An AI codebase audit is a fixed-scope, senior engineering review of an application's architecture, security, dependencies, testing, and deployment readiness, designed to catch the specific failure patterns AI coding tools can leave behind before real users do.
It exists because AI coding tools generate working code quickly without verifying that code is safe to run in production. The review step that used to happen implicitly, as a human developer wrote and understood each line, has to happen explicitly now. An audit is that step.
It produces three things: a production-readiness score, the real risks ranked in priority order, and a plan for what to fix first. That's different from a general code review, which usually checks a specific change rather than the whole system against a consistent standard.
A standard Mati Systems audit is fixed-scope, takes about a week, and costs $5,000 to $9,000, scored against the PRISM framework.
A regular code review checks a specific PR or feature. An AI codebase audit is a full-codebase review scoped specifically to the failure patterns AI coding tools introduce: duplicated logic, copied auth checks, unverified integrations, on top of standard architecture and security review.
Founders preparing to launch or raise, agencies delivering AI-assisted client work, and engineering teams whose review process wasn't built for AI-generated code volume.
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