Audit AI-written code before it reaches production.

During an audit, Guard reads your repo in a temporary workspace that is removed afterwards; the reports and history stay as product records. We don't train models on your code, and selected context may go to approved AI providers under their terms. GitHub App access is revocable any time, and every issue or pull request stays explicit and reviewable. For strict requirements, run Guard fully self-hosted with your own AI keys.

Classic scanners match code patterns. Guard audits the code and the running app together for the failure modes specific to AI-written software: hallucinated dependencies, fake-green tests, and exposed routes. Findings arrive in plain language as reviewable GitHub issues, and bounded fixes can become pull requests.

Yes. The self-hosted deployment runs in your own infrastructure with customer-controlled AI provider routes, so your code and reports stay under your control. In fully air-gapped deployments, data stays within your network. Email [email protected] to discuss your infrastructure, security, and deployment requirements with our team.

When a finding has a bounded, useful fix, Guard can prepare the change and open a narrow GitHub pull request with the relevant context and verification evidence. Your team reviews and merges it. Guard never merges or deploys code automatically, and findings that need a broader engineering decision remain issues for your team.

Yes. A project owner can invite teammates as members. Members can view the shared project data and run audits, while invite, removal, and project ownership controls stay with the owner.

Yes. We can help review the report, prioritize the findings, and work with your team on remediation that needs hands-on engineering. Email [email protected] with a link to the report or a short description of the project, and we will discuss the right format for the work.