Generative AI & LLM Integration

What we build

We integrate leading large language models including GPT-4o, Claude, Gemini, Mistral, and Llama into your products, workflows, and internal tools. We also design RAG pipelines, build prompt systems, and fine-tune models to understand your business as well as your best employee. Whether you are building a customer-facing AI product or automating internal knowledge work, we architect the integration so it is accurate, fast, secure, and easy to maintain.

01 GPT-4o, Claude, Gemini, and Llama integration

02 RAG pipeline design and vector database setup

03 LLM fine-tuning on private and domain-specific data

04 Prompt engineering and system prompt design

05 AI chatbots and conversational interfaces

06 Structured output and function calling

07 Semantic search and AI knowledge bases

08 Multi-modal AI combining text, image, and document processing

09 Secure and compliant LLM deployment

How we work

Every generative ai and llm integration engagement follows the same disciplined process. No surprises, no scope creep.

Step 1:  Use case definition and model selection

We identify exactly what you need the LLM to do and select the right model for accuracy, cost, and latency. Not every problem needs GPT-4o. We match the model to the task.

Step 2: Data preparation and RAG architecture

If your use case requires the model to work with your own documents, data, or knowledge base, we design and build the RAG pipeline that connects it all.

Step 3: Prompt engineering and system design

We engineer the prompts, system instructions, and output formats that make the model behave exactly as needed in your application.

Step 4: Integration and security implementation

We connect the LLM to your product or internal tools via API and implement the authentication, access control, and data handling policies your compliance requirements demand.

Step 5: Testing, evaluation, and deployment

We evaluate output quality rigorously before launch and set up logging, monitoring, and feedback loops so you can track and improve performance over time.

Technologies we use

We choose the right tool for the job, not the trendiest one.

• OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini 1.5 Pro, Mistral Large, Meta Llama 3

• LangChain and LlamaIndex for orchestration

• Vector databases: Pinecone, Weaviate, Chroma, pgvector, Qdrant

• Embedding models: OpenAI text-embedding, Cohere Embed, sentence-transformers

• Document processing: Unstructured.io, LlamaParse, PyMuPDF

• Fine-tuning: OpenAI fine-tuning API, Hugging Face PEFT, QLoRA

• Deployment: FastAPI, AWS Lambda, Google Cloud Run, Azure Functions

Who this is for

• Product companies wanting to add AI features without building from scratch

• Internal teams whose work involves reading, writing, classifying, or summarizing large volumes of text

• Companies with large document libraries or knowledge bases that need to become searchable and interactive

• Customer support teams looking to deploy intelligent self-service AI without hallucination risk

• Any business that has experimented with ChatGPT internally and wants to build something proper around it

Results you can expect

Days not months: A well-architected LLM integration can go from zero to production in 2 to 4 weeks.

Accuracy you can trust: RAG and fine-tuning eliminate the hallucination and irrelevance problems you get from vanilla LLM prompting.

Works on your data: The model understands your documents, your terminology, and your business context, not just generic internet knowledge.

Scales with demand: Serverless LLM deployments scale automatically and cost only what you use.

An LLM that knows your business, speaks your language, and works inside your systems is a completely different tool from a generic chatbot.