15 Best Large Language Model Solutions for Your Business

Updated February 2025

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. Popular examples of large language models, such as GPT, LLaMA, and Claude, are already helping businesses streamline operations, automate tasks, and enhance customer experiences.

As multimodal LLMs continue to evolve and gain the ability to process text, images, and video, businesses will benefit from even more advanced capabilities by 2026. The focus will shift toward improving efficiency by reducing energy consumption and minimizing biases, further enhancing their value in business operations.

As we look ahead, adopting LLMs will be crucial for businesses aiming to stay competitive in the next few years. Those that integrate LLMs into their processes will not only improve efficiency but also unlock new pathways for growth and innovation. This post will help you explore the 15 best large language models you can potentially adopt and exploit in business processes in numerous ways.

15 best large language models in 2025

Despite the LLMs becoming publicly available not so long ago, there is already quite an extensive range of options to try. Here are the 15 most widely used and capable large language model examples to consider using for business purposes. 

GPT-4o (OpenAI)

GPT-4, developed by OpenAI, is widely regarded as one of top large language models, offering advanced deep learning capabilities for businesses. The model’s advanced natural language understanding and generation offer significant benefits like:

• High-quality text processing
• Contextually relevant outputs
• Efficient automation of text-based tasks
• Multimodal capabilities (text, image, and video processing)
• Improved reasoning and problem-solving

For businesses, GPT-4 serves as a powerful tool across various industries. Common applications of this model include:

• Customer service: Automates and personalizes chatbot interactions, reduces response time, enhances self-service options, assists agents by suggesting responses, and analyzes customer sentiment
• Marketing and content creation: Generates high-quality, context-aware content for blogs, ads, and social media; optimizes SEO strategies, personalizes marketing campaigns, assists in A/B testing for campaign performance, and more
• Research and science: Assists in literature reviews, generates hypotheses, summarizes complex scientific papers, etc.
• Business intelligence: Summarizes reports, analyzes trends, and extracts insights from large datasets
• Education and training: Enhances learning materials, provides tutoring assistance, creates personalized educational content, develops interactive learning experiences, and supports corporate training programs

Get to know more about ChatGPT by checking out 100 stats to know about ChatGPT.

LaMDA (Google AI)

Google AI’s LaMDA (Language Model for Dialogue Applications) focuses on conversation-driven AI, adept at maintaining context over long dialogue sessions and spanning a broad range of conversational topics with a natural, human-like flow. Its key benefits include:

• Enhanced conversational abilities
• More natural user interactions
• Content retention over extended dialogues
• Improved engagement in digital communications
• Applications in diverse business contexts

For businesses, LaMDA is a powerful tool for creating intuitive and seamless user experiences across various applications. LaMDA is commonly applied to:

• Chatbots: Powers intelligent, context-aware chatbots capable of handling complex customer inquiries and providing personalized responses
• Virtual assistants: Supports voice and text-based virtual assistants, enhancing their ability to perform tasks and engage in meaningful conversations with users
• Customer service interfaces: Offers real-time, automated support that feels more human-like
• Product and service inquiries: Provides real-time, dynamic product recommendations and answers specific customer questions during the buying journey

LLaMA 3 (Meta AI)

In our list of AI large language models, we cannot omit LLaMA from Meta AI , which is engineered for deep language analysis and capable of interpreting and processing intricate language structures and understanding subtle language nuances. Its strengths are:

• Processing complex language patterns
• Accurate sentiment analysis
• Efficient text summarization
• Multilingual proficiency

LLaMA 3 is praised for its versatility and adaptability across NLP tasks. Its applications include:

• Sentiment analysis tools: Provides accurate sentiment analysis for social media monitoring, customer feedback, and market research
• Text summarization platforms: Enhances content aggregation by summarizing large documents, news articles, and reports
• Advanced language interpretation systems: Powers real-time translations, multilingual chatbots, and virtual assistants
• Content moderation and monitoring: Detects and filters harmful, inappropriate, or non-compliant language

Bloom 2 (BigScience)

As an open-source, collaborative initiative, Bloom 2 is a multi-faceted language model that supports a wide array of languages and dialects, making it highly inclusive and versatile for global linguistic tasks. It’s notable for:

• Fostering innovation through collaboration
• Versatility in diverse language tasks
• Encouraging community-driven development
• Extensive multilingual support
• Open-source and community-driven
• Use of ethical AI

Built with transparency and accessibility in mind, BLOOM 2 is optimized for a wide range of NLP tasks, among which: 

• Academic research: Supports linguistic studies, computational research, and others
• Large-scale content generation: Assists in creating multilingual articles, reports, and marketing materials similar to other AI language models for writing
• Collaborative AI projects: Provides a flexible foundation for developing customized AI applications
• Machine translation and localization: Enhances translation accuracy and adapts content for different cultural and linguistic contexts

PaLM 2 (Google AI)

PaLM 2 by Google AI specializes in multilingual processing, demonstrating strong capabilities in handling a variety of languages and dialects with advanced language comprehension and generation techniques. With its enhanced reasoning capabilities and efficiency, the model delivers high-quality language comprehension at scale. Its best features are:

• Handling intricate language nuances
• Robust multilingual capabilities
• Scalability in language processing
• Enhanced reasoning and coding abilities

The PaLM 2 model focuses on multilingual understanding and reasoning and finds applications in:

• Translation services: Provides high-quality, context-aware translations for businesses
• Creative writing: Assists in generating compelling narratives, marketing copy, and content ideas
• Multilingual content creation: Helps businesses localize their messages for culturally diverse audiences
• Knowledge retrieval and summarization: Extracts insights from large datasets, condensing complex information into easy-to-digest summaries

Cohere Command R+ (Cohere)

Cohere Command R+ is an advanced retrieval-augmented generation (RAG) language model designed for high adaptability, enterprise-grade AI applications. It’s designed for flexibility and ease of integration and delivers contextually relevant and highly accurate responses. The model has to offer:

• Adaptable language processing
• Real-time RAG
• User-friendly AI integration
• Automated customer support and content moderation
• Tailored for enterprise needs

For businesses, Cohere Command R+ is commonly used in business tools for:

• Customer support automation: Powers AI-driven chatbots and virtual assistants that provide instant, personalized responses
• Content moderation: Identifies and filters harmful or inappropriate content in real time
• Knowledge management and enterprise search: Retrieves and synthesizes company-specific information from vast datasets, improving decision-making

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Claude 3 (Anthropic)

Optimized for instantaneous responses and interactions, Claude 3 is particularly suited for real-time conversational AI, providing rapid and context-aware responses in dialogues. It features:

• Seamless, responsive dialogue
• Real-time interaction capabilities
• Enhanced user experiences in virtual assistance
• Strong ethical alignment

This model emphasizes safety and interpretability, and it’s primarily applied to:

• Interactive chatbots: Powers AI chats that improve engagement and provide accurate, real-time assistance
• Virtual assistants: Supports business automation by handling scheduling, knowledge retrieval, and contextual recommendations
• Customer service platforms: Enhances support operations with AI-driven ticket resolution, intelligent routing, etc.
• Content creation and summarization: Generates high-quality, context-aware texts

Learn more about the difference between Claude vs. ChatGPT.

ERNIE 4.0 (Baidu)

As a multimodal language model, ERNIE 4.0 is unique in its capability to understand and generate both text and image data, making it suitable for a wide range of integrated multimodal applications. Its strengths are:

• Blending text and image understanding
• Versatility in visual-textual applications
• Applications in image captioning and virtual reality
• Enhanced reasoning and knowledge retrieval

ERNIE is Baidu’s most advanced AI language model, designed for deep understanding, reasoning, and multimodal capabilities. This model is ideal for:

• Visual-textual applications: Supports tasks like image captioning, video analysis, and multimodal search
• Content creation and copywriting: Generates high-quality, contextually relevant text for marketing, publishing, and advertising
• Enterprise AI solutions: Enhances customer service, intelligent document processing, and knowledge management

Gemini 2.0 (Google DeepMind)

Focused on creativity and narrative, Gemini 2.0 is designed to excel in generating imaginative and engaging text, making it ideal for creative storytelling and content creation. It leverages powerful deep learning techniques and processes both text and images. It’s notable for:

• Novel narrative creation
• Engaging storytelling capabilities
• Creative content for marketing and scriptwriting
• Context-aware creativity

Gemini 2 is among the most advanced LLMs today. It’s ideal for:

• Storytelling apps: Generates compelling narratives, blog posts, ad copy, and scripts
• Marketing content tools: Develops personalized campaigns and SEO-friendly content
• Scriptwriting software: Helps to write and refine scripts for movies, TV shows, and videos
• Business intelligence: Analyzes trends, extracts insights, and summarizes data

If you’re unsure where to start with using LLMs in your business environment, don’t hesitate to reach out to qualified AI and ML experts from Intelliarts.

Megatron-Turing NLG (NVIDIA and Microsoft)

Megatron-Turing NLG, a collaborative effort between NVIDIA and Microsoft, is notable for its ability to process and analyze language at a large scale, handling extensive datasets with complex linguistic structures. It is known for:

• Handling large-scale language tasks
• Advanced research capabilities
• Sophisticated data analysis

The model remains one of the most powerful AI models for natural language generation (NLG) tasks. It’s used predominantly in:

• High-level research: Supports academic and corporate research teams working on large-scale language models and advanced ML techniques
• Complex language processing applications: Used for complex decision-making systems, customer service automation, and knowledge management
• BI and analytics: Extracts actionable insights from vast amounts of unstructured data, making it valuable for organizations in data-heavy industries

Jurassic-1 Jumbo (AI21 Labs)

Tailored for handling large and complex datasets in language processing, Jurassic-1 Jumbo excels in managing extensive linguistic tasks, offering robust capabilities in large-scale language comprehension and generation. Its benefits are:

• An extensive knowledge base for content generation
• High-quality, accurate text output
• Application across various domains

Known for its scale and sophistication, the model is used in:

• Automated content generation: Powers content creation tools for blogs, articles, and marketing materials
• Data analysis platforms: Processes large datasets to extract insights, identify trends, and generate summaries
• Language translation and summarization: Provides translation services and summarizes complex documents

Grok 3 (xAI)

Known for its efficiency and ability to process vast amounts of data, Grok 3 is designed to deliver high-quality, real-time responses in complex conversational contexts. It works best for math, science, and coding tasks and offers enhanced performance as compared to its predecessors. The model benefits with:

• Real-time language processing
• Advanced conversational AI
• High scalability

With its impressive conversational abilities and scalability, Grok 3 is applied across a variety of business sectors:

• Enterprise automation: Supports automation in operations, helping businesses streamline workflows and reduce manual tasks
• Business intelligence: Analyzes large volumes of data to extract insights, trends, and actionable intelligence
• Personalized marketing: Generates tailored content for marketing campaigns

Interested in using a LLM in your project? Drop Intelliarts a line, and our engineers will propel you to success. 

Mistral 7B (Mistral AI)

Mistral 7B is a highly efficient open-weight language model known for versatility and high-performance text generation. With just 7 billion parameters, it balances computational efficiency with strong language understanding, which makes it a perfect choice for those looking for a compact yet powerful AI model. Among its strengths:

• Lower resource requirements yet efficiency
• Strong reasoning and language comprehension
• Open-weight model
• Optimized for real-time applications

With its combination of efficiency, adaptability, and strong performance, the model is recognized as the best open-source LLM for code generation. It’s also well-suited for:

• Code generation and assistance: Generates and refines code snippets, boosting productivity in software engineering
• Automated content generation: Assists in writing blogs, reports, and marketing copy with coherent and contextually relevant outputs
• Chatbots and virtual assistants: Improves response accuracy for customer service solutions while reducing computational costs

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Falcon (TII)

Developed by the Technology Innovation Institute (TII) in Abu Dhabi, Falcon is a high-performance open-source large language model built with a focus on optimized training and deployment. The model’s key strengths include:

• Highly efficient architecture
• Open-source accessibility
• High performance

Falcon is an excellent choice for businesses looking for an adaptable and efficient AI language model. It has a variety of business use cases, most important of which are:

• Customer support: Enhances chatbots and virtual assistants with accurate, real-time responses
• Enterprise automation: Supports document summarization and data extraction
• Code generation and completion: Assists in writing, debugging, and optimizing code across various programming languages
• Scientific research and data analysis: Processes large-scale datasets and generates insights for research institutions

Qwen (Alibaba)

Qwen is an enterprise-focused LLM with a strong emphasis on multilingual processing, business intelligence, and scalability. It has a robust performance and was built to handle business automation, multilingual processing, and complex enterprise workflows. This model offers:

• Multilingual capabilities
• Easy integration into enterprise ecosystems
• Industry-specific adaptability

This LLM is optimized for complex corporate workflows and has many business applications:

• Enterprise AI: Automates workflows and enhances decision-making
• Document automation: Assists in intelligent document processing, summarization, and contract analysis
• Customer service automation: Improves performance of chatbots and virtual assistants for real-time, multilingual support
• E-commerce support: Enhances product recommendations, chatbot assistance, and personalized shopping experiences

What are the benefits of using LLMs and their applications?

In 2025, large language model companies are driving a revolution in business operations. LLMs are more than just AI assistants — they’re critical tools for business growth and efficiency. Whether it’s scaling multilingual support, extracting insights from large datasets, or automating content creation, businesses can get lots of value from LLM-powered solutions. Examples are provided in the below infographic:

Now, let’s get to know what business value you get by adopting LLMs, using the example of their applications:

1. Operational efficiency

LLMs allow businesses to automate complex workflows, like content generation, data analysis, and document processing to increase efficiency and productivity. For example, they can generate coherent and contextually relevant text based on prompts or data input. This capability is used for creating articles, reports, stories, and even programming code.

Examples: GPT-4 powers automated customer service agents for Duolingo Max, responding to thousands of queries in different languages. Another example is content creation and automatic writing solutions such as Wordsmith by Automated Insights.

Check our article about AI development for lead generation to know more about AI use cases.

2. Cost reduction

Implementing AI automation with the largest language models reduces operational costs by minimizing the need for human intervention. Businesses can cut expenses on customer service teams by deploying chatbots or virtual assistants that handle inquiries efficiently. Similarly, automating tasks like document summarization and translation reduces the reliance on outsourced services, leading to long-term savings.

Examples: Cohere tells how they helped Notion deploy their model into their workspace search, which resulted in reduced operational costs (linked to embedding and storage), as well as complexity.

3. Better customer experience

LLMs improve customer experience by enabling faster, more natural AI-driven interactions across multiple touchpoints. Chatbots and virtual assistants powered by LLMs can provide instant, context-aware responses, reducing wait times and improving user satisfaction. These interactions are also more personalized because the model analyzes preferences and previous conversations.

Examples: H&M uses a virtual assistant built based on Google’s Bard model, which provides customers with personalized shopping recommendations and styling advice. Also, virtual assistants and chatbots such as Apple’s Siri or Amazon’s Alexa add up to the customer engagement a lot. 

Learn more about the role of natural language processing in business by reading another of our blog posts. 

4. Scalability for global expansion

LLMs equipped with multilingual capabilities are essential for businesses looking to expand globally without the need for a full local workforce. LLM-powered solutions can translate text between various languages, enabling businesses to engage in seamless communication with customers and partners across the globe. This helps a lot in business, travel, and online communication.

Examples: AirAsia has a chatbot based on Meta’s LLaMA 3, which offers customer service in more than 15 languages. Another example includes translation services like Google Translate, which supports over 100 languages and can power up e-commerce platforms and travel agencies.

5. Improved decision-making

With the ability to summarize research papers, extract key findings, and offer predictive insights, LLMs assist decision-makers in numerous industries. For instance, the model can help assess the sentiment of text data, such as determining whether social media posts or product reviews are positive, negative, or neutral. This is particularly useful for brands and organizations looking to gauge public opinion or customer satisfaction.

Examples: Pharmaceutical companies like Pfizer use ChatGPT to speed up drug discovery for safety-related table summarization in clinical study reports. Another example includes tools like Brandwatch that analyze social media sentiment and help brands understand public opinion and improve their marketing strategies.

6. Speech recognition

When combined with speech recognition software, LLMs can transcribe spoken language into text, making information accessible to a wider audience. This technology underpins voice-activated assistants, transcription services, and real-time closed captioning.

Examples: Speech-to-text applications like Google’s Live Transcribe and Dragon NaturallySpeaking. For example, businesses in healthcare and customer support can streamline workflows thanks to voice-to-text transcription apps.

7. Personalized content recommendations

By analyzing a user’s past behavior and preferences, LLMs can suggest relevant and personalized content. This technology powers recommendation engines on platforms like Netflix, Spotify, and news websites and helps to improve user engagement and drive higher conversion rates.

Examples: The recommendation algorithms used by Netflix to suggest movies and shows or Spotify to suggest music. Both enhance user satisfaction and increase engagement rates.

8. Language modeling

LLMs are trained on vast datasets to predict the probability of a sequence of words, which helps in correcting grammar, completing partial sentences, and improving the fluency of machine-generated text. This makes them ideal for writing assistance and improving the quality of both machine-generated and human-generated content.

Examples: Writing assistance tools such as Grammarly to improve quality of writing across industries like education, marketing, and corporate communications. This helps to ensure that all written content is clear, professional, and error-free.

How to choose the right LLM for your business

Selecting the best AI language model for your business requires careful evaluation to ensure it meets your operational and scalability needs. Here’s a five-step guide to make it right:

1. Define your business needs

As always, you begin with goals and objectives to determine what you need the LLM to accomplish — whether it’s automating customer support, generating marketing content, processing legal documents, or coding. Different models excel at different tasks. For example, Gemini 2 is well-suited for creative text generation, while Mistral 7B is optimized for coding.

2. Consider budgeting

The next step is to consider open-source vs. proprietary LLMs to align with your budget. Open-source models like LLaMA 3 and Falcon provide cost-effective flexibility, while proprietary solutions such as Claude 3 or Gemini 2 offer premium features and support.

3. Ensure API integration and compatibility

The chosen LLM must seamlessly integrate with your existing tech stack and tools. Write out all the solutions that you need your LLM to be compatible with, such as CRM software, internal knowledge bases, or customer support tools. Models with robust APIs, such as Cohere’s Command R+, simplify implementation.

4. Pay attention to scalability and performance

Your LLM should efficiently scale with your business needs. If you require multilingual capabilities for global operations, consider a model like Qwen. For real-time responses, a high-speed option such as Claude 3 might work just well. A good idea here is to test performance under real-world conditions to ensure your LLM meets demand.

“If earlier an employee had to look through tons of articles to find the necessary information, now the data is at their fingertips. The employee can access the right article in a few minutes and even summarize its key points if needed.” — Says the Intelliarts’ customer after we implemented the data extraction chatbot

5. Optimize security

Opt for the LLM that enhances customer experience but also don’t forget about security and compliance. If your business deals with sensitive data, such as financial records or healthcare data, choose a model that supports encryption and enterprise-grade security. For example, Claude 3 is known for its AI safety measures and is designed to prevent unauthorized data leaks and biases. Besides, PaLM 2 works fine for industries like finance and healthcare, featuring enterprise-grade security and compliance support.

What are the challenges of using LLMs?

As with any other technology, LLMs have their limitations, related to the nature of innovation and the way it’s utilized. Here are some of the common concerns and challenges of large language models and ways to address them:

1. Security and compliance

What’s this? GDPR violations, data leaks, and intellectual property (IP) risks remain major concerns when deploying LLMs. Unauthorized access or improper data handling can lead to legal consequences. 

Solutions: Prioritize enterprise-grade security with encryption, access controls, and compliance-ready models like Azure OpenAI or Claude 3. Conduct routine audits to ensure compliance with GDPR, CCPA, and industry regulations.

2. Data privacy

What’s this? LLMs might inadvertently expose sensitive or private information when generating text. 

Solutions: Employ solid data anonymization techniques before training models, implement strict access controls, and use differential privacy to ensure individual data points are not traced back to the source.

3. Bias and fairness

What’s this? Even the best LLM models can propagate biases present in the training data and generate harmful or offensive content.

Solutions: Implement regular technical audits to find and address biases in language models, train with diverse datasets, and create strict ethical guidelines with an oversight committee to keep the use of language models in check.

4. Model hallucinations

What’s this? LLMs can produce factually incorrect information, leading to the spread of false information.

Solutions: Integrate fact-checking protocols and source-verification tools into the model’s output processes, and provide clear disclaimers about the potential for inaccuracies. Also, encourage user feedback to help identify and correct misinformation.

5. Fine-tuning costs

What’s this? Customizing LLMs for niche industries like legal, finance, or healthcare requires expensive training on domain-specific data, making implementation costly. This is especially an issue for smaller businesses.

Solutions: Use parameter-efficient tuning methods, leverage open-source alternatives like LLaMA 3, and explore cloud-based AI services to reduce infrastructure costs.

6. Sustainability and energy consumption

What’s this? Running LLMs at scale can have a significant environmental impact due to high energy consumption.

Solutions: Advance in developing more energy-efficient machine learning algorithms by using models like Mistral 7B. Also, invest in renewable energy sources for data centers, adopt serverless AI solutions,  and improve the energy efficiency of the hardware used.

Read also: AI in the renewable energy market

7. Lack of common sense

What’s this? LLMs often struggle with common-sense reasoning, leading to nonsensical or incorrect responses.

Solutions: Incorporate hybrid models that combine machine learning with rule-based systems, improve the dataset to cover more common-sense scenarios, and continually update the model with new data that fills the common-sense gap.

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Cost-effective LLMs for businesses

Selecting the right LLM also involves balancing performance with development costs. Here are some models offering optimal value:

• DeepSeek R1: This Chinese startup offers competitive pricing, making advanced AI accessible to a broader audience. The DeepSeek-R1 is 20 to 50 times cheaper as compared to OpenAI o1 model, depending on the task.
• Anthropic’s Claude 3.7 Sonnet: Known for its hybrid reasoning capabilities, this model is priced at $3 per million input tokens and $15 per million output tokens, providing a cost-effective solution for complex tasks. This can be compared to OpenAI’s prices again $15 and $60, respectively.
• OpenAI’s GPT-3.5: ChatGPT is still considered a budget-friendly option suitable for various applications, offering a balance between performance and cost.
• Google’s Gemini Flash Lite: Designed for simple, low-cost applications, this model offers an affordable entry point for businesses exploring AI solutions.

For a comprehensive comparison of LLM pricing, tools like the LLM API Pricing Calculator can assist in estimating costs based on specific usage needs.

Intelliarts’ experience

At Intelliarts, we apply LLMs to solve real-world challenges across multiple industries, from renewable energy to customer service to finances and legal. Our expertise includes developing AI-powered tools for:

Document data extraction

We developed a GPT-4-driven chatbot for an NGO focused on gun control and against gun violence, enabling quick and accurate data extraction from their extensive knowledge base. This tool reduced data retrieval time from hours to minutes, improving the organization’s ability to access critical information efficiently.

Customer service automation

For a cybersecurity education provider, we built an AI-driven customer service assistant to automate responses to student inquiries. This LLM-based chatbot streamlined the support process by providing real-time, precise answers to user questions in case they got stuck at the practical assignment.

PoC development

Intelliarts partnered with a legal firm to build a PoC that used ChatGPT for inmate classification in federal criminal matters. The solution aimed to simplify and speed up the process of filling out inmate security designation reports, potentially transforming legal workflows.

Go deeper into AI PoC development with our latest article. 

Process optimization

We helped a startup create an AI solution for contract analysis for small- and medium-sized businesses to optimize shipping contract discounts. By analyzing contract terms, this solution recommended the most cost-effective shipping options, offering businesses significant savings opportunities.

These are just a few examples of our success stories. Intelliarts also specializes in creating systems for Retrieval Augmented Generation (RAG), text summarization, and question-answering solutions that improve data accessibility and decision-making.

Final take

As we’ve explored, the potential of LLM is both diverse and valuable, offering 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 appropriate LLM 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.

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