Tired of AI chatbots that hallucinate, leak data, or give generic answers? I build production-grade RAG (Retrieval-Augmented Generation) systems and AI chatbots grounded in YOUR documents — with proper citations, access control, and multi-tenant isolation.
What I deliver:• Document ingestion pipelines (PDF, DOCX, TXT, web pages, Notion, Google Drive) • Vector database setup (ChromaDB, Pinecone, Weaviate, pgvector) • OpenAI / Anthropic / open-source LLM integration with proper prompt engineering • Embeddable chat widgets (React, vanilla JS) for your existing site • Multi-tenant workspaces with per-user chat history • Admin dashboard for document management & user access • Citation system showing source documents for every answer
My approach:I don't just wrap GPT in a UI. I build the full retrieval pipeline, evaluate accuracy on YOUR documents before delivery, and harden it for production (rate limits, auth, error handling, monitoring, cost controls).
Stack: Python (FastAPI) · Node.js · React / Next.js · ChromaDB · Pinecone · OpenAI API · LangChain · JWT / OAuth · AWS · Docker
Recent project — RAG Workspace: Multi-tenant document intelligence platform serving HR, ops, and compliance teams. Per-workspace document isolation, role-based admin console, per-user chat threads, full citation system.
Best fit for: SaaS companies adding "chat with your docs" features · internal knowledge bases for HR/compliance · customer-support deflection bots · document-heavy industries (legal, finance, real estate, education).
What you get: Production-deployed system on your cloud account · documented codebase you fully own · Loom walkthrough · 14 days post-launch bug-fix support.