What is an AI Product Audit, and what do I inspect?

An AI Product Audit is broader than code review. It maps what the product has become, where decisions conflict and which problems can be removed without a rewrite.

AI-built products can become structurally complex before the team has a shared model of the architecture. An audit starts by making that reality visible.

The audit layers

Product promise and feature scope.
Routes, domains and integration map.
Canonical sources of truth.
Network, polling, caching and cost behavior.
Authorization and secret boundaries.
UX complexity and mobile failure states.
Duplicate AI-generated abstractions.
Production and release topology.

1type AuditFinding = {2  severity: 'P0' | 'P1' | 'P2' | 'P3';3  evidence: string;4  impact: string;5  smallestFix: string;6  rewriteRequired: boolean;7};

A good audit does not manufacture more work. It also reveals what should not be built.

AI Product Audit
Architecture Audit
SaaS
Performance
Security

SER-02 · SER-02.04

From Prototype to Production

Turning a working MVP into a dependable product: production checks, modular refactoring, data migration, audits, rewrite decisions and release gates.

ENTRY-020From AI Prototype to Production Schema: Data Modeling and Migration Strategy ENTRY-015When should an MVP be rewritten, and when should it be rescued?

Case

PRJ-01 / Ordovia

The audit layers

From Prototype to Production

Has your product grown faster than its system?