[product]
Multi-agent AI code review, with compliance built in.
Autometric fans one serious diff out to bugs, security, performance, style, and compliance specialists, adds linked task context when tickets exist, then brings the result back through a Judge / Verifier and independent QA.
[review architecture]
multi-agent pipeline
[specialist reviewers]
Core reviewers stay on. Task Context joins when linked tickets exist.
- Security coverage for auth flaws, secret exposure, and unsafe data handling.
- Bug review that catches logic mistakes, brittle branches, and invalid assumptions.
- Performance review for hot-path regressions and wasteful operations.
- Style review to keep output readable and maintainable.
- Task Context when linked tickets add bug or acceptance details to the review.
- Compliance review for named framework packs on in-scope repositories.
[judge / verifier]
One review stream comes back, not six disconnected opinions.
Most AI code review tools show the first draft they generate. Autometric does not. The Judge / Verifier takes the reviewer outputs, consolidates duplicate findings, raises the confidence bar, and produces one ranked review stream that is easier for developers to trust.
A separate QA layer samples completed reviews over time, so quality can improve without widening the live noise floor.
[review quality]
Best-in-class review, before compliance even starts.
Security reviewer finding
Unsanitized user-controlled redirect target can enable open redirect abuse.
Bug reviewer finding
Nil branch on retryCount means timeout fallback never executes on first failure.
Performance reviewer finding
N+1 query path introduced in the reviewer summary endpoint for large repositories.
[task context]
Bug and feature tickets can change what the review expects.
When a pull request links a bug, Autometric can check for regression coverage. When it links a feature or task, it can walk acceptance criteria. That keeps review aligned to the work item, not just the changed lines.
[cost discipline]
Static pre-filtering keeps cost aligned with insight.
Autometric strips out trivial churn before inference. That means teams pay for review insight, not formatting noise, boilerplate changes, or low-risk diff volume that never needed a model in the first place.
[cta]
Want to evaluate the reviewer before anything else?
We can show the review engine, linked task context, and compliance layer in one flow against a sample diff.