Use cases

One generator, several jobs. All run on PostgreSQL today, and reach SQL Server and Oracle as those engines mature.

AI use cases

Where AI writes your database code, and you need to see, trust and hold what it produced.

AI behavioural transparency

AI wrote the function. The HTML report shows what it actually does, every branch, the moment you run it.

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Guardrail for AI-written code

Generated tests pin today's behaviour. An AI edit that breaks it fails before production.

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Generated, not guessed by AI

Tests derived from your code and run against the real database. Reproducible, and never a false pass, unlike a language model's best guess.

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Other use cases

Unit testing for developers

Generated unit tests and seed data for every routine, with branch coverage. Free.

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Integration testing for QA

Each entry point run with what it calls and fires. Coverage across the call map, every build.

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CI reports for DevOps

JUnit, coverage and HTML from every unit and integration run, wired into the pipeline that gates your merges.

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Behavioural change verification

Migration tools deploy the change. UnitAutogen proves the logic still behaves the same.

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Prove tenant isolation and security

rlsautotest proves the policies, seccover proves the code that bypasses them. Both, or no proof.

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Compliance evidence

Dated pgTAP tests and reports as supporting evidence for HIPAA and SOC 2 access controls.

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