I build Retrieval-Augmented Generation (RAG) systems that let applications answer questions using your own data — accurately and with context.

What I offer:

RAG pipeline design — document ingestion, chunking, embeddings, and vector search

Vector database setup — FAISS or similar, optimized for fast similarity search

LLM integration — connecting retrieval systems to models like Gemini for accurate, grounded responses

Custom Q&A / chatbot systems — built on your codebase, documents, or knowledge base

I built AskRAG, a tool that lets developers ask natural-language questions about any codebase and get accurate, context-aware answers — using FAISS for vector storage, sentence-transformer embeddings, and Gemini for generation. I understand both the ML side (embeddings, retrieval) and the engineering side (APIs, auth, deployment) needed to ship a working RAG system, not just a notebook demo.

If you want to add "chat with your data" functionality to your product, I can design and build it end-to-end.