How accurate is iDBQuery's natural-language-to-SQL?

iDBQuery makes natural-language-to-SQL accuracy verifiable rather than asking you to take it on faith: - It grounds every query in your real schema and semantic layer, so it uses columns that actually exist and mean what you think, instead of hallucinating a field. - It shows the SQL it wrote, so you (or a DBA) can read exactly how the number was produced. - It cites every figure back to source rows, so you can confirm the result against the underlying data. - When a question is ambiguous, it asks or states its assumption rather than guessing.

A worked example: ask 'average deal size for enterprise this quarter' and iDBQuery shows the SQL, the table, the filter for the enterprise segment, the date range, so any misinterpretation is visible at a glance, not buried.

The differentiator is the safety net. Raw text-to-SQL can be confidently wrong because it returns only a number; iDBQuery pairs the answer with its query and its citations, so accuracy is checkable and mistakes are cheap to catch and correct in a follow-up. Grounding in your actual schema and semantic layer also cuts errors at the source, especially on cryptic databases. No system is perfect, which is exactly why iDBQuery shows its work: the goal is answers you can trust because you can verify them, not a black box.