Reviewing AI-Generated Code at Enterprise Scale

AI coding assistants have changed the economics of software delivery. A feature that once took a sprint can be scaffolded in an afternoon. The bottleneck has moved from writing code to trusting it.

What we look for

• Security: injection risks, secrets handling, authentication and authorisation gaps.
• Correctness: edge cases and error paths that generated code often glosses over.
• Maintainability: duplication, inconsistent patterns and unclear ownership.
• Architecture: whether new code respects the boundaries of the existing system.
• Licensing and provenance: dependencies and snippets with incompatible terms.

Review as a service

Code Review As A Service gives engineering leaders an independent, repeatable audit of AI-assisted codebases — on a cadence that matches delivery, not a one-off health check that is out of date by the time it is read.