Retrieval-first support agent grounded in real docs

Challenge

A growing support function needed first-line automation that could answer product questions accurately without hallucinating. An earlier off-the-shelf chatbot had eroded trust by inventing answers, so the bar for the new system was retrieval grounded in real documentation.

Solution

We designed a RAG pipeline with hierarchical chunking, hybrid retrieval, and a re-ranking step before generation. The agent cites the source it used, hands off to a human on low confidence, and writes back every interaction for evaluation.

Architecture

FastAPI orchestrates retrieval, re-ranking, and generation. Vector storage holds embedded documentation, changelogs, and resolved tickets. Helpdesk integration handles escalation. A daily eval job samples conversations and flags regressions for human review.

Stack

Key Metrics

Outcomes

• Replaced a hallucination-prone chatbot with a retrieval-grounded agent
• Cut typical first-response time from hours to seconds for in-scope questions
• Freed the support team to focus on complex, non-routine cases
• Built an evaluation loop the team owns: every answer is reviewable