I will build custom rag agentic systems for your data

By restricting its search space. I build custom RAG pipelines that force the LLM to only answer using the exact snippets we retrieve from your database. If the answer is not in your documents, the model is programmed to simply say it does not know.

Almost anything your business uses daily. I write TypeScript parsers to clean and extract text from PDFs, spreadsheets, CSVs, live API feeds, or raw SQL databases so the AI can search through them accurately.

A basic wrapper just sends a prompt directly to an LLM, which leads to hallucinations and leaked data. A custom RAG system uses a dedicated pipeline to search your files first, extracts the most relevant context, and feeds only that secure data to the model for an accurate response.

I highly recommend PostgreSQL with the Supabase pgvector extension because it lets you keep your relational data and your vector embeddings in the same place. If your project scale requires a dedicated external vector database, I also work natively with Pinecone and FAISS.

Instead of letting the LLM freely decide what to do next, I build structured state machines using LangGraph and LangChain. This forces the AI agents to follow strict rules, validation steps, and fallback paths, keeping their behavior deterministic.

Yes. I write the core backend in Node.js, TypeScript, or Python, and then build a fast, minimal user interface using React and Next.js. This gives your team a clean web dashboard to interact with the RAG chatbot daily.

I build your application using open-source tools like PostgreSQL and LangChain to avoid costly third-party platform subscriptions. You only pay for your actual cloud hosting and your direct LLM token usage.

Yes. I write custom API endpoints using TypeScript and Node.js. This allows your autonomous AI agents to trigger actions, update records, and query data inside your current CRM, ERP, or internal databases.