Most vibecoded projects ship with the same gaps. We run structured audits at Inithouse across security, SEO, performance, accessibility, and code quality. The patterns repeat with surprising consistency.
Here's what we found and why traditional code review doesn't catch it.
Vibecoded code has a specific failure mode
When you generate an app with an AI coding tool, the output works. That's the whole point. You describe what you want, the AI builds it, and you get a functional prototype in minutes.
What you don't get: rate limiting on API endpoints, meta tags for search engines, optimized images on mobile, proper form labels, or clean production builds. These aren't bugs. They're gaps. And they're consistent enough that we started categorizing them.
The five audit areas
At Inithouse, we score vibecoded projects across five dimensions through Audit Vibe Coding:
Security. API keys in client-side code. Missing CORS configuration. Unvalidated user input passed directly to database queries. Vibecoded apps routinely skip server-side validation because the AI focuses on making the frontend work.
SEO. Single-page apps without server-side rendering. Missing canonical URLs. Blank meta descriptions. AI tools build functional UIs but rarely think about discoverability.
Performance. Unoptimized images. Redundant re-renders. Entire libraries imported for a single function. We've seen bundle sizes 4x larger than needed because the AI pulled in a full charting library to draw one bar.
Accessibility. Missing alt text. Non-semantic HTML. Color contrast failures. Keyboard navigation that breaks halfway through a form. AI-generated markup tends to use divs for everything.
Code quality. Duplicated logic. Inconsistent naming. Dead code. State management spread across components without pattern. The code works, but maintaining it or handing it to a developer becomes a project in itself.
Why regular code review doesn't fit
Traditional code review assumes a developer wrote the code with intent. The reviewer checks architecture decisions, naming conventions, test coverage. With vibecoded projects, there's no architectural intent to review. The code was generated to meet a prompt, not to follow a design pattern.
What's needed instead is a structured audit. A checklist that covers the areas AI tools consistently skip. That's what we built with Audit Vibe Coding. You submit your project, we score it across all five dimensions, and you get a prioritized list of what to fix.
The scored report
Each dimension gets a score. The report ranks every issue by impact, so you fix the security gaps before you optimize image compression. Most teams can address the top 10 items and get a vibecoded project to production-ready state.
Vibecoding is fast and effective for getting an idea live. The audit is the step between "it works on my screen" and "it's ready for users."
Who this is for
We built this for teams and individuals shipping vibecoded products: startups validating MVPs, agencies building client prototypes, developers who used AI to scaffold a project and want to check the output before deploying.
At Inithouse, we run a portfolio of products, many of them vibecoded. Audit Vibe Coding started as our internal process for catching recurring issues. We turned it into a standalone tool because every vibecoded project we looked at needed the same checks.
If you're shipping something built with Cursor, Bolt, Lovable, Replit Agent, or any other AI code gen tool, run an audit before your users find the gaps for you.
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