AI-Generated Code Audit | Razoyo
AI-Generated Code Audit · $2,500 Fixed-Price · Named Engineers
AI Tools Are Good at the Demo. Someone Still Has to Check What's Missing.
Missing auth boundaries, no test coverage, secrets left in client-side code, no separation between prototype scaffolding and real business logic. An AI-generated code audit finds these before real users do.
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I built this by prompting, and I have no idea what's actually underneath it.
It works in the demo. I don't know if it's safe with real user data.
Real people are using it now, and that's exactly what scares me.
What the Audit Actually Looks For
The specific failure signatures common to AI-generated codebases: missing or inconsistent auth boundaries, no real data model discipline, no test coverage, secrets left in client-side code, and no separation between prototype scaffolding and the actual business logic underneath it. These aren't generic code-review notes; they're the patterns that show up specifically when a system was built by prompting rather than by an engineering process.
Built With Lovable, Replit, Bolt.new, v0, Cursor, Bubble, DataButton or Windsurf?
Any of these. The tool isn't the point; the seven-dimension production-ready check is the same regardless of which one built the prototype: security and access, data integrity, architecture, test coverage, observability and alerting, a real deploy path, documented ownership.
The Deliverable
A Production Spec: $2,500 fixed, audit findings, risk ranking, and a remediation plan. Most of what a good prototype captures, the workflow, the intent, the rough data model, is worth keeping; the audit decides salvage versus rebuild on the evidence. Read the vibe-coded-to-production standard in full, including the FAQ on prompting's real limits and the offshore-price comparison.
Questions We Get Before the Call
Up to a point, and that point is real. Prompting adds features well on top of a sound foundation. It doesn't give you an access model, tests, or a deploy path.
Usually not. The workflow and data model a good prototype captures are usually worth keeping; the audit decides case by case.