Retrieval that works
RAG Pipeline Consulting & Development
Demos are easy; production RAG that stays accurate is not. I build retrieval pipelines with real evaluation, grounding, and guardrails - so answers are trustworthy, not just plausible.
What's included
Production RAG, not a notebook demo
Scope
- Chunking, embedding & vector store design (Pinecone, FAISS, Chroma, pgvector)
- Hybrid & re-ranked retrieval, query rewriting
- Grounding, citations & hallucination guardrails
- Evaluation harness & observability (Langfuse)
How it works
Engagement
- Retrieval quality baseline on your data
- Iterative pipeline design with eval gates
- Production deployment & monitoring
- Handover & team enablement
Proof
- FAISS-backed RAG pipeline serving live users on a careers platform
- NLP pipeline over 1M+ documents at 99% accuracy
- Deep knowledge-graph and retrieval work on a code-analysis platform
FAQ
Common Questions
Should I use RAG or fine-tuning?
Usually RAG first: it keeps knowledge fresh, is cheaper to update, and gives you citations. Fine-tuning is for style, format, or narrow tasks where the behavior - not the facts - needs to change. Many production systems combine both.
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