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Vibe Code Rescue

Product rescue

Know what is ready, risky, or blocking release

I review AI-generated code against the product, runtime, architecture, accessibility, and release constraints.

Audit the codebase

Staged example; no client data is shown.

A practical example

See a representative ai code audit context without client data or implied results.

Demo
Staged AI code audit with source diff, findings, severity, and release blockers

What it is

An AI code audit reviews generated or assisted code for correctness, architecture, maintainability, accessibility, performance, and release risk.

The problem

A prototype can look complete while hiding fragile state, missing failure paths, unsafe assumptions, and unclear ownership.

Best fit

  • teams inheriting AI-generated code
  • founders preparing a prototype for review
  • products with uncertain release readiness

What improves

An evidence-led review of AI-generated code, release blockers, and repair priorities.

  1. Main result

    Confirmed defects, risks, and unknowns become a prioritized decision record.

  2. Clearer experience

    Confirmed failures are separated from risks and cosmetic debt.

  3. Dependable delivery

    The team receives a prioritized path to a controlled release.

Possible scope

  • architecture and dependency review
  • focused reproduction and risk mapping
  • targeted frontend repair
  • tests, documentation, and release checks

Before work starts

  • Audit, stabilization, and rebuild work are estimated as distinct scopes.
  • Backend, infrastructure, privacy, and security issues receive the right specialist owner.

How I work

  1. Understand

    I clarify the users, business need, constraints, and available evidence.

  2. Shape

    I turn that context into a practical structure, prototype, and delivery plan.

  3. Build

    I implement the interface in testable slices, checking accessibility and performance.

  4. Ship

    I verify agreed flows, document ownership, and prepare a controlled release.

Clear ownership

I lead product thinking, UX, frontend engineering, accessibility, performance, and delivery. Backend, infrastructure, security, content, and domain specialists join when the work needs them. AI supports research, iteration, QA, and documentation; people remain accountable for decisions and release quality.

Common questions

What does an AI-generated code audit examine?
An AI code audit reviews generated or assisted code for correctness, architecture, maintainability, accessibility, performance, and release risk.
When should a team audit an AI-built prototype?
This service is best suited to teams inheriting AI-generated code, founders preparing a prototype for review, products with uncertain release readiness.
What does the audit deliver?
A typical scope can include architecture and dependency review, focused reproduction and risk mapping, targeted frontend repair, tests, documentation, and release checks. The final scope is agreed before work begins.

Plan your ai code audit

Share the current situation, who uses the product, and the next decision you need to make.
Audit the codebase