RAG Knowledge Assistant
Natural language querying over complex SQL data
§ overview
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 queries required a developer, creating a bottleneck that slowed down day-to-day decisions. They needed anyone on the team to get answers, instantly.
§ what we built
- →Natural language to SQL translation with schema-aware context injection for accurate queries
- →Pinecone vector store for semantic retrieval of relevant schema context and documentation
- →LangChain orchestration with conversational memory and multi-turn follow-up handling
- →Guardrails to prevent destructive queries and enforce read-only access at all times
- →Confidence scoring and source attribution on every response
- →Clean React chat interface accessible to non-technical users without SQL knowledge
§ results
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.
§ tech stack
Have a use case for AI agents or RAG?
We build retrieval-augmented systems that make your data actually useful — for your team, your customers, or both.