Codevetta launch risk review
I review AI-built products for the launch-blocking risks normal demos miss, including backend authorization, data boundaries, secrets, payments, webhooks, and generated code that nobody has checked closely yet.
First review target
Data access
If a signed-in user can change an ID and read another user's record, the launch risk is authorization.
Run the 3-minute checkA one-time launch risk review for solo founders and non-security builders who moved fast with AI coding tools and now need to know what could expose customer data or let users do things they should not.
Most expensive mistakes are not the bugs you see in the demo. They are the trust boundaries you did not know to test.
I specifically review for:
This is for you if:
Start with the free Vibe Coding Launch Risk Scanner if you want a quick first answer. It points to the first launch risk I would review.
Snyk, SonarQube, Semgrep, CodeQL, and AI code review tools are useful. I still use scanners. The problem is that a pre-launch founder usually gets a long list of possible issues, not a clear answer on what can hurt the product first.
Codevetta is a human review of the trust boundaries in the app: who can do what, which data they can reach, where secrets and webhooks live, and whether the code is safe enough to launch or hand off.
No. Use those tools if they fit your stack. I look at the product paths they do not understand well: account boundaries, plan checks, webhooks, imports, exports, background jobs, and handoff risk.
Usually, yes, if there is enough source code or configuration to inspect. If the app is mostly locked inside a platform and I cannot review the risky parts, I will say so before you pay.
You get a prioritized ship / don't ship yet recommendation tied to your product, not a pile of comments. I care most about the paths where real users, private data, payments, and permissions meet.
I've spent 30+ years building and shipping production software across startups, enterprise and government projects, and long-running products.
I've been working with LLMs and AI coding tools for over a year, so I understand both traditional development pitfalls and the new failure modes that come with AI-assisted code.
This service is usually used right before launch, after an AI-assisted build sprint, or before a handoff. Typical engagements are early-stage and small-to-medium-sized codebases: MVPs, single services, or pre-launch products. If your repo is not a fit, I'll say so before you pay.
If you have any specific questions or things to look at, include it in the email.
Not sure if this is a fit? Email me with a short description of your project before paying.
See the review process if you want the steps before sending over a repo.
You can also read more about me before sending over a repo.
