AI
AI Impact
AI Impact tracks what your AI tools actually deliver — from accepted suggestions to code shipped in production.
Measure cycle time, throughput, and quality changes against your pre-AI baseline to see the real difference AI is making on your team’s output.
Code to Production
- Track every AI-generated line from IDE acceptance to merged PR to production deploy — see exactly how much AI-assisted code is actually shipping versus getting rejected in review.
- Break down code-to-production metrics by tool, team, repo, and contributor type (human, AI-assisted, agent) to spot where AI is accelerating delivery and where it’s adding rework.
- Surface delivery checkpoints and bottlenecks in real time — know the moment AI-driven work stalls in review, fails CI, or gets reverted, instead of finding out in the next retro.
AI Checkpoints
- Pinpoint where AI-assisted work stalls — review queues, CI failures, merge conflicts, or post-deploy reverts — with checkpoints that map every stage of your pipeline.
- Catch delivery anomalies the moment they appear, not three sprints later — automatic alerts fire when cycle times spike, rework loops grow, or merge rates drop.
- Benchmark checkpoint health by team, repo, and contributor type to see exactly where AI is accelerating delivery and where it’s creating drag.
From AI activity to measurable impact
Separate AI tool usage from work shipped and quality maintained. Evaluate accepted suggestions, review activity, time to deliver, and changes in quality in the context of your team’s workflow. Correlation does not prove AI caused the change.
Explore the evidence: ROI research methodology · DORA, SPACE and DX frameworks.
Trusted by FORTUNE 500 enterprise companies
READY TO ADOPT A DEVELOPER PRODUCTIVITY INSIGHT PLATFORM?
