AI Build & Code Audit
Is your AI-generated or outsourced code actually production-grade?
AI tools and offshore teams can ship something that demos well and rots underneath — no tests, tangled architecture, silent security holes, and a codebase nobody can safely extend. I go through it line-by-line and system-by-system and tell you, in plain language, whether it is solid, salvageable, or a rebuild.
Who it's for
- Non-technical founders who built an MVP with Cursor, Lovable, Bolt, v0 or a freelancer
- Teams that inherited a codebase and do not trust it
- Anyone about to scale on top of code they did not write
You probably need this if…
What I look at
- Code quality, structure, and maintainability
- Security vulnerabilities and unsafe AI-generated patterns
- Architecture, data model, and scalability ceiling
- Test coverage, CI/CD, and release safety
- Dependency, licensing, and supply-chain risk
- Realistic effort to fix, harden, or rebuild
What you walk away with
Proof
The experience behind this service
Enterprise collaboration engineering for Google
Delivered enterprise collaboration engineering work for Google — the kind of high-bar engagement where engineering standards and delivery discipline are assumed, not requested.
COMPLY — a multi-agent AI governance system
Designed and built COMPLY: a multi-agent, RAG-based AI system that autonomously monitors 100+ delivery KPIs across every project, files findings to Jira by severity, and auto-escalates.
Re-architecting an entire delivery org around AI agents
Led the full transition of an 80-person engineering organisation to AI-native, agentic software delivery — now the exclusive delivery model across the firm.
FAQ
AI Build & Code Audit: your questions, answered.
Still unsure if this is the right fit? Book a free intro call and just ask.
What does an AI code audit actually include?
A full read of your codebase and how it fits together: code quality and structure, security vulnerabilities, the data model and architecture, test coverage and CI/CD, dependency and licensing risk, and how far it can scale before it breaks. You get a written report scored by severity, a prioritised fix list, and a plain-language verdict — solid, salvageable, or rebuild.
Can you tell whether my app was really built by AI or a freelancer?
Usually, yes. AI-generated and rushed offshore code leave recognisable fingerprints — invented logic that looks plausible but is wrong, missing or fake tests, copy-pasted patterns, and silent security holes. I led an 80-person firm's move to AI-native delivery and built a multi-agent code-governance system, so I know exactly how AI-built software fails, not just that it does.
My app works fine — why would I need a code audit?
Working in a demo and being production-grade are different things. The failures that matter — a security breach, an outage under load, a codebase nobody can safely extend — show up later, usually right when you scale or raise. An audit surfaces them while they are cheap to fix, before they cost you a customer or a round.
How long does a code audit take and what will it cost?
An AI-driven code audit is a fixed $500 with the report in 24 hours. A deeper, line-by-line manual review is scoped to the size of the codebase. Most people start with the fast fixed-price audit and go deeper only where it flags real risk.
Is my code kept confidential?
Always. Everything you share is treated as confidential and I'm happy to sign an NDA before we look at anything. Independent, confidential review is the entire point.
What happens after the audit — can you help fix it?
Yes. Many audits turn into ongoing work: a prioritised remediation plan, hands-on guidance for your team, or fractional-CTO support to see the fixes through. But there is no obligation — plenty of clients take the report and run with it themselves.
Get a straight answer about your build.
One free call. Bring your code, your quote, your architecture — or just your doubts. If I can't help, I'll tell you who can.