Can you work with our existing codebase?

Yes. We regularly integrate LLM capabilities into existing applications — whether it's adding AI features to a SaaS product, automating workflows, or building a new AI layer on top of your current stack.

LLM Development

Custom LLM Development Services

We build production-grade LLM systems — not demos. From GPT-4 integration to open-source fine-tuning, we architect LLM solutions that work at scale.

From $15,000· 4–18 weeks

Get a Fixed LLM Development Proposal in 72 Hours → See Case Studies

Capabilities

What We Build: LLM Systems for Production

LLM API Integration (OpenAI, Anthropic, Cohere)

Open-Source LLM Fine-Tuning (Llama, Mistral, Falcon)

LLM Orchestration with LangChain & LlamaIndex

Prompt Engineering & Optimization

LLM Evaluation & Testing Pipelines

Choosing the Right Approach

LLM Development vs RAG: Which Do You Need?

LLM Integration

Connect a pre-trained model (GPT-4, Claude) to your application via API. Fastest path to production. Best for general-purpose tasks like summarization, classification, and content generation.

Best when: You need AI features fast and your use case is general

RAG Pipeline

Add a retrieval layer so the LLM can answer questions from your proprietary data — documents, databases, knowledge bases. The model stays general but gains access to your specific information.

Best when: The LLM needs to know your business data

Fine-Tuning

Retrain an open-source model on your dataset to change its behavior, tone, or domain expertise. Higher upfront cost but lower per-query cost at scale and fully customized outputs.

Best when: You need specialized behavior at high volume

See our RAG pipeline development services →

Tech Stack

LLM Technology Stack

OpenAI GPT-4oAnthropic ClaudeLlama 3MistralLangChainLlamaIndexPythonFastAPICohereHugging Face

Process

Our LLM Development Process

Discovery

We analyze your use case, data, and existing systems to recommend the right LLM approach — integration, RAG, or fine-tuning.

Scoping

Detailed technical proposal with architecture diagram, model selection rationale, timeline, and fixed-scope pricing within 72 hours.

Build

Senior engineers build iteratively with weekly demos. Prompt engineering, model evaluation, and integration testing throughout.

Handoff

Production deployment, monitoring dashboards, cost optimization, documentation, and optional ongoing support.

Investment

LLM Development Cost & Timeline

All projects are fixed-scope — the price you agree to is the price you pay. No hourly billing, no scope creep.

Simple LLM Integration

$15K – $30K

4–6 weeks

Connect GPT-4 or Claude to your app via API. Includes prompt engineering, error handling, streaming responses, and production deployment.

Custom RAG System

$30K – $80K

8–14 weeks

Full RAG pipeline with document ingestion, vector database, retrieval optimization, and LLM integration. Your AI answers from your data.

Fine-Tuned Model

$40K – $100K

10–18 weeks

Fine-tune Llama, Mistral, or other open-source models on your dataset. Includes data preparation, training, evaluation, and deployment infrastructure.

Frequently Asked Questions

What LLM models do you work with?

We work with OpenAI GPT-4o, Anthropic Claude 3.5, Meta Llama 3, Mistral, Cohere, and other open-source models. We can also fine-tune models on your proprietary data for specialized use cases.

What's the difference between LLM integration, RAG, and fine-tuning?

LLM integration connects pre-trained models to your app via API — fastest and cheapest. RAG adds a retrieval layer so the LLM can answer from your proprietary data. Fine-tuning retrains a model on your dataset for specialized behavior. We help you choose the right approach based on your use case, data, and budget.

How much does custom LLM development cost?

Simple LLM integrations start at $15K–$30K (4–6 weeks). Custom RAG systems run $30K–$80K (8–14 weeks). Fine-tuned models cost $40K–$100K (10–18 weeks). All projects are fixed-scope with no overruns.

Can you integrate LLMs into our existing application?

Yes. We regularly add LLM capabilities to existing SaaS products, internal tools, and enterprise systems. Whether you need AI-powered search, document processing, chatbots, or workflow automation — we integrate into your current stack without rebuilding.

Related Case Study

LLM / Document Intelligence

LLM-Powered Document Intelligence

Built an LLM pipeline with OCR, classification, extraction, and validation — replacing 40+ hours/week of manual document review per analyst.

$200K/year saved · 95% accuracy

View Case Study

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Tell us your use case. We'll send a fixed-scope proposal with architecture, timeline, and pricing in 72 hours.

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