Project
Natural language data querying
- Role
- AI Engineer
- Context
- Global Hitss
- Stack
A chatbot that writes the SQL: whoever has the question stops depending on whoever can query.
The problem
The data that answered management questions lived in the database, and the only language that reached it was SQL. The people with the questions did not write the queries, and the people writing the queries were not the ones asking.
Turning a question into SQL is easy to describe and hard to guarantee: a wrong query does not fail, it returns a plausible number. And the user who cannot read SQL also has no way to check the SQL that was generated for them.
The solution
A chatbot that takes the question in natural language, generates the query against the client schema, runs it on the database and returns the result as written prose, with charts and filters requested in the conversation itself.
How a query moves through the system
- 01
Question
in natural language, with no table names and no syntax.
- 02
Schema
the ER diagram and the knowledge base enter as model context.
- 03
SQL
the question becomes a query over tables that actually exist.
- 04
Execution
the query runs on the database and returns rows, not text.
- 05
Answer
the result goes back to the model and comes out as prose, or as a chart when asked.