AI code verification tools
Check AI-written changes before merge with independent verification and a reviewable evidence record. Evaluate production controls against your own workflow.
For engineering and QA teams deciding how to check AI-written code before merge. A generated change still needs independent review, tests and an explicit release decision.
What Zof does
An independent check
Zof is an independent verification and control layer between AI and production. Start by checking the change, recording what was checked, and keeping missing evidence visible to the reviewer.
Verification chosen from the change
Zof determines which verification is appropriate from the change, the System Graph, the risk and your policy, rather than running every possible test on every change. Existing test suites and CI results count as evidence.
Platform evaluation
The Verification Engine is the platform path for broader testing and control. Confirm the checks, connectors, evidence retention and approval enforcement available in your proposed deployment.
From a change to a review decision
- 01
Select a change
Choose a supported repository and an explicit diff or changed files. Keep existing code review, unit tests and CI checks in place.
- 02
Inspect the result
Read findings, changed-file scope and any incomplete execution. A clean result means only that the checks that ran found no matching issue.
- 03
Make the decision
Review the result alongside executed tests. Configure your CI rules explicitly if a result should block merge. A report alone does not enforce a production gate.
What evidence you get
Results with their artifacts
Structured results, artifacts, traces and logs are kept together in an evidence bundle attached to the decision. Confirm the formats your reviewers need during evaluation.
Execution status
An incomplete or missing check is not a pass. Evidence should show which checks ran, which were skipped and which failed to complete.
Existing results count
Playwright, Cypress, Selenium and unit framework results keep running in your pipeline and become part of the evidence.
Prerequisites and limits
- Checks can miss defects. A passing result does not prove functional correctness, security or compliance.
- Supported languages, frameworks and connectors depend on the deployment. Confirm them on a representative repository before relying on a result.
- Running your tests means running trusted code. Agree which commands may execute, where, and with which credentials.
- Hosted integration, evidence retention and approval enforcement must be validated in a scoped evaluation for your deployment.
Start with one change
Bring a representative change, the tests you already run and your release rules to an evaluation on your own stack.