You already know your AI review bot is noisy. Now prove it.
Guardian measures which review comments actually lead to code changes. Yours. Not industry averages.
Your Tuesday afternoon
Your AI bot leaves 20 comments on a PR. You resolve them in bulk. Maybe 4 mattered.
Your junior dev accepts AI suggestions in 1.2 minutes. You spend 4.3 minutes per comment because you actually read them.
You know the signal-to-noise ratio is bad. You just can't prove it to your VP.
The numbers behind the noise
AI bot effectiveness
The percentage of AI bot review comments that actually lead to code changes. The rest is noise you absorb.
Junior review time
Average time a junior developer spends per AI bot comment. Fast. Maybe too fast.
Senior review time
Average time a senior engineer spends per AI bot comment. Because they actually evaluate it.
High "effectiveness" might mean compliant team, not good comments.
What you'll see
Per-Reviewer Breakdown
Who engages deeply with reviews. Who rubber-stamps. Data for coaching conversations, not punishment.
Bot Signal-to-Noise by Repo
Some repos get useful bot comments. Others get noise. See the difference across your codebase.
Coaching Data
Give your junior developers specific, data-backed feedback on review engagement. Growth-oriented, always.
The Slack Message
The data you need to write that message to your VP. In numbers they can act on.
"Found a tool that measures whether our AI review bot comments lead to code changes. Industry data: AI bots at 30-60% effectiveness vs ~90% for humans. Worth a look."