Is AI making your team better? Now you'll know.
Your company bought Copilot licenses, your engineers started using Cursor and Claude, and someone in leadership wants to know: is it working? Gitrevio's AI Impact Dashboard turns that question from a shrug into a data-backed answer.
What it measures
Gitrevio detects AI-authored code automatically. We use commit message patterns, AI tool footers (Co-Authored-By headers from Copilot, Cursor, Claude), structural heuristics, and optional tool API integrations to classify every commit with a confidence score.
Then we compare. AI-assisted PRs vs human-only PRs across every quality metric that matters: revert rates, lint errors, review round trips, time to merge, and test coverage.
No admin API integrations required for the base feature. If you do connect your Copilot admin API, we pull usage data too — but even without it, the heuristic detection catches the majority of AI-authored code.
Key metrics
Eight dimensions of AI impact, tracked continuously. Each metric is available per team, per repo, per individual, and org-wide.
Why this matters
Your company just bought 200 Copilot licenses at $19/seat/month. Six months later, someone asks: was it worth it? Without Gitrevio, that question gets a shrug. With Gitrevio, it gets data.
AI tool vendors report their own metrics — completions accepted, lines suggested. Those numbers are self-serving. Gitrevio measures what actually matters: did the code ship faster, did it break less, did the team's throughput improve?
The answer isn't always yes. Some teams thrive with AI tools. Others see no measurable benefit. Some see regressions in quality. You need to know which is which so you can adjust training, tooling, and investment.
Team-by-team comparison
Some teams adopt AI heavily, others don't. Compare them side by side to see which teams benefit most and which should change their approach.