RAG Systems
When an LLM needs to answer from documents, product data, or internal knowledge, the quality of retrieval and response policy matters more than model hype. I build grounded systems that can explain what they used, refuse when context is weak, and stay maintainable as the corpus grows.
AI-powered speech analytics platform that scans recorded calls for keywords, phrases, sentiment and conversational patterns, then transcribes and translates key moments.
Production-grade deep research agent that plans, fetches, and writes structured reports from URLs and documents with strict guardrails.
DocOps automation agent for production-grade document ingestion, grounded Q&A with citations/refusals, and an audit harness for evaluation.
Retrieval quality first. Prompting matters, but weak chunking, bad metadata, or no refusal policy will break a RAG product long before prompt wording becomes the main issue.
Yes. I can improve retrieval and answer behavior on top of an existing vector database and ingestion flow, or rebuild the weak parts if the current design is the bottleneck.