I will build a production ready rag system

Most RAG demos work once in a notebook and fall apart on real documents. I build retrieval systems that hold up: accurate answers on messy data, low latency, and clean code you can maintain.

I have shipped RAG in production, including a taxonomy-driven retrieval design that cut query latency by roughly 3 to 5 times over a naive setup. You get the architecture, the retrieval logic, and a service that runs, not a fragile wrapper.

What I deliver:

• Document ingestion, chunking, and embedding tuned to your data
• Vector search (Qdrant, FAISS, or your DB) with hybrid retrieval where it helps
• FastAPI service, Dockerized, with sane logging
• Clear handoff docs so your team owns it fully

I keep my own reusable tooling and frameworks. Your project-specific code and data are yours; my pre-existing libraries stay licensed for reuse.

Message me with your use case before ordering so I can scope it right.