RAG Knowledge Assistant

The Challenge

The client had years of operational data locked inside complex SQL databases — valuable for business decisions, but completely inaccessible to non-technical team members. Writing SQL queries required a developer, creating a bottleneck that slowed down day-to-day decisions. They needed a way for anyone on the team to ask questions and get answers, instantly.

What We Built

A RAG-based conversational assistant that sits in front of the client's SQL database and lets any team member query it in plain English. The system translates natural language questions into accurate SQL queries, retrieves the right data, and responds in clear, actionable language — with full conversational memory across a session.

• →Natural language to SQL translation with schema-aware context injection
• →Pinecone vector store for semantic retrieval of relevant schema context
• →LangChain orchestration with conversational memory and follow-up handling
• →Guardrails to prevent destructive queries and enforce read-only access
• →Confidence scoring and source attribution on every response
• →Clean React chat interface accessible to non-technical users

The Result

The assistant is now used daily by non-technical team members to make data-driven decisions without waiting on a developer. What previously required writing SQL or submitting a request to the data team now takes seconds in a chat interface. This is the core value of well-built RAG systems: invisible AI that makes the people using it genuinely more capable.