Build AI-Powered Document Q&A Module with RAG Pipeline for SaaS Platform

We're building a multi-tenant SaaS platform and need to integrate an AI-powered Q&A module that lets users upload documents and ask questions against them using a RAG (Retrieval-Augmented Generation) architecture. This is a focused, well-scoped task for someone who has done this before — not a learning project. What needs to be built: Document ingestion pipeline — PDF/text upload, chunking, embedding generation via OpenAI Vector store integration (Qdrant or Pinecone) for semantic retrieval RAG query flow — retrieve relevant chunks → pass to LLM → return grounded answer Basic REST API endpoint (/ask) that accepts a question + tenant/document context Simple frontend widget (React) that renders the chat interface inside the existing SaaS dashboard Per-tenant data isolation so one user's documents don't bleed into another's Stack we're already using: Next.js (frontend), Node.js/NestJS (backend), PostgreSQL, Docker OpenAI API for embeddings + completions What we're NOT asking for: A full SaaS build from scratch UI design or branding DevOps or deployment (we handle this) You're a good fit if you: Have shipped a RAG system before (not just tutorials) Understand multi-tenancy at the data layer, not just auth Can deliver clean, documented API code I can hand off to my team Communicate clearly and flag blockers early Deliverables: Working RAG pipeline (backend) with /ask API endpoint React chat widget integrated into a Next.js layout Basic README with setup instructions Short Loom walkthrough of how it works Budget: $30 fixed Timeline: 5–7 days Location preference: Open (async communication is fine) To apply, please answer: Have you built a RAG system before? Share a brief description or repo. Which vector DB would you use here and why? How would you handle per-tenant document isolation? Applications without answers to these questions will not be reviewed.