AI CODE / HUMAN TECHNICAL REVIEW

AI can produce code quickly. Somebody still needs to own the consequences.

We review AI-heavy PHP and Laravel codebases for architecture drift, hidden duplication, security problems, fragile integrations and code that appears plausible but does not fit the application around it.

AI code review illustration
WHY / LOCAL CORRECTNESS IS NOT ENOUGH

The problem is rarely syntax.

Generated code can be locally correct while still damaging the system-level design. The review focuses on how new code interacts with existing contracts, state, permissions, data, failure handling and operations.

  • Architecture consistency and duplicated abstractions
  • Authorization and security boundaries
  • Data-integrity and transaction risks
  • Duplicated or contradictory business logic
  • Queue, webhook and retry failure handling
  • Generated migrations and destructive schema assumptions
  • Test gaps and unverified edge cases
  • Maintainability and upgrade impact
REVIEW / FAILURE PATTERNS

AI tends to amplify whatever context it was missing.

The review looks for system-level inconsistencies that are easy to miss when each generated change was judged only by whether it compiled or appeared to work.

Parallel implementations

Multiple services, helpers or controller paths implementing the same business rule differently.

Happy-path integrations

Webhook or API code that works once but lacks idempotency, retries, signature validation or recoverable failure states.

Authorization drift

New endpoints or jobs that bypass the permission assumptions established elsewhere in the application.

Schema confidence

Migrations or model assumptions that treat production data as if the application were still an empty development database.

OUTPUT / BASELINE

Turn “the app seems to work” into a prioritized technical picture.

The outcome is not a style critique. It is a map of where generated code creates material risk, what deserves verification first, and which parts are safe enough to leave alone.