RAG Knowledge Assistant

Teams kept asking the same questions that were already answered somewhere in their docs, tickets and wikis. We built a retrieval-augmented assistant that ingests those sources, embeds them into a vector store, and answers in natural language — with citations back to the source so nothing is a black box.

The pipeline handles document chunking, embedding, hybrid retrieval and re-ranking, then grounds the model strictly in retrieved context to keep answers accurate. An evaluation suite and guardrails catch hallucinations and regressions before they reach users, and usage is monitored for both quality and cost.