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 ID exposed. Databases don't catch it. Linters don't catch it. LLMs reviewing their own SQL don't catch it.

sqlsure does — deterministically, in 0.1 ms, before the query runs.

The bug that ships to your dashboard

SELECT o.region, SUM(o.amount) AS revenue
FROM orders o
JOIN order_items i ON o.order_id = i.order_id
GROUP BY 1

Valid SQL. Runs clean. Every order's amount counted once per line item. The total is inflated, the dashboard looks normal, and nobody knows — that's the fan-out trap, one of nine silent-error classes sqlsure rejects with an exact fix:

✗ FANOUT — SUM(amount) after one-to-many join to order_items:
  amount will be double-counted.
fix: Pre-aggregate orders to [order_id] in a CTE before joining.

Where sqlsure sits

rejected? the fix hint goes back to the author — draft → check → fix → check → execute

No new language. Your dbt tests are the rulebook.

sqlsure judges SQL against facts your team already declared: a dbt unique test is a grain declaration, a relationships test is join cardinality, a one-line meta tag marks what's safe to sum. Plain PK/FK declarations work too. Rules are dictionary lookups, not LLM calls — same input, same verdict, every time, offline, and it never reads your data.

Three doors, one engine

Receipts, not promises. We ran sqlsure over the gold answers of Spider and BIRD — the benchmarks every text-to-SQL model is graded on. It mechanically found a schema defect affecting 13 questions and a gold answer that's provably wrong by 8× — from the exact bug class it targets. Ten of its fifteen BIRD flags were independently confirmed by a human expert review we discovered afterward. The full story →

What sqlsure is not

• Not a SQL generator. The agent stays the author; sqlsure stays the judge. Judges that also play stop being trusted.
• Not a semantic layer. dbt, Cube, and Snowflake define semantics; sqlsure enforces them on arbitrary SQL — including everything that never routes through a semantic layer.
• Not omniscient. Undeclared relationships come back "can't verify," never "looks fine." Honest uncertainty is a feature.

Start in 60 seconds

pip install sqlsure

# audit any dbt repo (no dbt install needed)
python -m sqlsure.scan path/to/repo --report report.md

# give your agent the inspector
claude mcp add sqlsure -- python -m sqlsure.mcp_server --manifest target/manifest.json