GoKawiilsqlsure
AI writes your SQL. sqlsure makes sure it's right.
A query can be perfectly valid, run without error, and return a number that's silently wrong — revenue double-counted by a join, an average summed, a patient identifier exposed. Databases don't catch this. Linters don't catch this. LLMs reviewing their own SQL don't catch this.
sqlsure does — deterministically, in 0.1 ms, before the query runs.
Proof, not promises: we ran sqlsure over the gold answers of the two benchmarks every text-to-SQL model is graded on. 2,568 expert-written queries, 45 flags, zero false alarms — including a BIRD dev gold answer that is provably wrong by 8× from the exact bug class sqlsure targets, and a schema defect now filed upstream.
How it works
sqlsure judges SQL against facts your team already declared — dbt unique tests become grain, relationships tests become join cardinality, one-line meta tags mark what's safe to sum. No new language to learn, no model to maintain by hand. Rules are dictionary lookups, not LLM calls: same input, same verdict, every time, offline.
Every rejection carries a machine-actionable fix , so AI agents self-repair: draft → check → fix → check → execute. In our benchmark, applying the fix verbatim produced a passing query 10/10 times.
Quick start
pip install sqlsure
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