AI Pull Request Review Your Team Will Actually Read
Review bots fail for one of two reasons: they say too much, or they are wrong often enough that people stop reading. Running a second independent model over the first review fixes both — the noise drops and what remains has been checked twice.
What happens when a pull request opens
- The webhook fires and CodeSightAI responds immediately, running the analysis in the background.
- Reviewer 1 reads the diff and reports findings with severities.
- Reviewer 2 audits that review, drops what it cannot support and adds what was missed.
- A summary comment is posted on the pull request; the full review is in the dashboard.
- Analyses that stall are failed automatically rather than left hanging.
Signals for the whole team
- A risk score derived from finding severity, not from a model's own confidence.
- Advisory quality gates based on what the review found.
- Automatic pull request labels from the file patterns that changed.
- Architecture diagrams summarising what a large pull request touches.
- Trend views for review volume and recurring risk areas.
Fixes, not just complaints
Findings carry a before/after suggestion. Where the fix is unambiguous you can commit it to the pull request branch from the dashboard, one at a time or in a batch, with the file verified before each write.
Frequently asked questions
How long does a review take?
Most reviews finish in a few minutes. The webhook is acknowledged immediately, so GitHub never waits on the analysis.
Does it block merges?
No. Quality gates are advisory. Nothing about your branch protection changes unless you decide to wire it up.
What happens on very large pull requests?
Very large diffs are summarised rather than reported line by line, and the highest severity findings are surfaced first.
Can I re-run a review?
Yes. Any pull request can be re-analysed from the dashboard after you push changes.
Free plan available. No credit card required.