AI Data Analyst Bot
Text-to-SQL and RAG for business-facing data access
The challenge
Business users want answers, not dashboards and table schemas. But AI access to data only works when retrieval, query generation, and governance are designed carefully. This project shows that AI analytics products still depend on solid data engineering.
How we solved it
- - Route requests between Text-to-SQL and RAG flows
- - Use retrieval to ground document-based answers
- - Use warehouse querying for structured analytical questions
- - Expose the experience through a lightweight application interface
Execution story
The application decides whether the user needs SQL generation or knowledge retrieval, then responds through a grounded workflow instead of a single opaque prompt.
AI without weak foundations
This project is useful on the site because it demonstrates an important point for the market: AI interfaces only create trust when the data platform underneath is reliable.
Why it matters for the publishing cycle
The topic performs well across GitHub, site content, and LinkedIn because it naturally connects engineering depth with a broader business audience.