Available for new projects — June 2026

AI Code Review
& Rescue

Built your app with Cursor, ChatGPT, Claude, or Copilot? I audit AI-generated codebases for security holes, missing error handling, scalability problems, and anti-patterns — then fix them before they become production incidents.

Everyone is building with AI now. The bottleneck is no longer writing code — it's knowing whether that code is safe to ship.

AI Tools I Audit Code From

CursorChatGPT (GPT-4o)Claude (Anthropic)GitHub Copilotv0 by VercelReplit AgentLovableBoltAmazon CodeWhispererAny LLM-generated code

What I Typically Find

AI tools optimize for working code, not secure, scalable, or maintainable code. These are the patterns I see in almost every AI-generated codebase.

🔓

Security Vulnerabilities

AI tools rarely enforce security. Common findings include unsanitized inputs, exposed API keys and secrets in code, missing authentication checks on routes, broken access control, and SQL injection vectors.

💥

Missing Error Handling

AI-generated code often swallows errors silently or crashes on unexpected input. I trace every failure path and add proper error boundaries, fallbacks, and user-facing error messages.

🐌

Performance & Scalability

N+1 database queries, missing indexes, no pagination on large datasets, synchronous blocking where async is needed — code that works for 10 users breaks at 1,000.

🏗️

Architecture Anti-Patterns

Business logic in the wrong layer, tightly coupled modules, no separation of concerns, God components, and duplicated code that makes future changes expensive and error-prone.

🔧

Hardcoded Configuration

Database URLs, third-party keys, environment-specific values — AI tools often hardcode these rather than using environment variables and config management.

🧪

Zero Test Coverage

AI tools skip tests unless explicitly prompted. Code going to production with no tests means regressions are invisible and refactoring is risky.

How It Works

01

Codebase Audit

Full read-through of your codebase — every file, every route, every query. I look for security, performance, architecture, and code quality issues.

02

Written Report

A prioritized written report covering every issue found, severity level (critical / high / medium / low), and a specific fix recommendation for each.

03

Code Fixes

I implement the agreed fixes directly in your codebase — security patches, refactors, query optimizations, and error handling — with clean, commented diffs.

04

Handover Call

A walkthrough call where I explain what was found, what was fixed, and what to watch for going forward. Optional ongoing retainer for continued hardening.

Pricing

Audit Only

From $500

3–5 days

Full codebase review and a prioritized written report with every issue and fix recommendation. You take the report to any developer.

  • Full codebase read-through
  • Prioritized issue report
  • Fix recommendations
  • Severity ratings (critical → low)
Most Popular

Audit + Fix

From $1,500

7–14 days

Everything in Audit Only, plus I implement all agreed fixes directly in your codebase. The most popular option.

  • Everything in Audit Only
  • Security patches implemented
  • Performance fixes applied
  • Refactoring completed
  • Handover call included

Ongoing Retainer

From $800/mo

Monthly

Continuous code review as your AI-assisted team ships new features. Catch problems before they reach production every sprint.

  • Weekly code review
  • PR review & feedback
  • Architecture guidance
  • Security monitoring
  • Priority response

Prices are starting points — exact scope and cost are confirmed after a free 15-minute scoping call.

Common Questions

My app 'works' — do I still need a code review?

Working and production-ready are different things. AI-generated code often works for happy-path scenarios but fails on edge cases, under load, or when someone probes for security weaknesses. Most critical bugs — SQL injection, broken auth, missing rate limits — are invisible until exploited. A review before launch is far cheaper than fixing a breach or outage after.

Which AI tools' output do you review?

Any AI-generated code: Cursor, ChatGPT (GPT-4o), Claude (Anthropic), GitHub Copilot, Amazon CodeWhisperer, v0 by Vercel, Replit Agent, Lovable, Bolt — or any combination. The output pattern is similar across all of them and I know exactly what to look for.

How long does an audit take?

A focused audit of a typical AI-generated MVP (5–20k lines) takes 3–5 business days. Larger codebases or ones requiring significant refactoring take 7–14 days. I provide a timeline estimate after an initial 15-minute scoping call.

Do you fix the issues or just report them?

Both options are available. Report-only engagements give you a detailed written audit you can hand to any developer. Full remediation engagements include me implementing the fixes. Most clients choose full remediation — it's faster and the context is still fresh from the audit.

What stack can you audit?

My primary stack is React, Next.js, Node.js, TypeScript, PostgreSQL, and MongoDB — which covers the majority of AI-built web applications. I can also review Python (Django/FastAPI) backends and Shopify codebases.

Ready to find out what's lurking in your codebase?

Share a brief description of your app — stack, rough size, what it does — and I'll reply with an honest scoping assessment within 24 hours.