Definition · AI in finance
Natural language query
Natural language query is asking data questions in plain language and getting structured answers. For natural language query, 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 NLQ, natural language querying
Why it matters
Understanding natural language query 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 lets finance teams ask questions in plain language and answers from a governed semantic model, so the result is a computed, reconcilable number, not a best guess.
In practice
Governance example
Teams use natural language query 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 lets finance teams ask questions in plain language and answers from a governed semantic model, so the result is a computed, reconcilable number, not a best guess.
In practice, teams should define natural language query with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding natural language query 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 lets finance teams ask questions in plain language and answers from a governed semantic model, so the result is a computed, reconcilable number, not a best guess.
A strong workflow for natural language query 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 lets finance teams ask questions in plain language and answers from a governed semantic model, so the result is a computed, reconcilable number, not a best guess.
FAQ
What is a natural language query?
Natural language query is asking data questions in plain language and getting structured answers. For natural language query, 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 does NLQ work over financial data?
To use natural language query, start with the decision, then confirm the source data, timing, calculation logic, and owner. The analysis is strongest when a reviewer can trace the answer back to the records that produced it.