Build a RAG AI That Answers Questions From Your Documents

Your documents, PDFs, manuals, and databases contain valuable knowledge — but searching through them manually wastes hours. I will build you a RAG (Retrieval-Augmented Generation) system that lets your users query your knowledge base using natural language and get accurate, sourced answers instantly. What you get: Document ingestion pipeline (PDF, DOCX, TXT, web pages), vector embeddings using OpenAI or open-source models, vector database setup (Pinecone, ChromaDB, or Weaviate), LLM integration (GPT-4o, Claude, or Llama), chat interface or API endpoint, source citation in every response, and deployment-ready code. Perfect for: internal knowledge bases, customer support bots, legal document Q&A, medical record search, and product documentation assistants. Message me with your use case.