large projects successfully completed
of customers return with new projects
years average length of our partnership
of our engineers are senior-level
Our Machine Learning Development Services
Machine Learning Consulting & Strategy
Not sure where ML will pay off first? We map your decisions and data sources, prioritize use cases by value and feasibility, then deliver an ML roadmap with success metrics and a PoC plan in 6–8 weeks.
Predictive Analytics Solutions
Churn prediction, risk and lead scoring, and customer analytics, validated against business KPIs. Our predictive model for a real estate startup processed 8TB of data and helped it grow 600% in 12 months. For a U.S. insurer, lead scoring cut 6% of inefficient leads and raised profit by 1.5% within months.
Forecasting & Decision Intelligence
We turn time-series forecasts into recommended actions: what to stock, when to service equipment, how much load to expect. For a top-tier manufacturer, we built a demand forecasting engine, integrated into its manufacturing system, that drives procurement and resource planning.
Computer Vision Solutions
Defect detection, visual inspection, image classification and OCR-based document processing. For a global PCB manufacturer, our prototype model double-checks polarity defects flagged by automated optical inspection with over 90% accuracy, to cut the false alarms that stop SMT lines.
Recommendation & Personalization Systems
Recommendation engines and next-best-action systems that decide which product, offer or lead goes to whom. For a U.S. health insurtech, our lead-routing model matches the most promising leads with top-performing agents, raising lead quality by 5% and agent conversion by 3%.
Anomaly Detection & Monitoring
Models that flag unusual transactions, sensor readings and system behavior before they become losses: fraud detection, predictive maintenance, operational monitoring. For a large manufacturer, our four ML models predict degradation of hydraulic system components with 98% average accuracy.
Why Opt for Custom ML Development?
Reduced customer churn by up to 15%
A custom churn model scores every customer by risk, so retention teams act while there is still time. McKinsey found that companies taking an analytics-based approach to managing their customer base can reduce churn by as much as 15%.
Enhanced visual content analysis with up to 90% better defect detection
Computer vision interprets images and video at production-line speed: product and quality inspection, fraud detection, damage assessment. According to McKinsey, AI-based visual inspection can improve defect detection by up to 90% compared to human inspection and raise quality-testing productivity by up to 50%.
Forecasting and trend identification with 20–50% fewer errors
Demand forecasting and trend analysis show when to build stock, how to plan production and where the market is heading. McKinsey estimates that ML-based forecasting can cut forecasting errors by 20–50%, reduce lost sales from stock-outs by up to 65%, and lower inventory by 20–50%.
Process automation of up to 30% of routine work
ML automates repetitive work such as image classification, document processing and routine data analysis. McKinsey puts the automation potential across business support functions at about 30%, and up to 90% for tasks such as IT service desk tickets.
Better data-informed decisions with 19x higher odds of above-average profit
ML turns raw data into decisions . In McKinsey’s DataMatics survey, companies that make intensive use of customer analytics were 23 times more likely to outperform competitors in new-customer acquisition and almost 19 times more likely to achieve above-average profitability.
Personalized customer engagement with a 10–15% revenue lift
Customers expect offers and experiences that fit their needs, and ML delivers them at scale from real-time data. McKinsey research shows that personalization most often drives a 10–15% revenue lift, and 71% of consumers expect personalized interactions.
Our Industry Expertise in Machine Learning Development
Our Success Stories
Custom Machine Learning Solutions Development Pipeline
Why Choose Intelliarts for Machine Learning Projects?
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92% senior ML and data engineering specialists
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Full-cycle custom machine learning development services
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Vertical domain expertise
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Dedicated contact point that doesn't change
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We always go extra mile
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Strong technology stack
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Many awards and recognitions
Our scientists work with ML algorithms and deep learning, backed by in-house data engineering for the pipelines models depend on. The team includes AWS-certified ML engineers, and 20% of them have over 10 years of experience.
Looking for one vendor for everything? We build ML models and the full-scale software around them, backed by our AI-native software engineering practice. Our client partnerships last over 4 years on average; the longest has reached 15 years.
Projects in renewable energy and e-mobility, agriculture, manufacturing, insurance and marketing taught us each industry’s data, constraints, and regulations.
A dedicated team of data scientists, ML engineers and BI experts works on your project. With 92% senior-level engineers and a 5-year median employee tenure, the people who start your project finish it.
We don’t stop at modeling. Our solutions are automated, deployed, integrated and managed, with ROI as the goal. In one case study, we helped the client reach $5–7 million in quarterly revenue.
OpenAI
Top AI Consulting Company 2026 by Vendorland
Top Generative AI Development Company by Selected Firms
Top AI Consulting Companies 2026 by TechReviewer
OpenAI Select Partner 2026
OpenAI
Top AI Consulting Company 2026 by Vendorland
Top Generative AI Development Company by Selected Firms
Top AI Consulting Companies 2026 by TechReviewer
OpenAI Select Partner 2026
Our Engagement Models
What Our Clients Say
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
Intelliarts developed an AI agent to streamline their traditional audit process conducted for Valency’s clients. We:
- Applied prompt engineering and fine-tuned LLMs
- Structured expert assessments into clear, easy-to-digest insights
- Reduced processing time from 5 minutes to a few seconds per query
- Used industry-specific tone and terminology for better quality
They treat everyone with respect and instill the sense of ownership into their developers, which is the quality we value the most in our internal and external developers.
Eleonora Ludin
Director of Engineering @HubSpot
Under the guidance of Intelliarts specialists, the company successfully implemented a translation portal, which:
- Helped to reduce the time spent on one translation from 5-20 minutes to a few seconds
- Added an ability to keep track of the translation status
- Made it possible to translate many documents in parallel
Overall, we’re comfortable with Intelliarts. We know that whatever they work on will be high value, which matters most.
Frank Miller
President @Ryffine
Intelliarts performed detailed data analysis to help an EV charging company to:
- Reduce downtimes and maintain their stations in top condition
- Provide deep insights into the equipment behaviors of EV chargers
- Recommend on data quality and quantity improvements
- Prepare data for building a predictive maintenance solution
Contact Us
Planning a machine learning project, or want to know whether your existing ML solution could perform better? As a machine learning consulting company with experienced ML engineers, Intelliarts is ready to help your team build predictive tools and smarter workflows.
FAQ
How long does it typically take to complete a machine learning project?
It depends on scope, data readiness and complexity. Most ML projects take a few months, and we typically deliver a working PoC in 6–8 weeks. Many of our ML projects have grown into long-term product development that lasts for years.
How do you ensure the quality and accuracy of machine learning models?
We validate every model on holdout data and time-based backtests against the agreed business KPI, and stress-test edge cases before launch. Explainability methods show which factors drive each prediction, so your domain experts can check the logic. After release, we monitor accuracy and data drift and retrain models when performance drops. Our models have reached over 90% accuracy in real-world projects.
How do you protect client data during machine learning development?
We follow secure development practices: data encryption, strict access controls and collecting only the data a model needs. Models can run in your own cloud account or on-premises, so sensitive data never has to leave your environment. We work in line with GDPR, CCPA, ISO 27001 and ISO 9001, and have delivered ML projects in regulated industries such as insurance.
![Machine Failure Prediction using Machine Learning [Case Study]](https://intelliarts.com/wp-content/uploads/2022/01/65e54c_c46767a478d0431c9582d190482327e3mv2.webp)
