What an AI code review finds that your own team misses
Every developer has had the experience of staring at a bug for an hour, then watching a colleague spot it in ten seconds. Familiarity is comfortable, and comfort is exactly what hides faults.
Now consider the scale. A modest business application is tens of thousands of lines. Nobody reads all of it. Reviews sample the parts that changed recently, which means the quiet corners are never looked at again after the day they were written.
That is where the expensive things live.
How an AI code audit works, step by step
We use AI to read all of it. Not a sample, not the recent commits — the whole codebase, in hours rather than weeks.
Then we throw most of it away.
A raw AI sweep produces hundreds of observations. Many are stylistic. Some are simply wrong. A handful are real, and one or two might be costing you money right now. The work that matters is separating those, and that work is done by an engineer, not a machine.
Every finding we report has been reproduced. We show you the fault happening before we tell you it exists. If we cannot reproduce it, it does not go in the report.
The AI reads your code, it never writes to it
The AI has read access. That is all.
It never commits, never opens a pull request, never touches your repository. Every proposed change is written up, reviewed by an engineer, put in front of you, and applied only once you have agreed to it — on a branch, so it can be undone.
This matters more than it sounds. The risk with AI-assisted development is not that the code is bad. It is that nobody can explain what changed, or why, six months later when it matters. An audit trail is not bureaucracy. It is the difference between a system you own and a system you merely host.
How findings are ranked, and by what they cost you
Most security tools rank findings red, amber and green. That tells you how the tool scores severity, not what a fault is costing you.
We rank by business impact. A theoretical vulnerability in an admin page three people can reach ranks below a validation rule quietly rejecting a segment of your customers at checkout. One is a risk. The other is a bill you are already paying.
To make that judgement we look beyond the code — at your logs, your analytics and where your traffic actually comes from. Faults that matter show up as numbers before they show up as errors.
Code review alongside Cyber Essentials and pen testing
Code review sits naturally alongside the rest of our security work. If you are already certifying under Cyber Essentials or running penetration testing, a code review answers the question those cannot: not “could someone break in”, but “is this code doing what you think it does”.
Many clients take it once, as a baseline. Others attach it to their managed IT support so that every significant release gets a second, independent pass before it reaches customers.
What an AI code review is not
It is not a rubber stamp. If your code is in good shape we will say so, and the report will be shorter and cheaper than you expected.
It is not a tool subscription. You are not buying access to a scanner. You are buying the judgement applied to its output.
And it is not a replacement for your developers. It is the second, independent pass that gives a codebase the distance your own team cannot easily bring to it.
AI code review scope and pricing
Tell us what the code does and roughly how large it is. We will come back with a fixed scope and a fixed price before any work begins, and you will know exactly what you are getting and what it costs.