A human senior review before your AI-built iOS app reaches production.
AI tools can produce a convincing demo quickly. They can also hide unsafe data handling, fragile state, invented APIs, concurrency defects and release problems behind a polished screen. I review the actual Swift project, run it, and turn the findings into a practical path to production.
A straightforward starting position
AI coding tools are genuinely useful. They compress the distance between an idea and something you can hold, and plenty of good software now starts that way. I use them myself.
The risk is not that the code was generated. It is that generated code arrives without the review, testing and platform judgement that normally accompany a contribution of that size. A demo that works on your phone and a product that survives real users, real networks, real devices and App Review are different things.
This service is not a verdict on your judgement. It is independent human accountability for a codebase that is about to carry real customers, real money or real data.
This is for you if…
- Founders who built an iOS MVP with an AI coding agent.
- Teams accepting AI-generated Swift into an existing repository.
- Investors or acquirers who need an independent technical view.
- Agencies taking over a “vibe-coded” native iOS project.
What I review
- Whether the project builds reproducibly on supported tooling.
- Data storage, secrets, authentication and permission handling.
- Swift concurrency, actor isolation and thread safety.
- State ownership, navigation and lifecycle correctness.
- Networking error handling, retries and offline assumptions.
- Dependency provenance, licences and abandoned packages.
- StoreKit, subscriptions and App Store readiness where present.
- Accessibility, privacy manifests and platform conventions.
- Test coverage of high-risk business behaviour.
- Maintainability by a human team after handover.
What you receive
- A production-readiness verdict, with its qualifications stated plainly.
- Critical findings, each with the evidence behind it.
- A prioritised remediation plan you can hand to an engineer.
- A debrief call and a period for follow-up questions.
- An optional remediation scope, quoted separately.
The same boundary applies as for any code audit: this is an expert engineering review, not a formal penetration test, a legal compliance certification, or a guarantee that no defect exists. Specialist security testing should be separately commissioned when required.
How the engagement is shaped
Most AI-built MVPs are small enough for a focused review of a few consulting days. I take the repository, build it on supported tooling, run it on a device, read the code that handles money, data and identity first, and work outwards from there.
You get the verdict and the findings whatever they say. If the answer is “this is closer to shippable than you feared, fix these four things”, that is the answer you get.
Relevant experience
Why this work suits me
I have shipped and operated App Store products end to end, including subscriptions, StoreKit, privacy manifests, App Review and phased releases, which is where AI-generated projects most often come apart.
I spent two years building an SDK to standards that had to pass tier-one banks' own security reviews: Keychain, certificate pinning, encryption and token lifecycle. And I use agentic AI tooling in my own work, so I have a practical sense of where these tools are strong and where they quietly guess.
Common questions
Is this confidential?
Yes. I am happy to sign a standard mutual NDA before receiving access, and confidentiality obligations are written into the engagement terms. Nothing about your product appears in my public material without your explicit permission.
Does the app need to be live already?
No, and pre-launch is often the better moment. Reviewing before release is considerably cheaper than reviewing after customers, investors or App Review have found the problem. A project that builds and runs is enough to start.
Do you use AI tools during the audit?
I use AI-assisted tooling the way I use static analysis: to widen coverage and surface candidates faster. Every finding in the report is one I have verified myself against your actual code. The point of this service is human accountability, so a machine does not get the last word.
Who owns code an AI tool generated?
Ownership and licensing of AI-assisted output is an evolving legal question and I am not a lawyer. What I can do is flag the practical risks I can see in your repository: dependency licences, code that appears lifted from a known source, and provider terms worth putting in front of a solicitor.
How is this different from running a linter?
Static analysis finds patterns. It does not know that your paywall unlocks on an unverified receipt, that a token is written somewhere it should not be, or that a screen mutates shared state from two places. Those failures need someone who understands both the platform and what your product is meant to do.
Can you fix what you find?
Often, yes, as a separate piece of work scoped after the findings. Keeping the audit and the remediation separate matters: it means the findings are not shaped by an interest in selling you the fix.
Related reading
Built it with AI and want a second opinion before launch?
Tell me what the app does, what you built it with, and where you are in the launch. I will reply by email with a view on what a review should cover.