For CEOs, CTOs, and team leads shipping AI-assisted products
Your team shipped fast with AI. Now make sure it doesn’t break.
AI tools help teams move quickly. But fast code and safe code are not the same thing. I review what your team built, find what could hurt you, and fix it - before your customers do.
Moving fast is good. Shipping risk is not.
Your team used Copilot, ChatGPT, or another AI tool to ship faster. That was the right call. The problem is that AI-generated code is optimistic by nature - it solves the problem in front of it, under ideal conditions, on the first try. It does not account for what happens when something goes wrong, when a user does the unexpected, or when a security researcher looks at your login endpoint.
Most of the time nothing happens. Until it does - and then it is a data breach, a failed audit, or a system that nobody on your team dares touch. I close that gap before it becomes your problem.
What you get out of it
No nasty surprises in production
AI tools cut corners you cannot see. I find the weak points - the inputs that are not validated, the access controls that are missing, the data that could leak - and fix them before they cost you.
A team that can keep moving
AI-generated code is often written in a way that only the AI understands. After the cleanup, your developers can read it, change it, and build on it without needing a rewrite six months from now.
Something you can stand behind
Whether it is a client demo, an investor, an auditor, or your own engineering team - you will know the code was reviewed by someone who does this for a living and will not embarrass you.
Not sure where your codebase stands?
Before spending anything, it helps to know what you are actually dealing with. This guide walks through the five areas I check on every AI-assisted project - written for people who manage software, not just people who write it.
The five things to check before you trust AI-generated code in production. No technical background required to read it.
How it works
1. A short call
We talk for 30 minutes. You tell me what was built, how, and what worries you. I tell you whether I can help and roughly what it will take. No charge, no obligation.
2. A clear picture
I go through the codebase and write up what I find - the risks, the problems, and what needs to happen in what order. You get a plain-language report you can act on or share with your team.
3. Fixed, not just flagged
I fix the issues directly in the code. Not a list of recommendations you have to hand off to someone else - the actual work, done, so you can move forward with confidence.
Frequently asked questions
What counts as AI-generated code?
Any code written or significantly shaped by a tool like GitHub Copilot, ChatGPT, Cursor, Claude, or similar. If a developer described a problem and accepted output rather than writing the logic from scratch, it qualifies. Partial suggestions count too - the risk is in what the AI filled in, not just full files it wrote.
We already have a senior developer on the team. Why would we need an external review?
Because the person who wrote or accepted the code is the worst person to review it. AI-generated code looks reasonable on the surface - that is why developers accept it. A fresh set of eyes from someone not invested in the outcome catches the gaps your team has normalised. This is not a criticism of your developers; it is just how code review works.
How long does a review take?
Most projects take between three and ten business days from access to final report, depending on codebase size and how many critical issues come up. I do not rush it, but I do not drag it out either. You will know the timeline before we start.
Do you fix the code or just report what is wrong?
Both, but the fix is the point. The report tells you what I found and why it matters. Then I fix it directly in the codebase - not a list of action items your team has to interpret and implement on their own. You get working, cleaner code, not just documentation of a problem.
What kind of access do you need?
Read access to the repository is enough for the review phase. For the fix phase, I work in a branch and open a pull request - your team reviews and merges. You stay in control of what goes into production.
We are under a deadline. Can this wait?
It can, but most of the companies that call me are already under a deadline - usually because something broke or an audit is coming up. The longer AI-generated code sits in production unreviewed, the more it gets built on top of. Fixing it gets harder and more expensive over time, not easier.
What languages and stacks do you work with?
Primarily PHP (Symfony, Laravel), JavaScript and TypeScript (Node.js, React, Next.js), and Python. If your stack is different, reach out and I will tell you honestly whether I can help or whether I know someone who can.
What does it cost?
It depends on the size of the codebase and the scope of the fix work. The initial call is free. After that I give you a fixed price before any work starts - no hourly billing, no surprises at the end.
Let’s talk about your codebase
Describe what your team built, what AI tools were involved, and what is making you nervous about it. I will tell you honestly what the risk looks like and what it would take to fix it.