Security & code quality,
not an afterthought.
AI tools write code fast, but they don't audit it. Hardcoded secrets, unvalidated inputs, and auth flows nobody reviewed are the most common findings when a senior engineer looks at an AI-built codebase for the first time.
A proper security and code quality review closes those gaps before real users, real traffic, or a real attacker find them, and leaves behind a codebase the next engineer can actually work in.
- Full audit of auth, data handling, dependencies, and secrets
- Refactoring for maintainability, not just a list of findings
- Quality standards that hold as the codebase keeps growing
AI-generated code
has a pattern.
Most AI-generated code runs fine right up until real users, real traffic, or a real security review show up. Auth flows get copied and pasted, secrets end up in the repo, and dependencies drift out of date because no one's watching. A senior engineer who has reviewed this pattern before catches it fast, fixes it properly, and leaves clear documentation behind, not just a patched build.
The risk you can't see
until someone looks.
- Secrets and keys committed to the repo
- Auth flows copied and pasted, never reviewed
- Dependencies drifting out of date, unwatched
- Dead code and unclear structure piling up
- Secrets rotated and managed properly
- Auth and access control reviewed and hardened
- Known CVEs patched, dependencies current
- Clean, documented, maintainable codebase
Every day unaudited is a day exposed.
Book a free call and find out what's actually in your codebase, before an attacker does.