Code review built for the way code gets written now
AI coding assistants write more code, faster, than any team could before. audit/bench exists to review that code with the same rigor a careful senior engineer would — before it reaches production.
Why this exists
Traditional linters catch syntax problems. They don't catch a service-role key bypassing row-level security, a shared mutable variable leaking data across requests, or Node syntax quietly failing inside a Deno edge function. Those are the mistakes AI assistants make constantly — because they produce code that looks rightwithout necessarily reasoning about the runtime, the framework, or who's allowed to call it.
audit/bench pairs LLM reasoning with static analysis — dependency graphs, dead code detection, secret scanning — so a review considers both what the code says and what the rest of the repository implies about it.
How an audit runs
Reads the code and routes it to the right specialists.
OWASP Top 10, auth/JWT issues, secrets, injection.
Edge cases, race conditions, hallucinated behavior.
Slow paths, N+1 queries, unnecessary work.
Framework misuse, structure, maintainability.
Untested edge cases and risky branches worth a dedicated test.
Turns each finding into a concrete, applicable fix.
Who it's for
- Individual developers
- Startup engineering teams
- Enterprise engineering organizations
- Security teams
- CTOs
What sets it apart
- AI-first code reasoning, not just pattern matching
- Multiple specialized review lenses per audit
- Context-aware repository analysis, not just single files
- Every finding ships with a root cause and a fix, not just a warning