RAG architecture
Design retrieval pipelines, chunking, metadata, embeddings, search, reranking and generation flows.
RAG DEVELOPMENT
Build AI that answers from
your real knowledge.
I design and build retrieval-augmented generation systems that connect language models to your documents, databases and knowledge sources so answers are grounded in information you control.
Build a RAG system →What I can build
Document ingestion
Process PDFs, websites, databases, knowledge bases and internal content into retrieval-ready data.
Hybrid retrieval
Combine semantic vector search with keyword, metadata and structured filtering when accuracy matters.
Reranking & relevance
Improve retrieval quality with rerankers, query rewriting and relevance evaluation.
Grounded responses
Add citations, provenance, confidence controls and structured answer formats.
RAG evaluation
Measure retrieval quality, answer relevance, faithfulness and failure modes before production.
Bring the problem, data or current prototype.
I can help scope the right architecture and turn it into a production-ready implementation.
Discuss your project →