SQLAI.ai

Attach a datasource or import table definitions, and the generator writes against your actual columns. It handles selects, filters, joins, aggregates and CTE-style requests. Naming errors drop sharply compared with prompt-only tools, because the model isn't inventing column names.

Paste a query and the validator points at syntax problems and structural risks before the query reaches a dashboard or a job. Teams that enforce rules can catch a missing filter here rather than in a production incident. It reads as a second pair of eyes, not a replacement for tests.

The optimizer takes a working but slow query and proposes a faster version, typically by reworking joins, pushing filters earlier or replacing a correlated subquery. You get the reasoning alongside the rewrite, so you can judge whether the trade-off fits your data. On optimizer-heavy work, credits disappear faster than on plain generation.

Drop in a query nobody documented and get a plain-language walkthrough of what each part does. This is the feature that saves the most time during a handover, since reading someone else's nested CASE statements is slower than reading a summary of them.

The SQL formatter normalizes indentation, keyword case and line breaks so a query looks the same as the rest of your repository. Conversion handles the move between engines, where date handling and string functions usually break first. Both are mechanical jobs that humans do badly at 6pm.

Paid workspaces can define generation rules: mandatory filters, tenant isolation, preferred join style, banned patterns. Those guardrails keep an AI SQL tool from producing queries that violate your data policy. Starter and above also unlock the public API and thread sharing, so a query can move from a chat into your own tooling.