[product]
Measure review quality, trust, and cost in one place.
Autometric analytics are built for more than vanity dashboards. They show where the review engine is trusted, where tuning is needed, and what each repository costs to review after pre-filtering.
Accepted findings
74%
High-signal review output across active repositories.
Median review time
38s
After static pre-filtering removes trivial churn.
Cost per review
$0.08
Measured where buyers care: per completed review.
[repo drill-down]
Know which repos trust the review and which need tuning.
payments-api
PCI + bugs
identity-service
security-heavy
developer-portal
style + regressions
[engineering view]
Platform owners can prove the reviewer is helping.
Review analytics matter because strong AI code review is still a rollout problem. Teams need to know where the reviewer is accepted, where false positives are clustering, and where the value is strong enough to expand scope.
[compliance view]
Compliance can see where enforcement is landing cleanly.
Because compliance runs on the same review engine, analytics can show where in-scope repositories are stable and where teams need more tuning before fail-closed behavior or wider framework rollout makes sense.
[cta]