[integration]
Full Autometric review, rendered natively in GitHub.
GitHub teams get the same best-in-class review engine for bugs, security, performance, style, and compliance. Autometric is not a lighter GitHub mode or a compliance-only overlay.
[github]
GitHub
Same best-in-class review engine. Native platform rendering. No compliance-only fallback mode.
[gitlab]
GitLab
Same best-in-class review engine. Native platform rendering. No compliance-only fallback mode.
[gerrit]
Gerrit
Same best-in-class review engine. Native platform rendering. No compliance-only fallback mode.
[perforce]
Perforce
Same best-in-class review engine. Native platform rendering. No compliance-only fallback mode.
Native review shape
Inline comments and status checks fit the GitHub pull-request flow teams already use.
Same engine, no downgrade
Specialist review depth stays on instead of collapsing to a compliance-only or lightweight mode.
Evidence path included
Findings can still carry control context and exportable evidence when repositories are in scope.
[best-in-class review]
The full review engine runs here too.
GitHub is often where teams first validate whether an AI reviewer is actually good. Autometric uses that surface to show real depth: bugs, security flaws, performance regressions, style drift, and control-aware findings when scope requires them.
[task context + governance]
Intent and evidence stay attached across the estate.
GitHub convenience does not remove enterprise requirements. RBAC, audit history, scoped framework packs, and deployment choices still matter once the rollout moves beyond a few enthusiastic developers. Linked tickets from connected task systems can still shape review expectations without changing where the review lands.
[compare callout]
Why this integration matters.
Most AI review products look strongest in GitHub. Autometric keeps that strength while extending the same model into Gerrit, Bitbucket Data Center, Azure DevOps, and Perforce.