LLM Development Services

What is LLM Development?

LLM (Large Language Model) development is the process of building, training, fine-tuning, or integrating AI models that can understand, generate, and reason with human language. These models power applications like chatbots, content generation, code assistants, document analysis, and intelligent automation systems.

Unihox specializes in helping enterprises leverage LLMs like GPT-4, Claude, Llama, and Mistral to build custom AI solutions. Whether you need a customer service chatbot, document processing system, or a domain-specific AI assistant, our team delivers production-ready solutions.

Our LLM Development Services

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Custom LLM Training

Train custom language models on your proprietary data for domain-specific AI that understands your business terminology and processes.

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Model Fine-Tuning

Fine-tune GPT-4, Llama, Mistral, or other models on your data to improve accuracy, reduce costs, and create specialized AI assistants.

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Prompt Engineering

Design and optimize prompts to maximize LLM performance. We create prompt templates, chains, and evaluation frameworks.

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LangChain Development

Build complex LLM applications using LangChain framework - agents, chains, memory systems, and tool integrations.

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API Integration

Integrate OpenAI, Anthropic, Google, or other LLM APIs into your existing applications with proper error handling and optimization.

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Enterprise Deployment

Deploy LLM solutions on-premise or cloud with security, scalability, monitoring, and compliance requirements.

LLM Use Cases

Customer Service Automation

AI chatbots that handle customer queries, resolve issues, and escalate complex cases to human agents.

Document Processing

Extract information from contracts, invoices, reports, and unstructured documents with high accuracy.

Code Generation & Review

AI assistants that write code, review pull requests, and help developers be more productive.

Content Generation

Generate marketing copy, product descriptions, blog posts, and personalized communications at scale.

Knowledge Management

Q&A systems over internal documentation, wikis, and knowledge bases for instant information retrieval.

Data Analysis & Insights

Natural language interfaces to databases, analytics, and business intelligence systems.

Frequently Asked Questions

What is LLM development?

LLM (Large Language Model) development involves building, training, or fine-tuning AI models that understand and generate human-like text. This includes custom model training, integrating existing models like GPT-4, Claude, or Llama, and developing applications that leverage these models for specific business use cases.

Which LLM models does Unihox work with?

Unihox works with all major LLM providers including OpenAI (GPT-4, GPT-4 Turbo), Anthropic (Claude 3), Meta (Llama 2, Llama 3), Google (Gemini, PaLM), Mistral AI, and open-source models. We help you choose the right model based on your requirements for accuracy, cost, speed, and data privacy.

What is the cost of LLM development?

LLM development costs vary based on project scope. Simple GPT-4 integrations start at $5,000-15,000. Custom fine-tuning projects range from $15,000-50,000. Enterprise LLM solutions with custom training can cost $50,000-200,000+. Contact us for a detailed quote based on your specific requirements.

How long does LLM development take?

Development timelines depend on complexity. Basic LLM integrations take 2-4 weeks. Fine-tuning existing models requires 4-8 weeks. Custom enterprise LLM solutions typically take 3-6 months. We provide detailed project timelines during consultation.

Can Unihox help with LLM fine-tuning?

Yes, Unihox specializes in LLM fine-tuning. We can fine-tune models like GPT-4, Llama, and Mistral on your proprietary data to create domain-specific AI models that understand your industry terminology, follow your brand voice, and deliver more accurate responses for your use case.

What is the difference between LLM and RAG?

LLM (Large Language Model) is the AI model itself that generates text. RAG (Retrieval-Augmented Generation) is an architecture that combines LLMs with external knowledge retrieval. RAG allows LLMs to access current, domain-specific information without retraining, making responses more accurate and reducing hallucinations.