Definition · AI in finance
Text-to-SQL
Text-to-sql is converting a natural language question into an executable SQL query. For text-to-sql, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Also known as natural language to SQL, NL2SQL
Why it matters
Understanding text-to-sql matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo can show the SQL behind a number, but resolves questions through a governed semantic model so figures reconcile across teams rather than depending on ad hoc queries.
In practice
Governance example
Teams use text-to-sql when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.
Pluvo example
Pluvo can show the SQL behind a number, but resolves questions through a governed semantic model so figures reconcile across teams rather than depending on ad hoc queries.
In practice, teams should define text-to-sql with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding text-to-sql matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo can show the SQL behind a number, but resolves questions through a governed semantic model so figures reconcile across teams rather than depending on ad hoc queries.
A strong workflow for text-to-sql separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Pluvo can show the SQL behind a number, but resolves questions through a governed semantic model so figures reconcile across teams rather than depending on ad hoc queries.
FAQ
What is text-to-SQL?
Text-to-sql is converting a natural language question into an executable SQL query. For text-to-sql, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
How accurate is text-to-SQL on real databases?
Measure text-to-sql against a defined standard: agreed source data, expected output, review threshold, and owner. That makes the answer testable instead of relying on whether the result merely looks plausible.