Production RAG System Over Your Documents

About this Gig

You get a working RAG system over your own documents that returns the right answer with a source you can click, not a confident guess. I ingest your files (PDF, Word, scanned docs, SharePoint, web), chunk and embed them, and build hybrid retrieval (vector plus keyword) on Weaviate, Qdrant, or pgvector. I tune chunking, embeddings, and query rewriting so retrieval actually surfaces the right passage, then wire it to an LLM with citations and guardrails against hallucination. You get a FastAPI service, a clean API or chat UI, and a measured quality baseline using RAGAS and DeepEval so accuracy is proven, not assumed. This is the same stack I used to ship hybrid RAG over a 600k+ document knowledge base in production and a secure RAG chatbot for a large South African bank. Delivered, deployed, and documented so your team can run it. This is a scoped, milestone-based engagement that starts with a short discovery call, with the final timeline confirmed after scoping. For a production RAG system that is typically around 6 to 8 weeks.

Requirements

A sample of the documents you want searchable (or a description plus volume and formats), the kinds of questions your users will ask, where it should live (your cloud or mine), and any accuracy, latency, or compliance requirements. A short call to align on scope helps.

Related Tags