I will build a custom rag chatbot for you

I build production-ready RAG (Retrieval-Augmented Generation) systems that let your chatbot answer questions from YOUR data docs, PDFs, website content, knowledge bases instead of generic AI responses.

Stack: Gemini API for generation, Qdrant for vector search, FastAPI/Next.js for the backend/frontend.

What you get:

- Document ingestion pipeline (PDF, docs, web scraping your source format)

- Vector embeddings + semantic search via Qdrant

- Chatbot interface (web widget or API endpoint)

- Source citation so answers are traceable, not hallucinated

- Deployment-ready code (FastAPI backend, optional Next.js frontend)

I've built this exact system for research automation and internal knowledge tools I know where RAG pipelines break (chunking strategy, retrieval precision, context window limits) and how to avoid it.