I build RAG chatbots that answer questions from YOUR content — documentation, PDFs, knowledge bases, policies — with cited sources, instead of making things up.
SEE IT WORKING FIRST
A live demo is on my profile: a chatbot that answers from a document knowledge base with inline citations, per-source relevance scores, and an honest "not in the knowledge base" refusal when the answer isn't there. That refusal behaviour is the difference between a RAG system you can trust and a liability.
WHAT YOU GET
• A chatbot trained on your documents — website docs, PDFs, Notion, help center, internal wikis
• Grounded answers with source citations, so users can verify every claim
• Honest refusal on out-of-scope questions instead of confident guessing
• Chat widget for your site, a standalone app, or an API for your product
• Deployed and handed over — hosting, docs, and a walkthrough included
HOW IT WORKS
Ingestion → chunking → embeddings → vector search with a relevance threshold → grounded answer with citations. Built with OpenAI / Gemini / Claude, LangChain where it fits, pgvector or Pinecone, on Next.js or FastAPI.
TYPICAL PROJECTS
Docs Q&A bot · customer-support deflection bot · internal knowledge assistant · "chat with your PDFs" · AI-powered search for your site.
WHY ME
Full-stack senior engineer — you get the whole product around the AI (auth, UI, database, deployment), not a notebook. Clear written communication, weekly demos, fixed-scope friendly.
Send me a message with what your bot should answer from, and I'll reply with a plan, a timeline, and a fixed quote. Small paid pilots available.