collection, storage, and processing in big data are pivotal

Key processes and aspects of data collection, storage, and processing are fundamental for successful AI & big data solution implementation. Currently, there is a range of big data technologies that serve three main stages of working with big data. Let’s delve deeper into each stage and review the existing solutions for fulfilling them.
Data collection is the process of gathering and measuring variables of interest or just information, from varied sources. Instruments for collecting data include:

Apache Flume: Distributed system for collecting, aggregating, and moving large amounts of log data from various sources to a centralized data store.
Apache Kafka: Distributed streaming platform that functions as a messaging system, event store, and real-time stream processor.
Logstash: Data processing pipeline that ingests data from multiple sources, transforms it, and sends it to destinations like Elasticsearch.
Amazon Kinesis: Web service that offers real-time data processing over large, distributed data streams, used for real-time analytics.
Google Pub/Sub: Real-time messaging service allowing reliable and asynchronous message exchanges between applications.

Data storage is the process of holding data in a digital format so it can be retrieved and used later. Instruments for storing data include:

Hadoop Distributed File System (HDFS): File system that stores large volumes of data across multiple machines, providing high throughput and fault tolerance.
Apache HBase: Non-relational, distributed database designed for quick access to large amounts of structured data.
MongoDB: Document-oriented NoSQL database known for its scalability and flexibility in high-volume data storage.
Amazon S3: Scalable object storage service for large-scale data storage, accessible globally.
Google BigTable: Distributed, column-oriented data store for large-scale applications requiring significant data storage and low-latency access.

Discover how big data can transform your business with Intelliarts.Learn more
Data processing is the process of converting raw data into meaningful information through a series of computational steps. Instruments for processing data include:

Apache Spark: Distributed system for big data workloads with libraries for SQL, machine learning, and stream processing.
Apache Flink: Framework that processes stateful computations over data streams, known for high throughput and low latency.
Hadoop MapReduce: Programming model that processes large data sets with a parallel, distributed algorithm on a cluster.
Google BigQuery: Serverless data warehouse for scalable analysis over large datasets with SQL.
Amazon Redshift: Petabyte-scale data warehouse service for analyzing data using standard SQL and BI tools.

It’s important to note that even a single big data pipeline is complex, and understanding its mechanism properly involves more than reviewing the detailed key phases. If you’re interested in delving deeper into big data components, types of big data pipeline architecture, and more, read another blog post by Intelliarts. 
Enabling AI techniques and algorithms is crucial for effective big data utilization
Let’s proceed with reviewing common AI techniques and algorithms, followed by explaining how exactly big data can come in handy here:

AI techniques and algorithms are essential for leveraging big data in AI processes. As we can conclude, the listed algorithms and techniques require high-quality data in extensive quantities to be built and operate in the desired way. Big data is exactly what can support AI in this matter, and here’s how:
1. Providing training data is key to AI success
Big data offers a vast reservoir of information from which these algorithms can learn. The diversity and volume of datasets provided by big data allow AI models to capture a wide range of patterns, anomalies, and correlations that might not be apparent in smaller datasets.
2. Enabling model refinement is critical for AI accuracy
With access to big data, AI algorithms can continuously refine and improve their predictions and decisions. As more data is collected and analyzed, these models can be retrained or updated to enhance their accuracy. This iterative process is crucial in fields where precision is vital, such as healthcare diagnostics, financial forecasting, and autonomous vehicles.
3. Facilitating complex decision-making is where big data shines
Big data enables more sophisticated AI models by providing the necessary depth and breadth of data required for complex decision-making. For instance, when it comes to data analytics and machine learning, a large dataset can help identify more nuanced and accurate predictions, which wouldn’t be possible with limited data.
4. Enhancing personalization in AI is crucial for customer satisfaction
In customer-facing applications like marketing and e-commerce, big data combined with AI algorithms allows for a high degree of personalization. By analyzing extensive data on consumer behavior, preferences, and interactions, AI models can deliver highly personalized recommendations and experiences to individual users.
Now, as the relationship between AI and big data and the means of big data is clear, we can examine how large, high-quality datasets can enhance the performance of ML models and software solutions built on them. 
Enhancing AI performance with big data is paramount for modern businesses
Here are three main ways in which big data can greatly improve the performance of AI-driven software solutions:

Improving AI accuracy: Big data allows AI to recognize complex patterns and relationships through training on extensive, diverse datasets. 
Reducing bias: When AI models are trained on a broad range of data, they are less likely to exhibit biased behaviors or decisions.
Enabling complex models: Complex AI models, such as deep learning neural networks, require massive amounts of data for effective training. Big data facilitates the development of these models by providing the necessary volume of training data. 

Here are some examples of data that can be provided using big data to enhance AI solutions for various industries:

Read also about machine learning applications in insurance industry. 
Challenges and considerations
There is a global discussion about the usage of AI and the solutions built with it. Gray Scott, a futurist and a techno-philosopher claimed:
“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.”
Here are some concerns that are frequently raised in regard to the use of AI:

Data privacy: Massive personal data collection risks breaches and unauthorized use. Ensuring ethical use and individual consent is challenging.
Algorithmic bias: AI can reflect and amplify biases present in training data, leading to discriminatory outcomes. 
Lack of transparency: Many AI systems are complex “black boxes,” making it hard to understand their decision-making processes. 
Regulatory compliance: Complying with data protection laws like GDPR or HIPPA is challenging, requiring careful navigation of the legal landscape around data use.
Ethical use and impact: Determining the ethical limits of AI use, particularly in areas impacting societal norms and individual lives, is a significant concern.

The listed concerns don’t indicate that AI shouldn’t be used. These are some of the modern-day challenges that are to be addressed as technology evolves and the approaches to its usage improve. One of the strategies for leveraging artificial intelligence wisely is shown in the infographic below:

Best practices for using AI with big data
As a company specializing in big data analytics and AI, Intelliarts sticks to and determines best practices for leveraging AI solutions reinforced with datasets provided by big data. Some of the points we would like to highlight in that regard are:
Prioritize data quality, diversity, and governance
This means ensuring the data is accurate, complete, and consistently formatted. Diverse data sources also matter; they prevent biases and improve model generalization. Implement a strict data governance policy, which involves:

Regular data audits
Clear data sourcing
Strict privacy controls

Remember that the quality of your AI predictions depends heavily on the quality of the data you feed into your models.
Ready to adopt AI and big data?Reach out to Intelliarts’ experts to kickstart your AI journey today.Contact us
Foster a culture of experimentation and model iteration
In AI development, being open to experimentation and continuous model iteration is key. This involves:

Regular model updates with new data sets
Testing different algorithms
Tweaking parameters

Encourage your team to view failures as learning opportunities. In our projects, we run A/B tests to compare different models and use the insights to refine our approach. This continuous loop of experimentation and learning is what keeps your AI models relevant and effective.
Always strategize for scalable infrastructure
We know well that scalable infrastructure is not a luxury but a necessity. As the stream of data grows, your infrastructure needs to evolve with it. 
As our recommendation, opt for cloud solutions for flexibility or invest in distributed computing for handling large datasets and intensive computations. Scalability ensures that your infrastructure won’t become a bottleneck when the AI models become more complex.
Align AI initiatives with real-world applications and business goals
AI shouldn’t exist in a vacuum. Aligning AI initiatives with real-world business objectives has always been key to creating impactful solutions. This means understanding the business context deeply and identifying problems that AI can effectively solve. 
So, the development should be all about building solutions that aren’t just technically sound but also deliver tangible business value, whether it’s through process optimization, enhancing customer experiences, for example by using AI recruitment software for hiring, or driving innovation.
Emphasize compliance and security
In today’s world, overlooking compliance and security in AI can lead to serious consequences. As such, the EU proposed AI rules violations that will result in fines equal to up to 6% of a company’s global turnover.
From personal experience, integrating strong security protocols and ensuring compliance with data protection laws from the start of your project is non-negotiable. This includes regular security audits, data encryption, and adhering to standards like GDPR. In the long run, this doesn’t only protect your projects but also builds trust with your users, a crucial element in the success of any AI application.
The Intelliarts experience with AI and big data
Here at Intelliarts, we are proficient with both AI-driven and big data strategies and solutions. Some of our related cases include the following: 
DDMR data pipeline case study

 
The challenge was to streamline data acquisition from Firefox and enhance the efficiency of handling extensive clickstream data. This was crucial since the company’s revenue generation, particularly in its sales department, heavily relied on the effective management of big data. 
The solution was the creation of a specialized browser application coupled with a comprehensive data management pipeline encompassing aspects like data gathering, storage, processing, and distribution. The project scaled impressively, evolving from a nascent stage to managing clusters equipped with 2000 cores and 3700 GB of RAM. As a result of this collaboration, our client experienced a significant surge in yearly revenue.
Discover additional details about how Intelliarts built a big data pipeline from another of our blog posts.
Real estate company case study

The challenge was to accurately forecast which homeowners were likely to list their properties for sale, preempting the actual market listing. The company was analyzing a vast array of over 1400 property and demographic variables across an extensive database exceeding 60 million records. Efficient processing of this voluminous raw data was imperative. 
The solution entailed relocating the data to Amazon S3 storage, executing data transformation, and leveraging AWS Glue for data processing. The resultant model successfully predicted approximately 70% of potential home listings, a figure significantly higher than the industry standard.
Final take
Big data provides the vast, diverse datasets required for AI algorithms to learn and make informed decisions, while AI offers advanced analytical capabilities to extract meaningful insights from large and complex datasets. AI works with big data well and the potential of their combined power is immense, promising significant advancements in different industries. 
Building from the ground up and integrating into the existing infrastructure an AI solution powered up with your business data is a challenging task. It should be trusted to engineers with a deep expertise in AI and ML only. Consider partnering with Intelliarts, as a big data consulting company. Our team will help you get the most value from your big data.
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#### The Role of IoT in Smart Grid Technology
The global smart grid market is forecasted to surpass $130 billion by 2028. It’s no wonder considering that the related but more established renewable energy market is worth nearly $1.1 trillion as of 2023 and is predicted to grow twofold over the next 7 years. It all boils down to the fact that smart usage of electricity in the form of IoT smart grid is a rising trend that can be exploited by various technology, energy, utility, and IoT companies.
Read also: AI in the renewable energy market
In this article, you’ll discover how smart grid works, why it’s better than traditional grids, and where is the connection between IoT and smart grid technology. On top of that, you’ll find IoT based smart grid systems and IoT applications in smart grid. Finally, you’ll learn about the potential future of smart grid technology  in IoT and the step-by-step flow for building IoT-enabled smart grid solutions with Intelliarts.
How does the smart grid work?
For starters, let’s familiarize ourselves with the main concept related to the topic.
A smart grid is an electrical grid that uses digital technology to monitor and manage the transport of electricity from all generation sources to meet the varying electricity demands of end users.
Smart grids are an enhancement over traditional power grids by incorporating a range of technological capabilities, which will be detailed in the below sections. 
It’s essential to understand that smart grids are not only about the management of electricity. At the modern-day level, smart grids can provide plenty of data, both technical and business. Among other things, information provided via smart grid can be used for empowering IoT tech.

Wrapping everything together, here’s how the smart grid operates across all procedures involved in the regular power cycle intended to manage electrical resources:

Generate: Smart grids facilitate the integration of diverse generation sources, including renewables, ensuring a flexible and sustainable energy supply.

Distribute: They employ advanced communication technologies to dynamically manage the distribution of electricity, enhancing the reliability and efficiency of power delivery.

Use: Smart grids enable real-time monitoring and control of energy consumption, allowing consumers to use electricity more efficiently through smart appliances and meters.

Control: These grids utilize automated control systems to respond instantly to changes in energy demand and supply, maintaining grid stability.

Store: Smart grids incorporate energy storage solutions that can store excess energy during low demand and release it during peak times, optimizing energy usage.

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Why are smart grids better than traditional grids?
As stated previously, smart grids are the advancement of traditional grids. They incorporate a range of technological capabilities allowing for better electrical resources management. Examples of such tech advancements include:

Smart meters

Smart appliances

Distributed energy resources (DERs)

Demand response technologies

Advanced Metering Infrastructure (AMI)

Energy storage systems

Grid automation technologies

Real-time monitoring and control systems

Communication networks

You can discover what such an improvement in the grid technology results into from the comparison table below:

How IoT works in smart grid tech
From a certain perspective, the usage of smart electricity management instruments combined with IoT devices is what makes a traditional grid a smart grid. 
Above all, smart grids feature a bi-directional flow of information between consumers and utility companies.
Moreover, the listed above IoT devices, both those operating on energy and those measuring all factors related to management and consumption generate lots of data.
It all boils down to the fact that smart grids give providers a complete overview of the consumption of electricity. It may not sound like smart grids in IIoT provide plenty of useful information, but in reality, the insights derived from such information are vast. Here are some examples of smart grid and IoT:

Real-time usage

Time-of-day, seasonality, and other patterns of usage

Condition data

Outages and issues

Now it’s time to move to exact use cases of exploiting such data in business. 
You may also be interested to discover the usage of IoT in supply chain management in another blog post by Intelliarts. 
What are IoT applications in smart grid
So, using the vast data from monitoring sensors in the smart grid, it’s possible to enhance IoT usage and energy management in these ways: 
#1 Solar farm monitoring
Solar farm monitoring systems utilize smart grid IoT to continuously track and analyze solar panel performance. This includes monitoring:

Weather conditions
Energy output
Panel efficiency 

Data collection helps to optimize energy production and predict maintenance needs, improving overall farm reliability and efficiency. Businesses from manufacturing, energy, and agriculture can benefit from this opportunity the most.
You can discover more about how solar farm monitoring works from the video below:

Read also how implementation of IoT in solar energy systems enhances real-time monitoring and optimization, contributing significantly to grid efficiency.
#2 IoT-based electric vehicle (EV) charging
Such IoT-based systems enable smart management of charging stations. These systems can adjust charging rates based on grid capacity and electricity pricing, provide real-time availability updates, and integrate with user apps for enhanced accessibility, EV energy consumption monitoring and usage tracking.
This IoT in EV application is of the most use for energy, logistics, and transportation companies.
#3 Battery monitoring 
Battery monitoring systems use IoT technology to track battery health, charge levels, and performance metrics in real time. This results into:

Optimal battery usage
Prolonged lifespan
Better safety and lower risks of potential failures

This can be used for both stationary and mobile devices operating on energy supplied by an IIoT smart grid. Battery finds vast usage across various business sectors with offline sites, so basically any business utilizing rechargeable batteries can take advantage of battery monitoring. 
#4 Smart meters
Smart meters are IoT devices that provide detailed and real-time data on electricity usage directly to consumers and utilities. They support dynamic pricing, enhance energy consumption tracking, and enable more precise billing and energy usage forecasting. Smart meters are of the most usage for energy and IoT companies.
#5 Predictive maintenance
Predictive maintenance in smart grids employs IoT sensors to monitor equipment conditions. This approach detects early signs of wear or malfunction, allowing for timely maintenance that minimizes downtime and extends asset life, thereby reducing operational costs. Predictive maintenance in smart grids can be used by businesses in manufacturing, logistics, transportation, energy, IoT, and other sectors. 
To explore how this technology benefits the manufacturing industry, check out our detailed guide on predictive maintenance for manufacturing.
#6 Energy supply smart management
Energy supply smart management leverages IoT to dynamically control and optimize power distribution based on real-time demand and supply data. This helps with the following:

Improve grid stability
Reduce energy wastage
Ensure a consistent, cost-effective energy supply

Smart energy supply management is critical, especially during notable spikes in electricity usage. By incorporating an IoT smart energy grid project, the regulation of power distribution during such events helps maintain grid integrity and prevents complete shutdowns. This application of IoT in the smart grid is particularly valuable to energy, utility, and IoT companies.
Real-life applications of smart energy grid using IoT you should know about
Now, when you understand what’s the use of IoT in smart grids, let’s explore smart grid technology in real life through concrete examples of how companies are applying it:
EV charging solution provider: OpenADR implementation for load management success story by Intelliarts

In this case, Intelliarts’ customer, which is the company that sells modern EV charging solutions, needed help with the mitigation of the risk of smart grid overload in their EV charging infrastructure. To enable this, Intelliarts implemented the OpenADR 2.0b specification that allows it to dynamically manage electricity demand in real-time, mitigating spikes in consumption.
The OpenADR protocol implementation was a big step forward. As a result of the project, Intelliarts’ customer achieved increased energy efficiency, better grid reliability, cost-saving, and eco-friendliness of their EV infrastructure. 
Lumin: Residential energy management success story by EcoTech innovations
Lumin, a company specializing in energy management solutions for residential properties, sought to enhance homeowners’ ability to manage and reduce their energy consumption efficiently. To address this, EcoTech Innovations developed an integrated smart panel system equipped with IoT technology and data analytics tools. 
At the end of the project, EcoTech Innovations delivered a comprehensive solution, known as the Lumin energy platform. The solution enabled Lumin’s customers to achieve significant cost savings, reduce their carbon emissions, and facilitate the easier adoption of green energy solutions. The system also provided capabilities for detailed energy consumption management.
Cisco: Grid modernization success story with BC Hydro by TechGrid Solutions
In this scenario, BC Hydro, a major utility provider, faced challenges in updating its ageing grid infrastructure to improve efficiency and reliability, particularly in incorporating IoT and smart grid technologies. In partnership with TechGrid Solutions,  Cisco undertook a project to overhaul BC Hydro’s grid management system. The focus was on implementing advanced smart metering and analytics technologies to enhance the utility’s operational capabilities.
By the project’s end, TechGrid Solutions, leveraging Cisco’s technology, had successfully equipped BC Hydro with a smart grid system. The modernized grid not only improved the reliability and efficiency of electricity supply to consumers but also supported the integration of renewable energy sources, setting a new standard for utility operations in the industry.
The future of smart grids with IoT
In the future, smart grid applications in IoT could enable entirely automated energy systems where homes and buildings adjust their own power consumption in real-time based on usage patterns, weather, and energy prices. Let’s take a look at two possible scenarios:
Scenario 1: Opportunity for electric vehicles to automatically charge during off-peak hours or when renewable energy production is high, optimizing both cost and energy use. In this case, electric vehicle data analytics helps to refine charging schedules.
Scenario 2: Ability for local microgrids to temporarily disconnect from the main grid and operate independently during outages or maintenance, significantly enhancing reliability and reducing disruption.
In their turn, businesses will be able to leverage IoT-enabled smart grids to automate and optimize their energy consumption, potentially qualifying for energy credits by participating in demand response programs. Additionally, the ability to generate and sell back excess energy to the grid can open new revenue streams, aligning profitability with sustainability initiatives.
Note: Any smart grids and IoT implementation project in business requires manufacturing software development services provided by a trusted development agency. 
You may also be interested in exploring how to get started with a smart factory from another blog post by Intelliarts. 
Building IoT-enabled smart energy solutions with Intelliarts

Smart energy solutions are of interest to two types of businesses:

Energy and utility companies, looking to modify their energy infrastructure and reinforce it with IoT.

Innovative tech companies, looking to exploit their IoT devices in the energy sector.

Variations are possible, but anyway, in this scenario, we are discussing either the improvement of energy infrastructure or utilization of IoT devices related to energy management, or both. 
Should you need a smart energy solution for your business, you need to understand what the process for creating your software will look like. Here’s what building an IoT-enabled smart energy solution with the Intelliarts team can look like:
Step 1: Define the problem and evaluate the technical feasibility
Above everything, you should identify the specific energy-related problems that you want to solve. Here are a few examples:

Reducing energy consumption

Optimizing load distribution

Enhancing renewable energy usage

Minimizing downtimes of IoT devices or energy machinery

Once you understand what exactly will be the goal of the project, you should proceed with the technical feasibility evaluation. This includes assessing the available technology and the infrastructure needed. Basically, in this stage, you need to ensure that your team both economically and technically can fulfill the project requirements, given their appropriate expertise in this matter.
Step 2: Design the system architecture
Next, you need a basic design of the IoT system architecture, which includes

Devices/Sensors: IoT units to monitor energy usage, temperature, light levels, etc.
Connectivity: Communication protocols and networks (Wi-Fi, Zigbee, LoRa, etc.) to connect devices
Data processing: Means of data aggregation, filtering, and analysis, either at the edge or in the cloud
User interface: The way users or admins are supposed to interact with the ready system, via mobile apps, web dashboards, etc. 

Explore our smart grid software development service.
Step 3: Select hardware and software
The choice of hardware boils down to manufacturing suitable ones if your company specializes in it or selecting appropriate IoT devices that are already available on the market. In the case of the latter, consider the following factors:

Purpose of hardware
Cost-efficiency
Compatibility
Reliability
Capability to withstand expected working conditions

As for software, you’ll likely need assistance from a trusted development agency to create and integrate a tailored solution for an IoT-based smart grid. 
In smart grids, software can play two roles:

Empowering IoT devices
Providing a solution to manage the IoT smart grid as well as gather and analyze data

Software should be both compatible with IoT devices and useful enough for users or admins of the end smart grid system. 
Step 4: Develop and test prototypes
Then, it’s necessary to create initial prototypes of the software solution for the IoT device — smart grid pair, and test how they work together. Leveraging automated IoT testing solutions can greatly streamline this stage by simulating real-world conditions, reducing manual effort, and improving accuracy across diverse device and network environments.  Here are some testing approaches to consider using:

Integration testing

Functional testing

Performance testing

Security testing

User acceptance testing

Step 5: Deploy
Start with a pilot project in a controlled environment to monitor how the system performs in a live setting. This approach allows for adjusting strategies and resolving any issues before full-scale deployment. Monitoring, as well as staff training and ongoing support, are mandatory at this stage. 
Step 6: Collect and analyze data
Finally, as the goal of IoT smart grids is to acquire data for further usage, it’s necessary to set the respective procedures up.
Real-time vs batch processing: Decide if the system requires real-time data processing or if batch processing is sufficient based on the needs of the application.
As for data analysis, here are some approaches you may consider using:

Descriptive Analytics
Predictive Analytics
Prescriptive Analytics

Additionally, it’s recommended to incorporate a feedback mechanism to continuously improve the system. Use the insights gained from data analysis to refine the deployment, enhance system performance, and better meet user needs.
Final Take
IoT applications in smart grid significantly upgrade traditional power systems by improving efficiency, reliability, and consumer interaction. As smart grids evolve with IoT integration, they promise automated energy adjustments, sustainable practices, and greater consumer participation, offering businesses operational efficiencies and new revenue opportunities. This makes smart grid technology a strategic investment for future-focused companies.
Consider partnering with a trusted development agency should you need an IoT-based smart grid solution. Here at Intelliarts, we’ve been on the market for more than 24 years, delivering software for businesses from across various industries like renewable energy software development. Our expert engineers can address your project needs on many levels. 
Need a tech consultation?Talk to expert

#### The Ultimate Guide to EV Software Regulations & Compliance
EV charging infrastructure is growing fast. According to the IEA Global EV Outlook 2025, more than 1.3 million public charging points were added worldwide over the past few years. This growth is expected to continue through 2026. As charging networks expand across regions, EV businesses may start struggling to comply with a growing set of standards and legal requirements.
In this article, you’ll review the key global and local regulations that impact EV charging businesses across the region. You’ll discover more about respective regulation bodies, the value of compliance, penalties for non-compliance, and how to choose the perfect-fit development partner for your project. 
Turn EV Compliance into a Competitive AdvantageGet expert insights on how EV software regulations shape architecture, security, and interoperability and how leaders can stay compliant.Check our white paper
Key global EV charging regulations
Before reviewing specific global rules, it is important to distinguish between EV charging standards and EV charging regulations. These terms are often used interchangeably, but they serve different roles in EV software development.
1. Global EV charging standard
A global EV charging standard defines technical specifications. These specifications ensure interoperability, safety, and structured communication between EVs, chargers, and backend systems. Standards describe how systems should work together. They are not legally binding on their own.
2. Global EV charging regulation
A global EV charging regulation is a legally enforceable framework. Governments or international regulatory bodies issue these rules. Regulations define what must be complied with to deploy, operate, or sell EV charging hardware and software.
In short, the difference between EV charging standards and regulations is that standards provide technical guidance, while regulations impose legal obligations. The terms are often used interchangeably because regulations frequently reference standards as mandatory compliance requirements.
With this distinction clarified, we can move to the key global EV charging regulations that shape the EV charging industry today.
For an insight into how software partners deal with industry standards, see the EV charging management software success story by Intelliarts.
#1 ISO 15118 for Plug & Charge and smart charging
ISO 15118 defines secure communication between EVs and charging stations. It enables Plug & Charge authentication and encrypted data exchange. Although it is a technical standard, many regulators reference it within binding EV charging regulations. ISO 15118 compliance-related questions are some of the most frequent ones based on Intelliarts’ consultation experience. 
Regulatory organization: International Organization for Standardization (ISO).
See the ISO 15118 standard description.
Key provisions:

Certificate-based vehicle and charger authentication
Encrypted communication channels
Secure exchange of identity and payment data
Support for smart charging and grid interaction

Applicable to: EV charger software, EV connect app providers, charging network operators, mobility service providers, and EV charging infrastructure projects that support Plug & Charge.
Penalties for non-compliance: ISO standards themselves do not impose monetary penalties. However, failure to support ISO 15118 can lead to exclusion from regulated public charging projects where compliance is mandatory. This requirement is reflected in the ISO eMobility standard references used by regulators.
#2 OCPP as a mandated interoperability requirement
OCPP defines how charging stations communicate with central management systems. While OCPP is not a regulation by itself, many governments and utilities mandate its use for public EV charging infrastructure.
Regulatory organization: Open Charge Alliance (OCA). Refer to the Open Charge Alliance and OCPP documentation for more information on the regulator.
Key provisions:

Standardized charger-to-backend communication
Remote monitoring and fault handling
Support for firmware updates
Vendor-neutral interoperability

Applicable to: EV charging software companies, charge point operators, EV charging management software providers, and publicly funded EV charging infrastructure projects.
Penalties for non-compliance: OCPP does not impose direct fines. Penalties usually take the form of procurement disqualification or loss of eligibility for public funding. Many government tenders explicitly require compliance, as stated by the Open Charge Alliance governance framework.
As a member of OCA, Intelliarts possesses substantial EV expertise, which allows us to provide technology consulting for businesses concerned with standard and regulation compliance. 
#3 OCPI for roaming and interoperability between charging networks
OCPI enables communication and data exchange between Charge Point Operators (CPOs) and e-Mobility Service Providers (eMSPs). While OCPI is not a regulation by itself, many governments and public infrastructure projects mandate its use to ensure seamless roaming, transparent pricing, and open access to EV charging networks.
Regulatory organization: EVRoaming Foundation. Refer to the EVRoaming Foundation and OCPI documentation for more information on the regulator.
Key provisions:

Real-time exchange of charging location and availability data
Token-based authentication for cross-network charging sessions
Transparent tariff and pricing information sharing
Automated billing and charge detail records (CDRs)
Support for roaming between different charging networks

Applicable to: EV charging software companies, charge point operators, e-mobility service providers, charging network operators, and publicly funded EV charging infrastructure projects.
Penalties for non-compliance: OCPI does not impose direct fines. Penalties usually take the form of procurement disqualification or loss of eligibility for public funding. Many government tenders explicitly require compliance, as stated by the EVRoaming Foundation governance framework.
#4 IEC 61851 safety and system requirements for EV charging
IEC 61851 defines electrical safety and operational requirements for conductive EV charging systems. Regulators frequently reference this standard as a baseline safety requirement for EV charging infrastructure.
Regulatory organization: International Electrotechnical Commission (IEC). See the IEC 61851 standard framework.
Key provisions:

Electrical safety requirements for charging equipment
Defined charging modes and configurations
Protection against overcurrent and electrical faults
Control and communication principles

Applicable to: EV charger manufacturers, infrastructure developers, and EV charging software integrated with hardware control systems.
Penalties for non-compliance: IEC standards do not define direct fines. Non-compliance can lead to certification denial, forced equipment removal, or legal liability after safety incidents. These enforcement actions follow national rules based on IEC conformity and certification guidance.
#5 UNECE WP.29 cybersecurity and software update regulations (R155 and R156)
UNECE WP.29 Regulations R155 and R156 define mandatory requirements for cybersecurity and software updates. R155 addresses cybersecurity management systems. R156 focuses on software update management, including over-the-air updates. These regulations directly affect EV charging software and charger firmware that interact with vehicles.
Regulatory organization: United Nations Economic Commission for Europe (UNECE), World Forum for Harmonization of Vehicle Regulations. See the official UNECE WP.29 overview.
Key provisions:

Cybersecurity risk management across the full vehicle lifecycle
Defined processes for secure software updates
Traceability of software versions and update history
Incident detection, reporting, and response requirements

Applicable to: Automakers, EV charging software companies, EV charger software vendors, fleet platforms, and EV charging management software that connects to vehicles or vehicle data.
Penalties for non-compliance: UNECE WP.29 does not define fixed monetary fines at the global level. The primary penalty is loss or denial of vehicle type approval, which prevents products from entering regulated markets. National authorities may also require recalls or suspend registrations under UNECE vehicle compliance rules.

Regional regulations for EV software and charging infrastructure
Regional regulations are legally binding requirements that apply within a specific jurisdiction, such as a country or economic bloc. They define what EV charging infrastructure must deliver in that market.
Global regulations and standards set broader baselines. Regional rules translate those baselines into enforceable programs, timelines, reporting duties, and penalties. Regional requirements also differ in funding conditions and consumer protections. That difference is why software teams must map features to each target market.
If your EV platform can’t pass regional compliance checks, scalability doesn’t matter. You won’t be allowed to deploy. — Alexander Barinov, Managing Partner at Intelliarts. 
Below are the most relevant regional frameworks for EV charging software and public charging infrastructure.
United States and NEVI
The United States uses a program-based approach. NEVI ties federal funding to minimum technical and operational requirements for public fast charging. This approach impacts site design, network operations, data reporting, and uptime targets.
National Electric Vehicle Infrastructure (NEVI) standards and requirements (23 CFR Part 680)
NEVI requirements establish minimum standards for federally funded public EV charging projects. They focus on reliability, interoperability, user experience, and data submission. These rules shape EV charging management software and charger software features.
Regulatory organization: Federal Highway Administration (FHWA), under the U.S. Department of Transportation. See the final rule in the Federal Register NEVI standards and requirements.
Key provisions:

Applies minimum standards to NEVI-funded and certain federally funded public charging projects 
Requires network connectivity and user-facing information availability, including mapping and real-time availability expectations referenced in Part 680
Sets technical station expectations commonly implemented in NEVI scopes, including multiple DC fast charging ports and CCS capability for DCFC ports

Links to technical requirement resources and what they request

The authoritative requirement text is in 23 CFR Part 680. It defines minimum standards, reporting, and operational expectations.
A practical, implementation-focused reference is the NEVI requirements appendix (example state reference). It summarizes items used in Requests for Proposal (RFPs), such as minimum Direct Current Fast Charging (DCFC) ports and Combined Charging System (CCS) connector expectations.

Applicable to: Charge point operators, EV charging software companies, charger OEMs, site hosts, and integrators building NEVI-funded public charging. It also applies to backend platforms that handle availability, reporting, and network operations for NEVI sites.
Penalties for non-compliance: NEVI is a funding program. Enforcement typically occurs through eligibility, project approval, corrective actions, and potential repayment or loss of funding under the program’s rules and federal-aid requirements. The core consequence is loss of compliance status for funded deployment requirements, not a single fixed federal fine amount in the rule text.
European Union and AFIR
The European Union uses a regulation-led approach. AFIR imposes binding requirements across Member States. It also creates user-facing obligations, especially around payment access, pricing transparency, and data provision. These requirements directly affect EV charger software and roaming integration.
Alternative Fuels Infrastructure Regulation (AFIR), Regulation (EU) 2023/1804
AFIR is the EU-wide regulation for deploying alternative fuels infrastructure, including public EV charging. It replaces the earlier directive model with binding requirements. It also standardizes user information and data-sharing expectations.
Regulatory organization: European Union, via the European Parliament and the Council. The official regulation text is available on EUR-Lex for Regulation (EU) 2023/1804.
Key provisions

Establishes binding deployment and usability requirements for EV charging infrastructure across the EU
Sets requirements related to payment access and user information, including pricing transparency obligations
Introduces data provision obligations for operators, including expectations for static and dynamic data availability over time

Links to technical requirement resources and what they request

The European Commission provides a policy and implementation context via the EU alternative fuels infrastructure page. It confirms AFIR’s application timeline and Fit for 55 context.
For the market context tied to EU charging buildout, use the EAFO report on BEV registrations rising in H1 2025.

AFIR is part of the Fit for 55 legislative package. Fit for 55 pushes emissions reduction targets and accelerates infrastructure deployment obligations. This creates pressure for scalable, interoperable charging software and better reporting capabilities. See the Fit for 55 overview and the Commission’s Fit for 55 proposals tracker.
Applicable to: Charge point operators, eMSPs, roaming providers, EV charging software companies, and public charging project owners operating in EU Member States. It also affects systems that expose data to platforms and enable ad hoc access and transparent pricing.
Penalties for non-compliance: AFIR requires Member States to establish penalties that are effective, proportionate, and dissuasive. The regulation does not set one EU-wide fine amount. Monetary penalties, therefore, vary by country and enforcement authority. The enforcement model is described in the  EU implementation context pages and AFIR references.
United Kingdom and PCPR
The UK uses a consumer-protection and reliability approach. The Public Charge Point Regulations define payment methods, transparency, and reliability expectations. They also impose explicit civil penalties. These rules drive clear requirements for EV charging management software and monitoring.
The Public Charge Point Regulations 2023 (PCPR)
PCPR imposes operational and consumer-facing requirements on public charge point operators. It addresses reliability metrics, payment access, price transparency, and data sharing. Guidance clarifies how operators should comply.
Regulatory organization: UK Government, under powers in the Automated and Electric Vehicles Act 2018. See the official instrument on legislation.gov.uk for The Public Charge Point Regulations 2023 and the PCPR guidance.
Key provisions:

Requires consumer-friendly pricing transparency and clear price display rules, supported by the guidance
Introduces reliability expectations for rapid networks, with compliance measurement details in the guidance
Mandates data standard usage for reliability measurement, with OCPI referenced in guidance for status reporting
Sets maximum civil penalty amounts for specific breaches, defined in the legislation

Applicable to: Public charge point operators and operators of publicly accessible charging infrastructure in the UK. It also affects EV charger software and backend systems that support payment flows, uptime monitoring, customer support, and public data obligations.
Penalties for non-compliance: The regulations define a maximum civil penalty of £10,000 per public charge point for specified breaches. This cap is stated directly in the legislation. See the PCPR civil penalty amount and the detailed penalty clause view in the Schedule paragraph on civil penalties.
Australia’s EV Collaborative Framework
Australia uses a coordinated national collaboration model. States and territories align on minimum operating standards for government-supported public chargers. The framework also covers data sharing and grid integration pathways. This approach shapes technical expectations for charger interoperability, payment access, and operational consistency.
National collaboration on EVs and minimum operating standards for public charging
Australia’s national collaboration sets a collective framework across states and territories. It includes minimum standards for government-supported public EV charging infrastructure. It also includes workstreams on data sharing and EV grid integration pathways.
Regulatory organization: Department of Climate Change, Energy, the Environment and Water (DCCEEW), with state and territory collaboration. See National collaboration on EVs.
Key provisions:

Applies to government-supported charging projects that started on or after 1 January 2024
Covers minimum expectations for payment methods, accessibility, and interoperability
Includes aligned work on communications protocols, cybersecurity, and smart functionality for EVSE
Supports common mechanisms for Electric Vehicle Supply Equipment (EVSE) data sharing and aligned installation rules

Applicable to: Government-supported public charging projects in Australia, including charge point operators and delivery partners. It also affects EV charging software used for payments, accessibility features, interoperability, and operational performance requirements on funded sites.
Penalties for non-compliance: The national collaboration page and standards focus on applicability and minimum expectations for government-supported projects. They do not publish a single national fine schedule on that page. Enforcement typically occurs through grant conditions, procurement requirements, and state or territory compliance mechanisms tied to project funding.
Curious about IoT in EV charging? Don’t hesitate to explore more in another blog post by Intelliarts. 

Why EV software regulations matter
EV software regulations define the minimum requirements for how charging platforms, charger software, and backend systems operate in regulated markets. As explained earlier, these rules affect cybersecurity, interoperability, payments, uptime, and data reporting. In practice, they determine whether EV charging infrastructure can be funded and legally operated.
To summarize, most problems appear when regulatory requirements are addressed too late. When EV software does not meet applicable regulations, deployment often stops before it starts. Non-compliant solutions are excluded from public charging programs and regulated tenders, especially in markets where access depends on frameworks such as NEVI, AFIR, or PCPR. Even in private projects, missing compliance leads to approval delays and limits market reach.
Looking for a trusted tech partner for your EV software project?  Explore our service
Once regulatory requirements are met, compliance stops being a blocker and starts supporting predictable growth and daily operations. Here’s a list of the most valuable outcomes you may expect to obtain due to compliance:
1. Market access and scalability
Regulatory compliance allows EV software to operate in multiple regions without repeated redesign. When core requirements are already in place, teams can follow known approval steps instead of making adjustments for each new market.
Key advantages include:

Eligibility for public charging programs and government-funded projects
Faster entry into new regional markets with fewer approval delays

This is especially important for EV charging software companies planning cross-border deployments.
As an example of how compliance affects market access, in one of our success stories, Intelliarts helped EV Connect join the UK market. It was done through charging technology developments that helped EV Connect to be in line with UK smart charging regulations. 
2. Trust with regulators, partners, and users
Intelliarts can confidently state that compliance also shapes how different stakeholders view an EV charging data analytics platform. For regulators, it confirms that safety and consumer protection rules are met. For infrastructure partners, it reduces integration and operational risk. For end users, it results in clearer pricing, secure payments, and more reliable charging access.
Together, these factors build confidence in the charging network. That confidence supports:

Long-term cooperation with municipalities, utilities, and fleet operators
Higher adoption of public charging networks and EV connect apps

3. Lower operational and legal risk over time
From an operational perspective, regulatory alignment makes day-to-day work more predictable. Clear requirements define what the system must do, how data should be reported, and how updates are handled. As a result, teams spend less time on emergency fixes and dispute resolution.
Over time, compliant systems benefit from:

Lower audit and certification effort
Easier updates when regulations change
Clearer internal ownership and documentation

4. Alignment with future EV charging infrastructure requirements
EV regulations continue to evolve alongside grid integration, cybersecurity expectations, and consumer protection rules. When software already meets current requirements, adapting to new ones is usually incremental rather than disruptive.
This makes it easier to introduce features such as smart charging, roaming, and advanced energy management while keeping the platform stable as EV charging infrastructure expands.
For information on how real‑time data analytics supports EV energy efficiency and compliance, see our insight EV energy management blog post. 
How to choose the right EV software development partner

Successfully choosing electric mobility solution providers or EV software development partners is half the battle. The right partner directly impacts product stability in live, regulated environments and brings practical experience beyond specifications.
By providing EV software development for more than 9 years, Intelliarts has been able to form a framework upon which businesses evaluate providers in the EV niche. Here’s a list of key factors:
#1. EV infrastructure experience
Hands-on experience with EV charging infrastructure means the team has supported systems in real operating environments. This experience matters because many issues only appear once a system is deployed and used at scale. Teams without this background often underestimate operational complexity and respond too late.
How to evaluate:

Ask for examples of deployed charging projects
Confirm that the systems were used in production
Ask what challenges appeared after launch
Check whether the team worked with live operational constraints

#2. Knowledge of EV regulations and compliance
Intelliarts’ insights show that experience with EV charging regulations involves understanding how rules influence system design, approvals, and rollout timelines. This matters because compliance issues tend to surface during audits or market entry, when changes are expensive and disruptive.
How to evaluate:

Ask which regulations or standards were addressed
Review how compliance is handled during testing
Ask how regulatory risks are identified early
Check whether compliance responsibilities are clearly defined

See how an EV partner like Intelliarts can contribute to an EV project of any complexity in our EV fleet management software success story. 
#3. Ability to scale EV charging software
Scalable EV charging software remains stable as usage increases and new markets are added. Systems designed without scalability in mind often perform well at first, then degrade once adoption grows.
How to evaluate:

Review architectural decisions and hosting setup
Ask how previous systems handled growth
Ask what limits were reached in earlier projects
Check how scaling issues were resolved

#4. System integration capabilities
Another key point, based on Intelliarts experts’ opinion, is integration capabilities. Strong integration allows software to exchange data reliably with external systems. This is critical when it comes to cost-effective EV charging optimization, as platforms depend on constant communication across services, which may be expensive to maintain. 
How to evaluate:

Review projects with multiple external integrations
Ask how integration failures are detected
Ask how partial failures are handled
Check experience with recovery and retries

#5. Operational support and long-term maintenance
Experience with operational support means the team understands how EV systems evolve over time. Many issues emerge months after deployment, once usage patterns change or infrastructure expands.
How to evaluate:

Ask what post-launch support includes
Check experience with live monitoring
Ask how long teams stay involved after delivery
Review documented support and escalation processes

There’s also an alternative method of EV software partner choosing. Here’s a core framework that visually represents what capabilities and in which category a trusted provider should have. Simply print the image or copy it in a photo editing program of your choice and check the boxes. This will provide you with a simple evaluation matrix byusing which you can compare multiple providers to each other. 
 
Choosing the right EV software development partner reduces delivery risk and long-term maintenance effort. Teams with practical EV experience, regulatory awareness, scalability knowledge, integration expertise, and operational support capability are better prepared to build and maintain reliable solutions in the EV charging industry.
Do you require EV software development services? Reach out to Intelliarts, to discuss opportunities for your best project. 
How Intelliarts helps ensure EV software compliance

Intelliarts supports EV charging businesses that operate in regulated, multi-market environments where EV software compliance is a core requirement. With more than 25 years of experience in the market and hands-on experience in the EV charging industry, we design, build, and scale EV charging software. We have delivered 90+ large-scale projects for clients worldwide, including EVConnect, Indigo Ag, Greenlane, and Schneider Electric. Our focus is on production-ready systems that integrate smoothly with infrastructure and remain stable as regulations evolve.
Intelliarts has been recognized among the Top 100 Software Development Companies of 2026 by industry reviewers.
Services offered

Custom software development
Technology consulting
Machine learning and LLM solutions
Data engineering and analytics
IoT solutions

Some of our key EV capabilities and expertise include:

Expertise in OCPP and ISO 15118 to enable interoperability, Plug & Charge, roaming readiness, and compliance.
Capability to include AI-driven features in your custom EV software solutions.
Expert knowledge of global and local EV standards and regulations for optimal compliance.
Capability to design and implement business-oriented analytical systems in EV software.

Intelliarts engineers provide custom software development emobility with interoperability, security, and auditability in mind to reduce the risk of late-stage redesigns. This approach is especially important for companies expanding across regions or operating public charging networks.
Beyond implementation, Intelliarts consults customers on EV charging regulations and architecture decisions to help manager regulatory expectations from the start. Our work is centered around helping EV charging software companies to scale confidently and deliver reliable user experiences across platforms, including EV apps.
Looking for a trusted provider of software services for renewable energy & e-mobility? Don’t hesitate to reach out to Intelliarts. Our experts are ready, willing, and able to contribute to your best project. 
The future of EV software regulations and what it means for your business
What the Intelliarts team, our customers, and other businesses in the industry see is that regulatory frameworks for EV charging software are changing beyond basic connector and safety standards. Rules are emerging around data privacy, cybersecurity, sustainability reporting, and interoperability. These trends reflect how governments and markets balance user protection, grid stability, and climate goals with rapid EV adoption.
In 2026 and beyond, we expect even EV software regulations to become stricter and harder to comply with due to zero emissions, ethical usage of AI, and green Earth policies. That’s exactly why every company dealing with EV software needs a tech partner they can trust. — Ihor Rudnyk, an eMobility Tech Specialist at Intelliarts
Emerging regulatory trends:

Data privacy protections are expanding, requiring stronger safeguards around user information and charging session data, influenced by broader laws such as the GDPR and national privacy regimes. 
Cybersecurity requirements are becoming more detailed and well-articulated, with initiatives like the EU Cyber Resilience Act proposing lifecycle security and incident reporting. 
Sustainability and green compliance obligations are influencing infrastructure deployment, tying charging behavior and energy use to emissions and efficiency goals.
Interoperability and data sharing expectations are increasing to support roaming and network coordination, as provided in the Eurelectric data interoperability study.
Consumer protection rules are tightening around pricing visibility and uptime standards, which reflects regulatory movement toward transparency for users.

To prepare for evolving standards and stay ahead of the regulatory curve, businesses should embed regulation tracking into product planning. That means the following:

Assigning clear responsibility for monitoring changes in key markets
Adding compliance checkpoints into development workflows
Aligning product roadmaps with upcoming regulatory timelines. 

Technical teams should build modular compliance layers so that adjustments for new privacy, security, or interoperability rules do not require major system redesign.
Regular internal reviews, participation in industry forums, and engagement with legal and standards advisors help surface emerging obligations early. By structuring development processes around foreseeable regulatory shifts, businesses reduce risk and maintain deployment momentum as EV charging regulations mature.

Final Take
EV charging software now sits at the center of regulation, infrastructure, and user experience. Global standards define how systems should work, while regional regulations decide where and how they can operate. Companies that design EV software with compliance in mind reduce rollout risk and avoid costly rework. Choosing experienced partners is what helps businesses scale across markets while keeping EV platforms stable, secure, and ready for future requirements.
Here at Intelliarts, with a team of senior staff engineers and EV experts, we can support your EV project with both consulting and development. We focus on long-term partnerships, have  90% customer return rate, and 25+ years of experience in the development, AI, and EV market. Should you need a provider you can trust, don’t hesitate to reach out to Intelliarts. 
Build a Future-Ready EV Platform with Senior ExpertsReduce rollout risk and avoid costly rework with our expertise in EV software and AIContact Us

#### The Use of Machine Learning for Customer Churn Prediction
For an insurance company, signing a new contract is only half the battle. Then go customer retention and loyalty, which are challenging to build but necessary for the long-term success of the company. 
Customer churn analysis in insurance can have a great impact on the insurer’s bottom line. A 5% monthly churn rate might sound pretty innocent to some people. Yet, compounded yearly, the insurance business may reveal that it’s losing a significant amount of its policyholders.
That’s why reducing customer churn rates is worth prioritizing, and the most reliable way to do it is by applying machine learning in insurance. In this article, we will walk you through customer churn prediction in insurance with the help of machine learning technology, discuss its opportunities and stages of implementation.

#### Time Series Analysis Database Guide for 2026
Real-time data has become essential for modern analytics and machine learning. In 2024, the global real-time analytics market reached $890.2 million, and it is projected to grow at a CAGR of 25.1% in the coming years. This growth highlights the rising importance of systems that can manage continuous, time-stamped information. A time series database (TSDB) technology supports this need by storing and organizing temporal data for forecasting, monitoring, and ML-driven insights.
In this post, you’ll find out what TSDB is and what its key characteristics are compared to traditional databases. Besides, you’ll see the connection of TSDB with ML applications and find out how to choose the best database for time series data in your project. 
The Intelliarts team has substantial experience with time-series data usage for predictive maintenance in EV and other niches. Learn more about our expertise in our portfolio. 
What is a time series analysis database?
A time series analysis database is a specialized data system designed to handle information that arrives as continuous, time-stamped sequences. 
Within the machine learning ecosystem, a time series analysis database serves as the foundation for models that rely on temporal context. These include AI-powered solutions used for:

Forecasting. Algorithms that predict future values based on historical patterns.
Anomaly detection. Systems that identify sudden or unusual deviations from normal behaviour.
Predictive maintenance. Solutions that estimate equipment failures in advance based on sensor trends.
Model performance monitoring. Solutions that track drift or degradation in an AI system through time-based metrics.

You can see that in each case, the models rely on long, continuous sequences of historical data to understand patterns and produce accurate predictions. A TSDB provides the clean, granular, and consistently structured data required by the pipelines that power these ML-driven systems.
Explore time series machine learning in greater detail in another blog post by Intelliarts. 
Definition of time series data
Time series data is information collected as data points indexed in time order, with each measurement reflecting the state of a system at a specific moment. 
This structure allows analysts and ML models to understand how behaviors, conditions, or signals change rather than viewing data as isolated snapshots.
Because time series captures continuous evolution, it helps reveal long-term trends, short-lived spikes, seasonality, cyclical patterns, or unexpected anomalies that would otherwise remain hidden. This temporal dimension is why it is fundamental to forecasting, anomaly detection, predictive maintenance, and real-time operational analytics.
Typical examples of time series data include:

Sensor readings from IoT devices
Stock or crypto price movements
Server and application performance metrics
Energy consumption measurements
Environmental data such as temperature, humidity, or air quality
Network traffic and bandwidth usage
Smart meter readings
Manufacturing machine outputs
Financial trading volumes
User activity or engagement metrics in digital products

Below is a sample time series dataset from a single environmental IoT sensor that monitors indoor air quality and ambient conditions in real time. Each timestamp shows the full state of the device, demonstrating how multi-metric time series data is captured and used for analytics, monitoring, and ML forecasting.

See more time series analysis examples in our corresponding blog post. 
Key characteristics of a time series analysis database
As we already mentioned, a time series analysis database is a specialized system built to manage data that arrives continuously and is indexed by time. Its internal architecture focuses on how temporal information is stored, compressed, and organized at scale. Here are characteristics that indicate how the TSDB design is suited exactly to the unique demands of temporal data:

A write-optimized storage engine built for continuous append-only workloads
Time-oriented indexing structures that organize data by intervals, buckets or shards
Compression algorithms tailored for sequential numeric data
Columnar storage layouts that reduce I/O and accelerate time-based scans
Native support for retention policies at the storage level (TTL, tiering, compaction)
Efficient memory and caching strategies for recent, high-resolution data

These characteristics allow a TSDB to capture and structure temporal data in a way that supports analytics, monitoring, and machine learning workflows.
When we work with long-running telemetry or sensor data, having good time series database changes everything. It keeps the data clean, fast to access, and easy to analyze, so our ML models can focus on real patterns instead of constant fixing. — Marta Vikhrak, ML Engineer at Intelliarts.
Core purpose of TSDB
The purpose of a TSDB can be defined as providing a dedicated system for managing data that evolves over time. 
TSDB captures continuous measurements and structures them as time-indexed sequences. Those sequences are available for further analysis, monitoring, and machine-learning workflows that depend on temporal patterns.
Putting it all into context, here are the responsibilities that TSDBs cover in operational systems, analytics pipelines, and ML environments:

Serving complete chronological datasets for trend, seasonality, and pattern analysis
Delivering precise time-based aggregations needed for dashboards, SLOs, and KPIs
Feeding ML pipelines with clean sequences for lag features, rolling windows, and temporal context
Supporting incident response and monitoring with fast access to the latest measurements
Maintaining historical continuity so that long-term modeling and regression analysis remain reliable
Providing accurate time alignment across metrics, devices, or systems for correlation or forecasting

These responsibilities describe how a TSDB behaves in real workloads. Since the importance of data analytics, decision-making, and ML is huge, TSDB can deliver a lot of business value by supporting these systems. 
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Contrast of TSDB with traditional databases
A time series analysis database is designed for continuous, time-indexed data, while traditional databases support broader transactional or document-focused workloads. To illustrate the differences clearly, the table below highlights how each system handles ingestion, storage, querying, and analytics needs.

Now that we know what time series data and time series analysis database are, let’s proceed to connecting them to ML applications and workflow. 
Why time series databases matter for Machine Learning?
TSDB is of particular usefulness for ML systems. That’s because, based on the Garbage-in-Garbage-Out (GIGO) rule, ML systems in lots of applications require extensive data, which, ideally, has time stamps. Here are all the ways in which TSDB supports ML: 
#1 Predictive maintenance for industrial equipment
What ML model does: Detects early signs of equipment failure using vibration, temperature, and pressure data. Needed to predict failures in turbines, pumps, industrial robots, or HVAC units.
How a TSDB supports it:

Ingests high-frequency sensor readings
Aligns multi-sensor time series
Maintains long-term histories needed for failure-pattern discovery

Why it outperforms traditional systems:

Traditional DBs cannot ingest thousands of metrics per second reliably
TSDBs are engineered for dense, high-frequency telemetry

Real-life example:

An EV charging provider engaged Intelliarts to prepare the foundation for a future predictive maintenance system. The team analyzed large volumes of historical charger logs, identified abnormal behavior, cleaned inconsistencies, and converted raw telemetry into structured, machine learning ready data. This helped the customer understand which patterns typically lead to charger downtime.
These preparations created the data quality and organization needed for the company to later introduce an ML-powered maintenance workflow. A TSDB could then support real-time monitoring, continuous data ingestion, and timely model updates.
Explore the predictive maintenance for EV chargers success story.
#2 Anomaly detection in server and application metrics
What ML model does: Identifies unusual spikes or drops in latency, traffic, CPU usage or error rates. It’s used to prevent outages and detect abnormal system behavior.
How a TSDB supports it:

Supplies clean rolling windows for training
Delivers real-time measurements with low-latency reads
Maintains data integrity during high-load events

Why it outperforms traditional systems:

Relational DBs become bottlenecks under rapid ingestion
TSDBs sustain high write throughput and fast streaming queries

#3 Demand forecasting in retail or logistics
What ML model does: Predicts product demand, inventory, and turnover using historical sales data, seasonality, and external signals. 
How a TSDB supports it:

Handles multiple temporal frequencies (daily, hourly, weekly)
Supports cross-metric correlations (inventory, visits, promotions)
Enables quick backtesting on long historical windows

Why it outperforms traditional systems:

General-purpose databases slow down during repeated historical scans
TSDB indexing makes sliding windows and rolling forecasts extremely fast

Real-life example:

A global electronics manufacturer faced challenges with stock availability and procurement planning. Demand patterns were inconsistent across hundreds of products. Intelliarts applied time series analysis to historical sales, pricing data, and product design-cycle behavior, uncovering clear seasonality and long-term demand signals.
The Intelliarts team built accurate forecasting models and integrated the results into the company’s dashboards. Procurement decisions became more precise, stockouts decreased, and marketing teams gained reliable insights for planning campaigns.
Read the machine learning in demand forecasting success story.
#4 Financial market forecasting and risk modeling
What ML model does: Forecasts short- and long-term market behavior using multivariate time series. It’s used in the finance domain to model price movement, volatility, and liquidity.
How a TSDB supports it:

Handles large volumes of tick-level or millisecond data
Provides tightly aligned sequences across instruments
Enables fast correlation and cross-instrument analysis

Why it outperforms traditional systems:

Relational systems choke on high-frequency financial data
TSDBs are tailor-made for dense, fast-moving temporal streams

#5 Real-time user behavior modeling
What ML model does: Analyzes user interactions over time to detect patterns that signal churn, interest shifts, or fraudulent behavior. 
How a TSDB supports it:

Reads recent interactions in milliseconds
Preserves chronological consistency across events
Supports rapid feature computation (session windows, recency, frequency)

Why it outperforms traditional systems:

Event tables grow too quickly and slow down SQL queries
TSDBs compress and index these sequences efficiently

Core features of a time series database
Now you have an in-depth understanding of TSDB and know how it’s applied in ML. Let’s continue by delving into the core features of time series database architecture to see what capabilities exactly make TSDB as useful as shown above. It also includes an input and output examples.
 
#1 High write throughput and compression
What it is: A capability within a time series database that ingests continuous, high-volume measurements while minimizing storage overhead. It uses a write-optimized engine and compression tuned for sequential numeric patterns.
What it does:

Accepts constant append-only writes from sensors, agents or telemetry pipelines.
Batches measurements into time-ordered blocks.
Applies temporal compression (delta, XOR, RLE) to minimize storage.
Writes compressed blocks into sequential, low-latency storage segments.

Input:

Time-stamped measurements
Sensor data, logs, metrics

Output:

Compressed, query-ready series
Storage-efficient historical blocks

#2 Efficient time-based querying and downsampling
What it is: A capability in a database for time series data that enables fast retrieval of time-indexed records across selected ranges. It also provides mechanisms to downsample dense historical series.
What it does:

Uses time-based indexing to locate relevant segments instantly.
Executes interval-based aggregations (avg, sum, min, max).
Produces downsampled views via bucketed rollups.
Scans compressed structures to reduce latency and I/O.

Input:

Time range parameters
Query filters and aggregation intervals

Output:

Fast aggregated results
Lower-resolution series for dashboards and ML

Any time we see a client switching from scattered logs to a proper TSDB, the difference is huge. They see trends clearly, catch anomalies earlier, and build predictive models without spending weeks just cleaning or stitching the data together. — Volodymyr Mudryi, a data scientist and machine learning engineer at Intelliarts. 
#3 Data retention and rollups
What it is: A capability of a database for time series analysis that manages the complete lifecycle of stored measurements through retention rules, archives and automated rollups for long-term efficiency.
What it does:

Removes or archives raw data after defined retention periods.
Runs scheduled rollups to summarize granular data.
Moves historical data into lower-cost storage layers.
Maintains consistency between raw, rolled-up and archived datasets.

Input:

Retention and compaction policies
High- and low-resolution time-series data

Output:

Cost-optimized long-term histories
Summarized datasets for trend analysis

Learn additionally about data preprocessing in machine learning in another of our blog posts. 
#4 Built-in analytics functions
What it is: A capability in a TSDB that performs native analytical operations such as aggregations, correlations, and lightweight forecasting, directly on stored temporal sequences. No external systems required. 
What it does:

Executes statistical functions across time windows.
Computes correlations and rate-of-change indicators.
Generates forecasts using built-in temporal models.
Returns pre-aggregated outputs without exporting data.

Input:

Stored time-series metrics
Analytical expressions, queries, or function calls

Output:

Trends, correlations, anomaly indicators
Forecast-ready signals for BI or ML pipelines

#5 Integration with ML frameworks and visualization tools
What it is: A capability of a time series data storage system that exposes APIs and connectors so ML pipelines, dashboards, and notebooks can access and use chronological datasets with minimal preparation.
What it does:

Serves aligned and cleaned sequences via SQL/REST/gRPC/streaming endpoints.
Supports feature extraction through query-based transformations.
Feeds visualization tools with real-time slices and aggregates.
Integrates into ML orchestration frameworks using dedicated connectors.

Input:

Requests from ML frameworks or visualization tools
Feature extraction or visualization parameters

Output:

ML-ready datasets for training and inference
Visualizable time-series outputs for dashboards and notebooks

Looking for a trusted provider of ML development services? Don’t hesitate to reach out to Intelliarts. 
How to choose the right time series database for your ML project
 
Selecting a TSDB is a decision that can greatly impact the performance and reliability of your ML pipelines. To help teams make the right choice, Intelliarts gathered practical, data-backed guidance based on real-world implementations across IoT, forecasting, anomaly detection, and telemetry pipelines.
Check the following key parameters to evaluate, why they matter, and the concrete performance metrics your TSDB should meet depending on your ML needs: 
Data volume, velocity, and retention needs
Your incoming data rate and retention strategy influence ingestion speed, storage cost, and query performance.
Why it matters: TSDBs differ dramatically in performance. Some write around 200,000 datapoints per second, while others exceed 1 to 5 million per second.
How to evaluate:

Estimate peak ingestion, for example, 100,000 points per second for sensor networks or more than 1 million per second for telemetry pipelines.
Map required retention windows (from 30 days to 5 years).

What to do:

If you expect more than 500,000 datapoints per second, choose a TSDB supporting at least 1 million sustained writes per second.
If retention exceeds one year, ensure compression ratios of 20 to 40 times and automated rollups (1 second to 1 minute to 1 hour).

Real-time vs. historical analytics focus
Decide whether your ML workflows rely more on live signals or large historical datasets.
Why it matters: Query latency varies widely. Some TSDBs return real-time results in 5 to 20 milliseconds, while others respond in 200 to 500 milliseconds.
How to evaluate:

Identify the query latency your ML tasks require, such as anomaly detection or long-window forecasting.

What to do:

For real-time workloads, choose a TSDB with under 50 milliseconds of query latency and streaming ingestion.
For historical analytics, ensure it can scan hundreds of millions of rows in under 500 milliseconds with indexed time filters.

Integration with your existing data stack and ML pipelines
Your TSDB must connect cleanly to your ETL processes, notebook environment, and model training workflows.
Why it matters: Weak integration adds minutes of overhead to pipelines that should run in seconds.
How to evaluate:

Verify support for SQL, REST, and gRPC.
Confirm Python client fetch latency under 200 milliseconds.
Check batch export speed of 100 to 500 MB per second for training data.

What to do:

If your ML workflow depends on notebooks or feature stores, choose a TSDB that can deliver aligned, ready-to-train batches of 10 to 50 million rows in 1 to 2 seconds.

Scalability and cost constraints 
A future-proof TSDB must scale predictably without generating cost spikes.
Why it matters: Storage pricing varies. Some databases cost 0.10 to 0.20 dollars per GB per month, while others reach 1 to 5 dollars per GB at higher tiers.
How to evaluate:

Check ingestion scaling, where each additional node adds 500,000 to 1 million writes per second.
Compare cloud storage pricing and compression efficiency.

What to do:

For growing pipelines, choose a TSDB that maintains 20 to 40 times compression, scales to more than 10 million writes per second, and uses hot, warm, and cold tiered storage to reduce long-term costs by 50 to 80 percent.

Important note: No TSDB, especially a proprietary one, can be ideal based on the listed and over parameters. If you are unsatisfied with the options available on the market, consider the development of a custom TSDB solution. 
Final take
Time series databases provide high-quality chronological data that supports forecasting, anomaly detection, and predictive maintenance. Their architecture helps teams handle fast ingestion, structured storage, and efficient time-based queries. With the right TSDB, ML workflows become more reliable and cost-efficient. The choice depends on data volume, latency needs, and integration requirements. When selected carefully, a TSDB can improve performance, reduce overhead, and support long-term analytical growth.
Should you be looking for a trusted provider to drive your TSDB project, consider Intelliarts, an ML development company, as your best choice. We have over 25 years of experience in the market, more than 80 large projects under our belt, and a 90% customer return rate. With 92% of senior staff engineers in-house, we are ready, willing, and able to create your custom TSDB and make it work for your best ML project.
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#### Time Series Data Examples: 6 Real-Life Use Cases & Modern Methods for 2026
The value of business analytics has hardly ever been underestimated. Time series forecasting business covers trends that can be spotted over time. It’s one of the ways to analyze and make use of vast business data that companies gather during their daily operations. 
Valued at $18.9 billion, the Analytics-as-a-Service (AaaS) market, which also covers forms of real-time time series analysis distributed as services, is projected to rise to $58 billion by the year 2027. It clearly shows the rising popularity of analytics in business, conducted by both external and in-house means.
In this post, you’ll discover real-time time series analysis, how it works, and which models are commonly used. You’ll also review six top time series data examples in businesses by famous companies.
Time series data examples
In our experience, real-world examples are the best way to show how time series analysis works in practice and what kind of business value it can create. Let’s review six top time series uses cases of companies across different business domains, highlighting an example of time series forecasting in business for each case:
#1 Time series in the electronic interconnect industry: Case of a manufacturing company

At Intelliarts, we’ve been working with time series data for over 5 years. This time our customer was a service provider in the electronic interconnect industry, known for manufacturing and distributing connectors, cables, optics, and more.
Case background: The company faced challenges in predicting demand and improving stock availability. To address this, the Intelliarts ML team developed a machine learning prediction and forecasting engine. This engine was designed to detect patterns in the manufacturer’s data and make accurate sales predictions, aiding in procurement forecasts.
Case outcomes: The implementation of this machine learning model enabled the Intelliarts’ customer to anticipate future purchasing needs, manage resources more efficiently, and reduce costs and waste, leading to improved operational efficiency.
Read also about predictive maintenance benefits in manufacturing.
#2 Time series in healthcare: Case of smart mattress startup under NDA

Our customer was a health tech company that offered smart mattresses that could monitor patients’ heart rate and respiratory rate. 
Case background: Intelliarts helped apply time series analysis for early detection of respiratory complications by analyzing trends in heart rate and respiratory rate data collected by smart mattresses. Using a time series binary classification model with gradient boosting, the company aimed to predict complications 7–10 days before onset, focusing on achieving high Sensitivity while maintaining Specificity above 0.95.
Case outcomes: The model successfully achieved 47% Sensitivity with Specificity over 0.95, enabling early identification of nearly half of potential complications with minimal false alarms. This time series analysis case study confirms the feasibility of using the technology in smart healthcare devices to enhance patient monitoring and preventative care.
#3 Time series in e-mobility: Case of EV Connect

Our partner was an EV charging solutions provider offering a network of charging stations with 24/7 service support. 
Case background: The company faced a challenge of unexpected charger downtime and limited visibility into equipment behavior. To address this, our team analyzed historical EV charger data, collected via OCPP logs and charging sessions. We used real-time time series analysis and anomaly detection techniques.
The goal was to identify patterns in charger behavior over time, detect anomalies, including unusually long charging sessions, and prepare data for a machine learning–based predictive maintenance IoT system.
Case outcomes: The analysis provided deep insights into charger usage patterns, system performance, and failure indicators. Intelliarts identified abnormal behavior clusters and key anomalies, helping the company better understand the root causes of downtime. We also improved data quality, structured raw time-series data for ML use, and delivered recommendations for building a scalable predictive maintenance IoT solution.
Interested in implementing a time series analysis solution in your business and enhancing it with AI? Drop Intelliarts a line, and let our expert data engineers contribute to your best project.
#4 Time series in the financial sector: Case of Goldman Sachs
Goldman Sachs is a leading global investment banking, securities, and investment management firm. It’s known for its expertise in market risk management.
Case description: Goldman Sachs employed time series analysis in market risk management, particularly for Value at Risk (VaR) modelling, which estimates potential portfolio losses. A real-life example of time series data could be the analysis of daily stock prices or market volatility. This approach enabled Goldman Sachs to quantify the level of financial risk associated with their investment portfolios and make informed decisions to mitigate potential losses. 
Case outcomes: The use of time series analysis enabled Goldman Sachs to better manage market risk and make informed investment decisions. The firm’s VaR modelling has been noted to average record levels, highlighting its significance in its risk management strategy.
Thinking about time series analysis for your business?Intelliarts helps implement the right tailored solution for your needs.Let's talk
#5 Time series in the energy sector: Case of National Grid
National Grid is an electricity and gas utility company responsible for energy transmission and distribution.
Case description: In this time series analysis case study, National Grid applied time series analysis for electricity demand forecasting, crucial for grid stability and energy distribution planning. By predicting the electricity demand, National Grid could ensure that there was a sufficient supply of energy to meet the needs of consumers and businesses.
Case outcomes: Enhanced grid stability and optimized energy distribution achieved through the time series use cases show a positive impact on ensuring reliable electricity supply and preventing blackouts.
#6 Time series in aviation: Case of EasyJet

EasyJet is a European low-cost airline operating a fleet of over 300 aircrafts across domestic and international routes.
Case description: EasyJet employs time series–based predictive analytics to improve aircraft maintenance and reduce operational disruptions. By analyzing historical flight data and sensor signals over time, the company uses predictive models to detect anomalies and identify early signs of component failure. With machine learning for time series, EasyJet increased the number of monitored parameters from around 390 to up to 24,000. This significantly improved their ability to track equipment behavior and predict issues in advance.
Case outcomes: The use of real-life time series data and analysis helped EasyJet to avoid 35 flight cancellations and 12 delays in a single month. Overall, the approach has already prevented hundreds of cancellations and major delays, which also reduced maintenance inefficiencies.
Models for time series forecasting business
Based on our experience building forecasting and predictive analytics solutions, we discuss what models are used to analyze and forecast real-time time series data. Here we distinguish between classical vs foundation and deep learning models.
But first let’s familiarize with an example of what two-year time series analysis can look like in the chart below:
Source: Timescale.com
In the infographic, the reference data is colored black, the trend is colored red, and the seasonality is colored blue. You can observe cyclic fluctuations, and two large irregularities complete the decomposition. This graphic can be typical for the demand for seasonal goods and products, such as clothing.
Classical time series models
Classical or traditional time series models are designed to analyze patterns within a specific dataset. They are widely used for their interpretability, stability, and strong performance on structured data with clear seasonality and trends. Common models include:
1. ARIMA (Autoregressive Integrated Moving Average)
Key principles: Combines autoregressive (AR) and moving average (MA) models with differencing to make the time series stationary. It is characterized by three parameters: p (autoregressive order), d (degree of differencing), and q (moving average order).
Despite the rise of deep learning, recent research confirms that ARIMA remains a strong baseline model for structured, low-noise datasets with clear temporal patterns. Modern benchmarking studies show its competitive advantage in scenarios with limited data and lower complexity.
“In real-world time series projects, model choice is rarely about picking the most advanced algorithm. It’s about matching data characteristics to the right level of complexity. In many cases, a well-tuned classical model like ARIMA or SARIMA outperforms deep learning simply because the data is structured and limited.” – Volodymyr Mudryi, Data Scientist at Intelliarts
Example of ARIMA forecast residual time series and histogram for the residual distributions.
If we compare ARIMA vs LSTM (Long Short-Term Memory) as a deep learning approach, ARIMA typically performs better on smaller, structured datasets. Meanwhile, LSTM models tend to outperform ARIMA in capturing complex, non-linear patterns in large-scale time series.
Application: Univariate time series with trend and without seasonal components.
2. SARIMA (Seasonal ARIMA)
Key principles: Extends ARIMA by adding seasonal terms to handle seasonality in the data. It includes additional seasonal parameters: P (seasonal autoregressive order), D (seasonal differencing degree), Q (seasonal moving average order), and s (seasonal period).
SARIMA achieves robust performance in seasonal forecasting tasks such as epidemiology, energy, and transportation. A 2025 study on aviation forecasting demonstrated that a SARIMA model reached approximately 4.5% MAPE and produced reliable short-term predictions for passenger traffic.
Application: Time series with both trend and seasonality
3. Holt-Winters (Exponential Smoothing)
Key principles: Applies exponential smoothing to capture level, trend, and seasonal components in data, a common seasonal time series application with real data. It uses three smoothing parameters: α (level), β (trend), and γ (seasonality).
Holt–Winters remains a reliable approach for short-term forecasting in datasets with stable seasonal patterns. For example, a 2025 study on agricultural price forecasting demonstrates that Holt–Winters effectively captures trend and seasonality, with the multiplicative version providing more accurate results in the presence of changing seasonal intensity.
Application: Time series with trend and seasonality, especially when the data is relatively stable and the seasonal pattern is consistent.
4. VAR (Vector Autoregression)
Key principles: A multivariate extension of the AR model, capturing the linear interdependencies among multiple time series. Each variable is modeled as a linear combination of its own past values and the past values of other variables.
VAR and its extensions remain effective tools for modeling complex economic systems with multiple interacting variables. A 2025 study on inflation forecasting shows that VAR-based models incorporate both domestic and external drivers, such as money supply, GDP, exchange rates, and foreign inflation.
Application: Multivariate time series analysis, such as analyzing the relationship between economic indicators.
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5. GARCH (Generalized Autoregressive Conditional Heteroskedasticity)
Key principles: Models the conditional variance (volatility) of a time series as a function of its own past values and past variances. It is characterized by parameters p (order of the autoregressive term) and q (order of the moving average term).
GARCH is widely used in financial analytics to model volatility clustering, periods of high and low variability in asset prices. A 2025 study in high-frequency trading environments proves its ability to model dynamic volatility patterns in real-world financial data.
Application: Financial time series with volatility clustering. 
In our experience, these methods used in time series analysis can be applied simultaneously as part of a comprehensive time series strategy to provide several outcomes that will verify each other’s correctness and provide a broader picture.
Are you looking for a trusted provider to help you implement an AI-based time series analysis solution for your business? Drop Intelliarts a line, and let’s discuss opportunities.
Foundation models
Foundation models are pre-trained on large-scale temporal datasets and can generalize across different domains with minimal fine-tuning. Unlike classical methods that are typically built for a single dataset, these can learn patterns across multiple time series and adapt to new forecasting tasks more efficiently. This makes them particularly valuable for complex and high-volume environments. 
Some of the most notable models include:
1. TimeGPT
Key principles: TimeGPT is a foundation model for time series developed by Nixtla. It is trained on large-scale temporal data and designed to perform zero-shot and few-shot forecasting without extensive retraining. The model captures global patterns such as trends, seasonality, and anomalies across diverse datasets.
TimeGPT was trained in the largest collection of publicly available time series and can forecast unseen time series without re-training its parameters.
The model was trained on over 100+ billion data points across domains including finance, energy, healthcare, and IoT. This allows it to forecast unseen time series without retraining and significantly reduces the need for manual feature engineering and model tuning.
Application: Forecasting across multiple domains (finance, retail, energy) with minimal setup, especially when historical data is limited or fragmented.
2. Chronos
Key principles: Chronos is a transformer-based time series model by Amazon that treats forecasting as a sequence modeling problem. It uses tokenization of time series data and is pre-trained on a large corpus of public datasets.
Research shows that transformer-based models such as Amazon Chronos can significantly improve zero-shot forecasting accuracy across diverse datasets. It also reduces the need for task-specific training.
Application: Large-scale forecasting tasks, including demand prediction, anomaly detection, and probabilistic forecasting.
3. Moirai
Key principles: A modern foundation model by Salesforce designed for probabilistic time series forecasting across domains. It uses large-scale pretraining to generalize patterns and provide uncertainty-aware predictions.
The model is based on a masked encoder transformer and trained on the Large-scale Open Time Series Archive (LOTSA), which contains over 27 billion observations across nine domains.
Application: Cross-domain forecasting and enterprise-scale prediction tasks.
Deep learning models
Deep learning models are designed to capture complex, non-linear relationships in time series data. Unlike foundation models, they are typically trained on specific datasets but can model interactions between multiple variables and learn long-term dependencies.
These models are particularly effective for high-dimensional data and scenarios where multiple external factors influence outcomes.
Learn more about deep learning for predictive models in another of our blog posts.
1. TFL (Temporal Fusion Transformer)
Key principles: TFT is a deep learning model specifically designed for interpretable multi-horizon time series forecasting. It combines recurrent layers with attention mechanisms to capture both short-term dynamics and long-term dependencies, while also highlighting which variables are most important for predictions.

Research proves the strong performance of TFL in capturing complex temporal relationships in real-world scenarios, including energy demand forecasting and financial modeling. Also, recent applications show that TFL remains highly effective in environments with multiple external variables, outperforming traditional models in multi-horizon forecasting tasks.
Application: Complex forecasting scenarios with multiple inputs (e.g., pricing, weather, promotions), such as retail demand forecasting or energy consumption modeling.
2. N-BEATS (Neural Basis Expansion Analysis for Time Series)
Key principles: The model uses a deep neural architecture with residual connections to model trend and seasonality without predefined assumptions. It learns interpretable components directly from data.
N-BEATS remains one of the most effective architectures for capturing complex temporal patterns due to its modular design and ability to model nonlinear relationships without feature engineering. Additionally, research applying N-BEATS to real-world domains, such as agriculture and energy forecasting, demonstrates its flexibility and strong predictive performance across diverse datasets.
Application: High-accuracy forecasting for sales, demand, and financial data.
3. DeepAR
Key principles: This is a probabilistic forecasting model based on autoregressive recurrent neural networks. It learns patterns across multiple related time series and produces forecasts.
Research highlights the effectiveness of this method in real-world applications such as retail demand, finance, and logistic forecasting, as well as energy load prediction. In these scenarios, probabilistic outputs help businesses make more robust decisions under uncertainty. Also, the algorithms like DeepAR remain a strong baseline for multi-series forecasting, particularly in scenarios with large datasets and shared temporal patterns.
Application: Demand forecasting, inventory planning, and large-scale prediction tasks.
Forecasting methods vs business use cases
Different forecasting approaches are suited for different types of data and business goals. The table below summarizes which methods work best depending on the use case, data complexity, and scale.
Forecasting methods vs business use cases
Our team’s experience proves that in practice, the most effective forecasting systems combine multiple approaches. For example, businesses can use classical models for baseline predictions and foundation models to improve accuracy and scalability across large datasets.
“A good forecast is the one that actually supports a business decision. In production, we often trade a bit of accuracy for interpretability and faster iteration. – Volodymyr Mudryi, Data Scientist at Intelliarts
Final take
Time series analysis is a powerful statistical tool used to forecast future trends, manage risks, optimize inventory, and enhance decision-making. By leveraging time series models, businesses can gain insights into patterns and seasonality in their data, leading to improved operational efficiency and competitive advantage. Implementing time series analysis, especially with AI enhancements, can significantly benefit companies in finance, retail, energy, technology, and telecommunications.
Implementing a time series solution in business, automating it, and enhancing it with AI capabilities, require substantial experience and expertise. The right call is to entrust your project to qualified engineers. Consider Intelliarts data engineering consultants, we deliver cutting-edge and data-driven software solutions for over 24 years.
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#### Time Series Machine Learning: Models & Use Cases
Business information is a primary source of insights. But how do companies translate data points collected or recorded at regular time intervals into strategic actions and automate this analytics? Machine learning for time series is the answer. 
The global machine learning market is projected to reach $113.10 billion by 2025 and further grow to $503.40 billion by 2030. With 42% of enterprise-scale companies reporting the use of AI in their business operations, the application of fairly popular analytical methods like time series analysis is becoming more common. 
In this post, you’ll explore traditional statistical methods and find out why machine learning for time series is a superior approach to them. Besides, you’ll learn the most commonly used machine learning approaches and find out how to choose the right one. Finally, you’ll review several use cases of time series machine learning and examine best practices for implementing them.
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Traditional statistical methods in business and why they are not enough
At all times, businesses have relied on data heavily to obtain useful insights and spot lucrative opportunities. Uncovering patterns in data is exactly what business statistics is used for. So, before delving into machine-aided statistics, let’s recall traditional approaches.
All models are wrong, but some are useful. — George E. P. Box, a British Statistician
Traditional statistical approaches and their usages
Main statistical approaches used to analyze data like sales trends, demand patterns, seasonality, etc. include:
#1 ARIMA (AutoRegressive Integrated Moving Average)

Use: Effective for univariate time series with trends or seasonality after differencing.
Strengths: Good interpretability, handles trend components well.
Limitations: Assumes linearity, sensitivity to outliers, poor performance with non-stationary or multivariate data.

#2 Naive Method

Use: Baseline model assuming the next value equals the last observed one.
Strengths: Extremely simple, useful for benchmarking.
Limitations: Poor accuracy in complex or volatile datasets.

#3 Holt-Winters (Triple Exponential Smoothing)

Use: Models level, trend, and seasonality for short-term forecasts.
Strengths: Easy to implement, good for seasonal data.
Limitations: Limited to univariate time series, sensitive to anomalies.

#4 Exponential Smoothing

Use: Weights recent observations more heavily for short-term forecasting.
Strengths: Fast, interpretable, and effective for stable series.
Limitations: Ineffective for complex seasonality or long-term forecasting.

#5 Linear Regression

Use: Predicts future values based on time and external regressors.
Strengths: Clear relationships, easy diagnostics.
Limitations: Assumes linear trends, a poor fit for complex temporal patterns.

Explore what time series analysis is and real-life case studies of its implementation in another blog post by Intelliarts. 
Reasons to opt for ML approaches for time series
From the limitations detailed above, we can draw several conclusions that indicate the weaknesses of traditional statistical methods. 

Linearity assumption by default: Traditional models fail to model non-linear patterns.
Natively univariate focus: Conventional models are not designed to handle multiple inputs natively.
High outlier sensitivity: Small anomalies and noise in data can skew predictions and lead to major inaccuracies.
Low scalability potential: Traditional approaches are complicated to scale to suit large-scale or high-frequency data, especially when operated by a human analyst. 

Note. Aside from the mentioned weaknesses, it’s also worth mentioning the human factor. After all, in traditional approaches, analysts still choose exact data sequences and statistical models themselves. In circumstances when data is vast and complicated, and outcomes from using different models contradict each other, even experienced specialists struggle to interpret results correctly. 
Ways how time series models in machine learning address those limitations are provided in the infographics below:

Core machine learning approaches for time series
Out of the numerous time series machine learning models and approaches, there are several suitable for time-series analysis. Let’s delve into the most common of them one by one:
#1 Supervised Learning models
These models use labeled historical data and engineered features (like lags and rolling means) to predict future values. Popular approaches include decision tree-based models, linear regression, and support vector regression. As an example:
Example: Decision Tree-based Models (Random Forest, XGBoost)
How it works: Converts time series into a tabular format and fits a model.

Strengths:

Handles non-linearity and outliers
Works with multivariate features
Interpretable (especially trees)

Weaknesses:

Requires manual feature engineering
Not sequential by design
Limited long-term memory

Best for: Sales forecasting, demand planning, financial time series
Explore the machine learning in demand forecasting success story by Intelliarts for an extra insight into our experience and expertise. It reveals how exactly our software development team handles such complex projects from businesses worldwide. 
#2 Deep Learning approaches
These models learn directly from sequences without manual feature engineering, capturing complex patterns over time. This includes RNN-based models (LSTM, GRU) and convolutional networks.
Example 1: Recurrent Neural Networks (RNN, LSTM, GRU)
How it works: Maintains a memory of past inputs via hidden states:

Strengths:

Learns long-term dependencies
Suited for sequential, time-ordered data
No need for manual features

Weaknesses:

High training cost
Needs lots of data
Difficult to interpret

Best for: IoT data, load forecasting, traffic predictions
Example 2: Convolutional Neural Networks (CNN for Time Series)
How it works: Applies 1D convolutional filters to detect local temporal patterns.

Strengths:

Fast to train
Effective for short-term dependencies
Good at capturing local patterns

Weaknesses:

Limited in modeling long sequences
Less effective without regular sampling

Best for: Activity recognition, anomaly detection, classification
Explore the data science service line by Intelliarts at our respective service page.  
#3 Hybrid & Automated ML solutions
Hybrid models combine strengths from different architectures (e.g., CNNs for local patterns and LSTMs or Transformers for sequence memory), while AutoML platforms automate key ML processes like feature selection, hyperparameter tuning, and model evaluation. 
Example: Hybrid Models (e.g., CNN + LSTM, Transformers)
How it works: CNN layers capture local features while LSTM or Transformer layers learn sequential dependencies.

Strengths:

High forecasting accuracy
Learns both short- and long-term patterns
Scales to large datasets

Weaknesses:

Requires high compute
Can be opaque
Complex to deploy

Best for: Financial forecasting, multivariate forecasting, real-time analytics
#4 Unsupervised Learning methods
Unsupervised models detect hidden patterns in time series without needing labeled output data. They are commonly used for anomaly detection, clustering, and pattern segmentation. Techniques include autoencoders, dimensionality reduction (PCA), and clustering algorithms like DBSCAN or k-Means.
Example: Autoencoders / Clustering (e.g., DBSCAN, k-Means)
How it works: Autoencoders reconstruct inputs, while a high reconstruction error indicates anomalies.

Strengths:

No labels needed
Detects anomalies or pattern shifts
Useful for exploratory tasks

Weaknesses:

Doesn’t forecast
Requires careful tuning
Results may be hard to interpret

Best for: Fraud detection, sensor drift, customer segmentation
#5 Reinforcement Learning for time series
Reinforcement Learning (RL) is used when decisions must be made over time with feedback, such as in inventory control or trading systems. RL agents learn policies by interacting with their environment, optimizing for cumulative rewards. Though complex, RL introduces adaptability and dynamic strategy formation into time series modeling.
Example: RL Agents for Forecasting and Control
How it works: Learns optimal actions based on rewards:

Strengths:

Learns dynamic, adaptive strategies
Optimizes for long-term outcomes
Useful where feedback is delayed

Weaknesses:

Complex to train
Needs simulation environments
High data and computing needs

Best for: Trading bots, pricing engines, supply chain control
The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present.— Paul Saffo, a Technology Forecaster
Note. Out of the listed approaches, supervised models, deep learning approaches, and hybrid and automated solutions are the ones specifically intended for time series machine learning. Unsupervised learning and reinforcement learning are also completely applicable, but keep in mind that unsupervised methods are not forecasting but rather anomaly detection tools, while reinforcement learning is intended for sequential decision-making rather than standard forecasting. 
Don’t hesitate to reach out to Intelliarts for custom machine learning services. With our expertise and experience, we can provide any of the mentioned ML solutions for time series analysis and more. 
How to choose the right time series machine learning approach?
Let’s explore how to choose one or another machine learning time series model based on different factors:
#1 Business goals

Forecasting: Use Supervised Learning, Deep Learning, or Hybrid Models to predict future values like sales, demand, or load.
Anomaly detection: Use Autoencoders or Clustering Methods to detect unexpected spikes, drops, or unusual behavior.
Classification: Use CNNs, RNNs, or Tree-based Models to label sequences such as fault types, customer behavior, or device states.
Decision-making: Use Reinforcement Learning to optimize sequential strategies in areas like trading, inventory, or pricing.

Explore the ML-powered error detection success story by Intelliarts to see how we used gradient boosting techniques to implement the ML system for error detection in fleets
#2 Data size considerations

Small datasets: Use Tree-based Models or Probabilistic Approaches for stable performance with limited data.
Large datasets: Use Deep Learning or Hybrid Models to capture complex patterns and scale across variables.

#3 Data frequency considerations

Low-frequency data: Use Simpler Models like XGBoost or Regression to model weekly or monthly trends.
High-frequency data: Use LSTM, CNNs, or Transformers to handle second-by-second or minute-level inputs.

#4 Data quality considerations

Univariate series: Use ARIMA, Exponential Smoothing, or Tree-based Models for single-variable trends.
Multivariate series: Use LSTM, XGBoost, or Transformer-based Models to incorporate multiple influencing variables.

#5 Time Series structure

Univariate series: Use ARIMA, Exponential Smoothing, or Tree-based Models for single-variable trends.
Multivariate series: Use LSTM, XGBoost, or Transformer-based Models to incorporate multiple influencing variables.

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Real-world use cases of time series ML
The typical use cases of time series machine learning models were briefly mentioned in the sections above. However, the time is just right to tailor them to real-world use cases from several business domains:
#1 Energy Industry
Use cases: Load forecasting, outage prediction, energy price forecasting, renewable output prediction
Data used: Smart meter data, weather conditions, grid sensor data, historical demand
What’s predicted: Energy consumption levels, grid stress points, generation patterns
Business benefits:

Balance supply and demand efficiently
Reduce operational costs and prevent blackouts
Support renewable energy integration

#2 Retail & eCommerce
Use cases: Sales forecasting, supply chain optimization, price elasticity modeling, demand sensing
Data used: POS data, online behavior, stock levels, promotions, seasonality
What’s predicted: Future product demand, delivery timing, sales trends
Business benefits:

Prevent overstock or stockouts
Boost campaign ROI through accurate timing
Improve inventory turnover

#3 Finance Industry
Use cases: Risk scoring, fraud detection, algorithmic trading, cash flow forecasting
Data used: Transaction logs, credit history, real-time activity, market data
What’s predicted: Fraudulent behavior, default risk, price movements
Business benefits:

Detect fraud early
Automate risk management
Enhance credit decision accuracy

#4 Healthcare
Use cases: Patient monitoring, hospital resource forecasting, early disease detection, treatment response prediction
Data used: Vital signs, EHR time series, medication logs, wearable device output
What’s predicted: Health deterioration, emergency risks, patient flow
Business benefits:

Enable proactive care
Reduce emergency visits
Optimize resource allocation

#5 Manufacturing
Use cases: Equipment failure prediction, predictive maintenance, quality control, production rate forecasting
Data used: Sensor data, operational logs, defect records, machine cycle times
What’s predicted: Component failure, process anomalies, downtime risks
Business benefits:

Minimize unplanned downtime
Cut maintenance costs
Improve product consistency

As you can conclude, ways to apply time series forecasting machine learning are many, and most businesses can opt to benefit from them. However, it’s worth mentioning that time series machine learning development and integration in any digital system a company may have in place require substantial expertise and experience. Therefore, it should be entrusted to qualified ML software engineering agencies only. 
Explore time series data examples in another blog post written by Intelliarts experts. 
 
Best practices for implementing time series ML
Finally, let’s proceed with best practices and tips on developing and implementing a time-series ML solution. To provide the most value for different groups of readers, we split our recommendations into business-focused and engineer-focused sections:
#1 Tips for businesses:

Define the business objective clearly. Identify whether your goal is forecasting, anomaly detection, classification, or strategic decision-making. Communicate your need to your software provider accordingly. 
Align expected ML output with actual business processes. Forecasts must integrate into operational workflows (e.g., dashboards, APIs, alerts). Without actionability, accurate predictions offer limited value.
Start small, then scale. Run a pilot on a specific product line, process, or segment before full deployment. Use early results to build internal trust and refine processes.
Understand data readiness. Ensure historical data is available, complete, and properly timestamped through robust data engineering processes. Don’t forget the Garbage In Garbage Out (GIGO) rule, which emphasizes the importance of supplying ML with high-quality data only. 

#2 Tips for engineers:
In the feature engineering stage, make sure to design features that capture time dynamics, which implies enhancing your dataset with time-relevant variables:

Lag features (e.g., yt−1,yt−2y_{t-1}, y_{t-2}yt−1​,yt−2​)
Rolling statistics (e.g., 7-day mean, rolling std)
Calendar/time indicators (e.g., month, holidays, hour)
Exogenous variables (e.g., weather, promotions)

Ensure no future data leaks into training by following these tips:

Never use future labels or future-derived features
Apply preprocessing (scaling, encoding) only on the training data before applying to the test sets

Watch for hidden leakage in joins or feature aggregation

Preserve temporal order when evaluating models by following these practices:

Split data chronologically, never randomly
Use rolling windows or expanding window validation
Always test on future data to simulate real-world performance

As extra advice, we highly recommend leveraging AutoML solutions like Google AutoML when resources such as time or knowledge are limited. Still, validate outputs manually before deploying.
Final take
Machine learning for time series overcomes the limitations of traditional statistical models by handling non-linear, multivariate, and large-scale data. It powers accurate forecasting, anomaly detection, and decision-making across industries like energy, retail, finance, and healthcare. With careful data preparation, time-aware validation, and continuous monitoring, engineers can offer businesses high-impact solutions that turn complex time series data into actionable insights.
Should you need assistance with time series machine learning development, don’t hesitate to reach out to Intelliarts. We’ve been in the software market for more than 25 years, delivering high-end solutions and tailored services. With over 90% of recurring customers and 92% of senior staff engineers, we are ready, willing, and able to tackle a project of any complexity at your request.
Make Smarter Decisions with Time Series MLTurn recurring data into strategic actions with machine learning for time series.Talk to an expert

#### Time Series Regression: Models & Use Cases
The global big data and business analytics market is projected to grow to $961 billion by 2032. A substantial portion comprises analytics software, most supporting time series regression analysis. This analytics method is widely used across business domains and is helpful in most forms of planning, forecasting, monitoring, and modeling. That’s exactly why it’s useful to be well-versed in forms of regression analytics and know how it can be used in your business area. 
In this blog post, you’ll find out about regression analysis of time series and its difference from standard regression analysis. Moreover, you’ll learn how to conduct a regression analysis in clear steps. You’ll also review business use cases of using data regression analysis, explore challenges and limitations, and find out whether this analysis method can work for your business.
From theory to practiceDiscover how tailored ML solutions can improve your time series forecasting.Talk to our experts
What is regression analysis with time series data?
Regression analysis with time series data is a statistical method used to model the relationship between a dependent variable and one or more independent variables over time.
In simple terms, regression analysis with time series data helps you understand how one thing changes over time in response to other factors. For example, it can show how sales depend on advertising spend, time of year, or economic trends while also considering patterns like seasonality or momentum from previous time points. More business use cases of regression analysis of time series are provided in the infographics below:

To have a complete picture, let’s compare time series regression analysis to standard regression. 
Standard regression is a statistical method used to examine the relationship between a dependent variable and one or more independent variables, assuming the data points are independent and identically distributed.
Standard regression does not account for time-based patterns, such as trend, seasonality, autocorrelation, etc. That’s what makes this method of analysis suitable for cross-sectional or non-temporal data.
A detailed comparison of these two statistical methods is provided in the table:

Explore the usage of AI regression analysis on agricultural use cases by Intelliarts.
How regression analysis works with time series data
Provided that you will likely be using a research analysis tool like Python libraries, Stata, IBM SPSS Statistics, etc., you don’t particularly need to understand the deep technicalities of the process. Alternatively, a dedicated ML and data science team may set up a model, which also doesn’t require in-house knowledge of how regression analysis works at core.
However, knowing how it’s applied in business and how the data in a business project is transformed from a series of numbers into insight is more valuable information. Here it is explained in 6 steps: 
Step 1: Define the problem and objective
Clearly state what you want to achieve. Are you forecasting future sales, predicting demand, or analyzing economic trends? To find out, define the following:

The target variable (e.g., daily sales)
Independent variables (e.g., marketing spend, day of week)
The forecasting horizon (e.g., next 30 days)
Business impact or decisions are dependent on this analysis

Step 2: Prepare and explore time-stamped data
According to the Garbage In Garbage Out (GIGO) rule, the accuracy of analysis largely depends on the quality of data used for it. To ensure the best possible results, perform the following actions:

Handling missing data: Fill, interpolate, or drop depending on business context
Adjusting granularity: Align data to a consistent frequency (e.g., daily, weekly)
Creating lag features: Include past values of the target or predictors (e.g., sales 1 day ago)
Exploring trends and seasonality: Visual inspection, decomposition, and plotting are key
Applying feature engineering if necessary: Time-based features like day of week, month, and holidays

Explore time series data examples for additional insight. 
Step 3: Choose an appropriate regression model
Based on data and the initial assumptions about the research, select a model:

Simple linear regression (with time or lags)
Autoregressive models (AR) or ARIMAX/ADL (autoregressive with exogenous variables)
Seasonal models if seasonality is present
Regularized regressions (Ridge, Lasso) for high-dimensional time-based features

More information on regression analysis forecasting models is provided in the below sections of the blog post. 
“A beautiful aspect of regression analysis is that you hold the other independent variables constant by merely including them in your model!” — Jim Frost, a statistician, author, and data analysis educator
Step 4: Train model, interpret results, test assumptions
Fit the model and ensure it meets critical assumptions:

Linearity between variables
No autocorrelated residuals (use Durbin-Watson test)
Homoscedasticity (constant variance of errors)
Normality of residuals if inference is required

For more information on time series analysis, explore the demand forecasting using machine learning success story by Intelliarts. 
Important. Simple regression analysis can be done using the models listed above and below in the semi-automatic mode. However, complex, fully automated regression analytics using ML and data pipelines should be set up only by qualified ML and data engineers.
Step 5: Validate with time-aware techniques
Traditional cross-validation won’t work due to time dependency. That’s why, when it comes to regression analysis of time series, it’s recommended to do the following:

Use walk-forward validation, expanding/rolling windows, or time series cross-validation
Evaluate using metrics like MAE, RMSE, or MAPE that reflect forecast accuracy over time
Compare against naive models (e.g., last value carried forward) to prove value

Discover more about time series machine learning in another of our blog posts. 
Step 6: Integrate into business pipelines or decision support systems
Finally, it’s necessary to make a time series regression model applicable for your business use cases. That’s what ML engineers would do in this stage: 

Deployment in dashboards, forecasting tools, or automated systems
Scheduling regular data refresh and retraining
Building alerts or decision triggers based on predicted values
Monitoring model drift and performance over time

Practical examples of how companies have used this method to solve actual business issues are provided further in the text. For now, let’s move on to the review of core regression analysis forecasting models. 
Core regression models for time series analysis
While models for regression analysis of time series are numerous, let’s focus on the five that are most frequently used. Most likely, you won’t have to actually apply formulas on your datasets yourself, as tools for research analysis are semi-automated, and ML solutions for these purposes are automated. Still, understanding the principles of models that your business data may be accessed upon might be helpful. 
#1 Linear regression
What it is: A basic model that assumes a linear relationship between one dependent variable and one or more independent variables over time.
How it works: Estimates coefficients that minimize the squared differences between actual and predicted values. Time is often included as a variable, but autocorrelation and temporal structure are not directly modeled.
Formula: Yₜ = β₀ + β₁Xₜ + εₜ
Where it is used: Short-term forecasting, baseline models, trend estimation, and business metrics tracking where relationships are stable and simple.
#2 Polynomial regression
What it is: An extension of linear regression that models non-linear relationships using powers of the independent variable(s).
How it works: Transforms the input variable(s) into higher-degree polynomials to capture curves in the data. Useful when the trend is non-linear over time.
Formula: Yₜ = β₀ + β₁Xₜ + β₂Xₜ² + … + βₙXₜⁿ + εₜ
Where it is used: Modeling curvilinear trends, such as growth followed by decay or saturation in economics, population modeling, and market lifecycle curves.
“All models are wrong, but some are useful.” — George E. P. Box, a statistician
#3 Multivariate (multiple) regression
What it is: A regression that models the relationship between a single time-dependent outcome and multiple independent variables.
How it works: Incorporates multiple predictors (e.g., price, season, weather) at the same time. Assumes independence across observations unless extended for time structure.
Formula: Yₜ = β₀ + β₁X₁ₜ + β₂X₂ₜ + … + βₖXₖₜ + εₜ
Where it is used: Used in demand forecasting, retail sales analysis, energy consumption modeling, and advertising impact studies.
#4 Autoregressive Distributed Lag (ADL) model
What it is: A model that combines past values of the dependent variable (autoregressive terms) with current and lagged values of explanatory variables (distributed lags).
How it works: Captures both internal momentum (e.g., Y depends on past Y) and external effects (e.g., X values over several time lags) in one framework.
Formula: Yₜ = α + φ₁Yₜ₋₁ + φ₂Yₜ₋₂ + … + φₚYₜ₋ₚ + β₀Xₜ + β₁Xₜ₋₁ + … + β_qXₜ₋_q + εₜ
Where it is used: Macroeconomic modeling, energy demand prediction, finance, and any domain where lagged interactions are expected.
#5 ARIMA with exogenous variables (ARIMAX)
What it is: An ARIMA model extended with external regressors (exogenous variables) that influence the forecast alongside autoregressive and moving average terms.
How it works technically: Integrates three components, autoregression (AR), differencing for stationarity (I), and moving average (MA), plus external inputs (X variables). Useful when both internal dynamics and external drivers matter.
Formula: Yₜ = φ(L)(1 – L)ᵈYₜ + θ(L)εₜ + βXₜ
Where it is used: Forecasting sales with promotions, economic forecasting with interest rates, weather-influenced load forecasting, and financial modeling.
Learn more about predictive maintenance using machine learning models in our post.
How to interpret and validate time series regression results: 5 strategies
Without further ado, here are proven tips and strategies that will help you achieve better regression analysis results. Alternatively, use these bits of advice to validate the outcomes of making custom regression models by ML engineers: 
#1 Check residuals over time
Plot residuals to see if errors are randomly distributed. Patterns may indicate missing variables or model misspecification. That’s why it’s essential to use ACF plots of residuals to detect autocorrelation.
#2 Compare forecast accuracy
Use time-aware metrics like MAE, RMSE, or MAPE. Benchmark against naive models (e.g., last period value). Consistently better performance validates model usefulness.
#3 Assess coefficient stability
Review signs and significance across rolling windows or retrained models. Inconsistent coefficients may suggest overfitting or non-stationary effects. Stability implies reliable long-term relationships.
#4 Monitor out-of-sample performance
Split the data chronologically and test on future periods. Walk-forward validation reveals how the model handles real forecasting conditions. Poor generalization indicates need for refinement.
#5 Align results with business context
Interpret findings in terms of known cycles, events, or policies. Validate with domain experts to ensure insights are realistic. If results contradict expectations, reassess assumptions or features.
Want to apply regression analysis to your business challenges?Reach out
Challenges and pitfalls in time series regression analysis  
Every technology, as well as a research method, has some limitations to be aware of. Here is an infographic depicting some challenges and pitfalls you may face when trying to implement regression analysis of time series in business:

We don’t provide more solutions to these challenges, as some of them are mentioned in the sections above. Should you have any particular issue in mind, don’t hesitate to apply for a technology consultation with our best data engineering and ML specialists.
Real-world use cases of regression analysis in time series for business
When assessing the suitability of a particular technology for a business, it’s always useful to understand how the solution is applied by other companies. Let’s find out how the use of regression analysis can prove beneficial on the example of three success stories:
Intelliarts success story: Enhancing demand forecasting with time series regression

Challenge: A global electronics manufacturer struggled with stockouts and overstock due to poor demand forecasting, leading to missed sales and inefficiencies.
Solution: Intelliarts applied time series regression models to analyze historical sales, pricing, and design cycle data. The solution identified product-specific demand patterns and predicted future sales with over 90% accuracy. It was integrated into dashboards with interpretable results for business users.
Results: The company improved procurement planning, reduced excess inventory, and enhanced marketing decisions. Insights on long design cycles also informed campaign timing, helping the team cut costs and align resources to real product demand.
Explore the success story
Danone success story: Improved promotion forecasting with time series regression

Challenge: Danone struggled to forecast the impact of trade promotions on short-shelf-life products, leading to high product waste and lost sales due to poor stock planning.
Solution: They implemented time series regression models to predict promotional lift, integrating historical sales, promotion schedules, and product expiry data. The system forecast demand during campaigns with 92% accuracy and automated stock decisions across the supply chain.
Results: Danone improved service levels to 98.6%, cut product obsolescence by 30%, and significantly reduced lost sales, turning trade promotion forecasting into a competitive advantage.
Explore success story
Walmart success story: Enhanced retail forecasting in the M5 competition

Challenge: Walmart needed accurate demand forecasts across thousands of items and stores, with data influenced by price, promotions, seasonality, and regional behavior.
Solution: Walmart released real sales data for the M5 competition, where top data science teams applied time series regression, ARIMA, and ensemble models. Forecasts incorporated exogenous variables like price changes and holidays to model real retail complexity.
Results: Winning models predicted daily item-level demand with high precision, proving the value of time series regression in large-scale retail forecasting and guiding Walmart’s internal planning improvements.
Explore success story
As of now, you know what regression analysis is, how it works, how it is used, and what the limitations of this time series method are. With these provided use cases in mind, let’s figure out when to use regression analysis and when not to.

Find out about machine learning applications in business in another blog post by Intelliarts.
Final take
Time series regression helps uncover how variables evolve over time, accounting for trends, seasonality, and lagged relationships. Models like linear, polynomial, ADL, or ARIMAX enable accurate forecasting in areas where timing matters, such as inventory planning, promotions, or economic analysis. Reliable results depend on well-prepared data, time-aware validation, and model stability, making this method a practical choice for businesses working with temporal data.
Should you need assistance with the automation of ML regression analysis of time series, Intelliarts has exactly the expertise. With more than 25 years on the market, providing software solutions, a 90% customer return rate, and 92% of senior staff engineers, we are ready, willing, and able to drive your best project.
Turn your data into resultsContact us

#### Top 10 Custom RAG Development Companies Globally
As enterprises scale up generative AI, building sustainable, governed systems is becoming a priority. According to Gartner’s 2025 Hype Cycle for Artificial Intelligence research, AI-ready data architectures are among the top strategic innovations. In this context, Retrieval-Augmented Generation (RAG) is gaining traction as an architecture that connects large language models to structured enterprise data.
In this post, you’ll learn more about the RAG development concept and why businesses need it to boost their generic AI models. Besides, you’ll review the top 10 RAG development companies and find out how to choose among them based on strict criteria. You will also observe a checklist and step-by-step instructions on starting a partnership with a provider of your choosing. 
Looking for a Trusted RAG Development Partner?From consulting to cost optimization - build scalable, production-ready RAG solutions.Explore RAG Services
What is custom RAG development and why does it matter?
Let’s get started by defining the main concept:
Custom RAG development is the process of designing and implementing a retrieval-augmented generation system tailored to a specific data source and business objective.
Unlike generic AI integrations, custom RAG development connects large language models (LLMs) with proprietary knowledge bases using structured retrieval pipelines. This approach enables enterprise AI systems to generate responses grounded in verified internal data rather than relying solely on pretrained model knowledge. 
RAG solves a specific problem faced by digitalized businesses: the lack of answer traceability and data integrity in highly regulated industries.
This challenge was exactly the case in one of the Intelliarts’ success stories, where we created a data extraction chatbot using RAG and ChatGPT paired together. 
And before delving any deeper into the topic, you need to recall what the RAG system, as a complete software solution, is built from. 
Core components of a RAG system typically include:

LLMs (e.g., OpenAI models)
Embedding models
Vector databases
Retriever layer
Re-ranking models
Orchestration frameworks (e.g., LangChain, LlamaIndex)
Data ingestion pipelines
Chunking and indexing mechanisms
Access control and security layer
Monitoring and evaluation modules
AI agents (optional)

Take a look at how a RAG-enabled LLM works on a user prompt with a vector database in the infographics below:

Software development companies like Intelliarts primarily focus on the design of the GenAI architecture, i.e., selection and orchestration of components in a particular way. The purpose is to align the end RAG system with business goals, scalability, and governance requirements.
You may explore in great detail what RAG models are and how to implement them in business in the corresponding blog post by Intelliarts. 
When do businesses need a custom RAG solution?
The RAG system connects LLMs to curated internal and external knowledge sources. This way, it directly makes responses grounded in verified information rather than the model’s generic database alone.
Therefore, retrieval augmented generation development handles limitations of standalone LLMs, which typically include the following:

Hallucinations in LLM outputs. Models generate inaccurate or unverifiable answers, RAG grounds responses in trusted data sources.

Need to leverage private or regulated data. Internal documents, policies, or sensitive datasets must be used securely and compliantly.

Lack of answer traceability and data integrity. Industries require source attribution, auditability, and controlled knowledge retrieval.

Fragmented knowledge across systems. Data is scattered across CRMs, ERPs, cloud storage, and internal tools.

Performance and scalability limits. AI prototypes must evolve into production-grade, enterprise AI systems.

Complex, domain-specific queries. Use cases demand contextual accuracy beyond generic public model knowledge.

At the basic scale, a generic language model can help extract some info or brainstorm ideas. A RAG-based system is what actually supports real decisions in business because it’s grounded in this business’s very own data. 
— Alexander Barinov, a Managing Partner at Intelliarts. 
So, the short answer is:
Businesses need a custom RAG solution every time their AI model has to work with additional data sources other than those that comprise its regular database. Otherwise, the result would be insufficient for any real-world business applications. 
This reason is exactly why companies require RAG experts and their custom development assistance. And now, we have come to the challenge of selecting the top RAG solution providers available in the industry.
You may be additionally interested in reviewing RAG system implementation best practices in another blog post by Intelliarts. 
How we evaluated the top RAG development companies
Intelliarts, as a trusted software development company with expertise in RAG, has its own set of evaluation criteria. These are formed mainly through strategy sessions with our customers, who express their concerns and preferences in choosing. 
Evaluation criteria list:

RAG architecture expertise. Demonstrated ability to design scalable retrieval pipelines, optimize embeddings, implement re-ranking, and manage orchestration. 
Experience with enterprise data. Hands-on experience integrating large, proprietary, and regulated datasets while maintaining governance, compliance, and data integrity.
Tech stack proficiency (LLMs, vector DBs, frameworks). Deep expertise across leading LLMs, vector databases, and RAG frameworks to ensure flexibility, vendor neutrality, and performance optimization. Since the underlying language model significantly impacts performance, it’s important to evaluate options such as Claude and ChatGPT before selecting a RAG technology stack.
Security and scalability. Ability to implement access controls, compliant encryption standards, and infrastructure capable of supporting high-volume, production-grade workloads.
Customization capabilities. Ability to tailor retrieval strategies, embedding configuration, and orchestration logic to specific business objectives and workflows.
Evaluation and optimization methodology. Use of measurable KPIs such as retrieval precision, hallucination rates, latency benchmarks, and continuous improvement processes.
Domain expertise. Industry-specific understanding that enhances context modeling, prompt engineering, and data structuring.
Post-deployment support. Commitment to ongoing monitoring, fine-tuning, scaling, and iterative system improvement.

If you are currently communicating with a number of RAG companies and trying to run a quick assessment of each, you need some sort of checklist. Just print the image below and fill it in manually for every company. Alternatively, use it as a basis for any evaluation framework you might already have in place.
 
Top 10 custom RAG development companies globally
Here’s a list of top RAG service providers operating at an international scale and meeting high standards based on the checklist above.
#1. Intelliarts

Intelliarts is a technology consulting and software engineering company founded in 1999. The company specializes in data engineering, machine learning, and custom RAG development services for enterprises. It combines 25 years of engineering experience with more than five years of focused AI and LLM practice. 
Intelliarts team includes Amazon Web Services (AWS)-certified engineers and 40 percent senior-level specialists, with innovation supported by PhD talent. Intelliarts reports that 90 percent of clients return with new projects. Typical PoC and Minimum Viable Product (MVP) delivery takes 6 to 8 weeks. The company works with complex enterprise data and builds scalable, secure RAG architectures aligned with compliance requirements.
Core RAG offerings:

End-to-end RAG architecture design and implementation
Enterprise data ingestion, normalization, and chunking
Embedding model selection and optimization
Vector database setup and search tuning
Retrieval and re-ranking strategies
Prompt orchestration and context injection
LLM integration for grounded response generation
Evaluation frameworks for accuracy and hallucination monitoring
Observability and performance tracking
Secure deployment with governance and access control

Typical use cases: Enterprise knowledge assistant, customer/support agent with grounded answers, research and analytics copilots, compliance and regulatory advisory assistant, technical documentation navigator. RAG systems can also enhance analytics workflows by providing contextual access to historical datasets, including time series data used for forecasting and trend analysis.
Learn more about Intelliarts’ RAG development services
#2. CaliberFocus
CaliberFocus is recognized for delivering tailored RAG services that build explainable AI systems for enterprises. Their work includes integration of business data with LLMs and design of RAG pipelines that handle domain-specific information retrieval and generation. 
Core RAG offerings:

RAG pipeline design
Data context integration with LLMs
Explainable and auditable response generation
Domain-specific retrieval strategies

Typical use cases: Knowledge search and Q&A, document intelligence workflows, compliance-aware assistants, enterprise search augmentation, and domain-specific decision support.
#3. Vstorm
Vstorm appears in market listings of RAG development firms and is noted for experience in the delivery of contextual AI systems and retrieval-augmented solutions. Their portfolio emphasizes AI agent work and contextual retrieval integrations.
Core RAG offerings:

Custom RAG solution delivery
Contextual AI and agentic system design
Retrieval-grounded conversational interfaces

Typical use cases: AI assistants with knowledge access, contextual search systems, automated support responders, document-centric query workflows, workflow automation with grounding.
#4. SoluLab
SoluLab is frequently listed among providers that build RAG-based systems integrating enterprise data with LLMs. Their services include custom retrieval augmented generation solutions tailored to business data sources.
Core RAG offerings:

RAG system implementation
Integration with enterprise knowledge repositories
Custom retrieval logic and LLM augmentation

Typical use cases: Enterprise Q&A systems, data-grounded assistants, knowledge discovery dashboards, contextual content generation, search-enhanced applications.
#5. DataRoot Labs
DataRoot Labs offers RAG-focused AI development services designed to strengthen generative systems with accurate, context-aware retrieval. Their services center on aligning LLM outputs with structured and unstructured enterprise data to produce accurate, relevant responses tailored to client workflows.
Core RAG offerings:

RAG pipeline development
Semantic search and knowledge retrieval systems
Integration with enterprise data sources (CRM, databases)
Retrieval optimization (ranking, filtering, hybrid search)

Typical use cases: Context-aware chatbots, internal knowledge assistants, document intelligence platforms, AI-powered search, and automated reporting.
Need a technical consultation on RAG development?Let Intelliarts experts provide you with a strategic framework.Contact Us
#6. Sphere Partners
Sphere Partners delivers custom retrieval augmented generation solutions that ground AI outputs in an organization’s internal documents and systems. They emphasize compliance, secure access control, and production-ready RAG deployments.
Core RAG offerings:

RAG discovery and assessment
Data auditing and ingestion
Retrieval architecture blueprinting
Secure RAG deployment

Typical use cases: Internal knowledge base assistants, product and operations Q&A, compliance support agents, leadership insight portals, and regulated knowledge retrieval.
#7. Rishabh Software
Rishabh Software offers tailored agentic RAG development services that connect proprietary enterprise data with LLMs to enhance knowledge access, decision-making, and content delivery.
Core RAG offerings:

Custom retrieval augmented generation workflows
Context-aware applications development
Integration of structured and unstructured data

Typical use cases: Decision support applications, knowledge retrieval agents, automated answer generation, business insights tools, data-rich assistants.
#8. ITRex Group
ITRex Group provides RAG-as-a-Service offerings that integrate retrieval pipelines with prompt optimization and LLM integration into enterprise systems. Their work includes building end-to-end RAG solutions tied into existing data ecosystems.
Core RAG offerings:

Custom retrieval pipeline implementation
Prompt augmentation and optimization
Seamless LLM integration

Typical use cases: Grounded conversational AI, enterprise search enhancement, contextual automation workflows, intelligent document query systems, and data-driven assistant tools.
#9. TechAhead
TechAhead offers RAG application development services that ground generative AI models in proprietary enterprise data for secure and scalable deployments. Their approach includes connecting company data sources with context-aware generation.
Core RAG offerings:

Enterprise-grade RAG service delivery
Data integration for retrieval workflows
Secure and scalable implementations

Typical use cases: Secure RAG assistants, enterprise knowledge retrieval, contextual AI for customer service, internal support bots, document-driven applications.
#10. Clover Dynamics
Clover Dynamics offers advanced RAG development services that combine large language models with dynamic information retrieval workflows. Their offerings include building custom RAG pipelines aligned with operational goals in industries like healthcare, law, and finance.
Core RAG offerings:

RAG pipeline development
Integration with structured and unstructured data
Enterprise-oriented retrieval systems

Typical use cases: Sector-specific knowledge systems, healthcare information support, legal data assistants, financial document processing, operational decision support.
  The Executive Guide to RAGExplore RAG strategies, architectural insights, and 10 real-world use cases in our latest white paper. Download now
Comparison table: Top RAG development companies
Find the visualized comparison of the top providers of RAG service providers in the table below:
 
Should you need RAG development assistance from an experienced team, don’t hesitate to contact Intelliarts. 
Custom RAG vs generic AI integrators
Still not sure if a RAG-driven, custom solution is superior to a generic, integrated AI model like ChatGPT or Claude AI? See the comparison in the table below:
 
 
Basically, there’s no need to consider which one is a better choice. A RAG-powered solution is always superior to any generic AI, as RAG is a direct enhancement on top of any AI model you might consider integrating and using. 
We typically see that teams operating AI require tracing back to a source, especially in regulated environments. Otherwise, the risk of using AI for them is simply too high. 
— Volodymyr Mudryi, a data scientist and ML expert at Intelliarts. 
How to choose the right RAG development partner
As of now, you have a list of top RAG development companies, a checklist on how to evaluate them, and a table with their direct comparison. Here are your next steps towards selecting a partner:

Shortlist candidates with proven RAG delivery. Check for RAG-focused service pages, public technical materials, and credible case evidence. Skip vendors who only mention RAG as a buzzword within generic AI services.
Run a structured discovery call. Use one agenda: data sources, access constraints, integrations, compliance, target users, and success metrics. A strong partner asks clarifying questions and proposes an approach based on constraints, not tools.
Request a written PoC plan. Ask for a 1–2 page plan that includes scope boundaries, deliverables, timeline, assumptions, risks, and KPIs. If they cannot define PoC inputs and outputs, the delivery risk is high.
Validate the proposed team. Confirm roles and seniority. You should see a solution architect, a data engineer, and an ML or RAG engineer assigned. Ask who owns retrieval design, evaluation, and deployment.
Check delivery governance. Agree on milestones, review cadence, acceptance criteria, and how changes are handled. RAG work requires iterative validation.
Confirm handover and post-launch support. Define what you get at the end: documentation, runbooks, monitoring, and knowledge transfer. Clarify whether ongoing optimization is included and what the support model looks like.

Results of the process: You select a RAG development partner with verified architectural competence, delivery discipline, and long-term support readiness. This is formalized through a clearly scoped PoC plan, defined KPIs, a documented architecture outline, an agreed team composition, a timeline, and post-launch support terms embedded in the contract.
Looking for custom AI solutions that are powered by RAG? Discuss your business needs with Intelliarts experts. 
Final take
Custom RAG development is needed to help convert general-purpose language models into enterprise-grade systems. This modification directly reduces hallucinations and supports compliance in regulated environments. However, the long-term value of RAG depends less on the tools chosen and more on architectural rigor, data modeling discipline, and delivery maturity. Selecting the right development partner is how you maximize the chances of making it a significant AI investment for your business rather than a short-term experiment.
At Intelliarts, we have curated expertise in RAG, whether it’s development and integration from the ground up or modifying an AI system already in place. With more than 25 years in the market delivering AI and ML software services, over 80 large-scale projects under our belt, and a 90% customer return rate, we present the top choice in the niche. Our experts are ready, willing, and able to contribute to your next RAG project.

#### Top 10 eMobility Software Development Companies
Electric mobility is moving into the mainstream as both vehicles and infrastructure scale worldwide. According to IEA, over the past few years, more than 17 million electric cars were sold globally, representing more than 20% of all new cars. During this period, more than 1.3 million public EV charging points added to the global network. These trends show that eMobility adoption and charging infrastructure build-out are accelerating.
In this article, you’ll find out more about the role of a software development partner in the electric mobility sector. Besides, you’ll explore the top 10 eMobility software development companies, and find out what to look for in custom e-mobility applications. 
Explore the EV charging management software expertise by Intelliarts, in our success story with a large Saudi Arabia’s E-mobility provider. 
What makes a top eMobility software development company?
To begin with, let’s define what software development in e-mobility is, to separate it from other dev niches:
Software development in e-mobility is the design, implementation, and maintenance of digital systems that enable electric transportation.
Naturally, the goal of custom software development emobility is to enable vehicles and other transportation-related devices to operate reliably, efficiently, and at scale. This dev domain sits at the intersection of automotive engineering, energy systems, IoT in EV charging, and cloud software.
And here’s what expertise, bits of knowledge, and direct contributions that a trusted provider brings to your electric mobility project: 

Expertise in EV infrastructure and smart charging platforms. Providers design software around real charging infrastructure constraints. They implement charger management, smart charging logic, and load balancing. This expertise allows providers to prevent downtime, avoid grid overloads, and optimize charging performance.
Scalable architecture for fleets and charging networks. Providers architect platforms with growth in mind. They account for rising numbers of vehicles, chargers, users, and data streams. Through modular and cloud-native design, providers ensure systems remain reliable as operations scale across regions.
Regulatory and standards-driven development. Providers embed compliance requirements directly into system design. They work with standards such as OCPP, ISO 15118, GDPR, OCPI, and regional energy regulations to make your solution compliant. This reduces regulatory risk and enables faster, smoother market expansion.
Enterprise-grade system integration. Providers connect electric mobility platforms with Enterprise Resource Planning (ERP) systems for finance and operations and with Transportation Management Systems (TMS) for logistics. They also integrate fleet management, billing, and energy management systems to support vehicle tracking, payments, and power monitoring.
Operational insight into eMobility data and workflows. Providers understand how EV systems behave in production environments. They work with vehicle telemetry, charger-to-cloud communication, and energy usage data. This enables providers to support monitoring, fault detection, and long-term optimization.

You may also be interested in exploring custom software development emobility expertise based on an example of a trusted partner like Intelliarts. 

The top 10 eMobility software development companies
Now, let’s get to the comprehensive overview of the top 10 eMobility software development partners, which you may consider for your best project. Remember, the comparison is based on publicly available information. For more focused insights, we recommend contacting the provider of your choice for additional information.
1. Intelliarts

Intelliarts is a software engineering and AI/ML services company with over 25 years on the market and over 9 years of specialization in eMobility software solutions. The company partners with enterprises and mid-sized businesses across eMobility, agritech, insurance, and digital marketing and has delivered 90+ large-scale projects for clients worldwide, including EVConnect, Indigo Ag, Greenlane, and Schneider Electric.
The firm has been recognized among the Top 100 Software Development Companies of 2026 by industry reviewers.
Services offered

Custom software development
Technology consulting
Machine learning and LLM solutions
Data engineering and analytics
IoT solutions

Expertise in EV software solutions

EV charge point management systems
Charging network platform development
Fleet and energy management systems
Integration with energy and enterprise systems
Predictive analytics and operational dashboards

Intelliarts stands out in the electric mobility sector by combining deep technical engineering with operational insight into EV charging management software, smart charging platforms, and more. Our engineering teams work directly with OCPP, OCPI, OpenADR, and ISO 15118 to strengthen interoperability, roaming readiness, and compliance. Over 92% of our engineers are senior-level specialists, enabling faster architectural decisions and fewer redesign cycles as platforms scale. With an average partnership length of 4+ years, our EV charging management software is built to run reliably in long-term, high-load environments.
Looking for a trusted e-mobility software partner for your project?  Don’t hesitate to reach out to us.Let's talk
#2 EV Connect

EV Connect offers cloud-based EV charging management software aimed at site hosts and network operators. Its platform focuses on operational control, visibility, and ease of deployment.
Services offered

Charging management software
Network monitoring tools
Driver-facing interfaces
Analytics and reporting dashboards
Access and user management

EV software expertise

Real-time charger status monitoring
Session and usage tracking
Billing and payment support
Infrastructure integration
Operational performance reporting

Interesting fact: EV Connect is actually a partner of Intelliarts, as these companies have had multiple projects, as for example, a predictive maintenance solution for EV chargers. 
#3 ChargePoint

ChargePoint is a global electric mobility company operating one of the world’s largest EV charging networks. Its software platforms support large-scale charging operations, driver interaction, and network-level analytics across commercial and public environments.
Services offered

EV charging network software
Charging station operations management
Mobile applications for drivers
Network analytics and reporting tools
Access control and user management

EV software expertise

Real-time charger monitoring
User authentication and authorization
Session management and scheduling
Usage analytics and performance tracking
Network reliability and uptime management

#4 EVBox

EVBox develops EV charging management software for businesses, municipalities, and charge point operators. Its platforms are designed to support diverse charging use cases and integration within broader energy ecosystems.
Services offered:

Charging management platforms
Network monitoring dashboards
Operational analytics tools
System integration services
User and asset management

EV software expertise:

AC and DC charging support
Multi-site charging network management
Energy usage monitoring
Integration with renewable energy systems
Reporting and compliance support

#5 Driivz

Driivz provides cloud-based EV charging and energy management software used by utilities, fleets, and charge point operators. The platform emphasizes monetization, scalability, and grid-aware charging operations.
Services offered:

EV charging management platform
Billing and settlement systems
Energy demand management tools
User and partner portals
Network operations dashboards

EV software expertise:

Smart charging and load management
Utility and grid integration
Tariff and pricing configuration
Multi-tenant platform architecture
Operational analytics and reporting

#6 AMPECO

AMPECO delivers a white-label EV charging software platform designed for operators managing complex charging networks. Its solution supports customization, scalability, and third-party integrations.
Services offered:

EV charging infrastructure software
White-label mobile and web applications
Operator and driver portals
API and integration services
Billing and payment management

EV software expertise:

OCPP-compliant charging control
Scalable fleet and station management
User access and role management
Pricing and tariff configuration
Reporting and operational insights

#7 Shell Recharge Solutions

Shell Recharge Solutions provides EV charging software as part of Shell’s electric mobility portfolio. Its platforms support public and commercial charging networks with a focus on usability and scale.
Services offered:

Charging network management software
Mobile applications for drivers
Payment and billing solutions
Network monitoring tools
Operational analytics

EV software expertise:

User authentication and access control
Payment processing workflows
Charger availability monitoring
Network performance analytics
Integration with broader mobility services

#8 ChargeLab

ChargeLab is an EV charging software provider focused on flexible deployments and partner-centric ecosystems. Its platform is commonly used by operators seeking adaptable and integration-friendly solutions.
Services offered:

Charging management software
Operator dashboards
API and partner integrations
Billing and pricing tools
Analytics and reporting

EV software expertise:

Live charging network visibility
Session and usage tracking
Custom billing workflows
Integration with third-party systems
Scalable deployment models

#9 Current

Current develops EV charging management software tailored to fleet and public charging environments. Its solutions focus on operational visibility and straightforward charging network management.
Services offered:

EV charging management software
Fleet-oriented operational tools
Network monitoring dashboards
Usage reporting features
Basic user management

EV software expertise:

Charging station integration
Fleet charging coordination
Session tracking and reporting
Operational visibility tools
Basic analytics capabilities

#10 YoCharge

YoCharge provides EV charging management software with an emphasis on white-label deployments and flexible payment options. Its platforms are used across commercial and residential charging setups.
Services offered:

EV charging management platform
Branded mobile applications
Operator dashboards
Payment and billing tools
Real-time station control

EV software expertise

QR-based charging workflows
Payment gateway integration
Session monitoring and control
User and access management
Operational reporting tools

You may also be interested in exploring the list of top electric mobility solutions in another our blog post. 
EV Regulations Every EV Leader Should KnowRead our practical guide to the rules shaping e-mobility software and EV charging platforms worldwide, featuring expert insights.Download the white paper
How to choose the right eMobility software development partner
Choosing the right partner for eMobility software development determines how well your electric mobility initiatives perform in real operations. In the fast-moving industry, software must support infrastructure, energy systems, and business processes at scale. 
Looking for a trusted provider of software services for renewable energy & e-mobility? Reach out to Intelliarts and let our experts consult you. 
Key factors to consider when choosing a software development partner
The following factors help distinguish capable long-term partners from short-term vendors. They should guide you whether you are choosing an off-the-shelf product or getting ready for a custom electric mobility project: 
#1 Fleet size and operational complexity
Fleet size refers to the number of electric vehicles, charging points, users, and operational scenarios a platform must support. In eMobility software development, this also includes vehicle usage patterns, charger density, geographic spread, and daily operational load. 
Fleet complexity directly impacts performance and reliability. Providers without relevant experience may underestimate system load or operational edge cases. A partner familiar with similar deployments reduces risk and improves delivery predictability.
Key considerations:

Number of vehicles
Charging patterns
Geographic coverage
Operational variability

Integration is where most eMobility projects succeed or fail. If billing, fleet systems, and energy data do not align, teams lose visibility. —  Alexander Barinov, a Managing Partner at Intelliarts.
#2 Scalability and growth readiness
Scalability refers to how well software supports future expansion without reworking core systems. In the electric mobility sector, growth often includes new fleets, charging locations, markets, and services. Software must be designed to accommodate this from the start.
Strong eMobility software development partners build modular, cloud-based systems. They separate core logic from extensions and plan for higher traffic and data volumes. 
Scalability protects long-term investment. As electric mobility initiatives expand, systems that cannot scale become costly bottlenecks. 
Key considerations:

Modular architecture
Cloud infrastructure
Multi-region support
Performance limits

Curious to read actual cases of top e-mobility service providers? Take a look at the EV fleet management software success story by Intelliarts. 
#3 Compliance with local and global regulations
Compliance covers how software aligns with legal and industry requirements across regions. In emobility service provider software, this includes EV standards, data protection laws, energy regulations, and sustainability reporting obligations.
Regulatory gaps can block market entry and create legal exposure. A provider familiar with the electric mobility industry helps companies expand confidently across borders while staying compliant.
Key considerations:

Industry standards support
Data protection rules
Energy regulations
Regional compliance experience 

#4 Integration requirements and ecosystem fit
Integration requirements define how eMobility platforms connect with existing business systems. This often includes ERP systems, TMS, fleet management tools, billing platforms, and energy systems.
A capable eMobility service provider designs stable APIs and reliable data flows. They ensure that operational, financial, and energy data move consistently across systems. 
Integration quality affects efficiency and visibility. Poorly integrated e-mobility applications limit reporting and slow decision-making.
Key considerations:

API design
Data consistency
System interoperability
Enterprise integration experience

#5 Domain expertise and delivery maturity
Domain expertise reflects a provider’s hands-on experience in electric mobility projects. This includes knowledge of EV operations, charging infrastructure, energy management, and production deployments within emobility partnerships.
Providers with domain maturity anticipate challenges early. They apply tested patterns, make informed technical decisions, and manage delivery risks. This expertise reduces project uncertainty and shortens time to value. A partner with proven experience contributes strategic insight on top of their development capacity.
Key considerations:

Relevant project experience
Production deployments
Cross-domain knowledge
Delivery processes

The table below provides a comparison of all the eMobility software development providers from the previous section by every factor mentioned: 
 
Don’t hesitate to utilize this framework in your decision-making flow. Score potential vendors and shortlist the best partners for your needs to maximize the chances of having a successful collaboration.
Advice on the regulatory landscape of eMobility when working with software developers
When working with software developers in eMobility, understanding the regulatory landscape early is essential to avoid costly rework and compliance risks in a fast-evolving market.

Ensure your developers have hands-on experience in the electric mobility industry and are familiar with regional energy, data protection, and charging standards.
Validate that compliance is built into the architecture, not added later, especially for billing, data security, and interoperability.
Require ongoing regulatory monitoring, as rules in the electric mobility sector change frequently across regions.
Choose partners who can document and demonstrate compliance through audits, certifications, and real deployments.

In eMobility projects, proactive regulatory alignment is as critical to long-term success as the software itself.
You may be additionally interested to find out cost-effective EV charging optimization practices in another our blog post. 
Key features to look for in eMobility software solutions
Here at Intelliarts, we often provide technology consultation, where, among other important aspects, we help with choosing an appropriate scope of functions for a project. While an off-the-shelf mobility service provider software may be packed with dozens of features, for a custom solution, it’s important to stay focused on what brings the most value.
Here’s the list of key features you most likely want to include in your e-mobility applications:

Smart charging orchestration. This feature dynamically balances charging loads and optimizes energy usage to reduce costs and avoid grid overload.
Charging station management. Is responsible for tracking charger status, availability, and performance to maintain uptime and efficient utilization.
Interoperability and protocol support. Should be present in an eMobility software solution to ensure compatibility with diverse hardware and industry standards.
Energy management and grid integration. Such functionality aligns charging behavior with grid conditions, renewable energy availability, and demand-response programs.
Fleet energy monitoring. Enables operators to track real-time energy consumption and identify efficiency gaps across vehicles and routes.
Billing and tariff management. This component automates pricing, invoicing, and payments while supporting flexible tariffs across users and charging scenarios.
Regulatory and sustainability compliance. Is responsible for supporting EV regulations, emissions reporting, and sustainability requirements across regions.
Data analytics and reporting. With this feature, operational data is transformed into insights for optimization, forecasting, and strategic decision-making.
Real-time vehicle tracking. Provides continuous visibility into vehicle location and status to improve coordination and service reliability.
Security and access control. Ensures protection of infrastructure, user data, and transactions through authentication, authorization, and encryption.

In eMobility projects, most problems do not come from chargers or vehicles. They come from software that was not designed for real operational load. — Ihor Rudnyk, an eMobility Tech Specialist at Intelliarts
Important note. When planning for custom software development emobility, you may want to omit some features or add others, like extended EV charging data analytics, integration capabilities, and so on. That’s normal practice. Use the list above to guide your decision-making on what scope of features to include, but don’t limit your product only to them. 

The future of eMobility software
eMobility software is entering a new phase, which is largely affected by increasing EV adoption and expanding charging infrastructure. However, it’s also important to account for rising regulatory pressure. 
The trends and predictions that may await the industry in 2026 and beyond are provided in the infographics below:
 
By 2026 and beyond, eMobility software will evolve from operational support tools into core infrastructure for the electric mobility industry. Platforms that successfully combine scalability, sustainability, and compliance will define market leaders. 
Final take
Choosing the right eMobility software approach is a practical business decision that affects daily operations and long-term growth. Success depends on working with experienced partners and understanding regulatory requirements. Software should integrate smoothly with existing systems and support future expansion. 
Looking for a trusted eMobility software development partner for your best project? Here at Intelliarts, we have more than 25 years of experience providing eMobility, energy, AI, ML, and other solutions under our belt. We are oriented towards long-term collaborations, with some of our projects spanning more than 15 years. We also have a 90% customer return rate and 92% senior staff engineers. Don’t hesitate to reach out for more details and consultation.
Drive your e-mobility project with our expert engineeringExplore our service

#### Top 7 Applications of Data Science in Business
How to use data science in business? With the software tools, methodologies, and capabilities it brings, data science can offer businesses a unique instrument to glean insights, streamline operations, and cultivate innovation.
According to the latest Polaris Market Research, today’s global data science platform market is valued at $95.31 billion and is projected to grow at an incredibly impressive CAGR of 27.6% by 2030. Likely, the trend will continue as data science-based solutions infiltrate businesses from different domains.
From this post, you’ll discover the value and impact of data science for companies and what are the top strategies for using this technology. Besides, you’ll get to know the components of a solid data science strategy and best practices for creating one. Examples of how data science transforms businesses are also included.
Importance of data in business
 
To begin with, the adoption of data science can notably revolutionize business in multiple ways. Digital transformation helps make better use of vast data generated through everyday operations. If utilized successfully, it can bring the following outcomes:
Improved decision-making
Probably the usage of data generates the most value when it impacts decision-making. By leveraging insights obtained through data science, businesses can now make choices based on concrete evidence rather than mere intuition. Alternatively, even if businesses used to exploit methods of data analysis, they can be notably enhanced and transformed, as well as largely automated, through data science approaches.
The detailed approach increases the likelihood of success, be it in launching a new product, entering a new market, or refining internal processes. In essence, data science provides businesses with modern-day analytical instruments needed to navigate an increasingly complex business landscape.
Enhanced customer insights
Understanding customers’ pain points is the cornerstone of any successful business strategy. After all, the opportunity to strike a chord with a potential buyer’s soul nearly guarantees sales. Data science plays a pivotal role in this by:

Analyzing consumer behavior. Tracking purchase history, browsing patterns, and feedback to understand preferences.
Predicting future trends. Anticipating what products or services customers might be interested in the future.
Segmenting the market. Grouping customers by similar behaviors or preferences to tailor marketing efforts.

With such deep insights, data science for business can bring better customer experience, fostering loyalty and driving revenue.
Increased efficiency and cost savings
Efficiency in making decisions, running business operations, distributing resources, etc. often translates to increased profitability. As an example, retailers using data analytics experienced an average 10% reduction in costs and a 10% increase in revenue.
Companies can identify bottlenecks, redundancies, or processes that can be optimized by employing data science techniques, like:

Classification
Clustering
Regression and time series analysis approaches
Anomaly detection
Visualization

This could range from automating mundane tasks to streamlining supply chain operations and optimizing logistics for faster delivery. The resulting operational improvements not only lead to faster, more efficient processes but also translate to significant cost savings, boosting the bottom line.
Better risk management and fraud detection
In today’s digital era, businesses are still vulnerable to various risks, especially financial health and cyber threats. Data science aids in:

Predictive modeling. Forecasting potential areas of financial, operational, or other machine learning risk assessment.
Enhancing security measures. Real-time monitoring, network analysis, geospatial analysis, anomaly detection, and other data science algorithms can help detect and counteract malicious activities. ML-based fraud detection also plays a crucial role in identifying and preventing fraudulent transactions.

The proactive detection and management of risks ensure that businesses remain secure, trustworthy, and compliant, safeguarding both their financial interests and reputation.
More innovation-driven products
Innovation is the driving force behind long-term business success. With data science, companies can:

Analyze market gaps. Identifying areas where customer needs are not met fully.
Test product concepts. Using data-driven feedback loops to help refine prototypes.
Optimize product launches. Analyzing market response to product launches to adjust strategies accordingly.

By embedding data science into the product development process, businesses can create offerings that are not only unique, but also resonate deeply with their target audience. This will help to ensure market success and sustained growth.
If you’re interested in data science, you will find it useful to learn more about building a big data strategy in another of our blog posts.
Why are there still few businesses using data science
As Oleksandr Stefanovskyi, the team lead of the R&D department of Intelliarts notices from his experience:
“Many businesses aren’t using data science because they regard its adoption as an expensive and complex intervention. They might not have suitable, cleansed, and representative datasets or know how to use them, as 33% of firms reported that they don’t trust their data. Some staff members might resist using new technology, lack data literacy as only 21% of workers claim to be confident in their skills, or have difficulty estimating the ROI from data science implementation. Plus, finding skilled engineers to integrate the technology can be complicated. Lastly, some businesses just don’t know how the data science application in business can help them solve their business issues.”
We can claim that younger companies and startups can start utilizing data science faster, as they tend to make decisions swiftly. As for well-established businesses, it’s easier for them to get started with data science as they have resources to purchase data. They can also allow themselves to implement innovations for a longer period, and they often don’t heavily rely on investors.
From the Intelliarts experience, businesses that opt to adopt data science don’t regret their choice. Our customers who shared their experience with data science practices claim an increase in the number of successful business decisions and positive outcomes from them, including competitive advantage, increased revenue, enhanced sustainability, and more.
If you’re unsure where to start with the adoption of data science, don’t hesitate to reach out to qualified experts from Intelliarts.
Business sectors in which data science is a must-have
Data science can be used in nearly all domains since all businesses naturally deal with data, and, respectively, need to utilize it effectively. Here is a list of industries and potential applications of data science within them:
 
The listed industries express the varying rates of data science adoption. While finance and retail are among the most rapidly transforming niches, the agriculture and energy sectors tend to start utilizing data science at a slower pace.
7 applications of data science in business
Application of data science in business, both as a technological field and as a practical approach, offers methods and strategies to process data effectively, making it extremely valuable for commercial use. The 7 best data science strategies to improve business outcomes include the following:
Generate unique insights
Above all, business owners need to have insights that can help them understand their clients’ pains and desires. Some of the insights that can be generated include:

Customer segmentation
Customer behavior patterns
Operational bottlenecks
Market trends
Product performance
Supply chain efficiencies
Employee productivity

This essential information will guide a strategy for satisfying clients in a way that’s beneficial for a business. In simple terms, insights can help to determine what services and products can be sold profitably.
Increase competitiveness and performance
In the data-driven world, those who harness data effectively often outpace their competitors. Data science tools and methodologies can benchmark your performance against industry standards. By analyzing this data, businesses can pinpoint strengths, identify areas that need improvement, and strategize accordingly. This proactive approach fosters a culture of continuous enhancement, making businesses more agile and robust.
Optimize and automate business processes
Operational inefficiencies can be costly. Data science enables companies to map out their entire workflow, identifying bottlenecks and inefficiencies. By leveraging algorithms, businesses can automate repetitive tasks, ensuring speed and accuracy. This not only saves time and resources but also allows human talent to focus on more strategic roles, driving overall business value.
Read also about predictive maintenance advantages and disadvantages for the manufacturing business.
Forecast the future of your business with patterns
Strategic forecasting can be done through the following approaches:

Trend analysis. Predict industry shifts and adapt strategies timely.
Seasonal forecasting. Anticipate demand during peak seasons.
Consumer behavior prediction. Understand the future actions of customers.

By recognizing patterns, businesses can anticipate market changes, ensuring they’re always a step ahead. Predictive models can help strategize inventory management, marketing campaigns, and more.
Enhance the customer experience through personalized recommendations
In today’s digital age, personalization is key. Consumers are more likely to engage with brands that offer tailored experiences. Using data science, businesses can analyze individual customer behaviors, preferences, and purchase histories. Such analysis allows companies to make personalized product or service recommendations, fostering customer loyalty, increasing retention, and maximizing revenue opportunities.
Drive decision-making with data-driven market analysis
Here are some of the techniques you can use to drive decision-making using data science to solve business problems:

Competitor analysis. Understand the strengths and weaknesses of competitors.
Market segmentation. Identify and target high-value customer groups.
Demand estimation. Predict and meet customer demand efficiently.

By conducting a thorough market analysis, businesses can make well-informed decisions. This data-driven approach helps in understanding market dynamics, spotting opportunities, and mitigating risks, ensuring more strategic and confident decision-making.
Identify and tap into new revenue streams using predictive analytics
Another data science application in business is providing an opportunity to predict future trends and spot opportunities before they become apparent. This might mean introducing a new product, entering a new market, or targeting a different consumer segment. By foreseeing these potential revenue streams, businesses can use market opportunities more effectively than their competitors. Or, detect potential risks and take action before they negatively impact the business.
Are you looking for a trusted provider to power up your data science project? Drop Intelliarts, the data science agency, a line.
Data science strategy components
One of the data science business applications is obtaining data and transforming it into useful knowledge includes several key components. See the infographic below to learn more:

Next, let’s get to know the best practices that can drive creating and executing a data science business strategy.
Best practices for a successful data science strategy
The mere adoption of data science isn’t sufficient. To truly reap its benefits, businesses must adhere to the following best practices that lay the groundwork for success:

Align with the right vendor. Partnering with vendors who understand your business can greatly help in building the system for generating relevant insights and making efficient data-driven solutions.
Gather and track the right metrics. Focus on key performance indicators (KPIs) that align with business objectives to drive meaningful change.
Set use-case-driven goals. Tailor data science initiatives to address specific business challenges for effective and tangible outcomes.
Demonstrate valuable results. Highlighting a clear ROI from data initiatives encourages stakeholder buy-in and further investment in data endeavors.
Invest in continuous training and upskilling. Keeping teams updated with the latest techniques ensures adaptability and optimum use of data tools.
Foster a collaborative environment between data scientists, IT, and business units. Integration of data scientists, IT, and business units streamlines operations and enhances the applicability of insights.
Ensure data quality and integrity. Accurate, clean data is foundational to data science strategizing. It ensures the retrieval of reliable insights and fosters trust in decisions made based on them.

If you’re interested in learning ways to obtain clean data using data pipelines in business, read another blog post of ours.
It’s safe to claim that theory is hardly useful if it’s not connected with practice. In the next section, we’ll explore some data science implementation success stories.
Examples of how data science helps your business
Let’s review some of the success stories showcasing how the data science application in business can be truly beneficial:
Home and car insurance company

One of the data science examples in business is home and car insurance company. The challenge was to help an insurance company replace its inefficient system for communicating with leads with a custom predictive lead-scoring solution. It was intended to help the customer prioritize leads better, raise efficiency, and optimize the usage of the company’s resources.
The solution was a predictive lead scoring model capable of forecasting how likely the lead would buy an insurance policy. This was achieved by using gradient boosting techniques that enable the recognition of patterns in vast amounts of tabular data. The custom insurance software enabled the customer to cut off approximately 6% of non-efficient leads, which resulted in a 1.5% increase in profit within a few months. Now the customer leads by the probability of sales success.
Real estate business

The challenge was to forecast which householders would sell a residence prior to the listing of the property for sale. The customer was monitoring over 1400 property and demographic data attributes. It also possesses a massive dataset comprising over 60 million data records. In that regard, the customer required effective processing of the raw data.
Re-uploading data to Amazon S3 data storage, concluding data transformation, and establishing data processing with the assistance of AWS Glue constituted the resolution. Using the processed data, the predictive model can identify a minimum of 70% of homeowners who intend to sell their homes. This result is significantly higher than the industry average.
AirBnB

Users of AirBnB had difficulty setting prices for housing opportunities, which either left money on the table if priced too low or led to few bookings if priced too high. So, to keep the competitiveness of the platform at a superior level, AirBnB decided to apply data science.
To assist hosts in setting a competitive and optimal price for their listings, AirBnB developed a dynamic pricing model using data science. This model, known as “Smart Pricing,” takes into account various factors including time of year, listing location, local demand, nearby events, amenities provided, etc.
Final take
Using data science to solve business problems is always a wise choice. After all, it helps companies make the best use of their valuable asset — data. By exploiting data science business strategies, applications of data science in business, and best practices, including the development of robust data pipelines, you can transform the way decisions are made in the company, leading to better outcomes for the business.
If you intend to get started with data engineering consulting services or creating and executing a data science strategy, reach out to Intelliarts — a provider of IT services that has been on the market for more than 24 years. Our team of expert data scientists is ready, willing, and able to help your business stay ahead.
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#### Top 7 Tips to Optimize Your Electric Fleet Charging
Global EV adoption is accelerating, with electric vehicles comprising more than 20% of all new car sales globally. At the same time, the EV charging infrastructure market is expected to grow at a CAGR above 25% through 2030. 
A modern energy management system (EMS) and fleet charging software enable control over charging decisions. As fleets scale, electric fleet charging optimization directly affects cost, uptime, and operational stability. Measurable improvements here are of high value for fleet operators, logistics companies, public transport operators, delivery fleets, and other companies involved. 
This post delves into the reasons for implementing EV fleet charging management. It also outlines seven key tips for optimizing EV fleet charging at both technological and organizational levels. The guide further shows what an optimized fleet charging workflow looks like and highlights common business mistakes companies may make.
What is electric fleet charging optimization and why is it critical?
Electric fleet charging optimization is the strategic coordination of charging schedules, energy usage, and infrastructure as a unified system. The ultimate goals of EV charging optimization are to minimize costs and ensure that vehicles are used in a time-effective manner.
If you’re wondering why EV fleet charging optimization can be tricky, here are some of the common challenges:

Coordination of charging across large and diverse fleets
Limited charging windows between vehicle shifts
Integration with route planning and duty schedules
Different battery levels and charging needs across vehicles
Scaling infrastructure as the fleet grows
Fragmented visibility across chargers, vehicles, and energy systems

And here are some of the charging-related factors that impact operational costs:

Utility tariff structure
Peak demand level
Time-of-use electricity rates
Number of vehicles charging at the same time
Charger power output
Total depot energy consumption
Session duration
Local grid capacity limits

Core capabilities of fleet energy management include intelligent scheduling, continuous monitoring, and real-time adjustments. These functions are supported by analytics, which, in turn, provide visibility into usage patterns. The final outcome is informed charging decisions to drive further optimization actions.
Learn more about cost-effective EV charging optimization practices in another blog post by Intelliarts.
The following sections will elaborate on the tips, best practices, and technologies to optimize your electric fleet charging. 
Tip #1 – How can smart charging reduce energy costs?
Without coordination, fleets often charge during peak tariff windows, increasing both energy costs and exposure to demand charges. Smart charging introduces controlled, data-driven behavior that keeps costs predictable while maintaining fleet readiness.
Approaches to cut EV charging costs through smart charging:
#1 Time-of-use optimization
Electric vehicle fleet charging schedules are shifted to off-peak tariff periods, often overnight. Non-critical sessions are delayed automatically, reducing the average cost per kWh.

Estimated impact: ~10-30% reduction in energy costs, depending on tariff spread.

#2 Demand response integration
Charging load is reduced or paused during grid stress events based on utility signals. This avoids expensive peak usage and unlocks incentive payments.

Estimated impact: ~5-15% additional savings through avoided peak pricing and incentives based on the above mentioned SceinceDirect research and supporting materials.

#3 Dynamic load balancing
Power is distributed across vehicles based on real-time capacity limits. This prevents simultaneous high-load charging and stabilizes total site demand. Load balancing is a core component of any advanced EV fleet charging infrastructure. 

Estimated impact: ~10-25% reduction in demand-related costs based on the above mentioned SceinceDirect research and supporting materials.

#4 Charging prioritization by operations
Vehicles are charged based on departure schedules and battery levels. Only critical vehicles receive early charging, while others are deferred.

Estimated impact: ~5-12% efficiency gain through better charger utilization and reduced overlap.

From Intelliarts experience supported by publicly available data and research, combining these smart charging strategies leads to 20-40% overall cost reduction for fleet charging operations. 
Additional savings include avoiding infrastructure upgrades and improved energy efficiency. In optimized cases, studies show even greater reductions when tariff-aware scheduling and load control are applied together.
Cutting energy costs: Success story by Intelliarts

Challenge: The customer is an EV charging platform provider expanding into fleet management. They needed a solution that could support different charging scenarios, including depot, on-route, and at-home charging, while keeping costs under control. 
Solution: Intelliarts built an EV fleet management solution with a clear focus on lowering costs and improving day-to-day efficiency. Vehicles that need to leave sooner get priority, while others are scheduled later, which helps avoid unnecessary overlap and wasted energy. On top of that, the system adds smart energy control through power limits and load balancing. 
Results: The solution keeps demand under control and prevents expensive spikes. It also tracks at-home charging and calculates costs based on actual electricity rates. In practice, this means lower energy bills, better use of charging infrastructure, and more predictable fleet operations.
Explore EV fleet charging case study.
Tip #2 – Why is load balancing essential for depot charging?
Load balancing is the process of distributing available electrical power across multiple chargers. This technology is what allows businesses to keep total demand within site limits and still ensure that vehicles get charged. 
As a C-level manager or a business owner, you might be concerned with the following:

Grid overload. When multiple vehicles start charging at full power, total demand can exceed the site’s capacity within minutes. This can trigger shutdowns, slow down all sessions, or lead to penalties for exceeding contracted limits, directly impacting operations.
Expensive infrastructure upgrades. Without load balancing, growing demand often leads to costly grid upgrades or new equipment. In many cases, the issue comes from inefficient power distribution rather than actual capacity shortages.

The table below shows distinctions between having a dynamic load balancing vs having static or even no set load balancing rules at all:
Tip #3 – How does charging scheduling improve fleet availability?
Here at Intelliarts, when providing technology consulting, our experts often note that poor fleet availability is actually a cause of uncoordinated charging rather than a lack of infrastructure. 
A lack of optimization in that regard leads to critical vehicles not being ready, while low-priority ones occupy chargers without urgency. Charging scheduling helps to manage charging sessions with consideration of the following factors:

Actual vehicle usage
Departure times
Operational priorities
Battery state of charge
Charger availability 

That’s how you optimize the system for charging the priority vehicles first. Adhering to additional fleet management trends may also notably help. 
Core scheduling approaches in EV fleet management include:

Route-based scheduling. Charging follows planned routes and departure times. Vehicles with earlier shifts are charged first, while others are scheduled later to avoid overlap.
Priority-based charging. Vehicles are ranked by battery level, next trip, or importance. Power is allocated to ensure critical vehicles are not delayed.
Integration with telematics systems. Telematics data provides real-time insights into location and battery status. Charging plans adjust dynamically to reduce idle time and improve accuracy.

Improvements to be expected:

Higher fleet availability at departure times
Reduced charging conflicts at depots
More efficient use of the charging infrastructure
Better alignment between operations and energy consumption

In practice, charging scheduling becomes a key component of EV fleet optimization, ensuring vehicles are charged based on real business needs rather than static rules.
Discover more about EV fleet management in another blog post by Intelliarts. 
Tip #4 – How can fleet charging software improve visibility?
Without a unified system, data for all electric fleet charging operations is often split across chargers, vehicles, and external tools. This makes it difficult to track charging status, identify inefficiencies, or respond quickly to issues. As fleets scale, this lack of visibility leads to higher costs and operational blind spots.
Key capabilities that improve visibility: 

Centralized dashboard. All charging operations are displayed in one interface.
Real-time monitoring. The system tracks charging activity as it happens. 
Charging analytics. Historical and real-time data are used to identify patterns in energy usage, peak demand, and charger utilization. 

How visibility supports better decisions:

Faster identification of operational issues
Clear understanding of energy usage across the fleet
Better planning of schedules and electric vehicle fleet charging infrastructure
Improved control over fleet charging costs

See the example of an optimization dashboard in the diagram below:

While being more of a general approach for an actual optimization dashboard, this diagram combines vehicle data, charging status, and energy usage in one place. Great for fast validation of detecting bottlenecks for improving cost control and planning further. 
Need a technical consultation on optimizing electric fleet charging?Let Intelliarts experts assist
Tip #5 – How can telematics and route planning support charging efficiency?
Charging efficiency becomes harder to maintain when vehicle usage, routes, and energy needs change throughout the day. 
Here are two main issues related to charging efficiency:

Unpredictable usage. Vehicle schedules often change during the day. Without real-time data, charging plans become inaccurate, leading to undercharged vehicles or unnecessary early charging.
Range anxiety in a fleet context. For fleet operators, this is not just about one vehicle. If battery levels are not tracked accurately, there is a risk that vehicles will not complete planned routes, affecting service reliability.

Telematics and route planning support charging efficiency by providing both real-time and predictive inputs for charging decisions. They are based on the following factors:

Battery level
Location
Recent energy consumption
Planned route distance
Next departure time
Charging history and patterns

Route planning adds context by linking charging to trip distance and departure timing, so energy is allocated based on actual needs rather than estimates.
This works through integration between the fleet management system and charging software. Such a level of coordination is increasingly expected from modern EV charging fleet services and advanced fleet platforms.
In early stages, fleet charging looks manageable. Once the number of vehicles grows and return times start overlapping, energy demand becomes concentrated within a very short window, and that’s where both costs and coordination issues start to surface. —  Jurij Aulikh, an eMobility Software Engineer at Intelliarts
Tip #6 – How do you minimize peak demand charges?
Peak demand charges are minimized by controlling site load, so short-term spikes do not define your billing level. In depot charging, this usually means managing how many vehicles charge at high power at the same time.
Core peak demand management methods include: 
#1. Peak shaving. Caps total site load by reducing charging power when demand approaches a threshold.
Formulas: Peak Demand = max (total site load, kW)
Cost = Peak Demand × Demand Rate ($/kW) → Lower peak = lower cost
#2. Battery storage integration. Supplies stored energy during peak periods to reduce grid draw.
Formula: Grid Load = Total Load − Battery Output → Reduces billed peak demand
Expert advice by Intelliarts: Battery storage can be complemented by emerging approaches such as Vehicle-to-Grid (V2G), where EVs act as distributed energy assets and feed power back to the grid during peak demand periods. V2G technology, when implemented properly, further reduces grid load and demand charges.
#3. Smart algorithms. Adjust charging timing and power based on demand, tariffs, and fleet priorities.
Formula (conceptual): Optimized Load(t) = Σ charging powerᵢ(t) ≤ Demand Limit → Keeps total load under cost threshold
Peak demand cost comparison across charging strategies is provided in the infographic below:

Note: The values shown are illustrative estimates based on typical EV fleet charging scenarios. Actual rates and resulting costs vary for every business situation. 
Tip #7 – When should you consider custom fleet charging software?
Custom fleet charging software becomes relevant when standard tools no longer support the scale, complexity, or control required for your operations. Experience of Intelliarts engineers shows that this usually happens when the charging logic becomes too complex because of operational constraints or when the following scenarios are applicable:

Multi-depot operations. Coordinating load, reporting, and charging policies across locations becomes difficult without centralized and flexible control.
Complex integrations. When data needs to sync in real time, standard platforms often fall short or require workarounds.
Scalability beyond standard workflows. More advanced logic is needed to manage prioritization, energy distribution, and cost control at scale.

See the general decision framework in the table below:

Still not sure how to approach the decision?
Through our many success stories, Intelliarts found out that the platform approach works well enough at the earliest stages of EV businesses. As operations expand, limitations appear in control, integration, and optimization. At that point, transitioning to custom fleet charging software starts appearing as a cost-effective and nearly critical strategic investment. 
Should you need technical consultation or assistance, don’t hesitate to reach out to Intelliarts and request our EV software development services. We will guide you through the decision based on your business context and help implement your best project. 
What does an optimized fleet charging workflow look like?
An optimized EV fleet charging workflow ensures that every charging decision is predictable, justified, and aligned with operations. The system should be capable of coordinating vehicle data, charging logic, and infrastructure in a way that supports both cost control and fleet readiness.
Adding more chargers increases theoretical capacity, but it doesn’t improve how efficiently that capacity is used. Without coordination, utilization remains uneven, and costs stay high. — Ihor Rudnyk, an eMobility Tech Specialist at Intelliarts
Key characteristics of an optimized workflow:

Charging reflects actual fleet operations. Vehicles with earlier departures or longer routes naturally move to the front of the queue. Others are scheduled later, which keeps energy usage aligned with real demand and avoids unnecessary overlap.
Vehicle data is continuously collected via telematics. Charging decisions rely on live inputs such as battery level, location, route assignment, and energy consumption. This keeps electric fleet charging aligned with actual vehicle usage throughout the day.
Charging demand is forecasted per vehicle. Energy requirements are calculated based on planned routes and departure times. Each vehicle has a defined charging target and deadline, which reduces both undercharging risk and unnecessary energy use.
Smart algorithms schedule charging sessions. A scheduling engine assigns when each vehicle should charge and at what power level. Priorities, tariffs, and site constraints are taken into account to avoid uncontrolled or simultaneous charging.
Load is balanced across available chargers. Power is distributed in real time to keep total site demand within limits. This stabilizes energy usage, prevents overload, and reduces peak demand charges across the EV fleet charging infrastructure.
Performance is monitored and continuously adjusted. Charging analytics track how sessions are executed, including energy usage and charger utilization. This feedback is used to refine scheduling and improve efficiency over time.

The infographic below shows the end-to-end electric fleet charging workflow, from telematics data to optimized charging execution. 
It explains how fleet charging software and an energy management system turn vehicle data into scheduling and load balancing decisions. It also represents a continuous optimization loop where charging analytics improve future EV fleet charging performance and costs. Work for fleet management for public transport as well as for private vehicle fleets.

Common mistakes in fleet charging optimization
Sometimes inefficiency is hidden in fairly avoidable operational and system-level mistakes. Here are some of the most common issues the Intelliarts team has seen companies make in electric fleet charging:

Ignoring tariff structures
Overprovisioning infrastructure
Lack of integration between systems
Manual scheduling
No real-time analytics
No load balancing across chargers
Charging all vehicles at arrival time (no staggering)
No prioritization based on operations
Poor visibility into charger utilization
Ignoring peak demand thresholds
No alignment between routes and charging plans

While detecting and fixing every bottleneck may be unrealistic, it is definitely worth pursuing excellence. After all, every bit of optimization results in potential financial gains.
Optimizing EV fleet charging requires specialized expertiseThe Intelliarts team handles EV fleet charging projects of any complexity.Talk to our experts
Final take
EV fleet charging optimization depends on how charging decisions are structured and executed. A scalable EV charging software architecture connects data, decision logic, and execution into a continuous system. As fleets grow, the ability to monitor, adjust, and optimize charging operations supports efficiency, cost control, reliability, and long-term scalability.
Intelliarts, as a trusted provider of software development services, has substantial expertise in EV solutions, with over 80 large projects successfully completed. We are also proud to maintain a 90% customer return rate and have 92% senior-level engineers on our team. Let our experts contribute to your electric fleet charging development with all our experience and industry-leading practices. 
Contact us

#### Top 8 Fleet Management Technology Trends for 2025
The usage of vehicles in business has grown over time due to an increasing number of logistics, energy, manufacturing, and other companies. But what’s the biggest challenge in owning fleets of vehicles? The answer is effective management. From sustainable energy usage to incorporating smart communication measures in vehicles — these fleet management industry trends are reshaping operations across sectors and can cover all that and more. 
In this post, you’ll explore the 8 biggest fleet industry trends in 2025, including innovative fleet management technologies, discover how the industry may evolve over time, and review real-life fleet management success stories, drawn from our 8 years of experience in creating EV solutions.
8 Trends in fleet management technology for 2025

The way companies operate with their fleets has substantially evolved over the past several decades thanks to rapid progress in fleet technologies like AI, IoT, and electrification. It brings us to the necessity to explore fleet management trends in great detail to find areas for improvement and ways to incorporate technology in business. 
#1 Incorporation of AI in fleet management
AI is transforming fleet management by automating decision-making, optimizing routes, enhancing predictive maintenance, and improving driver safety — as reflected in recent fleet management statistics showing cost reductions and improved efficiency. AI-powered analytics help fleets reduce fuel consumption, prevent breakdowns, and comply with regulations.
A report by Allied Market Research shows that the global automotive artificial intelligence market is projected to reach $405.3 billion by 2032, growing at a CAGR of 40.7% from 2023 to 2032. This suggests that AI-driven automation is becoming essential as fleets scale operations and seek cost savings.
Business issues this technology addresses:

Inefficient route planning that leads to higher fuel costs
Unplanned vehicle breakdowns causing operational delays
Inconsistent driver behavior impacting safety and compliance
Data overload without actionable insights
Rising operational costs due to manual decision-making

Solutions in the market:

Lytx DriveCam — Fleet management using AI for safety and telematics
Geotab AI Insights — AI-driven fleet optimization tools
Fleet Complete — AI-enabled vehicle tracking and diagnostics
Omnitracs — AI-powered predictive maintenance and routing

Transform Your Fleet Management StrategyExplore exclusive insights from e-mobility industry leaders in our latest white paper.Get your copy now
#2 Advancing telematics solutions for fleets
Telematics technology is evolving beyond basic GPS tracking to offer real-time diagnostics, driver behavior analytics, fuel efficiency monitoring, and AI-powered predictive maintenance. With the rise of IoT, 5G connectivity, and cloud computing, fleet operators can gain deeper insights into vehicle performance, reduce costs, and improve safety. 
According to the Markets and Markets report, the global telematics market is expected to reach $9.0 billion, growing at a CAGR of 10.1%.
Business issues this technology addresses:

Lack of real-time visibility into fleet operations
High fuel consumption and operational costs
Inefficient maintenance scheduling leading to vehicle downtime
Safety concerns due to poor driver behavior
Compliance challenges with government regulations (e.g., ELD mandates)

Solutions in the market:

Geotab — Fleet telematics and tracking solutions
Samsara — AI-driven telematics platform with video safety features
Verizon Connect — GPS fleet tracking and compliance solutions
Teletrac Navman — Fleet management and ELD compliance tools

#3 Green fleet initiatives
Sustainability is becoming a priority in fleet management industry trends, with companies adopting green fleet initiatives to reduce carbon footprints, comply with environmental regulations, and lower fuel costs. Strategies include using alternative fuels, optimizing routes to minimize emissions, and incorporating electric and hybrid vehicles. 
According to the International Energy Agency (IEA), electric vehicle (EV) sales are expected to reach 45% of total vehicle sales by 2030, driving a shift toward greener fleet operations.
Business issues this technology addresses:

Rising fuel costs and dependency on fossil fuels
Stricter government emissions regulations
Corporate sustainability and ESG (Environmental, Social, and Governance) requirements
Limited infrastructure for alternative fuels
Balancing cost-effectiveness with environmental goals

Solutions in the market:

ChargePoint — EV charging solutions for fleets
Geotab Sustainability — Fleet carbon footprint tracking
Shell Fleet Solutions — Alternative fuel options and carbon offset programs
Tesla Semi — Electric trucks for sustainable fleet management

Explore how IoT can boost renewable energy initiatives from another post written by Intelliarts experts. 
#4 Gradual vehicle electrification
The shift toward electric vehicles (EVs) in fleets is accelerating due to lower operating costs, government incentives, and advancements in battery technology. While full electrification is still in progress, many companies are integrating EVs gradually to balance cost and infrastructure readiness. 
The global electric vehicle market is projected to reach $823.75 billion by 2030, growing at a CAGR of 18.2% from 2021 to 2030. This trend is signaling a major shift in fleet management market trends toward sustainability and efficiency.
Business issues this technology addresses:

High initial investment in EVs and charging infrastructure
Limited charging stations affecting fleet operations
Battery range concerns for long-haul routes
Uncertainty around total cost of ownership compared to fuel-powered vehicles
Integration challenges with existing fleet management systems

Solutions in the market:

Intelliarts — Predictive maintenance solution for EV chargers
GM BrightDrop — Electric delivery vans for fleet operations
ABB E-Mobility — EV charging infrastructure for fleets

You may be curious to explore how to track and streamline your EV fleet, which is well-detailed by an Intelliarts expert in our corresponding blog post. 
#5 IoT and connected vehicles
The integration of Internet of Things (IoT) technology in fleet management is revolutionizing real-time tracking, predictive maintenance, and driver safety. Connected vehicles use sensors, cloud computing, and AI to optimize fleet performance, reduce downtime, and enhance operational efficiency. 
According to Allied Market Research, the global IoT in transportation market is expected to reach $495.57 billion by 2030, reflecting its impact on fleet management tech trends.
Business issues this technology addresses:

Limited real-time visibility into vehicle health and location
Unplanned vehicle breakdowns leading to costly downtime
Inefficiencies in fuel consumption and route optimization
Lack of proactive safety measures for drivers
Compliance challenges with regulatory reporting

Solutions in the market:

Intelliarts — EV fleet management software
Samsara Connected Operations — IoT-powered telematics and fleet analytics
Geotab IoT Solutions — Vehicle tracking and predictive maintenance
Verizon Connect Reveal — Connected fleet monitoring and insights
Teletrac Navman — IoT-enabled fleet intelligence platform

Get the insights on IoT analytics in automotive.Read more
#6 Digital twins for fleet optimization
Fleet technology called digital twins is reshaping management strategies by creating real-time virtual models of fleet operations. These models simulate vehicle performance, predict maintenance needs, and optimize logistics to reduce costs and downtime. By leveraging AI and IoT data, fleet managers can test scenarios and improve efficiency before making real-world changes.
According to Gartner, by 2027, over 75% of large enterprises will use digital twins to improve operations and asset management.
Business issues this technology addresses:

Difficulty in predicting vehicle failures and maintenance needs
Inefficiencies in fleet route planning and fuel usage
High costs of unexpected breakdowns and operational disruptions
Lack of real-time insights into vehicle and driver performance
Challenges in optimizing asset utilization across a fleet

Solutions in the market:

PTC ThingWorx — Digital twin platform for industrial and fleet operations
Siemens Digital Twin — Fleet simulation and predictive analytics
Microsoft Azure Digital Twins — Cloud-based digital twin solutions
Bosch Digital Twin — IoT-powered fleet performance modeling

“It’s easy to observe how digital twins transform from an experimental gimmick into technology for everyday usage.” — Volodymyr Mudryi, an AI and ML engineer at Intelliarts. 
#7 Integration of V2X (vehicle-to-everything) communication
V2X communication is a game-changer in trends in fleet management technology, enabling real-time data exchange between vehicles, infrastructure, pedestrians, and networks. This technology enhances safety, reduces traffic congestion, and improves fuel efficiency by allowing vehicles to anticipate road conditions. 
The global automotive V2X market is projected to grow from $0.5 billion in 2023 to $9.5 billion by 2030, at a CAGR of 51.9%. Presumably, it’s driven by the rise of smart cities and autonomous driving solutions.
Business issues this technology addresses:

Limited real-time awareness of road hazards and traffic conditions
Increased fuel consumption due to inefficient route adjustments
High accident rates due to lack of vehicle-to-vehicle communication
Challenges in coordinating fleet movements in urban environments
Regulatory pressure to improve road safety and emissions

Explore how V2X works on use case examples in the video below:

Solutions in the market:

Autotalks — V2X chipsets for connected vehicle communication
Qualcomm C-V2X — Cellular V2X technology for fleet safety
Cohda Wireless — V2X solutions for fleet management
Savari V2X — Smart transportation and connected vehicle solutions

You may additionally be interested in exploring use cases of ML in the manufacturing in another blog post by Intelliarts. 
#8 Enhanced driver safety monitoring systems
Driver safety monitoring systems highlight the growing role of technology in fleet safety construction, especially in high-risk industries like logistics and energy. These systems leverage AI, telematics, and biometrics to detect fatigue, distraction, and unsafe driving behaviors and help fleet managers reduce accidents, lower insurance costs, and comply with safety regulations. The tools are also often integrated with mobile workforce management software, helping fleet managers monitor drivers, assign tasks, and respond to safety issues in real time.
According to the National Highway Traffic Safety Administration (NHTSA), drowsy driving causes over 100,000 crashes annually in the U.S. only. This fact emphasizes the need for advanced monitoring solutions.
Business issues this technology addresses:

High accident rates due to driver fatigue and distraction
Rising insurance premiums linked to unsafe driving behavior
Limited real-time monitoring of driver activity
Compliance challenges with safety regulations
Difficulty in providing immediate feedback and coaching to drivers

Solutions in the market:

Samsara AI Dash Cams — Real-time driver safety monitoring
Nauto – AI-driven driver behavior monitoring
Omnitracs Critical Event Video — Fleet safety and event recording
SmartDrive — Video-based driver safety coaching

What’s also interesting is that the trends we can observe in the present day, are likely to be preconditions for future changes. 
Future outlook and predictions
Now, let’s take a closer look at how the listed fleet management trends may evolve over time and what assumptions for the future we can make today:
#1 EV adoption will accelerate, limited only by infrastructure shortages
The global push for sustainable fleets will intensify, with governments and corporations prioritizing EV adoption. However, infrastructure gaps, such as charging station availability and battery performance limitations, will remain obstacles.
Prediction: By 2030, over 50% of new commercial fleet vehicle purchases will be electric.
Recommendation from our tech lead: It’s best for fleet managers to develop phased EV adoption strategies and invest in private charging infrastructure to mitigate range limitations.
#2 Sustainability regulations will push green fleet initiatives forward
Global sustainability regulations will become more stringent, compelling fleet operators to adopt greener practices. Carbon emissions tracking, alternative fuels, and EV integration will become regulatory requirements rather than voluntary initiatives.
Prediction: The global carbon footprint management market is projected to reach $14.74 billion by 2028.
Recommendation from our PM: It’s worth for companies should proactively adopt sustainability-focused fleet management strategies, including carbon tracking software, alternative fuels, and efficient route optimization.
#3 Fleet management software will become fully cloud-native and AI-driven
It’s likely that cloud-based fleet management platforms will become the industry standard, integrating real-time data, AI-driven analytics, and automated decision-making processes.
Prediction: By 2027, it’s forecasted that nearly half of all Industrial IoT (IIoT) applications will incorporate AI elements. 
Recommendation from our partner: Fleet operators should shift to cloud-based platforms that support different types of fleet management software, including predictive maintenance, telematics, and sustainability tools. This helps to improve scalability, enable real-time data analysis, and enhance overall operational efficiency.
Intelliarts experience
Here are two out of the many success stories by Intelliarts, describing our experience in providing electric vehicle software development services, IoT, and fleet management technologies:
Next-Gen EV Fleet Management Software Development

Challenge:
A major EV charging provider — EV Connect, sought to expand into fleet management, enabling operators to monitor vehicles, optimize charging schedules, and reduce costs. The company required a solution that could support depot, on-route, and at-home charging while ensuring energy efficiency.
Solution:
One of the notable fleet management use cases was when Intelliarts developed an advanced EV fleet management system that allowed fleet operators to track vehicle statuses, manage charging infrastructure, and optimize energy consumption. The platform integrated telematics solutions such as Geotab and NFI Connect for precise vehicle tracking, energy use monitoring, and smart charging prioritization based on real-time data. 
Results:
The solution improved operational efficiency by enabling real-time station monitoring and automated charge scheduling. It reduced fleet downtime and energy costs through predictive analytics, smart load management, and optimized charging decisions. Fleet operators gained better control over expenses and fleet performance, enhancing overall business sustainability.
Explore success story
Predictive Maintenance Solution for EV Chargers

Challenge:
EV Connect, a leading provider of EV charging solutions, needed to enhance the reliability of its charging stations by reducing unexpected failures and minimizing downtime. The company sought a predictive maintenance (PdM) system capable of analyzing equipment behavior, detecting anomalies, and optimizing maintenance schedules based on real-time data.
Solution:
Intelliarts conducted a detailed data analysis to prepare EV Connect for implementing a PdM system. The project involved transforming raw data from log records into structured formats, performing anomaly detection to identify early failure indicators, and analyzing historical data to uncover usage patterns. 
Results:
Intelliarts provided actionable insights on EV charger behavior, improving system reliability and maintenance efficiency. The company gained a better understanding of failure patterns, allowing for proactive maintenance planning. Additionally, Intelliarts’ data quality recommendations helped EV Connect refine its PdM model, optimizing tariff adjustments, system performance, and overall customer experience.
Explore success story
Final take
The future of fleet management is shaped by AI, IoT, and sustainability-driven innovations. As automation advances, fleets will operate with greater efficiency, reduced costs, and improved safety. The shift toward electric and connected vehicles will accelerate, with cloud-native platforms enabling real-time data-driven decision-making. Companies that embrace these technologies early will gain a competitive edge.
Should you need assistance with applying technology solutions to solve fleet management difficulties in your business, consider partnering with a trusted software company. Here at Intelliarts, with more than 24 years of experience in the market, we are ready, willing, and able to contribute to your best project with our expertise. 
Smarter EV Fleet ManagementAutomate maintenance, optimize routes, and simplify EV fleet operations.Learn more

#### Top Big Data Trends for 2025 and Beyond
About 402.74 million terabytes of data are generated each day. This data is used for enhanced decision making and for gaining insights. The difficulty lies in the proper utilization of big data adjusted for current industry tendencies. 
In this post, you’ll discover foundational concepts of big data, explore top big data trends for 2024-2028, and review specific applications of big data across industries. Besides, you’ll get to examine several real-life success stories of utilizing big data in businesses. 
We help unlock the power of your data with expert big data consultingExplore more
Foundational concepts in big data
Let’s get started by delving into the “5 Vs” of big data, which are volume, velocity, variety, veracity, and value:
#1. Volume is the vast amount of data generated every second from various sources, including social media, sensors, transactions, videos, and more.
Basically, everything to the sheer mass of big data that a company can access.
#2. Velocity is the speed at which new data is generated and the pace at which it must be processed to meet demand. 
It happens that data is generated and gathered in real-time while processed in batches at certain periods or vice versa. The slowest process determines the end velocity. 
#3. Variety is the different types of data, i.e., structured, semi-structured, or unstructured data, and the variability of sources, like text, images, videos, sensor data, and more.
In most scenarios, greater variety is better overall. Yet, it increases the overall complexity of any big data solution and raises standardization and unification issues. 
#4. Veracity is the quality, reliability, and trustworthiness of data.
High veracity data has fewer inaccuracies and inconsistencies, making it more suitable for analysis.
#5. Value is the potential economic and business benefit that can be derived from data. 
When analysis of particular data can contribute the most to extracting insights and advance business objectives, this data is regarded as high-value. Value is the most crucial V as it drives the decision to invest in big data technologies and analytics. 
Aside from the five Vs, there are also concepts of batch and real-time processing of data:

Batch processing is collecting and processing large volumes of data at scheduled intervals or specific times.
The main characteristic of batch processing is that the data is accumulated over a period in this case, stored, and then processed in a single batch.
Real-time processing is the continuous input, processing, and output of data. 
With real-time processing, handling of data and moving it across the data pipeline occurs almost instantly when the data is received, allowing for immediate analysis and action.
You can discover the key differences between batch and real-time processing in the table below:

Top trends in big data for 2025
Big data continues to evolve, shaping the way businesses make decisions and innovate. The latest data analytics trends in 2025 highlight the integration of AI, real-time analytics, and edge computing, driving smarter, faster, and more scalable solutions. These advancements are unlocking unprecedented opportunities across industries:
#1 Edge computing integration
Edge computing is about processing data closer to its source, such as IoT devices, sensors, and edge servers, instead of relying solely on centralized cloud data centers. This reduces the need to send large volumes of data to centralized cloud data centers, thereby addressing latency and bandwidth issues, which are still urgent in all big data-centered applications. 
Another example is solar panel fault detection, which focuses on real-time processing of big data obtained from infrared cameras to determine any faults, like overheated spots, early.
Prospects for this trend:

Increasing adoption in healthcare, transportation, and retail sectors.
Development of more sophisticated edge computing devices and solutions.
Greater integration with existing cloud infrastructure.
Enhanced real-time analytics capabilities.
Improved data security and privacy measures.

#2 Artificial intelligence, machine learning, and big data synergy
The synergy between AI and ML with big data involves leveraging these technologies to enhance data processing and analytics capabilities. AI and ML can automate the extraction of insights from large datasets, identify patterns, and make predictions like in case of EV data analytics. 
For those interested in mastering these advanced skills, big data training courses offer comprehensive knowledge on integrating AI and ML for more sophisticated data-driven decision-making, enabling organizations to uncover insights that would be difficult to detect manually.
Prospects for this trend:

Greater accuracy and efficiency in predictive analytics and forecasting.
Increased personalization in customer interactions and experiences.
Expanded use in anomaly detection and predictive maintenance.
Development of more intuitive AI-driven data analytics platforms.
Enhanced ability to process and analyze unstructured data, such as text and images.

You may discover how big data preparation contributes to machine learning in another blog post by Intelliarts. 
#3 Advanced analytics and predictive modeling
Advanced analytics and predictive modeling in big data involve using complex statistical techniques and algorithms to analyze historical data and predict future trends. These methods help organizations understand underlying patterns, make data-driven decisions, and anticipate future events. Advanced analytics can provide deeper insights into data, while predictive modeling helps forecast outcomes based on historical data.
Prospects for this trend:

More accurate demand forecasting and inventory optimization in supply chains.
Enhanced ability to target marketing efforts based on predictive customer behavior.
Improved healthcare outcomes through predictive modeling of patient data.
Expansion into new areas such as environmental sustainability and climate change analysis.
Development of real-time predictive models that continuously learn from new data.

#4 Real-time data processing and analytics
Real-time data processing and analytics involve continuous processing of data as it is generated, enabling immediate insights and decision-making. Big data analytics trends here mostly concern applications that require up-to-the-minute information, such as financial trading, fraud detection, and live monitoring systems,  often requiring continuous scraping of websites without getting blocked to maintain real-time data feeds.
Prospects for this trend:

Increased use in industries requiring instant data insights, such as finance, telecommunications, and e-commerce.
Development of more robust and scalable real-time analytics platforms.
Enhanced ability to handle high-velocity data streams from IoT devices.
Improved customer experiences through real-time personalization and responsiveness.
Greater adoption of streaming data technologies, such as Apache Kafka and Flink.

You may be additionally interested in exploring how big data collection works in another blog post by Intelliarts. 
#5 Blockchain applications in big data
Blockchain technology, known for its secure and transparent nature, is being increasingly applied in big data to enhance data integrity, security, and traceability. Regarding big data, blockchain can provide a decentralized and tamper-proof ledger for recording transactions and data exchanges. This, in its turn, benefits the accuracy and trustworthiness of big data taken from blockchain-based sources.  (Mind that engaging VARA license advisory support can help ensure compliance obligations are accurately understood and met, minimizing potential regulatory risks.)
Prospects for this trend:

Increased use in sectors requiring high data integrity, such as finance, healthcare, and supply chain management.
Development of blockchain-based data sharing and storage solutions.
Enhanced security and privacy through immutable and decentralized data records.
Greater transparency and accountability in data transactions.
Integration with other big data technologies to create secure data ecosystems.

#6 Privacy and security considerations
As the volume and variety of data grow, so do the challenges of ensuring its privacy and security. Data is a valuable asset that should be protected. This trend in big data focuses on developing advanced methods and technologies to protect sensitive information from breaches, unauthorized access, and misuse. Many organizations are now partnering with the best cloud security companies to strengthen their data protection frameworks and ensure compliance with evolving privacy regulations.
Prospects for this trend:

Increased adoption of advanced encryption methods and privacy-preserving techniques.
Development of comprehensive data governance frameworks and policies.
Enhanced focus on compliance with data protection regulations, such as GDPR and CCPA.
Greater investment in cybersecurity measures to protect big data assets.
The emergence of new technologies and approaches for secure multi-party computation and federated learning.

Data privacy and security are major concerns across industries. We aim to mitigate cybersecurity challenges by constantly upgrading our protection methods, which requires research, innovation, investment, and, of course, following current best practices in the niche — Oleksandr Stefanovskyi, an ML team lead at Intelliarts. 
Let’s discuss how we can assist you in your big data projectReach out to Intelliarts
#7 Hybrid and multi-cloud adoption
Hybrid and multi-cloud adoption involves using a combination of on-premises, private cloud, and public cloud services to manage and analyze big data. This approach offers greater flexibility, scalability, and cost efficiency in utilizing big data overall, which is a concern of many small and medium-sized companies. Hybrid and multi-cloud adoption allows for the leveraging of the best features of each cloud environment.
Prospects for this trend:

Increased flexibility in managing data across different cloud environments.
Enhanced disaster recovery and business continuity capabilities.
Better cost management through optimized use of different cloud services.
Greater collaboration and data sharing across hybrid cloud ecosystems.
Development of unified cloud management platforms for seamless integration.

#8 Increased usage of data lakes
The data lakehouse, a new paradigm combining the benefits of data lakes and data warehouses, is gaining traction in the big data landscape. Giants like Databricks and Snowflake lead this trend by offering solutions that provide both the scalability of data lakes and the performance and reliability of data warehouses. This hybrid approach simplifies data management and enables advanced analytics on a unified platform, eliminating data silos and reducing the need for complex data pipelines.
Prospects for this trend:

Greater adoption of data lakehouse architectures in industries needing scalable and flexible data storage.
Improved performance and cost-efficiency in handling structured and unstructured data.
Enhanced real-time analytics capabilities through unified data processing.
Accelerated integration with machine learning and AI tools for more efficient data modeling.
Broader support for collaboration across data teams by simplifying data governance and access control.

Databricks and Snowflake are regarded as one of the largest providers of data lake solutions on the market. 
#9 Data governance and compliance
Data governance and compliance are becoming increasingly important as organizations handle larger volumes of sensitive and regulated data. It’s especially critical when it comes to medical, personal, and financial data. For example, as healthcare technology trends continue to evolve, there’s a stronger emphasis on data governance to meet regulatory standards and ensure the accuracy and integrity of patient data. This trend focuses on establishing policies, processes, and controls to ensure data quality, integrity, and compliance with requirements. 
Prospects for this trend:

Increased adoption of comprehensive data governance frameworks.
Enhanced focus on data quality and integrity to support accurate analytics.
Development of automated compliance tools to streamline regulatory adherence.
Greater emphasis on ethical data use and privacy protection.
Improved transparency and accountability in data management practices.

#10 Quantum computing impact on big data
Quantum computing has the potential to revolutionize big data analytics by providing unprecedented computational power to solve complex problems. It’s difficult to project the exact development of this trend, but it’s intended to enhance data processing, optimization, and predictive modeling capabilities far beyond the capabilities of classical computing.
Prospects for this trend:

Development of quantum algorithms specifically designed for big data applications.
Enhanced ability to analyze and process massive datasets at unprecedented speeds.
Improvement in optimization problems, such as supply chain management and financial modeling.
Greater investment in quantum computing research and development.
Collaboration between academia, industry, and government to accelerate quantum computing advancements.

#11 Sustainable and ethical big data practices
Sustainability, renewable energy, and other concerns resulted in the emergence of sustainable and ethical big data practices. As an example, a single ChatGPT conversation uses about fifty centiliters of water, equivalent to one plastic bottle. So, this trend focuses on minimizing the environmental impact of data operations and ensuring the ethical use of data by emphasizing the importance of energy-efficient data centers and responsible data sourcing.
Prospects for this trend:

Increased adoption of energy-efficient technologies and practices in data centers.
Greater emphasis on responsible data sourcing and ethical data use.
Development of frameworks and guidelines for ethical AI and data analytics.
Enhanced transparency and accountability in data practices to build public trust.
Collaboration between industry and regulatory bodies to promote sustainable and ethical data practices.

Every successful big data project starts by creating a big data strategy. Discover how to develop it in another of our blog posts. 
Challenges and solutions in big data

As with any other technology, the latest trends in big data analytics present both opportunities and challenges. Many companies, especially small and medium-sized ones, are hesitant about getting started with big data technology innovation. Here are some of their concerns and basic solutions to them:
#1 Scalability issues
Managing and processing vast data volumes is difficult, leading to slow performance and increased costs.
Solution: Use distributed computing frameworks and cloud-based solutions for scalable resources and efficient processing.
#2 Data quality and integration challenges
Integrating diverse data sources can result in inconsistencies and unreliable insights.
Solution: Employ data cleaning tools, robust ELT processes, and strong data governance policies to maintain data integrity.
#3 Talent shortage
A shortage of skilled professionals hinders effective data analysis and utilization.
Solution: Invest in training programs, partner with educational institutions, and leverage AI tools to fill the talent gap.
#4 Ethical and regulatory considerations
Handling personal and sensitive data requires compliance with stringent regulations.
Solution: Implement data privacy frameworks, enforce ethical guidelines, and apply data anonymization techniques.
#5 Data security
Increased data volume and access points heighten the risk of breaches and cyber attacks.
Solution: Implement advanced security measures, conduct regular audits, and establish strict access control policies.
#6 Real-time processing
Real-time data processing is challenging with large datasets, leading to delays.
Solution: Utilize stream processing frameworks and in-memory computing for faster, real-time data analysis.
#7 SATA storage capacity
Efficient and cost-effective storage of vast data volumes, especially unstructured data, is challenging.
Solution: Use NoSQL databases, data compression techniques, and hybrid storage solutions to optimize storage.
Explore big data challenges, practices, and mistakes with Bernard Marr, an author, popular keynote speaker, futurist, and strategic business and technology advisor, in the video below:

Big data use cases by industry
Here are examples of applications of big data tailored to the specific needs of businesses in particular industries:
#1 Healthcare
In healthcare, big data-based solutions are used to drive significant improvements in patient outcomes, operational efficiencies, and cost reductions.
Examples:

Predictive analytics for patient admissions and disease outbreaks.
Personalized medicine to tailor treatments to individual patients based on genetic and other data.
Predictive maintenance to anticipate equipment failures and schedule maintenance.

#2 Finance
As for finance, big data-based solutions are used to enhance risk management, customer insights, and operational efficiency. 
Examples:

Fraud detection to identify and mitigate fraudulent transactions.
Risk management solutions to assess and manage financial risks.
Algorithmic trading to utilize big data for high-frequency trading strategies.

#3 Manufacturing
When it comes to manufacturing, big data-based solutions drive improvements in production processes, quality control, and supply chain management. 
Examples:

Predictive maintenance in manufacturing industry to anticipate equipment failures and schedule maintenance.
Quality control solutions to monitor production processes and maintain standards.
Supply chain optimization to improve logistics and inventory management.

#4 Retail
Here, big data-based solutions enhance customer experience, inventory management, and sales strategies. 
Examples:

Customer personalization to offer tailored product recommendations.
Inventory management to optimize stock levels to meet demand.
Sales forecasting to predict future sales trends and customer demand.
Market basket analysis to understand purchasing patterns.

#5 Renewable energy
Finally, in renewable energy, big data-based solutions are used to optimize energy production, distribution, and consumption. 
Examples:

Energy forecasting to predict energy demand and supply for better grid management.
Consumer energy management to provide insights and recommendations for reducing energy consumption.
Predictive maintenance to schedule maintenance for wind turbines and solar panels.
Grid management solutions to balance supply and demand in real-time.

Looking for a trusted tech partner to help implement big data in your business infrastructure and processes? Reach out to Intelliarts, and let’s discuss your needs. 
Success stories of big data implementation 
Here at Intelliarts, we have substantial experience with developing and implementing software solutions operating on big data. Here are two of our many related success stories: 
ML solution for searching and scoring leads in marketing

Challenge:
A midsize US-based insurance company (under NDA) specializing in home and auto insurance faced challenges with its traditional lead-scoring approach. With multiple lead sources and a complex sales funnel, the company struggled with labor-intensive processes and inefficient resource allocation, often wasting time on low-conversion leads.
Solution:
Intelliarts created a predictive lead-scoring model using machine learning to rank leads by conversion probability, allowing agents to focus on high probability leads. The solution was further refined with prospect prediction and integrated into the company’s systems using Amazon SageMaker.
Outcomes:
The ML-powered lead scoring model increased the insurer’s profitability by 1.5% in a few months, cutting out 6% of non-efficient leads and raising overall lead quality. The company now benefits from a data-driven approach, removing guesswork and optimizing resource use, with high-probability leads showing a 3.5 higher conversion rate than average. This transformation showcases how leveraging big data can directly impact business efficiency and profitability.
Explore ML solution for searching and scoring leads case study in full.
All-purpose big data platform for data marketplace

Challenge:
DDMR, a US-based market research company specializing in clickstream data, needed a robust solution to handle the growing complexity and volume of their data. The challenge was to develop an end-to-end big data pipeline that could efficiently manage, process, and transform massive datasets into actionable insights.
Solution:
As part of our data engineering consulting services, Intelliarts developed an end-to-end big data pipeline for data collection, storage, processing, and delivery using Databricks, AWS, Spark, and Snowflake. We redesigned the infrastructure for scalability and reliability, optimized storage costs with a hot and cold data approach, automated DevOps processes, and enhanced data security to comply with GDPR.
Outcomes:
The new pipeline enabled DDMR to process large volumes of data daily and translate them into actionable insights. With our solution, the company multiplied its revenue several times and experienced an efficiency boost. Besides, DDMR expanded its customer base and improved data accessibility, leveraging newly acquired big data capabilities.
Explore big data platform case study in full.
Future trends and predictions for big data 
Beyond the listed trends, it’s possible to predict several other changes inflicted by the rise of big data utilization:

Expansion of data literacy: As big data continues to grow in importance, there will be an increased focus on data literacy across organizations. Employees across all levels will need to understand how to interpret and utilize data insights to make informed decisions.
Data monetization models: Organizations already view data as an asset that can be monetized. New business models may emerge around data marketplaces, where data owners can trade datasets. 
Unified data platforms: Potentially, we should expect the industry to head towards developing unified data platforms that integrate various data sources, structured, unstructured, and semi-structured, into a single environment. These could be both in-house business solutions and B2B products. 

As for the union of big data with other technologies, the following scenarios are possible:

Faster 5G networks will boost data transmission and connect more IoT devices, creating massive real-time data for advanced analytics.
Blockchain will become a standard and ensure secure, transparent, and trusted data sharing across entities.
NLP advancements will enhance machine understanding and analysis of human language in text-heavy data.
New synthetic data generation approaches will help to create high-quality, privacy-safe datasets for training machine learning models without using real data.
Digital twins will be used extensively to simulate, predict, and optimize processes using virtual replicas of physical entities with big data analytics.

You may be interested in exploring how to use digital twins in manufacturing in another blog post by Intelliarts. 
Final take
Big data industry trends for 2025 and beyond promise enhanced data-driven decision-making, optimized operations, and better customer experiences. They include:

Edge computing integration
Artificial intelligence, machine learning, and big data synergy
Advanced analytics and predictive modeling
Real-time data processing and analytics
Blockchain applications in big data
Privacy and security considerations
Hybrid and multi-cloud adoption
Data governance and compliance
Quantum computing impact on big data
Sustainable and ethical big data practices

It’s essential to understand that keeping up with every big data trend while resolving related challenges is a complex task that requires substantial AI and ML expertise that businesses can acquire from external vendors.
Entrust the implementation of your big data project to Intelliarts. With more than 24 years in the market, serving as technology consulting firm and delivering big data, software development, high tech, and other solutions, we are ready, willing, and able to help you get the most out of your data. 
 Leverage your data Learn how

#### Top MLOps Tools in 2026 and Their ROI [Based on Real Projects]
TL;DR

MLOps tools should be evaluated by ROI levers: time-to-production, failure rate, operating cost, governance, and manual effort.
MLflow and W&B help standardize experiment tracking, model versions, and release decisions.
Kubeflow, Airflow, and Flyte support repeatable pipelines, validation, retraining, and production handoff.
SageMaker, Vertex AI, and Azure ML fit teams already committed to one cloud.
Evidently and Arize help detect drift, production issues, and model degradation earlier.
This article maps tools to MLOps layers and real ROI patterns, including Intelliarts engagements.

AI adoption has outpaced MLOps maturity. McKinsey’s 2025 State of AI report says 88% of organizations use AI in at least one business function, yet only about one-third are scaling AI across the enterprise. In the 2025–2026 DataTalks.Club ML and MLOps survey, only 33% reported standardized deployment and monitoring.
For enterprise teams, the bottleneck is usually the launch and ownership of the model after the release. This is where the suite of Machine Learning Operations (MLOps) software solutions can come in handy. 
In this article, we compare the top MLOps tools in 2026 and explain where they create measurable ROI across experiment management, pipelines, deployment, monitoring, and governance, based on Intelliarts’ project experience.
What problems do MLOps tools solve?
MLOps tools help teams improve upon a model that performs well in a controlled experiment to make it reliable, traceable, monitored, and easier to improve in production.
For business owners, the absence of MLOps is a delayed ROI from ML investments. For engineering teams, it shows up as inconsistent deployments, unclear ownership, and growing maintenance costs.
A successful experiment may be impossible to reproduce months later or in a different environment due to broken handoffs across data, engineering, infrastructure, and business teams. 
Wrong feature versions, differences between AI models, and a multitude of other factors add up to the overall complexity of the issue. To aid with all the above-mentioned, a practical MLOps stack usually covers five core layers:

Data and artifact versioning: Keeps datasets, features, model files, and training artifacts traceable. This helps teams avoid inconsistent results and supports audit readiness.
Experiment tracking and model registry: Records parameters, metrics, runs, and approved model versions. This reduces duplicated experiments and makes model promotion easier.
Orchestration and pipelines: Turns training, validation, and retraining into repeatable workflows. This improves release speed and reduces manual handovers.
Serving and deployment: Packages models for production and manages scaling, rollback, and environment consistency. This lowers deployment risk.
Monitoring, drift, and governance: Tracks model quality, latency, data drift, and compliance signals. This helps teams detect failures before they affect users or revenue.

So, from Intelliarts’ experience, the value of investing in the MLOps stack and the effort results in producing a more reliable operating model. This leads directly to fewer failed audits, faster model releases, lower operating costs, and, respectively, high ROI overall. 
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How we selected the “top” MLOps tools for this article
To make this proposed suite of MLOps tools useful, Intelliarts experts grounded our selection based on their relevance to production ML environments and other factors specified below. 
Our goal was to highlight technologies that help companies transition from isolated ML experiments or small-scale pilots to well-governed systems. Drawing on Intelliarts’ engineering experience, we evaluated each tool against the following criteria:

Operational maturity: We assessed how well each MLOps tool supports the transition from experimentation to production. The focus was on reproducibility, controlled deployment, and the ability to keep models stable after release.
Enterprise decision risk: Priority went to MLOps tools that reduce risks tied to delayed launches, model degradation, and unclear ownership. These are the problems that usually turn ML initiatives into expensive maintenance work.
Architecture fit: Each tool was assessed in the context where it usually performs best. This includes cloud-native environments, Kubernetes-based platforms, regulated data ecosystems, and hybrid enterprise stacks.
Implementation trade-offs: Intelliarts experts considered the adoption cost behind each tool. A strong platform still needs the right skills, governance model, and internal standards to produce ROI.
Proof of value: The final selection favored MLOps tools with visible ROI patterns in production projects and public case studies. The strongest signals were faster releases, better traceability, and fewer issues after deployment.

Important note: In this article, “top” does not mean every tool is the best choice for every team and business scenario. It rather reflects that a particular tech stack choice would suit common enterprise scenarios where MLOps investments are needed. 
“Managed MLOps work best when the cloud strategy is already clear. It’s important to pre-plan usage to both avoid vendor lock-in and maximize impact.” — Andrii Shutka, an experienced data engineer and DevOps/MLOps at Intelliarts.
Tool landscape at a glance: 2026 MLOps stack
The 2026 MLOps landscape is expanding because AI adoption is moving faster than most organizations can mature in regard to their AI integration. McKinsey reports that 88% of organizations use AI in at least one business function, yet only about one-third are scaling AI programs.
MLOps maturity is still uneven. In a 2025–2026 DataTalks.Club survey, only 33% of respondents reported standardized deployment and monitoring.
That explains why tooling decisions now revolve around architecture fit, automation depth, governance needs, and ownership. Now, let’s move on to the application of MLOps technology in actual business scenarios:
Which MLOps tools show up most in real projects?
Across production projects, MLOps tools usually fall into a few stable categories, which basically revolve around either orchestration, deployment, monitoring, versioning, or experiment tracking. Altogether, these comprise the main MLOps implementations. 
Here’s the list of unified MLOps and data platforms distributed across their usability layers:
[table id=16 /]
It’s safe to claim that stronger MLOps implementation usually comes from choosing a suite of tools that fit the company’s goals perfectly.  
This is where Intelliarts helps you make an optimal choice and build a great stack. We define a set of MLOps tools per layer and align them with the company’s architecture to both reap the MLOps advantages and avoid having an unworkable stack. 
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Comparison table: Top MLOps tools and where they shine
As specified above, the right MLOps tool choice is the one that fits the company’s cloud, skills, delivery model, and risk profile. 
In Intelliarts’ practice, direct ROI usually appears in areas teams can measure quickly: reduced manual work, fewer repeated handovers, less rework, faster release cycles, and lower compliance effort. Indirect ROI builds over time through more reliable ML systems, better customer experience, and stronger business performance.
The table below compares flagship MLOps tools by their core role, typical users, ROI levers, and relevant Intelliarts project patterns.
[table id=17 /]
Explore the entire Intelliarts’ portfolio page to learn more about our projects and customers, as well as the expertise we can offer for your business. 
How to measure ROI of MLOps tools in real projects
MLOps ROI is calculated from the financial value created by the stack, compared with implementation and operating costs. 
Intelliarts team reminds you that some metrics can be converted directly into money, such as saved engineering hours or reduced compliance effort. Others work as supporting indicators, showing whether the stack improves release speed, reliability, and production control. 
See the five key ROI dimensions plus one final MLOps project ROI formula in the infographics below:

Important note: Before introducing an MLOps tool, teams should capture a baseline for the workflow it targets. For example:

MLflow can be measured against experiment reproducibility and approval time.
SageMaker Pipelines can be measured against deployment lead time, failed runs, and manual release steps.

From our engineering team (callout): A quick example of the impact of MLOps for enterprise comes from a manufacturing engagement under NDA. In that case, model release time dropped from about six weeks to ten days after introducing MLflow and a lightweight CI/CD pipeline. That is roughly a 76% reduction, or about 32 days saved per release cycle, mainly by standardizing model artifacts and replacing manual deployment scripts.
Tool deep dives: What they do and where they pay back

Tool comparisons are definitely helpful when each category is tied to a specific operating problem. To make sure of that, the sections below look at the MLOps tools that usually shape production stacks and explain where they create measurable value. 
MLflow & W&B: Are experiment tracking tools worth the overhead?
Business problem
Experiment tracking becomes worth the overhead when model decisions can no longer rely on notebooks, spreadsheets, or informal team memory. The cost shows up during release: teams lose time proving which run produced the selected model, which data and parameters supported it, and whether the result can be reproduced.
What these MLOps tools do
MLflow and Weights & Biases work as development MLOps solutions for controlled model experimentation. They give data science and engineering teams a shared system for recording experiments, comparing model versions, and preparing selected candidates for release.

Track model runs, parameters, metrics, and artifacts.
Store approved models in a searchable registry.
Compare experiments without rebuilding results manually.
Connect model versions to CI/CD and release workflows.
Support LLM evaluation, prompt testing, and quality checks, especially in W&B.

Why they pay back

Reduce repeated experiments caused by lost metrics or unclear run history.
Shorten release preparation by making model versions and artifacts explicit.
Improve rollback decisions because previous model versions stay traceable.
Speed up onboarding by giving new engineers a clear experiment history.
Support audit and governance work without manual evidence collection.

MLflow usually fits cloud, hybrid, and Kubernetes-based stacks because it can connect with CI/CD, object storage, Airflow, Kubeflow, or managed pipelines. W&B is more useful when experiment visibility, team collaboration, and LLM evaluation matter more than owning every part of the tracking layer.
Real project lens
Years ago, one Intelliarts partner under NDA had a manufacturing ML workflow where the bottleneck was release readiness, specifically. In a nutshell, every candidate model required manual checks across the feature schema and deployment configuration, which resulted in a critical overhead over time, as was evaluated by stakeholders.
Eventually, the partner resorted to a pilot workflow with MLflow, achieved through machine learning operations services provided by Intelliarts. This tool is used as the registry layer, and approved runs are connected to a lightweight CI/CD flow, resulting in more consistent release packaging. When scaled, this helped cut several days from each model iteration cycle.
Looking to request Intelliarts’ ML development services directly? Reach out, and we’ll contact you back
Kubeflow, Airflow, Flyte: Managing ML pipelines at scale
Business problem
Pipeline orchestration becomes critical when retraining stops being occasional work. Multiple models, changing data sources, validation rules, and release schedules quickly expose the limits of notebook-only workflows and manual cron jobs.
The cost usually appears in missed retraining, inconsistent pipeline runs, wasted compute, and weak visibility into where the workflow failed.
What these MLOps tools do
Kubeflow, Airflow, and Flyte are orchestration MLOps solutions for repeatable ML workflows. They structure the steps between data preparation and production release, so teams can run, test, schedule, and inspect pipelines with more control.

Define training and validation workflows as DAGs or reusable components.
Schedule retraining based on time, data availability, or business triggers.
Track pipeline runs, dependencies, failures, and execution history.
Reuse workflow logic across models, teams, or regions.
Connect ML jobs with Kubernetes, cloud services, and CI/CD workflows.

Why they pay back

Reduce manual coordination between data science, data engineering, and platform teams.
Make retraining easier to rerun when data drifts or business rules change.
Lower release risk by testing pipeline steps before production deployment.
Improve compute usage through scheduling and controlled execution.
Give teams clearer failure points when a pipeline breaks.

Architecture notes
Kubeflow fits teams that already have Kubernetes maturity and want stronger ownership of ML workflows. Airflow works well when ML pipelines need to connect with broader data engineering workflows. Flyte is a strong fit for typed, reusable workflows where pipeline reliability matters across many model runs.
Real project lens
A quick example is Spotify Engineering rebuilding its forecasting infrastructure for weekly and on-demand runs across 180+ markets. The workflow split data QA, training, tuning, checks, and visualization into controlled components, reducing heavy tuning processes from several months on one machine to a few hours.
SageMaker, Vertex AI, Azure ML: When does a managed platform make sense?
Business problem
Managed MLOps platforms make sense when the bottleneck is platform assembly. Building tracking, pipelines, deployment, monitoring, access control, and governance from separate components can take months before the first model reaches production.
For cloud-first companies, that delay often costs more than platform fees. The team spends too much time wiring infrastructure and too little time improving models, data quality, and release performance.
What these MLOps tools do
SageMaker, Vertex AI, and Azure ML are cloud-native MLOps platforms. They package core model lifecycle capabilities into one managed environment, so teams can train, evaluate, deploy, monitor, and govern models without building every layer themselves.

Manage training jobs, model versions, endpoints, and deployments.
Connect ML workflows with cloud storage, identity, security, and logging.
Support managed pipelines for validation, retraining, and release steps.
Provide monitoring features for model quality, drift, and runtime behavior.
Reduce custom infrastructure work for teams already committed to one cloud.

Why they pay back

Shorten the path to production for teams already on AWS, Google Cloud, or Azure.
Reduce platform maintenance work for ML engineers.
Improve integration with existing data lakes, IAM, logging, and CI/CD.
Make governance easier through cloud-native access control and audit trails.
Help small teams run production ML without owning a full custom platform.

The strongest fit is a company already standardized on one hyperscaler. SageMaker works best in AWS-heavy environments, Vertex AI in Google Cloud data stacks, and Azure ML in Microsoft-centered enterprise ecosystems. The trade-off is lock-in. Managed platforms simplify delivery, but they also shape how teams package models, govern access, and monitor behavior.
Real project lens
Google Cloud’s Cainz case shows where managed MLOps platforms pay back. The retailer used Vertex AI for demand forecasting across 209 stores, replacing a forecasting setup that struggled with seasonal products and short-term sales trends. The platform helped Cainz train large datasets more efficiently and reduce preprocessing time to 50 minutes regardless of store count. 
Important note: In Intelliarts’ practice, native MLOps platforms are great for shortening delivery, mainly when the client is already invested in one cloud. The decision becomes weaker when the client needs strong multi-cloud portability or expects to customize every layer of the ML lifecycle.
Looking for software engineering assistance from an expert AI/MLOps service provider? Don’t hesitate to reach out. 
Monitoring & drift tools (Evidently, Arize, Fiddler, etc.): Are they overkill?
Business problem
Monitoring becomes necessary when a model can fail without a visible system error. The endpoint still responds, but prediction quality drops because user behavior changes, input data shifts, labels arrive late, or an upstream integration starts sending different values.
For business teams, this can show up as bad recommendations, weaker risk scores, lower conversion, or missed anomalies. For engineering teams, the problem is harder: logs may confirm that the service is alive, while the model is already making worse decisions.
What these MLOps tools do
Evidently, Arize, Fiddler, and similar MLOps monitoring tools add observability after deployment. They track how inputs, predictions, performance, latency, and drift signals change in production.

Compare production data against reference datasets.
Detect data drift, concept drift, and prediction shifts.
Monitor latency, errors, and model quality signals.
Alert teams when behavior crosses defined thresholds.
Support root-cause analysis through dashboards and explainability views.

Why they pay back

Detect silent model degradation before users or revenue are affected.
Shorten the incident investigation by showing where the behavior changed.
Reduce unnecessary retraining by separating noise from meaningful drift.
Support governance with production evidence and model behavior history.
Improve SLA confidence for ML systems tied to customer-facing workflows.

Evidently is often useful for teams that want flexible open-source monitoring and custom drift checks. Arize and Fiddler fit teams that need deeper observability, explainability, and governance workflows across many production models.
Real project lens
Carnegie Mellon’s Software Engineering Institute tested drift monitoring on a DNS data exfiltration case. When adversarial behavior changed, the classifier’s performance dropped, but drift detection triggered retraining and restored strong post-drift performance. The case shows how MLOps monitoring tools can help teams detect behavior changes, separate real degradation from noise, and decide when retraining is justified.
Entrust your project to the AI and MLOps expertsContact us
Scenario matrix: Which MLOps tools fit which use cases?
Tool choice should follow the operating model. A small AWS-first team, a GCP-based enterprise, and a hybrid manufacturer do not need the same MLOps stack, even when they solve similar ML problems.
The matrix below shows practical starting points. Each setup can be expanded, but the first goal is to cover the critical layers without creating platform overhead.
[table id=18 /]
What Intelliarts experts suggest is sticking to simple patterns. So, lighter teams should start with managed platforms and a small tracking layer. 
Larger or regulated teams that need stronger governance and ownership, as well as are potentially users of high-risk products as per the EU AI Act and other regulations, should have stronger control over model releases.
A focused pilot has always been the safest way to prove the usefulness of planned technology implementation. If a better ROI is confirmed, then it’s time to strategize for scaling.” — Volodymyr Mudryi, a DS/ML engineer at Intelliarts
Common anti-patterns when adopting MLOps tools
We do not get tired of reminding that MLOps can’t be a paid add-on to a software platform. It should be a systematic approach in place instead, to actually achieve the desired ROI. 
The infographics below specify some of the most common anti-patterns in using MLOps tools:

Intelliarts experts strongly suggest keeping the first rollout narrow. For this, just choose the most complicated workflow and define the operating rules around it. Then you should measure whether the tool actually reduces release time, rework, incident response, compliance effort, or any other central parameter. 
How Intelliarts approaches MLOps tooling and ROI
Intelliarts approaches MLOps tooling using a business-oriented approach in the first place. The same tool stack will work in different scenarios because each one has a different release rhythm, risk profile, and governance load. 
Here’s a very brief methodology that we would base end-to-end MLOps solutions around, with adjustments for every particular business situation:

The methodology starts with evaluating the lifecycle of the current AI model in place, if there is any. Intelliarts maps where delivery slows down or becomes unreliable. Metrics like experiment handoff, release time, retraining delays, incident response, and others will define what the MLOps stack needs to improve.
The next step is a minimal, opinionated toolset for the client’s environment. Cloud-first teams may benefit from SageMaker, Vertex AI, or Azure ML. Hybrid or regulated teams may need more control over registries, pipelines, monitoring, and deployment gates.
ROI is validated through a focused pilot. Before-and-after metrics show whether the stack reduces manual work, release delays, production risk, or compliance effort before the MLOps ecosystem expands.

Important note: An actual project scope with clear steps tailored for your business will be provided as part of the consultation and project quote. 
For more information on Intelliarts’ expertise and service offerings, visit our data science consulting and data engineering consulting website pages. 
Final take
It’s generally accepted that there’s no universal best MLOps tool. All options available create value in different operating models and, by and large, serve different workflows. The strongest ROI comes from matching MLOps tools to clear production problems: release delays, manual handoffs, drift, failed deployments, and audit pressure. Start with measurable pain, choose a focused stack, prove value on a pilot, and expand only when the workflow improves. 
With more than 26 years of experience delivering AI and MLOps services to businesses worldwide, Intelliarts offers our unmatched expertise and experience to serve your business needs. 
We are proud to have a 90% customer return rate and 92% of our senior engineers are in-house. Our specialists can both consult you on business matters and help with any development and engineering needs to help you achieve optimal ROI for your best project. 
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#### Top Precision Agriculture Tools and Techniques by 2030
Over the last few years, agribusiness has been spending a lot of money on precision agriculture systems, such as GPS-guided tractors, soil sensors, and drones. Useful, yes, but often siloed. Markets and Markets projects the precision farming market to exceed $21 billion by the end of 2032 alone with a CAGR above 9.5%. But the actual change is happening to ag technology software solutions, which link these tools, process the data, and assist managers in making confident moves.
In this article, we will analyze the top precision agriculture tools that are relevant today: AI-based decision support systems, predictive irrigation platforms, satellite data analytics, and farm management software with advanced integrations. It is not the hardware that brings them together, but the intelligence.
From 1M to 2.6M acresTransform manual farm workflows into scalable software agritech systems.Explore the case study
Top software-driven tools leading precision agriculture by 2030
If the question is what is precision agriculture and why is it important, the short answer is unified data turned into timely action. Agribusinesses and farmers are now making more difficult decisions than ever before: how much water or fertilizer each zone requires, which fields to treat first, when to harvest as the weather changes.
Precision agriculture tools make such calls easier by converting real time information into understandable measures and consistent grower data collection as showcased in our case study. For example, NDVI (Normalized Difference Vegetable Index) makes use of satellite or drone imagery to display crop wellbeing, while VRT (Variable Rate Technology) adjusts inputs like fertilizers or water on the go. As AI crunches such streams of data, farms operate in a more efficient, greener, and more profitable way.
 
These categories illustrate how precision farming technologies and equipment are no longer about isolated tools. They represent connected, software-first ecosystems where data analytics drive every decision, from when to irrigate a row of wheat to how to schedule trucks for delivery. Below are precision agriculture technology examples that key agricultural players will focus on in the next few years.
AI-powered decision support systems
In simple terms, the AI-based decision support systems connect satellite images, IoT sensors, and past weather and soil data in order to make daily farming decisions. They are able to signal possible plant disease outbreaks and predict the pest threats and yields with remarkable precision. For example, a Plantix app could detect up to 120 species of pests by the end of 2023, and the efficiency is only growing. As a more recent example, see our AI applications in agriculture use cases that map detection to decisions.
Want to know how to improve sensor accuracy and reduce inefficiencies with the software?Read case study
These insights allow farmers to change the planting schedules or crop rotations before the issues emerge. It does not replace judgment. That said, what works in one field may not work in another, yet it provides managers with a better idea and lessens unexpected events in the season.
Precision irrigation and fertilization platforms
The prices for water are rising, and the prices of fertilizer are not going down either. Precision irrigation and fertilization platforms address this challenge by combining VRT with agronomic modeling of various streams of data. Multiple data sources provide the most benefit for farmers in this case and integrate data from soil moisture sensors, weather forecasts, satellite images, and historical yield maps. Based on these inputs, the software determines the time of irrigation, depending on the stage of the crop, weather forecasts, and water supply when soil sensors detect stress in a particular zone.

In the case of fertilizer application, precision irrigation and fertilization platforms consider the rate of nutrient uptake, soil variability, and yield potential. It is not about perfection but efficiency. There is a decrease in input costs, and resource use is improved in most of the operations. The sustainability information assists in certification schemes as well.
Will it address all irrigation issues? Probably not. Nevertheless, it reduces stress levels when making decisions at critical growth periods. The majority of the operations payback in 12 to 18 months. Others require more time when their disciplines are more complicated.
Satellite and drone data processing platforms
Satellite and drone platforms become the key in perceiving crop health by 2030. Through aerial survey, these systems can offer vegetation maps, indicate stressed regions and patterns across fields that cannot be observed when one walks the land.
What they do:

Monitor health of track plants by using such indices as NDVI and SAVI (Soil-Adjusted Vegetation Index) to identify which areas are performing well and which require maintenance.
Detect stress on the spot in some cases up to 10 days before the manifestation of symptoms and, thus, provide farmers with time to respond.
Integrate various forms of imagery, such as near-infrared and thermal, to develop a more comprehensive view of plant condition.

Why it matters:
These systems combine wide-satellite coverage and high-resolution drone shots, providing both an overview of large scale and small-scale field information. The outcome is intelligence which functions on individual plants to whole regions.
Real results:
Farms with NDVI and multispectral analytics have a 12-20% increment in yield and a 30% decline in field scanning. In a case in Europe, the outbreak of diseases was identified 1 week before using the conventional means, which saved time and resources.
2025-2030 upgrades:

The systems today are implemented in a browser, provide AI notifications about anomalies and can be integrated with farm management software like sensor inventory management systems or land sampling automation solutions.
Farmers can exploit advanced remote sensing without having a dedicated team of GIS.

Field teams can also streamline sampling as shown in our land sampling automation case study.
Predictive analytics for harvest planning and logistics
Farmers need to have a plan of harvesting crops to market in the appropriate time when sensors and images have not even opened better harvests. Predictive analytics systems utilize the health and field-variability data of NDVI systems and transform it into actionable harvest schedules. Also, they:

Determine the optimal time to harvest and where to deliver, with accuracy from 87% to 98% according to recent studies.
Consider previous harvest data, weather projections, and available workers to generate executable harvest calendars and routing plans.
Sync farm software (Enterprise Resource Planning/Supply Chain Management) so harvest figures automatically flow into storage and shipping plans, which studies associate with a 30,4% increase in total economic benefits.
Predict delays such as bad weather or truck shortages and surface mitigation options in advance.
Reduce guesswork for crews so less time is spent speculating and more on picking at the right time.

Farm management software with advanced integrations
Why is precision agriculture important? By the end of 2025, farm management software is no longer a record keeper. It has turned out to be a key point of convergence of equipment data, records of compliance, sustainability, and real time field updates. Managers no longer have to balance spreadsheets and disintegrated systems and can immediately have the full picture.
Modern platforms are cloud-based, which means you can monitor the performance of your farm at the office or in the field itself. They are modularly designed so that you can begin with a small tracking base and add predictive analytics as you scale. The API-first architecture enables easy integration of tractors, drones, sensors, and other precision farming equipment, including a sensor inventory management system like the one we developed.

In practice, dashboards combine satellite imagery, soil measurements, and weather projections to show the area where intervention is most required. Automation also saves time: certification reports that once took hours can now be generated in minutes, while equipment logs record all passes, fuel consumption, and maintenance notifications. Sustainability modules go further by calculating carbon footprints and creating ESG (Environmental, Social, and Governance) reports automatically, supporting both compliance and long-term farm strategy.
Farms with such precision farming systems can switch field observations to reports quicker if they have access to high-speed internet, which allows managers to be assured when they go to audit.
Considering the next action in sustainability reporting? We deliver solutions that transform field data into reliable records.Learn more
Techniques and trends making these tools smarter and more valuable by 2030
Precision agriculture technologies, along with every new technology in agriculture, are further enhanced when the data is more detailed, the models are localized, and the system is simple to combine. That is the trend that will have the most momentum by 2030.
Core trends and solutions shaping precision agriculture by 2025–2030 include:

Real-time remote sensing and crop sensors: Precision agriculture equipment and devices rely on continuous data streams to guide crop management decisions more accurately.
Geospatial fusion: Satellite imagery, drone surveys, soil sensor, and GPR (Ground Penetrating Radar) data are combined into a single field view, providing precise insights into crop health.
Region-tuned machine learning: Forecasting models are trained on local weather, soil, and yield history.
Prescription mapping: This technology converts predictions to zone-based irrigation and nutrient plans.
API-first integration: The API-first design is the standard of equipment, sensor, and ERP connection to integrate with low friction.
Edge-plus-cloud processing: this helps maintain both speed and depth in the field and analysis respectively.

What this unlocks for teams:

Earlier identification of stress with sufficient lead time to take action before crop yields are affected.
Optimization of resource utilization with variable rate behavior that is in line with the actual field conditions.
Less contaminated sustainability and compliance audits and buyer programs.
Reduced swivel-chair processes, as data does not flow back to the field to report.

The adoption of new tools is only going to grow, with regions like North and Latin America leading the way, according to the McKinsey and Company 2024 global farmer insights survey.

Now let’s consider these core tech innovations in more detail:
Remote sensing and geospatial data fusion
Technology and agriculture were always tightly connected. No longer does remote sensing in agriculture imply that a field scout will come back with notes. It simply means that farmers can now see live maps that are combined with satellite images, drone drills, alongside soil probes or weather station data. Combining multispectral, hyperspectral, thermal, and radar input can provide a system of this type with an ability to see leaf color, soil moisture, and canopy structure in surprising levels of detail.
The integrated perspective enables managers and farmers to move down from regional snapshots to specific corners of a field effortlessly. Similarly, satellites are able to cover large areas and emphasize stress patterns, drones take high-resolution images, and ground sensors provide the missing context. This data is then digested into useful maps that either direct irrigation, nutrient modification, or specific scouting.
Studies have shown that when drone and satellite data are combined, crop stress can be detected much sooner than usual, yield predictions can be made accurately, and the precision of classification can be enhanced. For farmers, it can lead to improved decisions made much faster, allow for better utilization of inputs, and improve records of sustainability programs.
Machine learning for pattern recognition and forecasting
Smart ML models trained on local weather, soil, and crop history can now pick up the subtle rhythms of each farm. To put simply, they cut down the amount of false alarms and sharpen yield forecasts. Here’s a brief breakdown of what pattern recognition model does:

Local data causes the model to take notice of what actually matters in a particular climate and soil, therefore, predictions are grounded in reality.
Images and sensors provide early warning to farmers with days of additional lead time to combat pests or plant diseases.
Prescription maps convert forecasts to zone by zone water and fertilizer rates, which, in turn, can save resources.
Resource planners can get more exact dates on harvest crews, trucks, and storage.
Explainable AI tools like SHAP and LIME can show which factors are driving each alert, so agronomists can trust and refine the advice.

API-first architectures and integration flexibility
Modern ag technology stacks touch satellites, IoT sensors, machinery, and business software. An API-first design keeps these parts talking without expensive custom links. Specifically:

Cloud APIs pull satellite, sensor, and farm-management data into one place.
Edge computing handles instant alerts and machine controls when the internet is weak.
Modular, cloud-native services let farms add compliance or sustainability tools without rewriting the core system.
Strong authentication and audit trails protect sensitive field and business records.

The result is faster reporting, smoother workflows, and a platform that can grow from a pilot to an enterprise network without breaking stride.
Off the shelf or custom?
If your goal is to get your systems running this season, then an off-the-shelf solution is often enough. However, if your goals are more long-term that allow you to get better forecasts, tighten your current integrations (or add new ones), and ultimately get better compliance then a custom path tends to pay off.
What it boils down to is speed is valuable, but fit is priceless. Start with speed and invest in the fit where you need it the most. When fit matters, our agriculture custom software development delivers the exact models and integrations needed.

Here are a few simple rules from the Intelliarts experts you can follow if you are still deciding:

Go off‑the‑shelf for standard record‑keeping, basic analytics, and quick wins.
Build custom for unique crops, regional models, or multi‑system integrations across sensors, satellites, and equipment.
Blend both when a farm management core is solid but needs custom AI and ERP or SCM links.

In the case of a custom solution, you can get more than that. Usually we are talking about:

Data analytics that reflect local conditions, improving decisions and resource use on the ground.
APIs that keep imagery, sensors, and equipment synchronized, reducing the potential swivel‑chair work.
Reporting that fits buyer programs and audits without too much of a manual rework.

If you need a quick overview here’s a simple guide to the trade-offs and benefits of each system:

Conclusion
The smart agritech instruments of 2025-2030 are now going beyond automation. They can do more than simply anticipate. Now, top precision agriculture tools can spot plant disease before it can spread, cut irrigation time before the heat spike, and order trucks way in advance of harvest mayhem. If you’ve ever wondered what important role did advanced farming technologies play in helping farms stay resilient, the answer lies in their ability to turn data into timely, profitable action.
With 25 years of experience Intelliarts makes it easy to reach new goals by basing shiny tech on straightforward business mathematics, which growers never felt comfortable doing. 
Ready to find out what predictive software will do to your operation? A short conversation about readiness usually finds benefits that are right there in front of you.
Reach out to Intelliarts for a quick consultationContact Us

#### Top RAG Application Development Mistakes in Enterprise AI Projects: Intelliarts’ Experience
TL;DR

A RAG demo can be built in days, but a production RAG needs retrieval, access control, evaluation, and monitoring.
Most failures start before generation: weak ingestion, poor chunking, missing metadata, or outdated sources.
A stronger LLM cannot fix retrieval if the system misses the right source, version, or context.
Enterprise RAG should be measured by workflow outcomes: resolution time, citation accuracy, adoption, latency, and cost per query.
Long-term ownership matters: RAG needs re-indexing, feedback review, evaluation updates, and continuous optimization.

Enterprise AI has moved fast, but measurable value still lags behind adoption. According to McKinsey’s 2025 State of AI report, only 39% of respondents attribute any Earnings Before Interest and Taxes (EBIT) impact to AI, and most of them report an impact below 5%. 
Gartner points to the same production gap: at least 30% of gen AI projects are expected to be abandoned after proof of concept because of poor data quality, inadequate risk controls, rising costs, or unclear business value. RAG development often fails for these exact reasons. 
This article breaks down the mistakes that make RAG demos look promising but keep enterprise systems from becoming reliable, secure, and useful in daily workflows.
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Why RAG development is so easy to start and so easy to get wrong
A basic RAG prototype is quick to build. Basically, with some knowledge, a team can connect an LLM API to a vector store, load a small document set, and produce a working demo in a few days. 
That speed often creates false confidence. Early results look strong because the data is clean, the questions are expected, and the scope is narrow.
The gap becomes clear after rollout, because in practice, users ask vague, incomplete, and context-heavy questions while documents exist in multiple versions. Moreover, access rules differ across teams, regions, and functions. 
See how prototype RAG and production-ready RAG compare against each other in the table below:
[table id=31 /]
Given all that, if retrieval is weak, the system may return outdated or irrelevant context. At the end of the day, due to an inherited lack of observability in simple RAG prototypes, devs cannot see why the response generation fails or where the delay comes from.
Quick refresher: what enterprise-grade RAG development involves
Enterprise-grade RAG is an architecture for producing source-grounded outputs over controlled business knowledge. It combines retrieval across enterprise data with an LLM, so the model works from approved evidence instead of relying on pretraining or broad prompt context.
Core layers of enterprise RAG include:

Ingestion: Source selection, parsing quality, chunking strategy, metadata, embeddings, and refresh logic
Retrieval: Hybrid search, filtering, reranking, source priority, and access control
Generation: Prompt orchestration, citations, tools or agents, guardrails, and fallback behavior
Evaluation and monitoring: Relevance, groundedness, drift, latency, cost, feedback, and error traces

Based on what the Intelliarts team observed, the architecture only works when these layers are designed around enterprise constraints. On top of that, security rules must apply before content reaches the model. More than that, there are AI compliance requirements, such as the EU AI Act’s provisions, that define, among many others:

Logging and retention rules
Data residency requirements
Audit trails
Access control policies
PII and sensitive data handling
Regulatory reporting

As for software integrations, they are used to place RAG inside the systems where workflows reside. For example, integrations with ticketing, CRM, BI, document management, or internal knowledge platforms are typically used. 
Most RAG mistakes come from shortcutting one layer and expecting another layer to compensate. From Intelliarts experience, in lots of instances, better prompts are used to fix weak ingestion and poor data quality. 
Or, a higher-performing model is used to compensate for improper retrieval. None of these and similar measures show acceptable results. That’s why every single layer, process, and choice must be to the point in RAG development. 
Explore how RAG works in great detail in another technical blog post by Intelliarts.
Mistake #1: Treating RAG as a chatbot UI
When auditing an AI project, our ML experts confirmed that this issue is usually a result of having a narrow interface decision as the core of a broader product strategy. 
Why it happens
Many enterprise RAG projects start as a Q&A layer on top of internal documents. Yet a chatbot interface does not define the workflow, user intent, or business value behind the system. 
Another part of the problem is anchoring on the demo before they define where the feature belongs, what task it should improve, and which metric will prove success.
How it shows up in production
When RAG is designed around the capability to provide basically any response, users get broad answers that are hard to verify or act on. As a result, chatbot-first RAG usually breaks down when:

Users receive generic answers that do not support a clear decision
The system does not fit ticket handling, reporting, review, or approval workflows
ROI cannot be measured because the app is not tied to task-level metrics
Adoption drops because employees need to leave their daily tools

How to fix it
Design RAG as a capability inside a real workflow. For each feature, define the target user, workflow step, expected output, and measurement. 
When it comes to, for example, support, this may include resolution time, escalation rate, and answer acceptance. For compliance, it may include review time, citation accuracy, and rejected responses.
As Intelliarts experts often emphasize, RAG works better when it is embedded into tools employees already use, such as CRM, wiki, ticketing, BI, or document management systems.
Mistake #2: Poor data ingestion, chunking, and index design
Poor ingestion weakens RAG at the evidence layer. Once outdated, duplicated, or poorly structured content enters the index, the system can produce fluent answers that are difficult to trust, audit, or operationalize. 
Why it happens
Teams index enterprise content before defining source authority, freshness, hierarchy, and required metadata. As a result, regional, outdated, or customer-specific documents may compete in retrieval as if they were equally valid. 
Chunking can damage meaning, too. A rule may lose its exception, a table may lose its explanation, and a section may lose the heading that defines its scope.
How it shows up in production
The RAG application retrieves context that looks relevant but is wrong for the business case. 
Common production symptoms include:

Outdated, duplicated, or low-trust sources appearing in generated answers
Tables, PDFs, and long-form documents losing context during parsing or chunking
Regional policies, product versions, or legal clauses being mixed
Similar queries returning different sources because ranking logic is unstable
Slow retrieval caused by broad indexes with weak filtering and poor metadata coverage

How to fix it
Intelliarts recommends starting with a source audit: what should be indexed, who owns it, how often it changes, which version is authoritative, and which content should be excluded.
Then, define chunking and metadata rules before scaling. Chunks should preserve sections, definitions, exceptions, tables, and references when the answer depends on their relationship. Metadata should cover owner, date, version, jurisdiction, product, customer segment, source type, and priority.
Before rollout, validate retrieval with real user queries. Check whether the right sources appear in the top results, whether filters improve precision, and whether the retrieved context is strong enough for the LLM to answer without filling gaps.
“When we audit a RAG system, we start with the retrieved context. If the correct source is missing from the top results, prompt tuning will only make the failure harder to see.” — Andrii Shutka, DS Engineer and MLOps at Intelliarts.
Mistake #3: Ignoring access control, security, and compliance
Security gaps in RAG usually start when retrieval is designed without the same access rules that govern enterprise systems.
Why it happens
Early RAG prototypes often run on public data, test files, or internal documents that all pilot users can access. This makes the system look safe until it connects to real knowledge bases, where permissions vary by role, region, department, contract, and legal status.
The main risk is assuming that access control can be added later. In production, the retriever must know which documents, fields, and records each user is allowed to see before context reaches the LLM.
How it shows up in production
Security reviews may block rollout because the application cannot prove who can access what. Even worse, users may receive answers based on documents from another department, customer account, region, or restricted project.
Common production symptoms include:

Users seeing content outside their role, region, or business unit
Sensitive documents entering prompts without proper permission checks
Legal or compliance teams requiring manual approval for wider rollout
Audit teams unable to trace which source was used in an answer
Data residency, retention, or logging rules being handled after launch
RAG endpoints missing the same testing applied to other enterprise APIs

How to fix it
Build access control into the retrieval layer from the start. Index security metadata with every document and enforce permissions at query time, including document-level and field-level restrictions where needed.
RAG development should also account for data residency, logging, retention, audit trails, and incident response. Security and compliance teams need to be involved before the pilot expands, not after risks appear in review.
For production systems, add threat modeling, permission testing, and penetration testing for RAG endpoints. The goal is to make every retrieved source traceable, allowed for the user, and compliant with the organization’s governance rules.
Explore our work in cybersecurity. Read the case study.
Mistake #4: Under-investing in evaluation, metrics, and feedback loops
Oftentimes, a RAG system may look strong in selected examples, but the team has no reliable way to prove whether answer quality improves, degrades, or varies across use cases.
Why it happens
Intelliarts team noted that the problem almost always lies in a weak quality baseline. For example, one stakeholder may care about completeness, another about concise answers, and another about source accuracy.
The problem is the missing definition of a good answer. A legal assistant, support copilot, and internal knowledge search tool should not use the same evaluation criteria. 
Each application needs its own thresholds for relevance, groundedness, completeness, safety, tone, and citation quality.
How it shows up in production
Quality debates become anecdotal. Users report that answers are “wrong” or “not useful,” but the team cannot see whether the issue comes from retrieval, prompting, model behavior, or source quality.
Common production symptoms include:

No trendline for answer quality over time
Prompt or model changes causing silent performance drops
High-confidence answers with weak or missing citations
Good answers for common questions but poor results for edge cases
User feedback staying in support tickets instead of improving the system
Declining adoption after a small number of unreliable answers

How to fix it
The Intelliarts team recommends building an evaluation loop with curated test sets, human review, offline tests, online metrics, and A/B experiments. The test set should include common questions, edge cases, ambiguous prompts, and cases where the system should refuse to answer.
Feedback also needs to become part of the RAG development services process. Capture thumbs up or down, comments, corrected answers, and failed searches in the UI. Then use that data to improve retrieval, prompts, source quality, and evaluation criteria.
Mistake #5: Over-engineering the model, under-engineering retrieval
In many failed RAG systems, the generation layer receives poor context, and the team keeps tuning everything around it.
Why it happens
The assumption is that a stronger LLM will resolve ambiguity, fill missing context, and work around noisy sources.
However, from Intelliarts experience, that approach hides the real failure point and makes detecting the source of degradation increasingly more difficult. If the right documents are not retrieved for core queries, the model is forced to reason from incomplete or incorrect evidence. A better generation may improve fluency, but it will not make the answer more reliable.
How it shows up in production
The system becomes more complex while quality stays inconsistent. Teams add longer prompts, reranking steps, chains, or agents to compensate for weak retrieval, which increases latency and cost without solving the root problem.
Common production symptoms include:

Correct answers depending on prompt wording rather than stable retrieval
Complex chains added to fix issues that start in the index
Higher infrastructure cost with little improvement in answer quality
Slow response times caused by too many model calls or retrieval steps
Difficult debugging because failures can come from retrieval, prompting, tools, or generation
Teams changing models before checking whether the right sources were retrieved

How to fix it
Use a retrieval-first approach in the ML development process. For priority use cases, test whether the right documents appear in the top results before changing prompts, models, or agents.
Once baseline retrieval is stable, add advanced RAG development patterns only where they solve a clear problem. Query rewriting can help with vague user input. Multi-step retrieval can support questions that require several sources. Structured retrieval can work better for tables, knowledge graphs, product catalogs, or compliance rules.
Looking for a trusted partner with RAG expertise?The Intelliarts team specializes in RAG solutions that drive business efficiencyRequest white paper
Mistake #6: Skipping production readiness: latency, cost, and observability
RAG fails in production when teams do not define how fast, how expensive, and how traceable each answer should be before users start relying on it. 
Why it happens
Unfortunately, the Intelliarts team has seen that teams responsible for prototyping usually don’t test operating limits well. They may check whether the response is useful, but not how many retrieval calls, model calls, tokens, tools, and retries it takes to produce that response.
That creates a blind spot. A flow that feels acceptable in a demo can become too slow or expensive when used across support, compliance, sales, or internal knowledge workflows.
How it shows up in production
The system starts creating operational friction when the following factors are noticeable:

Slow responses that push users back to search, wiki, or manual review
Cost spikes caused by large context windows, repeated retrieval, or unnecessary model calls
No visibility into which documents were retrieved for each answer
No tracing across retrieval, prompts, tools, model responses, and errors
Teams unable to separate model latency from retrieval latency
Quality issues that cannot be reproduced because the logs are incomplete

How to fix it
Define production targets before rollout. Each use case should have limits for response time, cost per query, availability, and acceptable failure behavior. Then tune the system against those limits. 
Adjust the number of retrieved documents, index type, reranking strategy, model choice, context size, and tool usage. Add caching or response reuse where repeated questions are common.
Observability should cover the full RAG pipeline: user query, retrieved sources, filters applied, prompt version, model response, latency, cost, errors, and user feedback. 
Mistake #7: Treating RAG development as a one-off project instead of an ongoing service
What we’re hearing from customers is that RAG quality decays when software ownership ends shortly after the launch. That’s because data changes, users change, and retrieval logic needs continuous support and maintenance to stay reliable even with the same LLMs. 
Not to mention that switching to new AI models can drastically undermine the workflow if left unvalidated and unadjusted for. 
Why it happens
Many RAG initiatives are funded as one-time builds. The team delivers a pilot or production release, but no one owns the backlog for re-indexing, evaluation, prompt updates, source quality, or user feedback. This creates a maintenance gap.
“I keep emphasizing that any RAG system needs ownership after launch. Re-indexing rules, feedback reviews, evaluation updates, and source-quality checks are only part of the maintenance process, but they play a major role in whether answer quality stays consistent or declines over time.” — Alexander Barinov, a managing partner at Intelliarts.
How it shows up in production
The system becomes less useful over time if:

New documents are missing from the index
Outdated sources continue appearing in answers
Retrieval settings no longer match how users search
Prompts stay unchanged after workflows evolve
Feedback is collected, but not turned into improvements
No team is accountable for quality, cost, adoption, or roadmap decisions

How to fix it
Position RAG as an operating capability with clear ownership. Assign a product owner, technical owner, quality process, and backlog for ongoing RAG development services. 
The operating model should include regular re-indexing, data quality checks, evaluation cycles, model reviews, prompt updates, and UX improvements. Usage data and user feedback should feed directly into this roadmap.
At Intelliarts, long-term RAG work is typically structured in stages: initial build, production stabilization, and continuous optimization. This keeps the system aligned with changing data, workflows, and business priorities after the first release.
Need assistance with a complicated RAG project?Intelliarts team provides consultation and end-to-end AI, ML, and RAG development.Contact us
The most common RAG mistakes, symptoms, and how to fix them
The seven mistakes above explain why enterprise RAG projects fail at the architecture, governance, and operating model level. You can note that these were mostly strategy and management-layer challenges. 
The table below goes one layer deeper and shows measurable technical defects teams can check during a production RAG audit.
[table id=32 /]
Use this table as an audit checklist for production RAG. Keep in mind that when multiple patterns are discovered, the issue is usually not the LLM itself, but the retrieval pipeline, data controls, or operating process around it. 
How Intelliarts approaches enterprise RAG development services

Here at Intelliarts, we treat RAG as a complex data and product architecture matter and never let our developments result in an LLM implementation risk for customers afterwards. 
The work starts with the use case, the decision the system should support, and the evidence needed to support that decision. Only then does the team design retrieval, generation, evaluation, and monitoring around enterprise constraints.
A typical engagement follows four stages:

Discovery and data audit: Define the target workflow, users, source systems, access rules, data quality issues, and success metrics.

Pilot with clear KPIs: Build a focused RAG application for one workflow and validate retrieval quality, answer accuracy, latency, and adoption.

Scaling: Add security controls, permission-aware retrieval, observability, cost limits, fallback behavior, and integration with existing systems.

Ongoing optimization: Re-index sources, review failed queries, update prompts, monitor drift, and improve UX as usage patterns change.

Success story #1: RAG-enabled data-extraction chatbot
This approach can be observed in Intelliarts’ data extraction chatbot development using the ChatGPT case. Our team built an information retrieval system over an internal knowledge base, added RAG for source-grounded answers, routed tabular queries separately, and included references so users could verify the output. 
The result was a reduction in search and analysis time from hours to minutes.
ML pipeline for the Data Extraction Chatbot
Success story #2: GPT solution for contract analysis
Another relevant example is Intelliarts’ GPT-driven solution for contract analysis. The system helps analyze and compare delivery contract discounts, which makes it a good example of AI tied to a defined business decision instead of an open-ended chat interface.
Intelliarts helps you advance from a working demo or an MVP and scale your RAG project to an enterprise-level system. As a result, you obtain a solution for daily use in production that offers accurate retrieval, controlled access, traceable answers, acceptable latency, and continuous improvement after launch.
How LLMs can convert tables into one format
Final take
RAG development fails when teams treat retrieval, governance, evaluation, and operations as secondary work. The core lesson is simple: production RAG depends on the evidence base around the model. 
Reliable systems need clean ingestion and permission-aware retrieval as well as measurable answer quality, cost, and latency controls, and continuous ownership after launch. Use the mistakes above as an audit checklist for existing initiatives or as a planning framework for new RAG applications built for real enterprise use.
The Intelliarts team is a trusted software development agency specializing in AI, ML, and RAG development. We have 26+ years of experience delivering tailored solutions to businesses worldwide and are proud of our 90%+ customer return rate. Should you need technology consultation, end-to-end development, or any other assistance, don’t hesitate to reach out. 
Looking for expert opinion on your RAG project?Let AI/ML specialists at Intelliarts provide any advisory or development assistanceReach out
 

#### Top Strategies for Cost-Effective EV Charging Optimization
The US alone is projected to require about 28 million EV chargers by 2030, with the majority being Level 1 and Level 2 chargers in private homes, workplaces, and multifamily properties. These stats connect well with the evidence that smart EV charging can save 15% to 30% of electricity costs. The big idea? EV charging providers have a lot to offer to the market, especially if they implement cost-effective EV charging optimization. 
In this post, you, as an EV charging business, will learn 4 best strategies for cost-effective, more profitable charging. From leveraging time of use (TOU) charging to scalability tips, using renewable energy sources, and data-driven optimization, you’ll have a firm grasp of all that and more. You’ll also get to know some common pitfalls and find out how to avoid them by the end of the post. 
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Smart charging: The foundation of cost efficiency in EV industry
Smart charging is an intelligent approach that optimizes the charging of electric vehicles (EVs) based on factors like energy demand, grid capacity, electricity prices, and user preferences.
There is a clear path to a sustainable-energy Earth. It doesn’t require destroying natural habitats. It doesn’t require us to be austere and stop using electricity and be in the cold or something. — Elon Musk
Explore the key benefits of smart charging, compared to traditional charging methods, in the table below:
 
Let’s proceed with reviewing four EV charging strategies suitable for any energy business, from a startup to an established EV charging network. 
Strategy #1: Leveraging Time-of-Use (TOU) EV сharging
Time-of-Use EV charging is a pricing strategy where electricity costs vary based on the time of day. 
Time-of-Use (TOU) rates are defined by utility companies, typically under the regulation of a public utilities commission or energy regulatory authority in each region. Here’s the breakdown of providers who are involved in defining and reporting TOU prices:
#1 Utility providers. E.g., PG&E, Con Edison, EDF. They provide TOU rate structures based on supply-demand patterns, infrastructure needs, and energy source costs.
#2 Regulatory bodies. E.g., CPUC in California, Ofgem in the UK, NERC in the US. Such bodies approve or modify TOU plans to ensure fairness, grid stability, and consumer protection.
#3 Grid operators. E.g., CAISO, PJM. These providers supply real-time data on grid load and rates that influence TOU planning.
An EV charging business should understand that utility providers and regulatory bodies define electricity costs and rates, while grid operators, which are direct providers of electricity to businesses and individuals, only report what the charges are at a particular time of the day. 
At the most basic level, TOU explains why rates are lower during off-peak hours (e.g., overnight) and higher during peak demand times (e.g., early evening).
Now, when the concept of this EV charging strategy and the parties involved are defined, we can delve into TOU models.
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Understanding TOU rates and pricing models
Here’s the breakdown of TOU rates and the hours they are respectively effective for:

Peak hours. High demand periods (e.g., 4–9 PM). Highest rates.
Off-peak hours. Low demand times (e.g., 10 PM–6 AM). Low rates.
Super off-peak hours. Extra low demand times (e.g., 9 AM–2 PM). Lowest rates. 
Mid-peak hours. Transitional periods with moderate rates.

As for TOU pricing models, set and approved, once again by utility providers and regulatory bodies, they are the following:

Static TOU: Same daily schedule year-round.
Seasonal TOU: Different rates based on time of year.
Dynamic TOU (Real-time pricing): Rates change hourly based on wholesale prices or grid load.

For consumers and smaller businesses, static or seasonal TOU is the most common option, while larger EV networks may leverage real-time pricing TOU options. 
The comparison of residential vs. business EV charging rate plans on the example of California – PG&E is shown in the infographic below:
 
As you can see, businesses have slightly different TOU options compared to those effective for residents. Based on this example of PGE, they also pay subscription costs in addition to the volumetric rate. 
Automation tools for TOU scheduling
The key question, around which this post is centered, is how to achieve a cost-effective EV charging optimization in business. As you could already note, TOU is basically several new factors in the already data-intensive scheduling calculations. So, the response here is the usage of specialized automation tools for EV charging that takes into account the TOU rates.
Examples are the following:
#1 ChargeLab
Cloud-based EV charger management with automated TOU pricing integration for utility programs. Supports Open Charge Point Protocol (OCPP).
Website: https://www.chargelab.co/
#2 Ampcontrol
AI-driven smart charging platform offering automated TOU scheduling, load management, and grid-responsive charging strategies.
Website: https://www.ampcontrol.io/
#3 Ev.energy
This platform automates charging during the cheapest and cleanest energy windows. It can integrate directly with utilities and supports smart home charging.
Website: https://ev.energy/
#4 Emporia Energy
Home energy management system with TOU-aware EV charging, solar optimization, and detailed usage analytics.
Website: https://www.emporiaenergy.com/
Note. The provided software solutions are examples of what you might opt for from the smart EV charging tools available on the market, rather than your ideal options. You’ll likely need a custom data analytics solution or advanced customization of a third-party one. Both a solution and a service that ML development agencies can provide. 
Explore details on smart EV charging data analytics opportunities in another blog post by Intelliarts. 
Strategy #2: Infrastructure selection & strategic scalability
This EV charging station planning strategy is basically a combination of two aspects. These are short-term considerations — infrastructure selection for current business purposes and long-term considerations — strategic scaling in the future. Let’s delve into these two strategy components separately, starting with infrastructure selection:
Infrastructure selection 
Beyond legal considerations, EV infrastructure strategy revolves around the selection of physical locations, ensuring sufficient electrical capacity, acquiring supporting systems, and more. While it’s hard to predict what actions a particular business should take, here are some suggested priorities:

Conduct a site selection analysis. This should include the evaluation of high-traffic areas, EV adoption trends, and accessibility.
Make a grid capacity assessment. Coordinate with utilities to assess available load and plan upgrades if needed.
Decide on charger type allocation. Decide between Level 2, DC fast chargers, or mixed infrastructure based on use cases.
Consider power distribution. Unless power distribution is pre-planned by, for example, a business estate owner, you need to set up wiring, transformers, breaker panels, and other electrical system components. 
Conduct physical layout planning. Optimize charging station placement, prioritizing user convenience, safety, and space efficiency.

Strategic scalability
Strategic scalability ensures your EV charging infrastructure can grow in line with rising EV adoption, user demand, and evolving technology, without requiring major overhauls. Key aspects to take into consideration here are the following: 

Stick to standardized equipment choices: Select only open-protocol chargers (e.g., OCPP-compliant) for easier interoperability.
Data-driven growth planning. Use software platforms that scale easily across new stations, users, and energy sources as the data stream expands. On top of that, utilize analytics to monitor usage patterns and identify optimal expansion timing.

Partnership development. Build relationships with property owners, utilities, and municipalities. This may get you better TOU pricing, faster rollout times, and more. 
Future-proof technology readiness. Ensure you stay or plan for becoming compatible with upcoming technologies like V2G (Vehicle-to-Grid) or wireless charging.

Explore the predictive maintenance analytics success story by Intelliarts 
Strategy #3: Adding renewable sources of energy
Cost-effective EV charging optimization shouldn’t necessarily be fully centered around getting the best TOU rates or making your infrastructure as streamlined and optimal as possible. Throwing into your EV charging station system a percentage of sustainable energy sources is regarded as a great practice. Not only does it help achieve a more cost-effective EV charging optimization, but it may also assist you in gaining a reputation among progressive customers who care about their CO2 emissions. As a smart EV charging business, your options here are:
#1 Solar panel installations on rooftops or nearby land.
Installing photovoltaic panels on available building rooftops or nearby plots allows you to generate clean electricity directly on-site. This reduces reliance on grid power, lowers energy bills, and qualifies you for local renewable energy incentives or tax credits in many regions.
#2 On-site wind turbines where feasible.
If the location allows for sufficient and consistent wind flow, small to mid-sized wind turbines can supplement your charging station’s energy needs. Wind energy can be particularly useful for rural or coastal stations aiming for partial or full off-grid functionality.
#3 Green energy contracts with utility providers. 
Partnering with electricity suppliers that offer certified green energy plans enables your station to operate using renewable energy even if you don’t produce it on-site. This is a quick way to reduce your carbon footprint and meet sustainability goals without major infrastructure investments.
Additional tip: When working with renewable sources of energy, you may find out that on some days, energy income is plenty, while on other days, there’s a lack of it. It’s recommended to install an energy storage system to store excess renewable energy for peak times. It would allow not to waste precious energy as well as to better rebalance the grid to a renewable energy ratio. 
Sustainability is no longer about doing less harm. It’s about doing more good. — Jochen Zeitz, CEO of Harley-Davidson
Strategy #4: Getting started with data-driven charging optimization 
Reflecting on the importance of real-time data for any analytical software and, subsequently, the entire data-driven EV charging optimization, let’s recall the Garbage In Garbage Out (GIGO) rule. This axiom basically proves that if one supplies the system with inadequate data, it will yield invalid insights. It’s also true that if the system isn’t intended to work with supplied data, it won’t work well either. It brings us to two major consideration points: suitable data analytics software for cost-effective EV charging optimization and key metrics to track.
Data analytics software for smart EV charging
Here are several examples of such data analytics software available on the market: 
#1 Ampcontrol Analytics
A module within Ampcontrol’s platform focused on granular data analytics for EV infrastructure strategy.
Website: https://www.ampcontrol.io/
#2 WeaveGrid
Data analytics and grid orchestration software that connects EV charging behavior with utility systems.
Website: https://www.weavegrid.com/
#3 Driivz
A cloud-based EV charging management platform that includes built-in analytics for performance monitoring, predictive maintenance, and usage optimization.
Website: https://www.driivz.com/
Important note. No publicly available solution is fully tailored to your analytics needs. The best data analytics software is one either custom or customized by request. That’s why it’s recommended to apply for assistance from professionals with domain expertise in emobility software development, like Intelliarts, should you need a specialized solution. 
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Key data metrics to track for EV charging optimization 
Lastly, here are the data types and metrics a business needs to gather for their EV charging strategies:
#1 Station utilization rate
The percentage of time a charging station is actively used versus its total available time.
Measured in: Percent (%), calculated as (active usage time / total time available) × 100.
#2 Cost per charging session
The average cost incurred to deliver one charging session, including electricity, demand charges, and operational costs.
Measured in: Currency per session (e.g., USD/session or EUR/session).
#3 Average session duration
The typical length of a charging session from plug-in to disconnect.
Measured in: Minutes or hours per session.
#4 Peak vs. off-peak charging ratio
The ratio of charging sessions during peak electricity rate periods compared to off-peak periods.
Measured in: Ratio or percent of sessions during each pricing period.
#5 Load variance across stations
The distribution of energy demand across multiple chargers or locations.
Measured in: Standard deviation of kWh usage or current (amps) across chargers.
#6 Charger downtime or failure rate
The percentage of time a charger is unavailable due to faults, maintenance, or connectivity issues.
Measured in: Percent (%) of total time or number of failures per 1,000 sessions.
#7 Renewable energy usage or CO₂ savings
The proportion of charging energy sourced from renewables or the amount of CO₂ emissions avoided.
Measured in: Percent (%) of renewable energy or kilograms/tons of CO₂ saved.
Note. When touching upon the topic of EV charging, I have to mention load balancing, i.e., the distribution of electrical demand evenly across the power grid to prevent overloads. You can explore load balancing and power grid in great detail in another blog post by Intelliarts. However, this aspect is a concern of energy companies, rather than EV charging infrastructure providers. That’s why it’s not a part of this article. You just need to be aware of this matter to understand capacity limits and fluctuations in electrical load. 
 
5 Common EV charging optimization pitfalls and solutions to them
Above everything, I have to mention that any technical intervention requires substantial software engineering expertise, which only software development providers or in-house dev teams can possess. Therefore, engineering aspects should be covered for your business by experts. That’s why, instead of delving into technical hurdles, in this section, I decided to focus on five strategic-level pitfalls related to cost-effective EV charging optimization. 
Pitfall 1: Ignoring TOU and utility rate structures
Many EV charging stations operate without regard for local electricity pricing models, leading to unnecessary costs during peak hours.
Solution: It’s recommended to align charging schedules with time-of-use (TOU) rates using automation tools. This allows charging during off-peak periods when electricity is cheaper.
Pitfall 2: Poor site scalability planning
Another common mistake is building for current demand only, with no room for infrastructure scaling or meeting higher load demands, resulting in costly future upgrades.
Solution: Often, the right call is to develop infrastructure using modular EV charging hardware rather than solid, consumer-centered stations, which are inherently limited in their capability to support growing demand. If such hardware is not available or not optimal for other reasons, it’s still normally possible to account for the need to add additional charging stations, install more solar panels or wind turbines, etc. The goal is to plan for scalability from the beginning and consider long-term demand using predictive analytics in advance. 
Planning to invest in the electric vehicle software development services? Intelliarts experts are here to help.
Pitfall 3: Underutilizing smart charging data
Collecting charging and usage data without translating it into actionable insights leaves optimization opportunities on the table.
Solution: If your EV charging and data analytics solutions don’t have smart analytics dashboards and visualization tools, it’s recommended to utilize external solutions like Power BI or Tableau. However, custom ML data analytics software can prove to be a better-suited solution. Insights to look for include usage trends, inefficiencies, opportunities for enhanced charger deployment and energy distribution, and more. 
Pitfall 4: Not integrating renewable energy
Not including solar or wind energy sources keeps businesses fully dependent on grid power and exposed to pricing volatility. Renewable sources may not suffice for some EV charging business needs. However, when they do, investing in solar or wind energy infrastructure is highly recommended. 
Solution: Integrate on-site renewable energy where possible. This reduces carbon footprint, ensures supply stability, and opens access to green energy incentives. It also may make a company eligible for additional funding, for example, from the Alternative Fuels Infrastructure Facility (AFIF). It’s the EU’s program focused on facilitating the deployment of alternative fuel infrastructure. 
Pitfall 5: Choosing non-interoperable equipment
Some providers lock themselves into one hardware or software ecosystem, making it hard to switch vendors or integrate new services later.
Solution: Always select hardware and platforms that follow open standards like OCPP. Before purchase, verify that your solution can connect with third-party platforms (e.g., fleet systems, billing tools) via open APIs.

Final take
To build a profitable and future-proof EV charging business, providers must focus on cost-effective optimization strategies. Key actions include leveraging TOU pricing with automation, planning scalable infrastructure, integrating renewable energy, and using smart data analytics. Avoiding common pitfalls like ignoring rate structures or choosing closed hardware can make a major difference. Together, these steps ensure lower costs, improved grid compatibility, and long-term growth in the evolving EV niche. 
The Intelliarts software development team has been on the market for more than 25 years already, delivering high-end solutions. With more than 90% of recurring customers and substantial EV expertise, we are ready, willing, and able to take on your next EV charging optimization project.
Looking to Scale Your Renewable Energy Business?Intelliarts delivers custom tech solutions tailored to your goals—from concept to launch.Contact us

#### Types of Sensors Shaping the Future of Smart Farming
Smart sensors help manage water waste, soil loss, and livestock tracking. Set to reach $7.27 billion in market value, they’ll soon be a must-have for every farm.
Agriculture feeds the world and supports local economies, but it also feels the full weight of modern times. Cities expand, pollution rises, soil loses quality, and water becomes harder to manage. If you work in this space, you know traditional farming doesn’t stretch as far as it used to, so it might be time for a change.
Analysts at Fortune Business Insights project the agriculture sensors market to reach $7.27 billion by 2034, making it one of the most in-demand technologies in the field. Smart sensors cover a number of tasks, from temperature monitoring to tracking livestock, painting a full picture of your farm’s health.
Here, we’ll list the main types of agricultural sensors, explain what each does best, and show how to use them for maximum impact, both for your business and for the planet.
What are the sensors used in smart agriculture?
Agricultural sensors are devices that collect information from your farm environment and send it to become actionable data. They can track soil moisture, humidity, light levels, crop health, etc.
Basically, sensors are the eyes and ears of modern farming. They react to physical or chemical changes, such as a change in soil moisture, and convert that into an electrical signal or data.
The real value of agricultural sensors is about turning data into automated decisions. When sensor networks connect directly with irrigation, fertilization, or analytics systems, farms move from observation to real-time optimization.
 – Martha Kufalska, Agritech Solution Expert @Intelliarts
In some cases, sensors are only for monitoring. In others, they can trigger an action like watering a crop when the soil is too dry.

Why invest in smart sensors?
Climate change creates difficult conditions for farmers. Droughts, heavy rain, and extreme temperatures interfere with planning and schedules, while demand for food rises every second. Clearly, the farmers of today must adapt to these changes.
Agricultural sensors help detect stress, prevent losses, and make smarter choices. When you get direct information about the state of your farm, you don’t have to rely on visual checks alone and can base decisions on measurements.

Studies show that sensors and precision farming practices increase crop yields by 10-20%, and in some cases even more. 
Sensors also reduce waste. IoT networks cut fertilizer use by 50% and water use by 30%, not affecting yields.

Over time, small savings become tangible. Many farms reach a positive ROI (return on investment) within 2-5 years after installing sensor-based systems thanks to lower input costs and higher productivity.
For example, in one Intelliarts project with agritech company Indigo Ag, our engineers integrated sensor data and automated pipelines for processing field records such as seed types, locations, and irrigation data. The solution reduced manual data entry from around 8 hours to just 1–2 minutes, allowing agronomists to focus on analysis rather than administrative tasks. The latter had a significant ROI impact as a result. 
Sensors collect the data, and only the right software turns it into smarter farming.Build your agritech solution
Types of smart agriculture sensors
Agriculture sensors cover a range of tasks, yet the most effective use of IoT is an interconnected network that spans all areas of your operation. Let’s break down the most common sensor types one by one.

Soil and nutrient sensors
Roots and leaves achieve balanced water flow only when soil moisture is optimal, and the same goes for sustenance. Sensors measure moisture, temperature, pH, and nutrient levels.
How it works:

Moisture sensors are placed in the soil to measure how much water is present at different depths.
pH and nutrient sensors check the soil’s chemical makeup, including nitrogen, phosphorus, potassium, and acidity.
All this data goes to a dashboard or irrigation system so you can adjust watering and fertilizing in real time.

Benefits: You water only when needed, waste less fertilizer, and provide crops with better growing conditions.
Weather and climate sensors
Farms that use real-time weather data make better decisions about when to plant or protect crops. Studies of precision farming show crop yields can be 20% higher when using weather sensors.
How it works:

Temperature sensors use electronic thermometers and hygrometers.
Rainfall sensors collect precipitation data with tipping buckets or optical sensors.
Wind sensors measure speed and direction.
Data is transmitted for alerts and scheduling.

Local microclimates can vary significantly even within the same farm. Weather sensors placed across fields provide localized data that helps farmers schedule spraying, planting, and harvesting far more accurately than relying on regional forecasts.
 – Martha Kufalska, Agritech Solution Expert @Intelliarts
Benefits: Better timing for planting, spraying, and harvesting, and lower risk of weather damage.
Crop health and imaging sensors
Crop disease often starts before visible signs. By the time you notice yellow leaves, the damage might already be done. Imaging sensors catch the small changes and gather data for targeted treatments.
How it works:

NDVI (Normalized Difference Vegetation Index) sensors measure red and near-infrared light reflected by plant canopies, indicating plant health, chlorophyll content, and biomass.
Multispectral and hyperspectral cameras capture images in different wavelengths.
AI or machine learning analyzes data to find signs of pests, diseases, or nutrient shortages.

Benefits: Earlier detection of problems, fewer crop losses, and better field planning.
Learn more about farm equipment imaging in today’s agricultural businesses.
Livestock monitoring sensors
Health issues in animals are harder to notice in large herds. A single cow that eats less can trigger a disease spread, which is why livestock sensors monitor activity and feeding habits 24/7.
How it works:

Wearable sensors track temperature, heart rate, and movement.
RFID (Radio Frequency Identification) tags and GPS collars show location and activity.
The data is sent to farm software that flags unusual behavior.

Benefits: Faster response to illness and better-planned feeding and grazing.
Water quality sensors
Water monitoring tools give you lab results instantly, no waiting. Sensor-driven irrigation systems have been shown to improve yields by 8–20 % and help save water at the same time.
How it works:

Sensors measure pH, salinity, oxygen levels, turbidity, and contaminants.
The results go to a dashboard or irrigation system.

Benefits: Safer water use, lower risk of stress on crops and livestock, and better water control.
See how our sensor management system cut water use by 10x.Explore our case study
Automated machinery sensors
IoT tech and smart sensors in tractors and harvesters can save $5,000-$15,000 per machine per year just from better fuel use, fewer overlaps, and less downtime.
Automation also improves how farms manage operational data across large agricultural environments. In one Intelliarts case study, integrating sensor-driven data pipelines and automated workflows helped our agritech partner scale its internal data infrastructure, contributing to 260% operational growth while maintaining reliable data processing across multiple farming locations.
How it works:

GPS systems guide tractors and planters for accurate field work.
Load sensors monitor harvesting machines to prevent overload.
Drones with cameras scan fields for crop health and field mapping.
Data integrates with farm management systems to automate operations.

Benefits: Reduce manual labor, save resources, and improve farm efficiency.
For your convenience, here’s a short summary of the topic in a table with key sensor types, use cases, and benefits:

Final thoughts
With the weather getting less predictable and food demand going up, guessing is a risky game. Sensor-based farming is what keeps your farm steady, productive, and able to last for the long run.
Smart farm sensors track soil, weather, crops, animals, water, and equipment, and when they work together, you get a full picture of what’s really going on in your fields. You can see problems before they appear, and stop wasting fertilizer and water.
However, sensors alone are only part of the equation. To unlock their full value, farms need robust software platforms that collect, analyze, and transform raw sensor data into actionable insights. With 25+ years of experience in software engineering, Intelliarts helps agribusinesses turn agricultural data into practical solutions. If you’re looking for a partner with exclusive expertise, 90% retention rate, and lasting quality as a core value, contact us to schedule a consultation.

#### Using AI and IoT Green Supply Chain Technologies in 2025
The study by PwC indicated that, 46% of companies have fully adopted IoT in their supply chains, second only to cloud-based platforms. That basically means that soon enough, the magnitude of companies resisting such a transformation will start falling off. 
Moreover, projections indicate that by 2033, the IoT in supply chain management market will reach $41.8 billion, with a compound annual growth rate (CAGR) of 12.9% from 2023 to 2033. It clearly shows the high rate of adoption of IoT green supply chain technology. 
In this post, you’ll explore challenges with implementing green supply chains with IoT technology as well as the role of IoT in business. Besides, you’ll find out what AI in green chains issues are there and how to solve them. Moreover, the post offers AI and IoT in supply chain implementation strategies and real-life cases. 
Current challenges in sustainable supply chains
Let’s begin by reviewing an IoT green supply chain issues that still complicate digital transformation for businesses in the niche:

Using IoT in smart grid is one of ways to address energy and sustainability challenges. Explore more in another of our blog posts. 
Implementing IoT & AI in green supply chain strategies
We are already aware of both sustainable supply chains and AI implementation in green chains issues. This brings us to the necessity to delve into strategies to build an IoT green supply chain with IoT and AI: 

#1 Sustainable Supplier and Resource Management
The strategy lies in using IoT to monitor supplier sustainability practices and AI to analyze compliance with environmental standards.
Implementation steps:

Integrate IoT tracking for supplier emissions and resource use
Collect and centralize supplier sustainability data
Use AI to assess and score supplier environmental impact
Collaborate on eco-friendly production improvements
Conduct regular audits and performance reviews

Expected impact: Greater supply chain transparency, eco-friendly sourcing, and stronger sustainability compliance.
Who benefits: Automotive companies, Fashion and apparel brands, Food and beverage companies. 
Explore additionally what green supply management is in the video below:

#2 AI-powered predictive logistics
The strategy involves using AI and IoT to forecast demand, optimize inventory, and enhance transportation efficiency, reducing waste and emissions.
Implementation steps:

Collect real-time and historical logistics data
Train AI models for demand forecasting
Optimize inventory levels dynamically
Automate route planning with AI-driven insights
Adjust supply chain operations based on predictions

Expected impact: Lower logistics costs, reduced overproduction, and minimized fuel consumption.
Who benefits: E-commerce companies, FMCG companies, Logistics and delivery services.
#3 IoT-enabled reverse logistics for circular economy
The strategy focuses on using IoT to track product returns, repairs, refurbishments, and recycling efforts to promote a circular economy and reduce waste.
Implementation steps:

Install IoT sensors in products to track usage and lifespan
Implement AI to assess the feasibility of repair vs. recycling
Automate return logistics using IoT tracking
Use data-driven insights to optimize refurbishment processes
Establish partnerships for efficient material recycling

Expected impact: Extended product lifespan, reduced waste, and cost savings from refurbished or recycled materials.
Who benefits: Electronics manufacturers, Automotive companies, Consumer goods brands.
Exploring the role of IoT in sustainable supply chains
One of the possible answers to the challenges mentioned above is IoT and AI-driven software. That’s why knowing how IoT enhances green supply chains can prove valuable knowledge for any decision-maker. Here are these roles described in detail:
#1 Carbon footprint monitoring
Implementing IoT in sustainable supply chain strategies enables businesses to track emissions across logistics and production. Smart sensors collect real-time data on fuel consumption, energy use, and waste generation, helping companies optimize processes and reduce carbon output.
Key IoT-driven footprint monitoring features here include:

Real-time CO₂ emission tracking in vehicles. 
Energy consumption analytics and identification of energy-intensive operations.
Waste reduction insights. 

For additional insights, explore our other article on AI in renewable energy. 
#2 Circular Economy Enablement
The green supply chain with IoT promotes circular economy practices by enabling better tracking, reuse, and recycling of materials. Smart IoT-powered monitoring systems improve resource efficiency and waste management.
IoT-enabled circular economy solutions include:

Material lifecycle tracking to monitor product condition and enable reuse.
Automated refurbishing alerts for proactive maintenance and repair.
Smart waste separation using IoT-driven sorting technologies for better recycling.

Exploring IoT for a sustainable supply chain? Our experts are ready to assist.Learn more
#3 Smart Packaging Optimization
Optimizing packaging with IoT supply chain sustainability solutions reduces material waste while ensuring product protection. IoT sensors embedded in packaging help maintain product quality and minimize excess materials.
IoT-powered packaging improvements include:

Temperature and humidity monitoring to ensure perishable goods remain intact.
Shock and impact detection to prevent damage and reduce unnecessary waste.
Lightweight, reusable, and biodegradable packaging insights for eco-friendly logistics.

#4 Demand-Driven Production Optimization
With IoT in green supply chain management, businesses can move from overproduction to demand-driven models, aligning production with real-time market demand.
IoT-driven production efficiency includes:

Automated demand forecasting using real-time sensor data and analytics.
Dynamic production scheduling to prevent excess inventory and resource waste.
Operational efficiency tracking to minimize production downtime and reduce emissions.

#5 Sustainable Supplier Management
Ensuring suppliers align with sustainability goals is easier with IoT for green logistics. IoT-powered monitoring systems provide real-time transparency into supplier operations, helping businesses select responsible partners.
Key IoT-enabled supplier management features include:

Emission tracking across supplier networks to ensure compliance with environmental standards.
Energy efficiency monitoring to identify high-impact areas for sustainability improvements.
Ethical sourcing verification through IoT-driven material origin tracking.

Basically, IoT supply chain sustainability can be achieved in numerous mutually complementary ways. Naturally, some businesses, like transportation companies, may be more interested in carbon emission monitoring, while others, such as manufacturers, are more concerned about smart packaging. Discovering where green supply chains with IoT can be of best usage is the first step to digital transformation. 
 
Explore additional details on how to use IoT in supply chain management in another blog post by Intelliarts. 
Common AI in green chains implementation issues
As a continuation of the description of IoT supply chain sustainability issues, we need to mention what challenges exactly make it difficult to apply AI technologies in green chains. Recommendation on how those issues can be addressed is included: 
#1 Data accuracy and bias
AI models rely on data, but inaccurate or biased inputs can lead to flawed sustainability decisions. This impacts carbon tracking, waste reduction, and supplier evaluation.
Solution: Use diverse, high-quality datasets and regularly audit AI models to eliminate bias and improve decision accuracy.
You may be interested in learning additionally about data preparation in ML, which is explored in detail in another our blog post.
#2 High energy consumption of AI models
AI algorithms, especially deep learning models, require vast computing power, increasing carbon emissions and offsetting sustainability benefits.
Solution: Optimize AI algorithms for energy efficiency and use green data centers powered by renewable energy.
#3 Lack of standardized sustainability metrics
AI struggles with inconsistent definitions of sustainability across industries, making it hard to compare and track green supply chain progress.
Solution: Develop industry-wide AI-friendly sustainability frameworks for uniform reporting and analysis.
#4 Ethical and transparency concerns
AI-driven sustainability decisions often lack transparency, making it hard to validate eco-friendly claims and ensure ethical sourcing.
Solution: Implement explainable AI (XAI) models that provide clear insights into decision-making processes.
#5 AI model scalability across global supply chains
AI solutions may work in limited regions but struggle to scale across diverse global operations with varying regulations and infrastructures.
Solution: Develop modular AI models adaptable to different supply chain environments and compliance requirements.
#6 Security risks in AI-driven supply chains
AI-powered logistics and sustainability platforms are vulnerable to cyber threats, which can manipulate data or disrupt operations.
Solution: Strengthen AI cybersecurity with encryption, access control, and regular audits. For physical security, an access control system can also protect critical infrastructure by controlling who can enter sensitive areas.

In essence, it’s possible to minimize concerns and limitations of AI and IoT green supply chains. However, it often requires AI development services from a trusted dev partner to handle technical aspects. 
Real-life IoT solutions for sustainable supply chains
Let’s get through two real-world success stories of how famous companies faced sustainable supply chain issues and addressed them through software solutions:
Blustream’s IoT-enabled storage monitoring system: Success story by Intelliarts
Background:
Blustream Corporation needed to prevent product spoilage due to temperature and humidity fluctuations in its supply chain. The company partnered with Intelliarts to develop a mobile app synced with IoT sensors for real-time condition monitoring.
Solution:
Intelliarts built an app that connects to IoT sensors via Bluetooth, tracking temperature and humidity. It sends real-time alerts and provides analytics to help businesses optimize storage environments.
Results:
The solution reduced product spoilage and storage risks, resulting in cost savings and improved supply chain efficiency. It also expanded Blustream’s B2B customer base.
Explore success story
Walmart’s IoT-enabled food safety system
Background: Walmart needed to improve food safety and reduce waste in its supply chain, especially for perishable items like fruits and vegetables.
Solution: The company deployed IoT sensors in refrigerated trucks and storage units. These sensors track temperature, humidity, and shelf life, ensuring optimal conditions during transportation. Any deviations trigger alerts, allowing quick corrective action.
Results: Walmart cut food waste by 15%, improved compliance with safety regulations, and ensured fresher products for customers. The IoT system also reduced energy consumption in refrigeration, aligning with Walmart’s sustainability goals.
Explore success story
Looking for an IoT-enabled food safety solution like Walmart’s?Let Intelliarts power your green supply AI & IoT project.Contact Us
Maersk’s smart containers for efficient shipping
Background: Global shipping giant Maersk faced challenges in tracking container conditions, leading to inefficiencies, spoilage, and delays in the supply chain.
Solution: Maersk introduced IoT-enabled “smart containers” equipped with real-time tracking sensors. These devices monitor temperature, humidity, and location, providing instant data to logistics teams. The system integrates with cloud analytics to optimize routes and prevent product spoilage.
Results: The implementation reduced cargo loss by 30%, improved supply chain visibility, and enabled predictive maintenance. Maersk’s customers gained real-time insights into shipments, increasing efficiency and reducing delays, contributing to more sustainable logistics.
Explore success story
Final take
IoT green supply chain technologies can be reinforced with advanced analytics, real-time monitoring, and predictive capabilities. It offers optimized energy use, enhanced transparency, and minimized waste through data-driven decision-making. They enable precise demand forecasts, efficient logistics, and automated sustainability compliance, reducing carbon emissions and operational costs. This approach creates a more sustainable and resilient supply chain ecosystem.
Having a trusted tech partner for your IoT green supply chain project is half the battle. Should you need professional assistance, don’t hesitate to reach out to the Intelliarts team. With more than 24 years of experience and hundreds of projects under our belt, we are ready, willing, and able to provide technical consulting, execution, and support. 

#### Using AI Proof of Concept Effectively
Any software development project is a risk. It especially concerns the development of innovative machine learning solutions. The development of an AI proof of concept (PoC) which is basically a prototype of a proposed solution, can help a business validate the idea with limited spending.
C-level executives are commonly rational about investing resources into projects. This post will help you get an idea of how an AI PoC can help minimize the risks of wasting the budget and improve the solution’s performance. It will also delve you into the steps of creating your first AI PoC and familiarize you with some successful proof of concept examples.
Why is creating a PoC important to AI development?
Going with PoC development first is the right call nearly at all times. Here are some of the reasons why:

It allows to validate the feasibility of an AI solution before large-scale investing. For instance, when considering generative AI in marketing, an AI PoC can serve as a testing ground for refining the solution functionality before committing to large-scale investments.
It enables the verification of the AI model’s interpretability. A PoC helps to ensure that the AI model is transparent and can be trusted, which is essential for user acceptance and compliance with certain regulations.
It helps to identify potential technical challenges, risks, and limitations early on. It also offers an opportunity to develop strategies to overcome these hurdles early in the project.
It enables to visualize the value and potential impact of the AI solution. It allows investors and stakeholders to obtain a clear picture and provide valuable input for improvements.
It allows to make informed decisions. An AI PoC fosters buy-in from stakeholders, bridging the gap between technical teams and decision-makers.

After creating and examining an AI PoC, a company may come to one out of three possible outcomes. These are basically successful or failed prototype or a prototype that needs further improvement. Here are some conclusions that derive from the outcomes and possible actions to take:

Successful AI PoC

It demonstrates that the technology is viable and capable of achieving the desired objectives. The next step is to implement an AI proof of concept by scaling the solution, optimizing its performance, and addressing any potential limitations or challenges identified.

AI PoC that requires improvement

Although the prototype is promising, it necessitates further refinement to meet the desired performance criteria or address specific limitations. Model retraining or integrating an AI & big data solution may be required. Enhancing the prototype so it can achieve the desired outcomes and further retesting is due.

AI PoC that haven’t met expectations

This outcome calls for a careful evaluation of the reasons behind the failure, such as insufficient data, flawed model architecture, or inadequate algorithms. Based on this analysis, it may be necessary to revisit the problem statement, reassess the approach, and potentially explore alternative AI solutions, abandoning the initial PoC early.
The big idea here is that any outcome of a PoC is useful to the project. It either helps to produce a better solution or saves a considerable amount of resources that overwise would be wasted on an unsuccessful solution.
Need a reliable AI team for your PoC? Intelliarts is here to help.Let's talk
Scenarios when AI PoC is a must
It’s safe to claim that creating a prototype of an intended solution first is the right call at all times. However, in some cases, it’s even more crucial to start with a solid AI PoC. These include:
1. Innovative technology or method
If an AI project is based on a technology or a method that has not been previously proven, then a PoC is to validate the technology and an approach.
2. High-risk projects
If the AI project is considered high-risk, a PoC is a must. Reasons for a project to be high-risk include: significant capital investments needed, the impact of an AI PoC on critical business or its involvement with sensitive data, highly regulated business domains, etc. An AI PoC helps to mitigate risk by testing the solution in a controlled environment.
3. Complex projects
The AI project usually involves the usage of complex business processes, innovative algorithms, or datasets of bad quality. The PoC will ensure that the system can manage the complexity and yield the desired outcomes. It also allows stakeholders to understand the interplay between different elements in the system.
4. Uncertain data quality or availability
Sometimes it may be difficult to claim the suitability of the data for the proposed AI solution. An AI PoC helps to address issues of uncertain data quality or availability preventing the wastage of resources on larger projects.
Additionally, business owners or AI engineers may be willing to start with an AI PoC when the project requirements are not sufficient or for other significant reasons.
  One PagerRevolutionize Insurance: Enhance Claims with Computer Vision Car Damage Detection Download
Scenarios when it’s possible to go without AI PoC
The PoC approach is extremely helpful in the vast majority of cases. But here are some cases in which going without this phase comes with minimal risks to a project’s success:
1. Small projects with low risk
Lower-scale solutions, especially ones that have likely been tested and validated extensively by others in the industry, reduce the need to conduct feasibility testing.
2. Well-established and widely used technologies
If the AI project’s idea and its implementation approach are well documented from functional and technical perspectives and there are comprehensive guides and successful use cases, you might not need a PoC.
3. Shortage of time
An AI PoC offers a quick, focused validation of the AI solution feasibility, reducing risk before diving into full-scale development. It can quickly demonstrate the potential benefits, or the lack thereof, saving valuable time in the decision-making process and offering a faster go-to-market.
4. Minor changes or updates to existing well-known system
If a project has already passed the initial stages of development and testing, and a development team can likely on prior work to understand the viability and potential of the solution, a PoC may not be required.
The big idea to keep in mind — a PoC is to make project investments more justified and the end cost of a solution more optimal. Skipping the PoC phase is unlikely to offer you any cost advantage.
Benefits that businesses gain from AI proof of concept
For the consideration of C-level executives, AI PoC has what to offer in exchange for a feasible investment. Here are some of the AI proof of concept benefits:

Risk reduction. Early identification of issues due to a PoC mitigates the technical and functional risks of full-scale implementation.
Resource optimization. A prototype allows businesses to assess required resources, preventing over or under-allocation.
Confidence boost. A successful demonstration of a PoC reassures stakeholders of the project’s feasibility and potential returns.
Market validation. A prototype provides an opportunity to gauge customer response and validate market demand for the solution.
Standard compliance assurance. A PoC helps ascertain that the solution meets industry standards and regulatory requirements.
Cost reduction. Early identification of potential issues can prevent costly fixes or modifications in the future.
Roadmap provision. The outcomes of a PoC can guide the development of a more accurate and effective project plan.

5 steps to guide your first AI proof of concept
Now, when we’re ready to build a successful PoC, let’s review the steps to follow on this path. From a certain perspective, the AI PoC development process is pretty much the same as with regular software solution development, but on a lower scale. Here is how a 5-step AI PoC creation will look like in most times:

#1 Determine what problems you want to solve with AI
Preparing for an AI proof of concept starts with assessing organization’s needs and challenges. It’s followed by identifying where automation or predictive AI can enhance efficiency and productivity, adding real value this way. A clear problem statement serves as a roadmap, ensuring that stakeholders and engineers are on the same page. At this stage, an AI design prototyping tool can help visualize assumptions and align business expectations with technical possibilities.
Besides, engaging with different business units is also due. This phase involves a fair amount of brainstorming and discussion to ensure that the AI initiative is focused, targeted, and able to deliver substantial business benefits.
#2 Data selection and preparation
According to the GIGO concept, the quality of AI model training heavily depends on the quality of prepared data. So, it’s crucial to identify relevant sources of structured and unstructured data and prepare datasets.
Data preparation involves cleaning the data, removing inconsistencies, and filling in missing values. A robust data governance framework should be put in place to manage, protect, and enhance the value of data assets.
Read also: Data Preparation in Machine Learning
#3 Designing and building an AI PoC
Companies may choose to develop a proof of concept themselves or outsource the work to a software development agency that specializes in AI. In the case of the latter, you get external expertise and flexibility in project management.
When designing an AI solution, you should carefully consider organization’s technical capabilities, resources, and timelines. You should also be ready to experiment with different algorithms and models to find the one that suits the intended solution the best.
Should you need a trusted partner for building PoC, don’t hesitate to reach out to Intelliarts. Our team provides custom machine learning solutions.
#4 AI PoC evaluation
After creating the AI model, you should evaluate it thoughtfully. The performance test involves using a portion of your dataset to test the model and confirm whether it can generate valuable, actionable insights. Metrics such as accuracy, precision, and recall can be used to measure the model performance.
It’s also essential to evaluate the AI model from the business perspective. Does it solve the problem you initially set out to tackle? Is it providing actionable insights that can lead to better decision-making or operational efficiency? An AI PoC is hardly about technical perfection. It’s rather about assessing the potential value of the future solution to the organization.
#5 AI PoC revalidation or scaling
Basically, this step brings to one of the three AI PoC outcomes, which are successful, partially successful, and failed PoC. If the AI model has proven its effectiveness and value to a certain degree, it’s time to re-validate the concept. Re-validation involves refining the model based on learnings from the initial deployment, adjusting parameters, or adding more data to improve its performance.
Scaling up means expanding the use of the AI model. It’s moving toward the full-scale development of a high-performing AI solution using the experience gained from a PoC. Scaling up should only be considered when the model has proven the potential to bring value.
Read also about the AI for network marketing use case.
Intelliarts experience with AI PoC
Here at Intelliarts, it’s our common practice to start an AI project with a PoC. In cases when a client is unsure about the task or when it’s impossible to know exactly if the solution will work, a PoC serves as our coverage of some kind.
Typically, work on a PoC lasts about 6-8 weeks. We test the viability of an idea and task requirements and produce a basic, limited version of the intended solution. A PoC performs the task but is restricted in terms of functionality and performance. It also doesn’t take into account edge cases, doesn’t work very fast, etc.
We validate whether the model created under a PoC solves the problem well or not. If it was decided to move forward with the project, we proceed with fully-fledged development. We retrain, improve, tune, evaluate, and only then deploy a finished model. Basically, with a PoC, we undergo the full AI development process, as shown in the infographics below, but on a limited scale.
Sometimes we, along with our clients, may decide to not implement AI proof of concept. For one, when there is a clear understanding of the task or past experience with similar solutions, for another, straightforward tasks also may not require a PoC.
Use cases of AI PoC from Intelliarts portfolio
Here are three AI PoC examples developed by Intelliarts to test the possibility of solving a proposed business problem:
Computer vision demo ML model
This computer vision in insurance demo presents a car damage detection ML solution. As a PoC, it offers limited functionality, which is enough for an overview and testing of its usability, such as the capability to identify damaged areas and car parts. The business challenge intended to solve with this PoC was automated claims processing in insurance.
The computer vision demo can be accessed in the Hugging Face environment.

You may discover more about engineering a two-model computer vision solution from our other article.
Visual search demo ML model
This demo presents a visual search solution. As a PoC, it offers limited capabilities to suggest goods and products available on the web-based on an input image or video frame. The business challenge intended to solve with this PoC was visual searching in e-commerce.
The visual search demo can be accessed in the Hugging Face environment.

You may discover more about visual search in E-Commerce and demo ML model from our other article.
List stacking and predictive model for real estate businesses
The solution presents an ML-based system that prioritizes homeowners based on their motivation to sell the property and make a discount. In this project, we started with a PoC which later was scaled to a fully-fledged solution. The business challenge intended to solve with this PoC was to predict a homeowner who is going to sell a house before the house is put up for sale.
Final Take
The impact of an AI PoC on a project’s outcomes is hard to overestimate. Despite there being instances when this stage can be omitted, for the majority of cases starting with an AI PoC is the right call. It provides a clear roadmap, minimizes risks, allows for better resource allocation, enhances the performance of an end solution, and many more.
Entrusting your AI endeavor to an organization that specializes in IT consulting services, can prove to be a game-changer. With a strong focus on an AI Proof of Concept, our highly experienced team can ensure that your journey toward digital transformation is both productive and successful, maximizing the value of your investments with our AI development services.
Need an AI PoC?Get expert support from Intelliarts to turn your AI concept into a working proof of concept.Get in touch
 

#### Using GPT, LLaMA, PaLM in Insurance & Marketing
The market size in the Generative AI market is projected to reach $66.62 billion in 2024 and then show an annual growth rate of 20.80%, resulting in a market volume of $207.00 billion by 2030. It means that more and more companies have used Generative AI-driven solutions or started offering products and services on their basis, and this tendency will remain strong for years.
In this post, you’ll delve into the technical differences between GPT, LLaMA, and PaLM text generation models. You’ll also discover their possible applications and review a real-life case of using a text generation model in business.
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GPT vs. LLaMA vs. PaLM text generation models: What is the difference?
Let’s get started by giving text generation models a primary definition:
The text generation model is an ML-based solution that automatically creates human-like text based on input data and pre-trained language patterns.
Text generation models primarily operate on the Transformer algorithm, a type of deep learning model that uses mechanisms like self-attention to process and generate language. In the field of NLP and text generation, other algorithms like Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Hidden Markov Models (HMM), etc., can also be used.
A detailed comparison of GPT, LLaMA, and PalM text generation models is shown in the infographic below:
The main differences between these models lie in their scale, i.e., the number of parameters and their underlying architectures, since despite using the same transformer model, their specifics can vary, as well as their intended applications or focus areas. For example:

GPT models are often used in applications like chatbots, content generation, and language translation.
LLaMA is designed to be a research tool that helps in understanding the workings of large language models and improving them.
PaLM is designed to demonstrate the capabilities of the Pathways system, which aims to create more general and efficient AI models.

Each model represents a different approach to solving the challenges of natural language processing and generation.
If you’re interested in exploring text generation models, you can also be curious about the role of NLP in various industries.
And if you feel like you need to learn additional tech details on how text generation models, in particular, GPT, work, take a look at the video below:

Applications of text generation models in marketing
GPT, LLaMA, PaLM, and other text generation models are fairly widespread and are gaining more popularity in various domains. Healthcare, manufacturing, education, and e-commerce are only a few of them. Now, let’s focus on two industries where text generation models are used the most, starting with marketing.

Despite LLMs being a relatively new, publicly available technology, generative AI for marketing already offers a broad range of applications. They offer to simplify processes and enhance various aspects of marketing strategies.
#1 GPT in marketing use cases
Here are some of the possible applications of the GPT models in marketing and ways of using ChatGPT for marketing software.

Content creation

Generation of blogs, social media posts, and other types of long and short-form content. 
Example of a basic prompt: “Create a short, engaging social media post to promote a 20% off sale for a new line of running shoes.”

Customer service

Powering chatbots for 24/7 customer support and answering FAQs. 
Example of a basic prompt: “Write a 30-word FAQ about how to choose a running shoe size.”
Launch your custom GPT: Our team offers ChatGPT application development services.
#2 LLaMA in marketing use cases
Examples of LLaMa use cases in marketing include:

Efficient language analysis:

Analyzing large sets of customer feedback or academic texts where computational resources are limited.
Example of a basic prompt: “Summarize key themes from customer reviews on our latest product.”

Market research and trends analysis:

Use case: Analyzing consumer trends, social media sentiments, and market research data to provide actionable insights.
Example of a basic prompt: “Analyze recent social media trends to identify emerging preferences in the eco-friendly products market.”

Language model optimization for specific audiences:

Fine-tuning the model for specific demographic or geographic audiences to create more targeted and effective marketing messages.
Example of a basic prompt: “Optimize language model to enhance engagement with the 18-25 age group in European markets.”
#3 PaLM in marketing use cases
The ways to exploit PaLM models in marketing are the following: 

Advanced query resolution:

Answering complex, multistep customer queries that require understanding context and subtleties.
Example of a basic prompt: “Explain the differences between various insurance policies for a customer looking for comprehensive coverage.”

Multi-tasking language applications:

Handling tasks that involve switching between languages or integrating different types of data, like text and numbers.
Example of a basic prompt: “Generate a report that integrates financial data analysis with explanatory text for a quarterly business review.”

Cross-channel marketing strategy development:

PaLM use cases also include developing comprehensive marketing strategies using marketing analytics software that integrate insights across various channels like social media, email, and web content.
Example of a basic prompt: “Formulate a cross-channel marketing strategy that synergizes social media campaigns with email marketing for a new product launch.”
It’s important to understand that, basically, all three text generation models can cover all the listed applications. Yet, we attributed one or another use cases to models that are a better fit for them, considering their specifics.
Applications of text generation models in insurance
Applications of text generation models in insurance are vast. They are intended to enhance both efficiency and the customer experience. Let’s delve into several key areas where these models are particularly useful.
#1 GPT in insurance use cases
Here are some of the possible applications of the GPT models in insurance: 

Policy information and FAQs:

Providing detailed policy information and answering frequently asked questions on insurance websites.
Example of a basic prompt: “Explain the benefits of whole life insurance in a simple, understandable manner for website visitors.”

Claim processing assistance:

Assisting in the initial stages of claim processing by guiding customers through the required steps and documentation.
Example of a basic prompt: “Create a step-by-step guide for customers on how to file an auto insurance claim.”

Risk assessment reports:

Generating risk assessment reports based on customer data for underwriters and agents.
Example of a basic prompt: “Generate a risk profile for a potential client interested in high-value property insurance.”
Future-Proof Your Insurance BusinessAdopt the newest technology with Intelliarts to boost efficiency and profits.Learn how
#2 LLaMA in insurance use cases
Examples of LLaMa models usage in insurance include:

Efficient document review:

Quickly analyzing and summarizing lengthy insurance documents or policy agreements.
Example of basic prompt: “Summarize the key coverage points and exclusions from a 50-page health insurance policy document.”

Customized policy recommendations:

Offering personalized insurance policy recommendations based on customer profiles and historical data.
Example of a basic prompt: “Provide tailored life insurance policy recommendations for a 40-year-old client with two children.”

Data-driven market analysis:

Analyzing market trends and customer demographics to aid in developing new insurance products.
Example of a basic prompt: “Analyze demographic shifts in the past decade to identify potential new markets for health insurance products.”
#3 PaLM in insurance use cases
The ways to exploit PaLM models in insurance are the following: 

Complex query resolution:

Handling intricate customer inquiries that involve understanding and explaining complex insurance terms and conditions.
Example of a basic prompt: “Explain the differences between term and universal life insurance to a customer inquiring about long-term financial planning.”

Integrated customer service:

Providing comprehensive customer service by integrating information from various departments like claims, underwriting, and sales.
Example of a basic prompt: “Create a unified response to a customer who has queries about their premium adjustments, coverage details, and claim status.”

Automated underwriting:

Utilizing advanced algorithms for automated underwriting helps to assess risks and determine policy premiums more efficiently.
Example of a basic prompt: “Develop an automated underwriting process for evaluating applications for travel insurance, incorporating a range of risk factors.”
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Pro tips from the Intelliarts team to use when working with any AI-based model for the best outcomes:
#1. Be specific. Clearly define the context and the desired outcomes. Be especially cautious when requesting statistics and numbers, as if not provided with a request to include actual statistics only, Generative AI tends to generate abstract numbers. 
#2. Include examples. The chances of obtaining the desired outcome on the first attempt increase when you provide a reference. You can always specify changes to make with the reference.
#3. Don’t abuse AI. Always remember that Generative AI lacks human mentality, empathy, and understanding of complex concepts. Try to automate tedious tasks, but review the outcomes and make modifications to sensitive bits of information by yourself. 
Read also about insurance machine learning applications. 
Intelliarts experience: ChatGPT-based solution for inmate security designation
The Intelliarts’ customer, specializing in criminal matters and sentence mitigation strategies, requested us to validate the product idea for inmate security designation and custody classification through the use of ChatGPT. The solution was intended to help attorneys and lawyers optimize work processes and reduce time spent filling out the Inmate Load and Security Designation Form.
The solution by Intelliarts was the PoC — a machine learning model designed to optimize the efficiency of composing statements. We wrapped it into an application that can help users utilize a prompt effectively by simply uploading filled-out questionnaires and assessment rules for them.
Businesses in the legal industry, as well as lawyers, can use this document data extraction solution powered by OpenAI GPT solution to conduct a quick and effective evaluation of the likelihood of a certain person committing a crime.
You can access additional information on the Chat-GPT-enabled solution for inmate security designation by Intelliarts on our portfolio page. 
Final take
The integration of text generation models like GPT, LLaMA, and PaLM in industries such as marketing and insurance represents a significant leap in operational efficiency and customer engagement. These AI-driven solutions offer diverse applications ranging from content creation and customer service to risk assessment and policy customization. So no wonder 97% of business owners believe ChatGPT and other LLMs will benefit their business. 
Entrust Intelliarts, an AI and ML service provider with over 24 years of experience, your text generation model idea. We’ll bring industry-leading expertise and substantial experience to your project and help achieve the desired value-adding business outcomes. 
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#### Vehicle-to-Grid Technology Explained: Definition, Applications, Benefits
Switching transportation to electric power is more urgent than ever. Yet, electric vehicles place new pressure on the grid. Driving an EV about 160 km uses roughly the same electricity as a typical household needs for a day. Why?
Power systems were built for one-way traffic. Electricity flows in, gets used, and that’s it. Vehicle-to-grid technology (V2G) flips that logic. When parked and plugged in, EVs become batteries that push energy back to homes, buildings, and the grid when demand spikes. 
In this article, we’ll look at how V2G technology works, its key components, the benefits it offers to society, and the core differences between V2G, V1G, and V2H, drawing on our hands-on work with platforms like EVIQ and  EVConnect.
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What is vehicle-to-grid technology?
Vehicle-to-grid, or V2G, is a smart technology that lets car batteries discharge excess energy back to the power grid when they’re plugged in.
Normally, electricity flows one way: a power plant → grid → homes, factories, and cars.
V2G solutions invert that setup. Bidirectional charging stations can both charge EVs and send electricity back to the grid. In simple terms, V2G means:

EVs store electricity while charging
The grid can borrow some of the stored energy later
Power can move in both directions

A system like this is a part of a larger concept: vehicle-to-grid integration (VGI). Cars parked in garages become useful energy storage. Thousands of plugged-in cars stabilize the grid and supply power to nearby buildings.
How does V2G work in practice?
V2G is simple in concept, but in reality, it depends on a stack of hardware, software, and communication infrastructure. It starts when an EV connects to a bidirectional charger, which can both:

Deliver electricity to the vehicle battery
Send electricity from the battery back to the grid

Once connected, the software layer monitors electricity demand and available supply. It then decides when the vehicle should charge or discharge based on overall system needs.
A typical flow looks like this:

The EV connects to a bidirectional charger
The system monitors real-time grid demand
It sets a charging or discharging schedule
If the electricity supply is high, the car continues charging
If demand increases, the system uses energy from EV battery storage
Energy flows between the EV and the grid accordingly
All activity is recorded for billing and compensation

We’ve seen such grid balancing strategies many times in other forms. For example, some power plans offer lower prices during quiet hours to encourage charging when demand is lower.
What makes it even better? Once everything works, it largely takes care of itself. The system charges a vehicle when electricity is abundant, and sends power back when the grid needs it most.

What are the core components of a vehicle-to-grid system?
Vehicle-to-grid systems use a multi-layer infrastructure. Each part plays its role: storing energy, moving it, and coordinating when it flows to or from the grid. Let’s explore in detail.
1. V2G electric vehicles
In the V2G setup, EVs are basically mobile power banks. Their batteries store electricity during charging and can give some of that power back to the grid later.
Not every EV can do this yet. The car must support bidirectional power flow, which means electricity can move both into the battery and out of it. 
Main things the EV does:

Stores electricity in the battery
Supplies energy back to the grid when needed
Connects to the system through a compatible charger

From what we’ve learned, cars with larger batteries can return more electricity, which gives the grid a bigger buffer when demand climbs.
2. Bidirectional charging stations
A regular EV charger pushes electricity into the car. A bidirectional charger can also pull energy from the battery and send it back to the power network.
What a V2G EV charger can do:

Charge the EV battery
Move electricity from the battery to the grid
Pass data to grid management systems

3. Communication protocols (ISO 15118 and OCPP)
Vehicles, chargers, and grid software need a way to connect. Two standards guide most of this communication.

ISO 15118 – defines how an EV and a charging station exchange data. It supports automated charging control and bidirectional power transfer.
Open Charge Point Protocol (OCPP) –  connects charging stations with central management systems. Operators use it to watch stations, control charging sessions, and manage networks.

Combined, these protocols support communication, automation, remote control, and secure data handling. 
We’ve built similar logic a few times. For one client, we implemented OpenADR-based load management that connected energy systems to utility signals, giving operators control over how and when assets respond.
Explore protocol-first architectures.Learn why OCPP, OCPI, and ISO 15118 are key to scaling.Download our white paper
4. Energy Management System (EMS) software
The energy management system (EMS) is the main decision-maker for the entire V2G. It checks grid conditions in real time: electricity demand, available generation, and grid stability.
Based on that information, EMS decides when vehicles should charge normally, release some electricity, or stay idle. Everything happens automatically once the vehicle connects to the charger.
What the EMS does:

Optimizes charging and discharging schedules
Balances cost savings and battery health
Forecasts energy demand and supply

5. Aggregator/Virtual Power Plant (VPP) platform
A single EV doesn’t make much impact on the grid. But when hundreds are combined, they become a huge energy source. 
Many V2G systems use aggregation platforms to group vehicles, or distributed energy resources (DER), into one Virtual Power Plant (VPP) and operate at scale.
How a VPP works:

Unites many EVs into a controllable resource
Dispatches energy to the grid when needed
Participates in energy markets

Provides services like demand response and frequency regulation
 
Consult Intelliarts for custom V2G software and management solutions. Get in touch
What are the main applications of V2G technology?
V2G solutions already have many practical use cases, including peak shaving, frequency control, and congestion management. Let’s look at some real-world cases.
Grid balancing and frequency regulation
Electric grids must stay balanced. Supply and demand need to match from second to second. If they drift apart, the grid frequency moves outside the safe range, which can damage equipment or cause outages.
Electric vehicles connected through V2G correct these small swings. Studies show that a single EV can deliver up to about 20 kW back to the grid, which makes it suitable for frequency regulation programs.

Real-world case: In February 2024, a fleet of Nissan LEAF EVs in Australia helped stabilize the national grid during a transmission outage. When frequency dropped, the connected EVs delivered power back within 6 seconds, each at up to 5 kW.

Peak shaving
Mornings usually bring the highest consumption of electricity, so utilities use additional generation capacity, which increases costs. V2G redirects part of that demand to quieter hours.
In practice, we’ve found that vehicle-to-grid programs transfer 2.7% to 4.3% of charging loads from peak to off-peak periods at the city level. Even such a small reduction is important, as peak electricity is the most difficult to supply.

Real-world case: Renault Group, together with the Municipality of Utrecht in the Netherlands, deployed a V2G car-sharing fleet. Vehicles charge during off-peak hours and discharge energy during morning peaks, helping balance demand without extra generation.

Renewable energy integration
Existing storage systems can absorb only part of the renewable energy, while sudden surges (like strong winds) push them to their limits. Over our 9 years in the renewable energy field, we’ve seen this gap more than once.
V2G helps store that extra energy instead of letting it go to waste. Parked vehicles absorb surplus electricity when renewable output is high. Later, when production drops, those same vehicles send part of the stored energy back.

Real-world case: In April 2025, China launched multiple grid-connected V2G pilot stations that combine EV charging with onsite solar and storage systems to provide electricity back to the grid when needed.

Backup power for buildings (V2B)
Vehicle-to-grid ideas also work on a smaller scale. In this case, the energy flows between the car and a building, with the electric vehicle being a temporary backup battery. Many people call this vehicle-to-building, or V2B.

Real-world case:  During Hurricane Beryl in Texas, EVs (including Ford F‑150 Lightning and Tesla Cybertruck) supplied electricity to homes when the grid went down.

Fleet energy monetization
Large vehicle fleets create one of the strongest business cases for V2G, as we’ve seen in our projects. Since fleets spend long periods parked, they can connect hundreds of vehicles into a “virtual power plant.” 
When demand spikes, the system pulls small amounts of energy from those batteries, compensating the operator.

Real-world case: An independent analysis of a vehicle‑to‑grid pilot in Australia found that EV fleets could earn the equivalent of about C$10,700 per EV annually by participating in grid services.

What are the benefits of vehicle-to-grid systems?
Vehicle-to-grid systems help reduce energy costs, improve grid stability, and even monetize surplus energy by feeding it back when demand is high.
Even though V2G has been around since the 2000s, we see the industry only starting to reap its benefits. Fraunhofer Institutes for Transport & Environment estimate that widespread V2G use could save the EU up to €22 billion annually by 2040.
To give you a quick snapshot, here’s a concise table of the key benefits of V2G, describing its value for both operators and EV owners.
 
Discover real results from our EV fleet management project. Check out the case study
What are the technical and regulatory challenges of V2G?
Despite its promise, there’s still a long way to go before the vehicle-to-grid chargers market becomes popular. Most existing vehicles are still unidirectional, although progress is steadily being made. Some issues block wider V2G adoption:
Battery degradation concerns
One common concern is that frequent charging and discharging cycles can accelerate battery wear. Accordingly, this could reduce its lifespan and lead to higher replacement costs. 

Expert comment: We work with e-mobility systems, and we find this risk overstated. A discharge rate of around 3 kW, typical for V2G, is unlikely to have a noticeable impact on the battery. Since V2G relies on low-power AC charging, similar to home charging, it doesn’t appear to place unusual stress on the battery.

Regulatory uncertainty
Rules around V2G are still a bit unclear. There’s often no answer to who’s allowed to sell energy back to the grid, whether you need a special license or how taxes apply to that income.
For example, in some countries, only certified energy suppliers can participate in electricity markets. So even if your EV can technically send power back, you might not be legally allowed to sell it.
Payment is another grey area. In one region, you might get compensated at wholesale prices (which can drop below €0.05/kWh), while in another, subsidies might temporarily boost that to €0.20/kWh or more.

Expert comment: The main blocker here is not technology, but market structure. With clear rules, standardized settlement, and aggregator support, V2G scales quickly. Often, the systems are ready, it’s the market that isn’t.

Infrastructure costs
V2G could reduce the need for building new power plants, which sounds great in theory. But getting there isn’t cheap. A bidirectional charger alone can cost anywhere from €3,000 to €10,000, which is way more than a standard home charger.
Then there’s the grid itself. Upgrading local transformers and distribution lines across entire cities or regions can cost millions, and that’s before factoring in software and control systems. And even when funding is available, these upgrades can take years to plan and implement.

Expert comment: Smart grids, subsidies for chargers, and incentives for early adopters can help spread costs over time. From our side, we usually push customers toward pilots first, to gather more data before making bigger upgrades.

Standardization gaps
Right now, there’s no single standard that everyone follows for V2G. Different car brands, charger manufacturers, and software providers often use their own systems.
For example, the Nissan Leaf already supports bidirectional charging using CHAdeMO. In contrast, the Tesla Model 3 does not support V2G and uses its own charging system instead of ISO 15118.

Expert comment: Using ISO 15118 as a common base, along with OCPP and OCPI, makes things a lot easier in real life. Most of the complexity ends up in integration work instead of the technology itself if you don’t have that.

What’s the difference between V2G, V1G, and V2H?
Smart charging comes in more than one form. V1G, V2G, and V2H each have a different role, some simpler, some more advanced. Let’s break them down.
V1G (Smart One-Way Charging)
V1G is the basic form of smart charging. Energy flows only from the grid to the EV, but charging is optimized using station data.
It helps:

Schedule charging at cheaper or cleaner times
Reduce costs and peak load
Monitor and optimize energy use

V2H (Vehicle-to-Home)
V2H allows energy to flow both ways between an EV and a home or building. It helps balance fluctuating renewable energy and provides backup during outages.
It’s used for:

Powering homes or buildings with stored EV energy
Local energy balancing, especially with renewables

V2G (Vehicle-to-Grid)
With V2G, EVs can send electricity back to the grid through bidirectional charging. It also coordinates communication between vehicles, chargers, and control systems.
V2G can:

Balance grid demand and supply
Integrate renewable energy
Reduce system costs with stored energy at peak times

While still not widely deployed, examples exist, for instance, TEPCO in Australia has tested V2G systems using Nissan LEAFs in grid-balancing pilots.
 
When should companies invest in vehicle-to-grid solutions?
The short answer: when V2G stops being a technical possibility and becomes a predictable business case. 
For this to happen, a few key conditions should come together: a large enough fleet, volatile energy prices, and a clear path to working with the grid. Let’s go into detail.
1. When fleet scale thresholds
With a small fleet, V2G is more of an experiment than a strategy. The revenue (or savings) simply won’t justify the effort. But once you’re operating at scale (typically 50+ vehicles), even modest per-vehicle output adds up to something usable.
In our experience, the sweet spot is fleets with predictable downtime (overnight parking, scheduled routes). That’s when you can reliably “offer” energy to the grid without any drawbacks.
2. When energy prices create real arbitrage or savings
V2G only works financially if there’s a gap between when you buy electricity and when you sell (or avoid buying) it. If your energy costs are fixed, the upside is limited. 
For those of our partners who’re exposed to time-of-use tariffs or real-time pricing, the economics improve a lot. Even a €0.10–0.20/kWh spread can be huge at scale, so we generally recommend taking advantage of it where the setup allows.
3. When there’s a clear path to market participation
Technically, your vehicles might be V2G-ready, but that doesn’t mean you can monetize them. In many markets, you can’t just “sell electricity” directly. 
Working with aggregators or utility programs may be helpful here. They take care of market access, bundle capacity, and manage grid communication. Without them, most companies won’t get far. 
From what we’ve learned, V2G projects only move beyond pilots when there’s a defined partner and revenue model in place. Otherwise, they tend to stall at the proof-of-concept stage.

Conclusion
Growing demand for electricity puts new pressure on grids built for one-way power flow. Vehicle-to-grid (V2G) offers a smart solution: parked EVs can store energy and feed it back to the grid when it’s needed most. 
Of course, adoption challenges remain, but when fleets are large, energy prices fluctuate, and access to the grid is secured, V2G starts to have real business sense, making it a huge opportunity to explore.
At Intelliarts, we combine 20+ years of software expertise with deep e-mobility know-how. Our teams support companies with end-to-end software and ML development, including PoC delivery, and expertise in energy protocols like OCPI, OCPP, and OpenADR.
We treat your product as our own, stay focused on results, and build to fit real business needs. Reach out to start your project with a team that gets you.

#### Vendorland Ranks Intelliarts Among Top AI Consultants
Intelliarts is excited to announce its selection as one of the top AI consulting companies by Vendorland, a globally recognized platform for identifying top-performing technology firms. This acknowledgment highlights Intelliarts’ dedication to providing cutting-edge AI solutions and driving innovation for businesses worldwide.

Vendorland’s comprehensive evaluation process showcased Intelliarts’ strengths in AI consulting, including its forward-thinking strategies, successful project implementations, and commitment to customer satisfaction. This recognition underscores the company’s ability to deliver transformative solutions that empower organizations to adapt, grow, and thrive in the era of AI.
“We are proud to be recognized as one of the top AI consulting firms by Vendorland,” said Alexander Barinov, Managing Partner at Intelliarts. “This achievement reflects the hard work, expertise, and passion of our team, as well as our ongoing mission to help businesses unlock the potential of AI to achieve their goals.”
Custom AI SolutionsReady to unlock your industry potential with AI technology?Let's talk
Intelliarts specializes in a wide range of AI-driven services, including strategy development, machine learning model creation, data analysis, and AI-powered automation. By leveraging cutting-edge tools and platforms, Intelliarts crafts bespoke solutions that address clients’ unique needs across various industries, including healthcare, retail, finance, and manufacturing.
Find more information about Intelliarts and its AI consulting services.
About Intelliarts:
Intelliarts is a leading provider of AI consulting and software development services, specializing in data analytics, machine learning, and intelligent automation. With a focus on delivering tailored solutions, Intelliarts partners with organizations to drive growth, innovation, and operational efficiency.
About Vendorland:
Vendorland is a trusted global platform connecting businesses with the best technology service providers. By conducting in-depth evaluations and industry analyses, Vendorland identifies top companies that excel in delivering innovative solutions and exceptional client outcomes. 
 

#### Visual Search in E-commerce: Benefits and Real-Life Examples
Buyers often choose goods and products based on appearance, especially in the ecommerce industry. AI-based visual search and ecommerce blended relatively recently. Yet, companies have already started to exploit visual search in e-commerce.
The Intelliarts AI team of engineers participated in the visual product recognition challenge. We acquired useful insights while building a high-performing ML model for suggesting similar goods and products based on their images. And we are willing to share these insights with you throughout the article, so follow us here.
In this post, you’ll deepen into some technical aspects of building visual search for ecommerce. Besides, as a C-level executive, you may also be interested in the concrete business advantages of investing in such technology as well as in reviewing examples of strong market players and their experience with visual search.
Visual search in e-commerce: Brief overview
Let’s start exploring how to use visual search in ecommerce. The first step is getting an insight into the technology and its modern-day market.
From a technical perspective, visual search is a functionality enabled by a combination of AI-driven algorithms. The resulting solution can be composed of the two following technologies:

Object detection. It enables to locate objects of interest within a digital image and identify individual products based on visual attributes, i.e., color, texture, pattern, shape, etc.
Visual search engine. This technology utilizes ML algorithms to match identified products with ones in a database and suggest related products based on detected visual similarities.

From an e-commerce business perspective, visual search technology is all about the user experience. It helps simplify the process of searching for a particular product, which increases the chances of a purchase. At the same time, visual search offers a form of entertainment as an extra benefit.
Visual search e-commerce market size
Now, let’s attempt to predict how visual search engines will impact e-commerce in the future. This evolution is a critical part of the future of online search, where AI and human behavior are rewriting the rules of engagement. Estimated to surpass $14 billion by the end of 2023, the global visual search market will rise up to as much as $33 billion by 2028, according to historical data and forecasts. For the forecast period of 2021-2028, the Compound Annual Growth Rate (CAGR) of the market is predicted to be 17.5% respectively.
It brings to the conclusion that the interest to this relatively new technology is at a high level, and e-commerce businesses worldwide are likely to increasingly invest in visual AI in fashion ecommerce. Potentially, within the next decade, most e-commerce businesses will adopt visual search as part of their platforms’ functionality.

If the question “how visual search is transforming ecommerce” came across your mind, you are likely to be equally interested in exploring our machine learning guide for executives.
The benefits of visual search in e-commerce
There is a range of main business benefits related to adopting visual search technology. Examples include the following:
Improved search accuracy and efficiency
Search engines, including semantic ones, do a good job of helping customers find the products they are looking for. Yet, visual search technology raises the efficiency of the processes to a whole new level. With visual search, a customer can take a picture of the item they desire, and the ML-driven engine will identify matching or similar products without the need for an exact name or description of the item. Easier search for customers means a higher probability of a customer going for a purchase.
Reduce bounce rates
Higher bounce rates indicate that customers are likely uninterested in browsing the site for any reason or face difficulties making them abandon the site and go to another one. Since visual search as an additional e-commerce platform’s functionality can provide customers with relevant, accurate, and personalized search results, users are more likely to find products they are interested in. This way, customers are more engaged in browsing the platform.
Better shopping experience
Enhanced shopping experience via online is all about finding the necessary products via the online platform. Visual search engines enable customers to browse a wide range of products based on the picture of the item they desire. It could be any product. For example, if you’re looking for kids umbrellas online, visual search helps you find exactly what you saw in the picture, making the shopping process faster and easier. It saves time and effort, once again making a customer more likely to make a purchase from you.
Check on the below video to find out how video search functionality may work:

Enhanced offline shopping experience
Fashion AI search for ecommerce is tightly connected even from the perspective of running an offline business, such as a clothing store. Customers can take pictures of items they see in an exhibition hall and then find them and similar products via the store’s online platform. It’s especially useful when the desired item is absent on-site in needed sizes. Alternatively, visual search engines may help customers find a range of products they want to ask consultants about or try on if available on-site.
The creators of the challenge strongly believe that image-based search can be particularly handy when a customer observes the desired product in real life, movies, or media.
Product discovery
Since visual search technology not only finds exact matches but also suggests similar items, it plays a crucial role in product discovery. It means that it can engage customers in considering additional items from the same or similar categories compared to the initial interest. Helping customers discover items that may not have been found overwise leads to additional sales for businesses.
Increased sales and revenue
It can be stated that visual search in e-commerce increases revenue and sales. Customers are more likely to find and purchase things they are interested in when they find desired products with ease and simultaneously receive customized search suggestions. Offering a relevant coupon during this stage can further encourage purchases and enhance customer satisfaction. Furthermore, visual search and the accurate results it provides help make more informed purchasing decisions leading to reduced returns rates.
You may also find it useful to discover more about computer vision technology applications across industries.
Challenges and considerations of visual search in e-сommerce
Before deepening any further, let’s get to know the challenges and considerations of visual search technology in e-commerce. The list includes the following aspects:

Accuracy and relevance. Ensuring the search returns correct and pertinent product matches.
Large datasets. Managing and updating vast and varied image databases efficiently.
Speed and performance. Providing instantaneous search results without delays.
User privacy. Addressing concerns about storing and analyzing user-submitted images.
Adoption and usability. Ensuring users understand and adopt the new search feature effectively.
Diverse product representation. Handling varying angles, lighting, and quality of product images for consistent results.
Integration complexity. Seamlessly integrating visual search with existing e-commerce platforms and systems.

Handling these matters will significantly enhance the performance of your visual search solutions and increase the positive outcomes of such an intervention. 
 One PagerRevolutionize Product Discovery: Simplify Shopping with Visual Search Learn more
Metrics for tracking visual search success in e-commerce
The effect of incorporating visual search for retail industry players can be measured and assessed.
Business metrics
Now, let’s proceed with reviewing common business metrics typically utilized to evaluate the impact of integrating new technology.

Conversion rate

This metric measures the percentage of visitors who made a desired action, such as making a purchase or filling out a form. The conversion rate is calculated as the number of conversions divided by the number of website visitors and multiplied by 100%.

Click-through rate (CTR)

It’s a percentage of people who interacted with an ad and visited a website or landing page. CTR is calculated as the number of clicks divided by the number of ad impressions and multiplied by 100%.

Basket size

This refers to the average number of items in a shopping cart of a customer. This measure indicates how well the functionality of a platform performs in offering complementary products.

Bounce and exit rate

It’s the percentage of website visitors who left the website without taking any action. Bound and exit rate is calculated as the number of visitors who left the website divided by the total number of visitors and multiplied by 100%.
Visual search technology, when integrated into the website as an additional functionality, can have both direct and indirect impacts on the performance of the platform. This influence can be tracked by comparing the historical data for performance evaluated with the use of metrics to the performance after the integration of the visual search.
Technical metrics
Aside from business metrics of the success of visual search engines in an e-commerce project, there are technical ones. They help assess the performance of individually trained models for solving visual search tasks. Examples include:

Accuracy

It’s the percentage of correct predictions. Accuracy is calculated as the number of true positive and true negative predictions divided by the total number of predictions and multiplied by 100%.

Precision

It’s the estimation of how reliable a prediction for a particular class, i.e., prediction output, is. Precision for class 1 is calculated as the number of true positive predictions divided by the number of positive predictions and multiplied by 100%. Precision for class 2 is calculated in the same way but with negative values.

Recall

It’s the estimation of how well the model can predict a class. Recall for class 1 is calculated as the number of true positive predictions divided by the number of true positive and false negative predictions and multiplied by 100%. Recall for class 2 is calculated in the same way but with true negative and false positive values.

Mean average precision (mAP)

This metric is the evaluation of a model’s general precision. MAP is calculated as the sum of average precision values for each class divided by the number of classes.
In the visual search competition, the Intelliarts team of engineers used exactly this metric. It was so because our model was intended to find the 1000 most similar products based on a digital reference. Unlike the other three metrics, mAP suited the evaluation of such search results perfectly.
The representation of what’s understood under predicted and actual, as well as positive and negative values in the above formulas, is known as Confusion Matrix. It’s shown in the image below.
Read also, how visual search models in vehicle damage assessment can analyze images of damaged vehicles to accurately classify and quantify damage severity.

Implementing visual search in your e-commerce store
Merely being aware of the technology is not enough to have your innovative e-commerce store up and running. Here are some best practices for integrating the visual search functionality:

Dataset collection: Gather large, diverse sets of product images you need the model to be able to search among, and categorize them with accurate tags and metadata and label them.
Select or build a visual search engine: Choose from existing visual search platforms (like Google Cloud Vision or Clarifai) or build custom models using ML frameworks.
User interface integration. Incorporate a camera or upload feature on your website/app allowing customers to input their visual queries.
Optimize for mobile: Ensure visual search is seamless on mobile devices, considering the higher camera usage by mobile shoppers.
Educate customers: Promote and demonstrate the visual search feature to customers through tutorials, demos, and marketing campaigns.

By exploiting these tips, you can integrate a visual search functionality into your e-commerce store. Should you require technical assistance, don’t hesitate to reach out to a provider of ML solutions.
Real examples of visual search for e-commerce
Well-established businesses that operate in e-commerce have already adopted visual search functionality. Moreover, they have been using it successfully for years. Among such are the following companies:

ASOS

This British online fashion and cosmetic retailer has been operating in the e-commerce industry since 2000. It offers a “Style Match” feature, which is exactly an implementation of the visual search technology on the ASOS platform. 
Style Match functionality offers to take a picture of an object of interest using the phone’s camera or upload a picture from the photo library to run the search process. Then, the ML algorithms of the visual search engine and the IT infrastructure behind it will do the rest. ASOS claims that they don’t store the image, they just analyze the information from the visual, i.e., color, patterns, type of clothing, etc., to find a match and make personalized recommendations to customers. 

Alibaba

 Alibaba is a Chinese multinational company specializing in e-commerce. It’s regarded to be one of the fastest-growing retail platforms in the world. Alibaba connected manufacturers and wholesalers with retailers and individual customers, and the range of products traded via its website is vast.

Alibaba offers the “Image Search” functionality, which provides the same conventional capabilities to find product items based on uploaded digital imagery. As a complementary feature, the Image Search engine allows uploading a link to an image to run the search process effortlessly.

Amazon

Amazon is an American multinational technology company. Its mission is to be Earth’s most customer-centric company. Amazon exploits innovative technologies in its IT environment extensively. As such, 1-click shopping, personalized recommendations, and other industry-latest technology advancements.
In 2019, Amazon introduced the visual search functionality packed into the feature called “StyleSnap,” available through the Alexa app. As usual, the technology enables users to get the most relevant products to their search by simply uploading a photo and letting artificial intelligence perform the search.
Read also about the AI for network marketing use case.

BooHoo

 Boohoo is a UK-based online fashion retailer founded in 2006. It became a popular online store offering affordable and trendy clothing and having a presence in over 100 countries. BooHoo is considered to be one of the largest fast-fashion retailers in the world.

The visual search functionality that the BooHoo platform offers to its users is “Camera Search.” It was introduced back in 2017 with the goal of enhancing customer experience and, potentially, increasing the revenue from the business operation. The technology supports searching based on screenshots or pictures taken in real-time with the help of a phone’s camera.

Google

Google is a global technology company founded in 1998 and headquartered in Mountain View, California. It has become the world’s leading search engine, offering a wide range of internet services and products, including online advertising, cloud computing, software, and hardware. With its presence in over 190 countries, Google is considered one of the most influential and innovative companies in the world.
Circle to Search, introduced by Google in 2024, is a visual search feature designed to enhance user experience on Android smartphones. It allows users to search for information directly from their current screen by using simple gestures like circling, highlighting, or scribbling on content. This innovative tool aims to provide seamless access to information without the need to switch between apps, making the search process more intuitive and integrated.
You can discover more about Circle to Search functionality by Google from the video below: 

The listed companies are well-established businesses that made a fortune by pioneering innovations. It’s safe to say that integration of the visual search technology in their platform played an essential role in their success.
Technical visual search requirements and principles
A visual search functionality is a complex solution. Discover what components required for its creation are and what they serve for from the infographics below.

In the visual search competition, the creation of the ML solution followed exactly the detailed pattern. We had the database of product images separated into user and seller photos. The latter was our ground truth. At the same time, user photos, which were typically snapshots of products taken with a phone camera in cluttered scenes, were used for testing the ML model.
The role of AI and machine learning in e-commerce visual search
Now, let’s review some of the best practices for improving the publication of images via an e-commerce platform and optimizing a visual search solution:

Alt text

It’s important to include brief information about a product shown in an image. It helps search engines understand the context of an image which results in a higher likelihood of finding the necessary product.

Numerous images

Include multiple images of the same product. It doesn’t only allow customers to have a better overview of a product but also increases the chances of a search engine finding the item.

Keyword usage

Descriptions should contain several long-tail keywords to help the image appear in search results.

Product detection first

It’s recommended to have product detection as the first step of image processing with an artificial intelligence visual search solution. It ensures that no background elements will be treated as objects of interest, which might interfere with the accurate matching.

Visual transformers (ViTs)

It’s advised to ground a visual search solution on the ViT models family and utilize a Contrastive Language-Image Pretraining (CLIP) network. The latter can predict an accurate text snippet based on an image. Usage of the technology can give a huge boost to the accuracy and speed of search for a resulting solution.
The team of ML engineers of Intelliarts could see the high usefulness of these practices throughout the course of our projects.
Our experience with visual search
While working on a visual search for retail, we tried multiple approaches as a part of the AI competition. We started with Convolutional Neural Network (CNN) architectures as classical neural networks to build an ML solution on. Yet, we came to two important conclusions in the process:

Insight 1. ViT models family shows huge outperformance over classical CNN architectures.
Insight 2. It’s crucial to use re-ranking techniques when building visual search solutions. Evaluating search outcomes and moving more similar objects higher in the list of results adds to the usefulness of the solution overall.

You may give a try to the online demo that presents an interactive playground for the trained AI model. The demo shows the capabilities of a visual search engine to suggest goods and products available on the web based on an input image or video frame.
Final take
Visual search is a trending technology in the e-commerce niche. Such large market players as ASOS, Amazon, Alibaba, and BooHoo already offer visual search capabilities to the users of their platforms. The enhanced shopping experience, lower bounce rates, and higher revenue from business operations are only a few of the advantages the technology may offer.
Implementing an e-commerce visual search solution requires high expertise and experience. As a C-level executive, you may consider contacting a trusted machine learning development firm. This way, you will get professional help with building and integrating the visual search functionality into your platform, which may result in numerous business benefits for your company.
Thinking about e-commerce visual search solution?Intelliarts experts are here to help.Let's talk

#### What Are the Benefits and Challenges of Sustainable Agriculture
Modern agriculture tends to shift toward smarter and more efficient production as environmental limits and market pressures grow. Farmers now face the challenge of maintaining productivity while using fewer resources and protecting soil and water quality at the same time. According to a 2024 McKinsey & Company survey, 61% of farmers already use digital agronomy tools, and 51% rely on precision hardware. That clearly indicates that technology-driven sustainability is becoming a practical solution, not a distant goal.
In this post, you’ll find out about core benefits of sustainable agriculture, including its economic, social, and environmental impacts. You’ll also discover about physical and software technology supporting sustainable farming, review the limitations of this approach, and learn how to balance pros and cons. 
Drive Sustainability in Your Agriculture BusinessCustom software development for efficient and sustainable operations.Explore our service
What is sustainable farming?
Sustainable agriculture is a holistic approach to food production that is intended to help meet current needs while preserving resources for future generations. 
Sustainable farming, by default, integrates environmental stewardship, economic viability, and social responsibility into every decision, such as seed selection or harvest.
Unlike conventional methods that prioritize immediate yields, sustainable agriculture builds long-term resilience. It recognizes that healthy soil, clean water, and thriving ecosystems aren’t obstacles to productivity. They’re the foundation of it. Now that you know the answer to the question: “what is sustainable agriculture,” let’s proceed by reviewing the key principles of this concept.
Key principles of sustainable farming
The advantages of sustainable agriculture emerge from a framework of interconnected principles that guide farming operations. These fundamental concepts work together to create systems that are both productive and regenerative. The Intelliarts team strongly suggests reviewing these principles to understand sustainable agriculture advantages and the future of food production:

Resource efficiency. This principle focuses on minimizing waste of water, energy, and inputs while maximizing productivity. These advantages are achieved through precision management and smart technology that targets specific field conditions.
Soil health management. Soil management here includes building organic matter to protect soil structure. It also suggests fostering beneficial microorganisms that naturally enhance fertility and crop resilience over multiple growing seasons.
Biodiversity enhancement. Biodiversity strategy involves supporting diverse plant and animal life both in fields and at their margins. This also includes natural pest control and pollination systems that reduce dependence on chemical interventions.
Emission reduction. This principle encompasses decreasing greenhouse gas releases through carbon sequestration in soil, reduced tillage practices, and optimized input use. Such an approach can notably lower the carbon footprint of farm operations.
Water conservation. Conservation strategy emphasizes implementing efficient irrigation systems. It also leverages practices that protect both water quantity and quality, since both these parameters are important for agricultural and community needs.

Explore more about farm equipment imaging in modern agricultural businesses. 
Comparing farming approaches
The benefits of sustainable agriculture become clearer when contrasted with traditional methods. The differences between these approaches reveal not just alternative techniques, but fundamentally different philosophies about agriculture’s relationship with natural systems. Find the detailed comparison in the table below:

The role of technology in sustainable agriculture
Why is sustainable agriculture important in the digital age? Years ago, humanity simply didn’t have the means to sustain some of the most important sustainable farming practices as we understand them now. Luckily, modern farming technology is already capable of turning sustainability from an idea into everyday practice. 
Sustainability means producing wisely, not less. The future of farming depends on a balance between growth and care for the land. — Marta Kufalska, Agritech Solution Expert at Intelliarts
Where modern technology is applied to sustainable farming:

GPS guidance: Optimizes planting patterns and machinery paths.
Soil sensors: Track real-time moisture, pH, and nutrient levels.
Satellite imagery: Identifies crop stress, pest zones, and growth variations.
Drones: Capture aerial images to monitor fields and spot issues early.
Automated machinery: Handles planting, weeding, and harvesting with precision.
Drip irrigation systems: Deliver water directly to roots, reducing waste and evaporation.
Solar-powered pumps: Provide clean, renewable energy for irrigation and livestock watering.
Smart greenhouses: Control temperature, humidity, and CO₂ levels to optimize growth. Many of these systems increasingly rely on generative AI to support decision-making and operational planning, making it important for businesses to understand the differences between leading models such as Claude and ChatGPT.
Composters and bio-digesters: Turn organic waste into natural fertilizer or biogas.
Water filtration and recycling units: Reuse runoff and rainwater for irrigation.
Vertical farming systems: Maximize space and minimize resource use in controlled environments.

Does 260% higher operational scaling sound like promising results to you? Intelliarts knows how to achieve such results with centralized sensor management software for agtech businesses. 
Explore the sensor inventory management system success story by Intelliarts for more information on potential technology solutions in agriculture. 
The next sections will delve deeper into how these innovations bring tangible sustainable agriculture benefits. 
What are the benefits of sustainable farming?
Sustainable agriculture focuses on building a long-term balance between productivity, environmental care, and social responsibility. It ensures that farming remains viable for future generations while supporting the needs of today’s farmers and businesses. The benefits of sustainable farming can be seen across environmental, economic, and social dimensions.
Environmental benefits
Sustainable agriculture safeguards the planet’s natural systems and reduces the negative impact of farming on ecosystems. It emphasizes resource conservation, soil regeneration, and the responsible use of natural inputs.
Key environmental benefits of sustainable agriculture include:

Reduced chemical use and healthier soil through crop rotation and composting.
Water conservation with precision irrigation and moisture sensors.
Lower emissions and improved carbon capture through regenerative practices.
Biodiversity protection and natural pest control systems.
Prevention of erosion and restoration of degraded land.

Sustainable agriculture focuses on ecosystem balance. At the same time, the balance benefits both productivity and the environment. Earthwatch Europe research indicated that sustainable farming positively impacts both the Earth and communities. 
Economic benefits
From a financial perspective, sustainable agriculture offers opportunities for long-term stability and growth. A study on the costs and benefits of transition to sustainable agriculture shows positive transformations. As such, transitioning to sustainable agriculture requires upfront and ongoing costs, e.g, biodiversity features cost about $689/ha. Yet evidence shows lower fertiliser, pesticide, water, and fuel use, with stable or higher yields after 3–5 years and stronger long-term farm resilience.
Altogether, the major sustainable agriculture benefits include:

Cost savings from reduced input use and efficient resource management.
Access to new markets and certifications, such as organic and ESG-aligned supply chains.
Long-term profitability through improved soil fertility and yield stability.
Better resilience to climate risks and market fluctuations.
Increased investor confidence and higher land value over time.

It’s worth noting that economic stability and environmental care are not separate goals, as they work together under the sustainable agriculture concept.
Social and market benefits
In a scientific review on the socio-economic performance of agroecology and farming, it’s shown that sustainability influences social and market dynamics. The impact focuses on strengthening relationships between producers and consumers and supporting community well-being.
Key advantages of sustainable agriculture include:

Stronger consumer loyalty for eco-friendly, transparent production.
Compliance with ESG reporting and sustainability policies.
Brand differentiation for agribusinesses in competitive markets.
Improved working conditions and fairer income distribution in rural areas.
Enhanced collaboration between farmers, researchers, and tech innovators.

These outcomes explain why sustainable agriculture is so important. After all, it drives not just environmental recovery but also economic growth and social progress.

Don’t hesitate to additionally explore the benefits of precision farming.
Disadvantages of sustainable farming
While the benefits of sustainable agriculture are undeniable, the transition isn’t without its obstacles. Here are some limitations of sustainable farming, that every transitioning business should consider:
#1 Higher initial costs
Transitioning to sustainable agriculture often demands significant upfront investment. Farmers face expenses for:

Modern equipment and precision tools
Infrastructure upgrades (irrigation, storage, sensors)
Certification and compliance processes

 Without clear short-term ROI, many hesitate to adopt new systems despite long-term gains.
#2 Knowledge and skill gaps
Sustainable farming requires new competencies in data analysis, soil science, and eco-friendly techniques. Traditional farmers may struggle with:

Data literacy for IoT or farm management software
Understanding regenerative practices
Access to proper training and expert support

These gaps can slow adoption and limit results.
#3 Lower short-term yields
During the early transition, yields may temporarily drop as soil ecosystems recover and new practices stabilize. This can create financial strain before benefits, like improved soil fertility and reduced input costs, become visible.
#4 Market and policy challenges
Sustainable products don’t always receive equal market access or policy backing. Many regions lack strong subsidies, consistent eco-labeling standards, or distribution networks for certified goods. This inconsistency complicates scaling sustainability efforts.
Our experts help you navigate and overcome modern farming challenges with custom software.Contact Us
Data-driven decision making: Balancing the pros and cons of sustainable agriculture
Every innovation brings both opportunities and challenges. Sustainable agriculture is no exception. Farmers face high setup costs, complex data systems, and the need for specialized knowledge, yet the potential rewards include higher yields, lower waste, and long-term soil health.
Technology gives farmers data, but not direction. The real challenge is turning numbers into decisions that balance income, ecology, and time. — Yurii Bondarenko, Software Engineer at Intelliarts
Intelliarts bridges these challenges and opportunities through deep expertise in agricultural technology and data-driven transformation:

Precision farming platforms & IoT integration: Intelliarts builds robust IoT-enabled systems that gather real-time field data from sensors, equipment and weather stations. This empowers your team to make informed decisions that reduce inputs and improve yields.
AI & predictive analytics: Predictive analytics and other AI applications in agriculture forecast yields, predict pest outbreaks, and optimize resource inputs for maximum efficiency. With advanced machine-learning models, predictive analytics, and other AI applications in agriculture, Intelliarts helps forecast yields, anticipate pest or disease outbreaks, and optimize resource usage for maximum efficiency. These forecasts are often built on historical and real-time time series data collected from sensors, machinery, weather stations, and farm management systems.
Farm management software: Intelliarts delivers custom farm management systems that integrate multiple data sources, streamline record-keeping, track costs and support sustainability compliance. 
Automation technologies: From equipment tracking solutions to land sampling automation, we help to implement automation and analytics related to labor-intensive tasks to scale your sustainable farming practices.

3x increase in final field acres and reduced data entry time from weeks to hours or even minutes are results you can expect, as an agriculture business, with Intelliarts. 
Take a look at our grower data collection success story to explore another automation software example. 

Decision support systems (DSS): Intelliarts can develop custom scenario-modeling tools to guide planning and risk management. These systems help you respond proactively to changing field or market conditions.

The infographics below summarise this article’s insights on both physical and software technologies used in sustainable farming:
 
Here at Intelliarts, a trusted software development company, we have substantial expertise in addressing complex business challenges through technology. As provided above, we can help you make better use of physical devices for monitoring crops or soil, drones, green energy systems, and more. We do so by implementing decision-making, predictive analytics, automation, and other capabilities. We also have a strongly relevant domain expertise, acquired through working with companies like Indigo, an agritech innovator in the niche of farming and environmental sustainability.
Should you be interested in agriculture custom software development or technology consulting services, don’t hesitate to reach out. 
Final take
Sustainable farming represents a strategic shift toward smarter, more resilient agriculture. It unites environmental care, economic stability, and social responsibility in one framework. With the right technology and expertise, the transition becomes both achievable and profitable.
With more than 24 years of experience delivering software solutions and a 90% customer return rate, Intelliarts can offer to transform a traditional farming business into a sustainable one. We can integrate advanced software, analytics, and automation tools that help agribusinesses make data-driven, sustainable decisions. We focus on long-term partnerships and are ready, willing, and able to contribute to your best project.

#### What Is OpenADR & Why Is It Crucial for Energy Management
Implementation of OpenADR for energy management shows savings of up to 8% in total electricity bills as well as 20% load reduction for large businesses. On one side, demand response automation is becoming a new industry standard. On the other side, the incentive can offer huge business advantages for businesses, from infrastructure providers and utility companies to manufacturers. 
In this post, you’ll explore the concept and technicalities of OpenADR for energy management. Besides, you’ll find out how the technology works in detail, how it’s used for energy management, and what you should consider before getting started with OpenADR. 
What is OpenADR
Above everything, you need an in-depth insight into what OpenADR is and how exactly it works in business:
Open Automated Demand Response (OpenADR) is an open, standardized communication protocol used to automatically exchange signals for adjusting electricity consumption.
In other words, OpenADR is a communication standard like a common language for sending and receiving demand response signals.
Origins & evolution of OpenADR 
OpenADR originated in California in the early 2000s, when the state faced recurring energy crises and blackouts. To address the challenge, the California Energy Commission sponsored research at Lawrence Berkeley National Laboratory, where the first specification of Open Automated Demand Response was created. 
The idea was simple: instead of relying on phone calls, emails, or manual actions, utilities could send automated signals directly to customer systems, prompting them to reduce or shift load during peak periods. Today, OpenADR has moved far beyond its California roots. 
You can see the timeline of OpenADR adoption in the infographics below:

Core principles of OpenADR 
At its core, OpenADR follows several principles that regulate the way this technology operates. They are as follows:

Automated demand response without manual actions.
Communication of all systems through a standardized protocol.
Protected data exchange with secure, encrypted messaging.
Scaling from individual devices to entire energy networks.
Support for flexible use cases like peak reduction, DER, and EVs.

Main purposes of using OpenADR
Deriving from the definition of OpenADR, the technology helps energy providers and grid operators to balance demand and supply by automating demand response events. But that’s just the tip of the iceberg. Here are the main reasons to utilize OpenADR:

Peak load management: Automatically reduce or shift consumption during high-demand periods, helping utilities avoid costly grid reinforcement.
Renewable integration: Align consumption with variable solar and wind generation, reducing curtailment and maximizing clean energy use.
DER coordination: Connect storage, EVs, and flexible loads into aggregated resources that participate in demand response markets.
Cost optimization: Enable customers to benefit from dynamic pricing and incentive programs by automating load adjustments.
Grid resilience: Provide fast, reliable demand-side response during emergencies or contingencies, improving overall system stability.
Global standardization: Offer a proven, internationally recognized protocol (IEC 62746) that ensures interoperability across vendors and regions.

In the end, OpenADR boils down to enabling energy businesses to exchange information so they can operate smart grids better. 
See how we build custom software for renewable energy innovators.View details
How OpenADR works
In essence, OpenADR operates through a two-node architecture, where the Virtual Top Node (VTN) and the Virtual End Node (VEN) exchange automated signals between energy providers and customers. 
OpenADR key components (VTN & VEN)
VTN is the central server run by a utility or aggregator that sends demand response signals and collects performance data.
Essentially, VTN is the “command center” in the OpenADR architecture.
At the same time:
VEN is the customer-side client that receives signals from the VTN, executes load adjustments, and reports results back.
Basically, every device or piece of equipment, such as an EV charger or an industrial machine, connected to the grid, or an example of VEN. 
OpenADR communication process
The process usually begins when a grid operator or utility detects a need to balance supply and demand, for example, during peak load, renewable variability, or a grid contingency. This triggers the creation of a demand response event, which is packaged as an OpenADR signal.
A demand response event is a scheduled or real-time request from a utility or grid operator for customers to reduce or shift their electricity consumption.
The exact procedure of how OpenADR works is provided in the infographics below:

Event creation. The operator defines an event (start, duration, priority) and signal type (level/price/shed/temporal).
Notification delivery. VTN sends oadrDistributeEvent; VEN receives via poll or push and validates signatures.
Opt decision. VEN evaluates constraints and responds per event: opt-in/opt-out, with acknowledgements.
Strategy mapping. VEN translates signals into control actions (e.g., Level 2 → HVAC +1.5°C, EVSE rate limit, battery discharge).
Ramp & execute. After any ramp period, devices adjust setpoints/loads automatically for the event window.
Monitoring & reports. VEN sends acks/telemetry and periodic reports (oadrCreate/UpdateReport) showing kW curtailed or shifted.
Modify/cancel handling. If VTN updates/cancels the event, VEN adapts immediately and confirms.
Event end & recovery. VEN restores normal schedules while managing rebound effects.

A demand response event is conducted at frequencies defined by program rules or real-time grid needs. They may follow scheduled patterns (daily peak shaving, time-of-use windows) or be triggered dynamically by emergencies, market prices, or renewable fluctuations.
Learn additionally about IoT in renewable energy from another blog post by Intelliarts. 
 
Now, let’s proceed with reviewing key OpenADR components, the communication process, and the intricacies of integrating the technology with existing systems. 
Integration of OpenADR with existing systems
Let’s see how OpenADR operates in pairs with some of the traditional systems your business might have in place already: 
#1 EMS and BMS
The OpenADR protocol allows existing energy and building management systems (BMS) to automate HVAC, lighting, and industrial load controls. This reduces peak demand charges and improves operational efficiency without the need to establish new infrastructure.
Explore power grid load balancing in another blog post of ours. 
#2 IoT devices
Smart thermostats, sensors, and EV chargers can serve as demand response enabling devices. With OpenADR integration, businesses gain granular control over individual assets, enabling faster response and better load management.
#3 Distributed Energy Resources (DERs)
Assets such as rooftop solar, batteries, and EV fleets are coordinated through open automated demand response. Businesses can maximize self-consumption, monetize flexibility, and participate in demand response markets.
Interested in building your own electric truck charging infrastructure? Take a read of our respective blog post. 
Turn Smart Charging into a Grid AssetLearn how AFIR, NEVI, and other global regulations are reshaping EV infrastructure and how this integrates e-mobility.Get your copy now
Why OpenADR matters for energy management
Here are some of the many business benefits of openADR for energy management, with supporting sources indicated. 
#1 Grid stability & resilience
OpenADR 3.0 simplifies messaging and helps grid owners manage forecasted and real-time power demand adjustments, improving system reliability and avoiding peaks. First OpenADR 3.0 certified products announced in 2025, including E.ON’s SWITCH platform, have shown superior results. 
#2 Cost savings
In a 2014 LBNL demonstration of Auto-DR using OpenADR at four large commercial buildings in New York, enabling automated responses to day-ahead hourly pricing led to bill savings of 1.1% to 8.0% of total summer electricity bills.
#3 Sustainability & ESG alignment
OpenADR adoption helps integrate DERs (solar, batteries, EVs), enabling cleaner generation and flexibility, contributing towards sustainability and emission reductions. The 2024 Annual Report of the OpenADR Alliance points to growth in managing distributed energy resources using the OpenADR standard. 
#4 Interoperability & scalability
The Pearlstone case study (UK/Europe) demonstrates OpenADR being used across a multi-site portfolio of buildings, giving smaller and geographically dispersed facilities access to demand response markets using a cloud-based, low-cost platform. This shows how it scales and interoperates across vendors and regions. 
#5 Compliance & future-proofing
OpenADR 3.0 certification has begun (e.g., first certified VTN and VEN in 2025), enabling organizations to meet evolving regulatory demands and integrate modern DR & DER programs. As such, there was an announcement of the first OpenADR 3.0 certified products by E.ON, EVoke Systems, and Universal Devices in March 2025. 
Demand response has tremendous potential to help with long-term reliability needs as well as daily system balancing needs. — Debra Lew, Executive Director of the Energy Systems Integration Group

Challenges of using OpenADR & key considerations
Here are the main challenges and considerations that any business must know before getting started with an openADR for energy management. 
#1 High upfront investment
Integration and setup can require significant capital, especially for large facilities. At the same time, the end ROI largely depends on efficient deployment and early performance gains, so substantially cutting costs is not recommended. 
Solution: Start with pilot projects to validate benefits before scaling system-wide.
#2 Cybersecurity requirements
Protecting sensitive grid and facility data is critical to avoid breaches. Compromised Demand Response (DR) systems can undermine trust and grid reliability.
Solution: Developers like Intelliarts use end-to-end encryption, secure APIs, and routine vulnerability testing to address this difficulty. 
#3 High technical expertise needed
Implementing OpenADR for an energy management system requires specialized knowledge of energy systems and IT. Without it, integration risks delays and inefficiencies.
Solution: Partner with certified system integrators experienced in OpenADR.
#4 Certification matters
Non-certified devices may not fully comply with OpenADR specifications, leading to communication failures, security gaps, or regulatory non-compliance. 
Solution: Prioritize OpenADR-certified hardware and software during procurement.
#5 Difficult change management
Shifting to automated demand response requires internal process adjustments. Without stakeholder buy-in, adoption can stall or underperform.
Solution: Provide staff training and clear communication on operational benefits.
#6 Ongoing maintenance
Systems require continuous monitoring and updates to stay effective. Neglecting maintenance can erode savings and reduce reliability.
Solution: Plan for regular system audits and software updates. 

Pro tip: Any OpenADR system in place is required to comply with standards, including IEC 62746 and OASIS Energy Interoperation (EI). Ensuring compliance on the dev stage presents a difficulty, unless experienced tech partners are involved. However, given the successful compliance, the criticality of the challenges mentioned above diminishes.
How to get started with OpenADR
Finally, here’s a short guide that the Intelliarts team prepared for companies that are looking to integrate OpenADR in their business:
#1 Assessing readiness
The first step is understanding your facility’s energy profile. This includes:

Analyzing consumption patterns
Identifying peak load drivers
Evaluating existing DER assets such as solar, storage, or EV fleets

A readiness assessment clarifies where demand response opportunities can deliver the strongest ROI. This is also an obligatory preparation step before connecting with technology service vendors. 
You can’t manage what you don’t measure — understanding your energy profile is the foundation of every successful demand response strategy. — Alexander Barinov, Managing Partner at Intelliarts
#2 Choosing the right vendor
This is a tricky part. Depending on your business and your objectives for the project, you may need an integration with EMS, BMS, and DER platforms, custom software, additional hardware, etc. Once your intentions for the demand response automation project are clear, start selecting an OpenADR development provider. 
Remember, trusted vendors and technology partners reduce integration risks and help you qualify for incentive programs without costly rework.
Explore an openADR protocol implementation success story by Intelliarts. 
#3 Integration roadmap
With the right technology partner, integration becomes a guided process rather than a challenge. Your partner connects the OpenADR protocol to EMS, BMS, and IoT platforms, configures control strategies, and runs test events. This structured roadmap ensures smooth adoption, minimal disruption, and measurable performance improvements from the start.
Learn more about smart grids in IoT in another of our blog posts. 
#4 Training & change management
Adopting OpenADR changes how facilities interact with the grid. Here are the types of staff members that will likely need additional training:

Facility managers
Operators
IT staff 

The listed professional will require clear procedures for event notifications, opt-in/opt-out decisions, and system reporting. Training ensures events are executed smoothly, while change management avoids operational surprises, such as unexpected HVAC shifts during peak hours.
#5 Scaling & future expansion
Most companies begin with one site or system to validate performance. Once event responses and savings are proven, OpenADR can be extended to additional facilities, larger DER portfolios, or EV fleets. Scaling gradually helps standardize processes across regions while aligning with evolving certification and regulatory requirements, such as OpenADR Certification Program (2.0b / 3.0) and the IEC 62746 Standard.
Final take
In a nutshell, demand response in energy management enables businesses to cut energy costs, enhance grid stability, and integrate DERs using a standardized, certified protocol. Success requires assessing readiness, selecting the right vendor, and ensuring smooth integration with EMS, BMS, and IoT systems. With proper training, monitoring, and scaling, companies can turn energy flexibility into measurable savings, compliance, and resilience.
Implementing OpenADR for energy management requires technical knowledge and experience with the technology. Should you require assistance, from a consultation to a full-scale development, reach out to Intelliarts. With more than 25 years of experience, 92% of senior staff engineers, and over 80 large projects under our belt, we can address your business needs. 
Need expert development?From consultation to full-scale development, let our senior staff address your business needs.Contact Us

### Pages

#### Blog
URL: https://intelliarts.com/blog/

#### Careers
URL: https://intelliarts.com/careers/

#### Company Overview
URL: https://intelliarts.com/company-overview/

#### Contact Us
URL: https://intelliarts.com/contact-us/

#### Cookies Policy
Last updated: 27.09.2023
This Cookies Policy (‘Policy’) is a part of the Terms of Use [https://intelliarts.com/terms-of-use/] and Privacy Policy [https://intelliarts.com/privacy-policy/] of Intelliarts. All the terms in this Policy are used with the same meaning as in these documents.
This Policy describes the details of processing your data as the Visitor of our Website [intelliarts.com] using cookies and other automated data collection means.
The Policy defines:

What are cookies
Which cookies we use on the Website and why, and whether they collect your personal data
How long we store the collected data
What your choices are as to the use of cookies, etc.

What do cookies mean?
We use cookies and other automated data collection means when you visit any page of our Website. Cookies are small blocks of data that are stored on your browser when you use websites or other services. They are usually installed on most websites to enable different features, for example, to facilitate navigation on the website, remember choices, or serve you with the ads that best reflect your interests.
A cookie file usually contains the name of the domain it comes from, its “expiry time”, and a randomly selected number identifying this file. In some cases, cookies are used to collect personal data, such as IP addresses and data linked to the IP address (traffic data). The usage of such cookies is regulated by data protection laws. That is why you as a Visitor obtain more rights to control the collection and processing of some data.
What data do we automatically collect via the Website and why?
We use cookies to let you navigate between pages efficiently, for statistical analysis purposes, and to improve the user experience. Cookies allow us to recognize and count the number of visitors and see how visitors move around our Website when they are using it. With the help of such information, we improve the way our Website works, for example, by ensuring that Visitors are finding what they are looking for easily.
If we are talking about personal data, with the use of cookies we collect IP addresses of our Visitors. An IP address is a number that an internet service provider assigns to the computer of a Visitor to the Website. The IP address allows you to access the Internet. In most cases, it is assigned dynamically to the computer, i.e. it changes every time you connect to the Internet. We use your IP address to:

Diagnose technical problems with the server
Create statistical analyses (e.g. determine from which regions we record most visits)
For security purposes and possible identification of unwanted automatic programs for viewing content that burden the server

We also collect your version of the browser, time zone, your domain, and your location such as country and city, based on IP address according to open data. Besides, we collect information about the web pages or sites you visited just before or just after using the website.
Which types of cookies do we use?
1. Depending on the storage period, we use two types of cookies:

Session cookies: After the end of a browser session or turning off the computer, the saved information is deleted from the device’s storage
Permanent cookies: They are stored in the memory of the Visitor’s end device and remain there until they are deleted or expired

2. Depending on who installed cookies, they are divided into two types:

Cookies are set by us called first-party cookies
We also use third-party cookies – cookies from a domain different from the domain of our Website

3. Depending on their purpose, we may install to your browser the following types of cookies:

Strictly necessary cookies

We use this type of cookie for our Website to function properly. For instance, we use cookies to authenticate the Visitor on the Website and ensure the Visitor’s session on the Website. These cookies are set only by us.

Marketing (advertising) cookies

We use these cookies to track visitors across the Website, to display ads relevant and engaging for the individual Visitor and thereby more valuable for publishers and third-party advertisers.
These cookies are set by us and third parties like Google.

Analytics cookies

These cookies are used to collect information on how visitors use our Website. We use the information for the analysis and research as well as audience audit. In particular, this allows us to create anonymous statistics that help to understand how Visitors use the Website, which allows for improving its structure and content.
These cookies are set by us and the third-party analytics service Google Analytics. The data collected in this way is stored in aggregated form and usually does not constitute personally identifiable information.
How does the Company deal with data collected through the cookies?
The cookies usage message was the first message you were likely to see when you visited our Website. In most cases, we do not ask you for consent to install them if we install cookies that are strictly required for the functioning of the Website in general and some of its features in particular (e.g. the very option to refuse some cookies).
Data that we collect with the cookies, where it is personal data, is processed in accordance with provisions of our Privacy Policy.
Are cookies safe and can I disable them?
The cookie mechanism is safe for the computers of the Website Visitors. In particular, it is not possible for viruses or other unwanted software or malicious software to enter the Visitor’s computers in this way.
You can regulate your choice on our banner. You have the right to choose whether or not to accept cookies, and later, if you already have chosen certain settings and want to change them – by using a special cookie icon in the left bottom corner.
Otherwise, you may choose the appropriate settings of your browser. Please refer to the following links to find out how you can change the settings of popular web browsers in the use of cookies:
For the users of Internet Explorer
For the users of Firefox
For the users of Chrome
For the users of Safari web and iOS
For the users of Android
Contact information
You can contact us if you have any questions about our Cookies Policy.
Intelliarts Consulting
31 Vitovskogo Str, Staryi Sambir,
Lviv oblast, 82000, Ukraine
Email: info@intelliarts.com

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#### Home
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#### Privacy Policy
Last updated: 27.09.2023
This Website https://intelliarts.com/ (“Website”) was created for Intelliarts Consulting, LLC (“Company”, “Intelliarts”, “we”, “us”). Your privacy is important to us. Therefore, we invest great efforts to be transparent about processing your personal data with the use of our Website and otherwise.
Who we are
The owner of the Website is Intelliarts. As a result, Intelliarts is the controller of personal data obtained with the use of the Website and processed under this Privacy Policy. This means that we decide what personal data should be processed, how, and why.
When we refer to Intelliarts, we mean a company registered under the law of Ukraine (state register number – 33914949) with a registered address at 31 Vitovskogo Str, Staryi Sambir, Lviv oblast, 82000, Ukraine.
If you have questions, concerns, or complaints, or would like to exercise your rights, we are available at info@intelliarts.com.
You can also complain to the supervisory body in your country if you are unhappy with how we have used your personal data.
Scope of our privacy policy
Our privacy policy (“Privacy Policy”) describes the purposes for which we use your personal data (you are our “Visitor”), with whom we may share it, where your personal data is stored, and the measures we take to protect its security. It also aims to tell you about your rights with respect to your personal data and how to contact us regarding our privacy practices.
This Privacy Policy covers the personal data we process when you interact with our Website and with us through the means indicated on the Website. Before using our Website, please read our Privacy Policy carefully as it may affect your rights.
Age of our Users
Our Website is not directed at individuals under 18. We do not knowingly collect personal data from children under 18. If we become aware that a child under 18 has provided us with personal data, we will take steps to delete such information.
Detailed description of how we process your data
Below you will find a detailed information about:

What data we may process and when we have got it
Purposes of processing your data
Legal basis for processing
Retention period

Please be aware we may collect your personal data (such as IP address, the country you are visiting our Website from, or language preferences) with the use of automated technologies like cookies. For more information, see our Cookies Policy, which is an integral part of this Privacy Policy.
Other types of data we process are described below.
1. Data we use to respond to your requests
a. Nature and purpose(s) of processing
We process this data after you address us with your requests to:

Administer the Visitors’ requests sent via our «Contact us» form
Allow you to download our white papers or similar reports
Interact with your responses related to our vacancies
Offer you and discuss our partnership/job opportunities

b. Personal data we process
Data you provide when you fill out a contact form:

First and last name
Your company and position
E-mail
The request (your questions, comments or feedback)

Data you provide when you want to download our white papers or similar reports:

Name
Business email

Data related to your responses to our vacancies. It may include various information about you that is relevant to your job and that you chose to provide — for example, first name, last name, email, phone number, LinkedIn profile, cover letter, and your work experience, including your CV or other content that you share with us and that could include personal data.
We encourage you not to share sensitive personal data with us (such as data about your health, sexual orientation, or religious beliefs), as well as excessive data that we do not need to resolve your issue.
c. Legal basis for processing (if applicable)
Entering into and performance of a contract (Article 6.1(b) GDPR)
Legitimate interest (Article 6.1(f) GDPR)
d. Data retention period
We may store your personal data mentioned above from the moment you provided data as long as we need such data to answer your requests, but in no case it will be longer than 12 months following the receipt of your requests.
If we receive your data as a response to our vacancy, and if we decide not to cooperate, your data will be removed within 3 months following the rejection.
2. Data we use to send our marketing information
a. Nature and purpose(s) of processing
We process this data after you complete the form or address us with your requests and directly agree to receive our marketing information, or when you separately subscribe to our newsletters to send you the information about our services and news and to conduct analytics.
b. Personal data we process
Email address
c. Legal basis for processing (if applicable)
Consent (Article 6.1(a) GDPR)
Legitimate interest (Article 6.1(f) GDPR)
d. Data retention period
We may store your personal data mentioned above from the moment you provided data as long as you unsubscribe from receiving our newsletters.
We may also process your personal data to perform legal duties, responsibilities, and obligations and to comply with laws and regulations that apply to us. For example, we could store data about emails and names of subscribers to our newsletters, as well as their choices and consent – to make sure we comply with laws on e-communication.
Your rights when we process your personal data
GDPR and other countries’ privacy laws provide certain rights for data subjects. But basically, you have the following rights with respect to your personal data:

Right of access. You may ask to access and obtain a copy of the data about you that we collected if required. This also refers to the right to know which personal data about you we process, why, and how.
Right to rectification. You may ask to update the data or correct any inaccuracies if you know we have some outdated personal data about you.
Right to erasure. You may ask to delete the data we retain in certain circumstances, such as when you withdraw consent we have earlier received from you.
Right to restriction of processing. You may request to restrict the use of your data in certain circumstances, such as when you have objected to our use of such data. But we will first verify whether we have overriding legitimate grounds to use it.
Right to data portability. In some jurisdictions such as the EU, you may ask to transfer your data to a third party in a structured, commonly used, and machine-readable format in circumstances where the data is processed with your consent or by automated means.
Right to object. You may object to our processing of your data in some cases, for example, when we process your data on the basis of our legitimate interest.
Right not to be subject to automated decision making. Please be aware that we do not make decisions about you based solely on automated processing (including profiling), which produces legal effects concerning you or similarly significantly affects you.

If you want to exercise some other right with regard to your personal data in your jurisdiction, do not hesitate to contact us via the contact data indicated at the top of this Privacy Policy. We will use our best efforts to keep you satisfied.
How we share data
We share your data with third parties. We are not in the business of selling any of your data, including personal data to other third parties or advertisers.
We may pass on your data to our employees or verified independent contractors, including contracted private entrepreneurs who are based in Ukraine with sales, marketing, and recruiting functions. We always enter into non-disclosure and confidentiality agreements with those who have access to your data to ensure data protection.
We may share your data with service providers that help us operate, provide, support, and enhance our services. If a service provider needs to access your data, they do so under appropriate security and confidentiality measures designed to protect your data. For instance, we store your data using Google Cloud storage services (Google Workspace). We also use WordPress, Reply.io, and HubSpot CRM to create the forms, collect, and store your responses, and send follow-ups, updates, and information about our products and other marketing we deem interesting to you. Through the use of Calendly, we allow you to easily book a meeting with us. We utilize Google Analytics and HotJar to collect analytics and statistics from the Website. To track applicants to our vacancies within our recruiting process, we also use TalentLyft.
You should always check the privacy settings and notices in these third-party services to understand how those third parties may use your data.
In exceptional circumstances, we may disclose data with law enforcement agencies, courts, fraud prevention agencies, or other third parties, where we think it is necessary to comply with applicable laws or regulations or to defend our legal rights (where possible, we will notify you of this type of disclosure).
International data transfers
When we use and share your data, it may be transferred to and processed in countries other than your country or residence.
Where we transfer your data, we put safeguards in place to ensure your data remains protected. For individuals in the UK or EEA, this means that your personal data may be transferred outside of the UK or EEA. When this is the case, it will be transferred to countries where we have compliant transfer mechanisms in place, in particular, the implemented European Commission’s Standard Contractual Clauses to agreements with entities and independent contractors the data is transferred to or other appropriate legal mechanisms to safeguard the transfer.
Security of data
We are committed to keeping our user’s personal data safe. We take appropriate security measures to protect your personal data from accidental loss or destruction, unlawful processing, or access to it.
All personnel are subject to full confidentiality, and any subcontractors and subprocessors are required to sign a confidentiality agreement if not full confidentiality is part of the main agreement between the parties. Also, any access by authorized personnel is logged.
We use verified contractors who might have access to the data as specified in this Privacy Policy and with whom relevant data processing agreements are concluded. Moreover, we guide and train our personnel to process your data securely. We also use technical measures to ensure your data is safe.
Updates to our Privacy Policy
We may amend or update this Privacy Policy from time to time. If we decide to do so, and the amendments will substantially affect your rights and legitimate interests, we will notify you of any changes via email and/or a notice on our Website. We will also indicate the “Last updated” date at the top of this document.

#### Terms of Use
Last updated on 27.09.2023
The terms of this agreement (“Terms”) govern the relationship between you as a visitor of our Website (“Visitor” or “you”) and Intelliarts Consulting, LLC (“Company”, “Intelliarts”, “we”, “us”), registered under the law of Ukraine (state register number – 33914949) with a registered address at 31 Vitovskogo Str, Staryi Sambir, Lviv oblast, 82000, Ukraine.
These Terms govern your usage as the Visitor of our Intelliarts Website [intelliarts.com] and all related pages (“Website”). By using our Website, in particular, by filling in the respective forms on the Website, you agree to be bound by these Terms.
Use of the Website is also governed by Intelliarts Privacy Policy. Your privacy is important to us. We designed our Privacy Policy to let you know how we collect and use your personal data. We encourage you to read the Privacy Policy carefully as it is an integral part of our Terms.
To the extent the Terms conflict with any other Intelliarts terms, policy, rules, or codes of conduct, the terms contained in these Terms shall prevail.
1. General provisions
1.1. Scope of our services
Our Website is an online platform which is designed for getting acquainted with the Company, showing our services and experience, and joining our team.
In particular, we provide you with access to our content, such as:

Information about our Company and team as well as the list of the active vacancies
Information about our services
Blog with our articles
Links to our social media
Newsletter subscription
Our contact information

Intelliarts reserves the right to update the Website in any way at its sole discretion. These Terms will also apply to any updates, enhancements, and new features on the Website not expressly mentioned hereunder and implemented after these Terms become effective.
1.2. Accessing the Website
The Website is intended for persons above the age of legal majority. If you are under the age of 18 (or under other age of majority under the law of your country), you may use the Website only with the consent of your legal guardian (parents or principal). You represent and warrant that your legal guardian has read, understood, and agreed to these Terms.
1.3. Suspension and termination of the services
Intelliarts may limit, suspend, terminate, modify access to the Website or portions thereof,  and deny answering to your requests, etc. if you are, or Intelliarts suspects that you are:

Failing to comply with any of these Terms or for any actual or suspected illegal or improper use of the Website, with or without notice to you
Breach any warranty you give us by accepting the Terms
Send us in your requests or via any forms available on the Website any excessively profane, hate-related, violent, obscene, harmful, threatening, malicious, or other illegal content
Creating risk or possible legal liabilities, infringing the intellectual property rights of third parties, or acting inconsistently with the letter or spirit of our Terms or policies

Upon termination of these Terms, all licenses and rights to use the Website shall immediately terminate.
Intelliarts reserves the right to stop offering and/or supporting the Website or part of the Website at any time either permanently or temporarily, at which point your license to use the Website or a part thereof will be automatically terminated or suspended. In such an event, Intelliarts shall not be required to provide refunds, benefits, or other compensation to users in connection with such discontinued elements of the Website.
1.4. Third-party links
You may have an opportunity to leave the Website via the links to third-party websites and services. For instance, we may provide you with links to our profiles on social media. Besides, our blog articles contain links to external resources. We include links to external resources that we trust and believe will be helpful. We can also post links to our clients’ websites for your perusal.
Such third-party links are not under the control of Intelliarts, and Intelliarts is not responsible for them. We may provide you with these links only for your convenience, and the inclusion of any such link does not imply the endorsement by Intelliarts of such third-party websites.
You use all third-party links at your own risk. You should apply a suitable level of caution when doing so. When you click on any of the third-party links, the applicable third party’s terms and privacy policies apply. We recommend you to read any such terms and policies carefully before using the respective third-party services.
2. Disclaimers, limitations, and indemnification
No provisions of these Terms will prejudice the statutory rights that you may have as a consumer of the services. The laws of some countries and jurisdictions do not allow us to exclude some kinds of warranties or limit our liability as stated in this section. So, the below terms may not fully apply to you. Instead, in such jurisdictions, the exclusions and limitations shall apply only to the extent permitted by the laws of such jurisdictions.
2.1. Disclaimers and limitations of liability
Disclaimer as to the warranties on the Website operation
You expressly agree that use of the Website is at your sole risk and is provided on an “as is” basis without warranties of any kind, either express or implied, including, but not limited to, warranties of title or implied warranties of non-infringement, merchantability, or fitness for a particular purpose. You may have other statutory rights, but the duration of statutorily required warranties, if any, shall be limited to the shortest period permitted by law. Intelliarts shall not be liable for delays, interruptions, service failures, and other problems inherent in the use of the internet and electronic communications or other systems outside the reasonable control of Intelliarts.
To the maximum extent permitted by law, Intelliarts does not make any representation, warranty, or guarantee that:

The use of the Website will be secure, timely, uninterrupted, or error-free
The Website will operate in combination with any other hardware, software, system, or data
The Website will meet your requirements or expectations
Any stored data will be accurate or reliable, or that any stored data will not be lost or corrupted
Errors or defects will be corrected

Disclaimer as to the warranties regarding the damages that may be caused to you
Intelliarts parties shall not be liable for any indirect damages, including for loss of profits, goodwill, or data, in any way whatsoever arising out of the use of, or inability to use the Website.
You acknowledge and agree that, to the fullest extent permitted by any applicable law, the disclaimers of liability contained herein apply to all damages or injury caused by or related to use of, or inability to use, the Website under any cause, including, without limitation, actions for breach of warranty, breach of contract, or tort.
We also do not make any warranties about the suitability, accuracy or completeness, and usefulness of the information materials available on the Website. You use any such materials and take any decisions based on such materials at your own risk. We disclaim any liability of the Company for the use of such materials.
Force majeure
Intelliarts shall not be liable for any delay or failure to perform resulting from causes outside the reasonable control of Intelliarts, including due to unforeseen circumstances. Such circumstances include, in particular, war, terrorism, riots, embargoes, acts of civil or military authorities, fire, floods, accidents, network infrastructure failures, strikes, or shortages of transportation facilities, fuel, energy, labor, or materials.
2.2. Indemnification
You agree to indemnify, save, and hold Intelliarts and Intelliarts parties harmless from any claims, losses, damages, liabilities, including legal fees and expenses, arising out of your use or misuse of the Website, any violation by you of these Terms, or any breach of the obligations, representations, and warranties that you made.
Intelliarts reserves the right at your expense to assume the exclusive defense and control of any matter for which you are required to indemnify Intelliarts, and you agree to cooperate with Intelliarts defense of these claims. Intelliarts will use reasonable efforts to notify you of any such claim, action, or proceeding upon becoming aware of it.
3. Intellectual property
3.1. Intellectual property rights of the Company
The visual interfaces, graphics, design, compilation, information, data, computer code, any software, articles, pictures, designs, logos, trademarks, and any other materials available on the Website constitute the intellectual property of Intelliarts and its partners. All the materials mentioned are also referred to as the “Company materials”. Intelliarts and its partners retain all the rights, title, and interest in and to the Company materials.
Intelliarts has the right to remove from the Website at any time any Company materials published thereon, as well as to publish any new Company materials.
3.2. License to the Visitor
Intelliarts grants you a non-exclusive, non-transferable, revocable, worldwide, limited license to access and use the Website solely for your own non-commercial purposes. You agree not to use the Website for any other purpose. Access to the Company materials posted within the Website is provided by Intelliarts without the right to public demonstration or notification, reproduction, any processing or adaptation, distribution or disclosure, sale, and without the right to any other use unless provided for in the Terms. You shall not use the Company materials for any purposes or in any manner other than allowed in these Terms. In particular, you are prohibited from selling or exploiting the Company materials for your own commercial purposes unless we provide you with explicit consent.
Intelliarts reserves and does not transfer to you any rights, including all intellectual property rights or other proprietary rights in connection with the Website and the Company materials. If we share certain materials with you, which are available for downloading (after your application for receipt) via the Website, we also do not transfer to you any rights in connection with such materials, except for the license indicated above.
3.3. License limitations
Any usage of the Website in violation of these Terms as well as attempts to use, encourage, or assist any other person to use the Website in such a way is strictly prohibited and can result in the immediate restriction of your access to the Website. By using the Website, you agree and warrant not to:

Without Intelliarts express written consent, use the Website or any part thereof for any commercial purpose, including but not limited to (1) communicating or facilitating any commercial advertisement or solicitation, or (2) exploiting the Company materials in any way, including by (sub)licencing, (re)selling, transferring, assigning, and distributing the Website to any third party in any way
Transmit unauthorized communications through the Website or the contact emails indicated on the Website, including junk mail, chain letters, spam, and any materials that promote malware, spyware, and downloadable items
Copy, modify, or distribute rights or Company materials or use any method to copy or distribute the Company materials except as specifically allowed in these Terms
Attempt to “frame” or “mirror” the Website or any Company materials without prior written permission of Intelliarts
Use the Website, intentionally or unintentionally, in connection with any violation of any applicable law or regulation, or do anything that promotes the violation of any applicable law or regulation

4. Miscellaneous
4.1. Assignment
Intelliarts may assign or delegate its rights and obligations under these Terms, in whole or in part, to any person or entity at any time without your consent.
4.2. Updates
Intelliarts reserves the right to change, modify, add, or remove portions of the Terms at any time by posting the amended Terms on or within the Website. You will be considered as having accepted such changes if you continue to use the Website after the amendments are made (unless we inform you on such changes otherwise). If at any point you do not agree to be bound by the current version of the Terms, please do not use the Website.
4.3. Applicable law and dispute resolution
The Terms and other relationships between you and the Intelliarts shall be governed by the laws of Ukraine (excluding any choice-of-law rules).
All disputes and disagreements that might arise from these Terms shall be resolved through negotiations. For the settlement of such disputes, e-mail correspondence with the authorized persons of Intelliarts shall be the effective and binding method of communication.
If the dispute cannot be resolved through negotiation within 30 calendar days, it shall be referred to and finally resolved by the appropriate court under the laws of Ukraine.
4.4. Contact information
If you have any questions concerning Intelliarts or the Terms, please contact us by email at info@intelliarts.com.
Thank you for taking the time to read our Terms.

### Expertise

#### AI Chatbot Development Services
URL: https://intelliarts.com/expertise/ai-chatbot-development-services/

#### Big Data
URL: https://intelliarts.com/expertise/big-data/

#### ChatGPT Development Services
URL: https://intelliarts.com/expertise/chatgpt-development-services/

#### Custom ML Solutions
URL: https://intelliarts.com/expertise/ml-development/

#### Data Science
URL: https://intelliarts.com/expertise/data-science-consulting/

#### IoT Solutions
URL: https://intelliarts.com/expertise/iot-solutions/

#### Large Language Model Development
URL: https://intelliarts.com/expertise/large-language-model-development/

#### RAG Development Services to Build Context-Aware AI
URL: https://intelliarts.com/expertise/rag-development-services/

### Services

#### Custom AI Solutions
URL: https://intelliarts.com/services/ai-development/

#### Data Engineering Consulting
URL: https://intelliarts.com/services/data-engineering/

#### Software Engineering Company
URL: https://intelliarts.com/services/software-engineering-company/

#### Technology Consulting
URL: https://intelliarts.com/services/technology-consulting/

### Industries

#### Agriculture Software Development Services
URL: https://intelliarts.com/industries/agriculture-software-development/

#### eMobility Solutions
URL: https://intelliarts.com/industries/emobility-solutions/

#### EV Software Development
URL: https://intelliarts.com/industries/ev-software-development/

#### Insurance Software Development Services
URL: https://intelliarts.com/industries/insurance-software-development/

#### Manufacturing Software Development Services
URL: https://intelliarts.com/industries/manufacturing-software-development/

#### Marketing Software Development
URL: https://intelliarts.com/industries/marketing-software-development-services/

#### OCPP/OCPI Development Services
URL: https://intelliarts.com/industries/ocpp-ocpi-development-services/

#### Renewable Energy Software Development
URL: https://intelliarts.com/industries/renewable-energy-software-development/

#### Smart Grid Software
URL: https://intelliarts.com/industries/smart-grid/

### Authors

#### Alexander Barinov
About Alexander
Alexander Barinov is a managing partner at Intelliarts who has been guiding the company’s strategic direction and supervising day-to-day activities for almost 20 years. He’s enthusiastic about data science and ML with vast experience in software engineering.
Alexander shares his valuable experience with the Intelliarts readers, which lies in:

Being tech-savvy — Alexander’s experience reaches more than 20 years in the technology industry
In-depth knowledge of agile methodologies, such as CRISP-DM and SCRUM
Active participation in various technology events, lecturing, and workshops
Diverse management background gained after managing multiple software development projects
Long-term cooperation with Intelliarts, from a software engineer to the upper management level
Special interest in artificial intelligence and machine learning, including certification in machine learning issued by Stanford University
MS degree in Technology Management

#### Andrii Shutka
About Andrii
Andrii Shutka is an experienced data engineer and DevOps/MLOps who has shifted from system administration. With over 20 years in the technology industry, Andrii gladly shares his insights about data science and MLOps in particular.
Andrii contributes to the Intelliarts blog thanks to his vast expertise:

3 years of experience working as a data engineer and MLOps and over 20 years in computer science and computer information technology
AWS and SnowPro certifications
Strong understanding of virtualization and cloud computing
MS degree in system engineering, computer science, and computer engineering
Special interest in artificial intelligence and machine learning
Purpose-driven professional with a skill set required for being a strong team player and working on projects implying collaboration with independent contributors
A professional chess player and very skillful at shooting sports. He also loves challenges and computer games

#### Mariana Franchuk
About Mariana
Mariana is an Agritech Solution Expert at Intelliarts with a strong foundation in software engineering and a passion for frontend technologies. She builds intuitive, user-centric interfaces that empower agritech solutions.
Mariana contributes to the Intelliarts blog by sharing practical insights from the field:

Solid understanding of agritech business needs and how to address them with modern software
Specialized in frontend development with a background in full-stack and mobile engineering
Over 5 years at Intelliarts, contributing to projects like Verity and Indigo
Started with mobile development in Swift, transitioning through a full-stack role, and now focusing entirely on frontend development for the Indigo project
Degree in Computer Science at Lviv Polytechnic National University
Enjoys reading, swimming, and creating beautiful embroidery pieces — a blend of logic and creativity that mirrors her approach to engineering

#### Marta Kufalska
About Marta
Marta is an Agritech Digital Solution Expert at Intelliarts, with 9+ years in software engineering, including 4+ years building complex Agritech products. She turns ambiguous agritech problems into clear product and technical direction.
Marta contributes to the Intelliarts blog by sharing practical insights from the field:

Deep understanding of agritech business needs and how to address them with scalable, data-driven software
Hands-on experience with data-heavy agritech and sustainability products, including field operations, grower workflows, remote sensing, sustainable programs, and carbon programs
Technical strategy, 0→1 product development, architecture, stakeholder alignment, and execution in the agritech domain
Strong focus on engineering leadership and interoperability solutions for agribusinesses and growers
Over 4 years at Intelliarts, contributing to agritech projects like Indigo Ag
Growing focus on applying AI and data to real agritech problems, improving grower workflows and decision-making
Does her best work when the problem is messy, the path isn’t obvious, and a team needs clarity, structure, and momentum

#### Oleksandr Stefanovskyi
About Oleksandr
Having a strong background in data science and software engineering, Oleksandr Stefanovskyi is a strategic technology partner at Intelliarts, as well as the head of the machine learning team. He’s a regular contributor to the Intelliarts blog, telling the nuances of building end-to-end machine learning pipelines.
Oleksandr benefits our blog with his knowledge in data science and ML coming from:

3 years of experience in leading ML projects out of more than 10 years in the software engineering industry overall
Strict adherence to the principles of CRISP-DM methodology for AI and data-heavy projects
Being an AWS-certified engineer
Deep expertise in software architecture design 
Extensive experience in designing and building data lakes and data warehouses for data-intensive applications
Effective management and conflict resolution skills developed after years of successful management of mixed development teams and leading research and development initiatives
Oleksandr loves taking photos on his smartphone and traveling across the world. He’s interested in Contemporary Art and reads 30+ books a year

#### Olena Zherebetska
About Olena
Being a tech enthusiast, Olena Zherebetska is a content manager at Intelliarts, writing about the latest news and innovations in data science and machine learning.
Olena is an experienced tech writer whose expertise extends to:

4 years of writing about IT and 7 years of writing experience generally
Researching the topics in the fields of data science and machine learning and converting the findings into the audience-specific content
Exploring the jungle of marketing with certificates in content marketing and digital marketing from Hubspot Academy
Creating various types of content, from academic papers to case studies, white papers, etc.
Tech lover who always follows the latest tech trends and advancements
MS in Foreign Languages and Literature
In her leisure time, Olena prefers reading world classics books, especially the Modern Period. She loves traveling and exploring new places and has visited 10 countries by the age of 25.

#### Volodymyr Mudryi
About Volodymyr
Volodymyr Mudryi is a talented data scientist and machine learning engineer who has more than 4 years of experience in the field. He’s a prominent writer on the Intelliarts blog and a former employee, currently developing an AI and computer vision startup focused on manufacturing. He covers a broad range of topics, from data science to deep learning, data mining, and NLP.
Volodymyr gladly shares his DS/ML expertise since he:

Specializes in classical machine learning
Has a primary focus on projects related to computer vision, clustering analysis, object detection, and tabular data classification
Is a certified ML engineer in Applied AI with DeepLearning, Advanced ML and Signal Processing, Fundamentals of Scalable Data Science, etc.
Seeks to change and improve the world through machine learning and math
Shares his skills and experience as a lecturer at Lviv Polytechnic National University
Delivered lectures on machine learning at IT Education Academy and AI House, the largest AI community in Ukraine
Takes part in competitions and hackathons devoted to ML, among them the Visual Product Recognition Challenge and Hack4Retail Hackathon
Volodymyr enjoys Ukrainian underground music and keeps up with emerging bands across various genres

#### Yuliia Zabudska
About Yuliia
Yullia is an e-Mobility Solution Expert at Intelliarts with over 8 years of experience in software development. She helps renewable energy businesses implement tailored software solutions to drive growth and optimize energy use.
Yullia is a recognized author in the Intelliarts blog, sharing her unique experience in e-mobility:

Specialized in EV charging solutions, V2G, EV fleet management, and power management systems
Proven ability to create scalable, future-ready solutions for energy optimization
Passionate about driving business growth through innovation and technical excellence
Highly skilled in leading teams to deliver high-quality, timely, and within-budget solutions
Regular participant in Google Hashcode, where her team ranked 17th among 224 Ukrainian teams in 2021
Master’s degree in Computer Systems
Enthusiastic about world history, books, handmade crafts, traveling, and space exploration

#### Yurii Laba
About Yurii
Yurii Laba is an experienced data scientist and machine learning engineer who translates data into valuable insights on a daily basis. He contributes to the Intelliarts blog, opening the curtain about AI/ML development to business leaders.
Yurii’s expertise includes:

4+ years of experience in data science
A hands-on experience in computer vision, anomaly detection, natural language processing, and so on
Strong engineering skills with a BS degree focused in Computer Science from Ukrainian Catholic University
MS degree in Innovations and Entrepreneurship obtained in Lviv Business School
Regular participation in popular AI/ML events and competitions, such as the Visual Product Recognition Challenge and Hack4Retail Hackathon
Public speaking on NLP in the workshop organized by AI House
A range of licenses and certificates, including SQL, Problem Solving, etc. from HackerRank, Neural Networks, NLP with Classification and Vector Spaces, Linear Regression for Business Statistics, and others
A deep interest and contribution to the development of the Ukrainian AI community, including the latest scientific research paper on word sense disambiguation in Ukrainian, which was presented at the UNLP workshop at the EACL conference.
Yurii is passionate about history and archeology, particularly the history of 20th-century Eastern Europe. He likes traveling to explore his native country, hiking in the mountains, and long-distance bicycle rides

### Success stories

#### AI Assistant for Cybersecurity Course
URL: https://intelliarts.com/success-stories/ai-assistant-development-for-cybersecurity/

#### AI Chat for Subscription-Based Financial Intelligence Platforms
URL: https://intelliarts.com/success-stories/rag-powered-ai-chat-for-finances/

#### AI Marketing Platform Development for Nowsite
URL: https://intelliarts.com/success-stories/ai-marketing-platform-for-nowsite/

#### AI-Powered Solution for Equipment Failure Prediction
URL: https://intelliarts.com/success-stories/asset-failure-prediction/

#### All-Purpose Big Data Pipeline for DDMR Data Marketplace
URL: https://intelliarts.com/success-stories/big-data-pipeline-for-ddmr/

#### Automated Content Localization System for HubSpot
URL: https://intelliarts.com/success-stories/content-localization-solution/

#### Automating Agritech with a Sensor Inventory Management System
URL: https://intelliarts.com/success-stories/sensor-management-software/

#### Automating Data Entry through Advanced Software Integrations
URL: https://intelliarts.com/success-stories/data-automation-for-indigo/

#### AWS Optimization & Database Migration for Real Estate
URL: https://intelliarts.com/success-stories/infrastructure-optimization-and-database-migration-in-real-estate/

#### B2B Job Sourcing Platform Case Study for Talent Matching
URL: https://intelliarts.com/success-stories/b2b-job-sourcing-platform/

#### Building Software Solution for Financial Reporting
URL: https://intelliarts.com/success-stories/business-report-automation/

#### ChatGPT-enabled Solution for Inmate Security Designation
URL: https://intelliarts.com/success-stories/chatgpt-enabled-solution-for-inmate-security-designation/

#### Compete: Technology Consulting for Delivery Optimization
URL: https://intelliarts.com/success-stories/technology-consulting-for-delivery-optimization/

#### Creating a Fully-fledged Customer Data Platform for Optimizely
URL: https://intelliarts.com/success-stories/customer-data-platform/

#### Creating Next-Gen EV Fleet Management Software
URL: https://intelliarts.com/success-stories/ev-fleet-management-solution/

#### Data Extraction Chatbot Development Using ChatGPT
URL: https://intelliarts.com/success-stories/chatgpt-data-extraction-chatbot/

#### Dell EMC: One-Stop Translation Web Portal
URL: https://intelliarts.com/success-stories/translation-portal/

#### Enhancing Demand Forecasting with Machine Learning
URL: https://intelliarts.com/success-stories/demand-forecast-with-machine-learning/

#### Enhancing Expert Assessments with an AI-powered Agent
URL: https://intelliarts.com/success-stories/ai-agent-for-smarter-expert-assessments/

#### EV Charging Management Software Platform for EVIQ
URL: https://intelliarts.com/success-stories/ev-charging-management-software-for-eviq/

#### GPT-driven Solution for Contract Analysis
URL: https://intelliarts.com/success-stories/gpt-driven-solution-for-contract-analysis/

#### Improving Scalability with a No-Code Toolkit for Verity Platform
URL: https://intelliarts.com/success-stories/no-code-toolkit-for-verity-platform/

#### In-Vehicle Truck Driver Support System Powered by AI
URL: https://intelliarts.com/success-stories/truck-driver-support-system/

#### Increasing Cold Calling Performance in Insurance
URL: https://intelliarts.com/success-stories/improving-cold-calling-success-rate/

#### Integration of Third-Party Software into HubSpot
URL: https://intelliarts.com/success-stories/third-party-software-integration/

#### Lightning-Fast Field Data Collection for Growers
URL: https://intelliarts.com/success-stories/lightning-fast-field-data-collection/

#### ML List Stacking & Predictive Modeling in Real Estate
URL: https://intelliarts.com/success-stories/predictive-model-for-real-estate/

#### ML that Cuts False Calls in SMT Lines
URL: https://intelliarts.com/success-stories/ml-false-calls-reduction/

#### ML to Boost Lead Quality & Agent Efficiency
URL: https://intelliarts.com/success-stories/improving-lead-quality/

#### ML-Based Respiratory Risk Prediction for Healthcare
URL: https://intelliarts.com/success-stories/time-series-ml-respiratory-exacerbations/

#### ML-powered Error Detection for Enhanced Fleet Maintenance Service
URL: https://intelliarts.com/success-stories/error-detection-for-fleet-maintenance/

#### OpenADR Implementation for Efficient Load Management
URL: https://intelliarts.com/success-stories/openadr-implementation-for-load-management/

#### Predicting Degradation in Hydraulic Systems
URL: https://intelliarts.com/success-stories/predicting-degradation-level/

#### Predictive Lead Scoring Model for Insurance Company
URL: https://intelliarts.com/success-stories/predictive-lead-scoring/

#### Predictive Maintenance Solution for EV Chargers
URL: https://intelliarts.com/success-stories/predictive-maintenance-for-ev-chargers/

#### Processing and Analyzing Extensive IoT Data for OptiMEAS
URL: https://intelliarts.com/success-stories/processing-iot-data/

#### Redesigning Indigo Ag’s Legacy System for Efficient Land Sampling
URL: https://intelliarts.com/success-stories/indigo-land-sampling-automation/

#### Smart Object Counting Using AI Technology
URL: https://intelliarts.com/success-stories/counting-objects-solution/

#### Smart Power Management System Development
URL: https://intelliarts.com/success-stories/smart-power-management-system/

#### Tech Consulting & Baby Monitoring App for Dorel
URL: https://intelliarts.com/success-stories/tech-consulting-for-dorel/

#### Tracking Storage Conditions with Blustream Mobile Application
URL: https://intelliarts.com/success-stories/tracking-storage-conditions-mobile-app/

#### XerosTech IoT Platform for Water & Energy Savings
URL: https://intelliarts.com/success-stories/xeros-technology-iot-platform-development/