Build an AI chatbot with RAG integration for our custom business knowledge base

We're a small startup looking to build an AI-powered chatbot that can answer questions based on our own internal business data. We need someone who understands RAG (Retrieval-Augmented Generation), vector databases, and LLM integration to build this from scratch and deploy it to production. WHAT WE NEED A working AI chatbot trained on our custom business documents and data RAG pipeline using a vector database (Pinecone, Weaviate, or similar) for accurate, context-aware responses LLM integration using OpenAI API or Gemini with proper prompt engineering A clean REST API (FastAPI or Node.js) to power the chatbot backend A simple frontend interface built in React or Next.js to interact with the bot Deployed via Docker on a cloud platform, fully production-ready Clean, well-documented code that's easy to maintain and scale TECH STACK WE PREFER Python · FastAPI · LangChain · OpenAI API · vector database · React or Next.js · PostgreSQL · Docker · cloud deployment YOU'RE THE RIGHT FIT IF YOU Have hands-on experience building RAG systems and AI chatbots for real production use Can handle the project end-to-end — backend, AI integration, frontend, and deployment Have worked with LangChain, OpenAI API, or Gemini and understand LLM prompt engineering Value clean architecture and long-term maintainability over quick fixes Communicate clearly and deliver with defined milestones TO APPLY, PLEASE SHARE A similar RAG or AI chatbot project you've built (link or description) Your preferred tech stack for this type of project Your estimated timeline for delivery