Analytics for Claude Code, measured on your repos

Claude Code can draft, refactor and ship real changes fast. But raw speed is a vanity metric. What matters is whether that velocity comes with higher quality and less rework, or whether it just moves the cost downstream into review, reverts and production incidents.

FeatureFactory attributes work to Claude Code on your own repositories and reports quality, velocity and rework side by side with your human baseline. Instead of a demo-day impression, you get evidence: is cycle time actually dropping, is the change failure rate holding, is the generated code surviving past the first week? Our approach to measuring AI-generated code keeps the attribution honest.

The result is a Claude Code analytics view your engineering leaders can trust - and act on. See it in the product tour.

Most teams roll out Claude Code, then have no idea whether it worked. Adoption dashboards tell you how many prompts ran; they don't tell you what shipped or what it cost you in rework. That gap is where budgets get cut and good tools get blamed for bad process.

FeatureFactory closes the loop by connecting agent activity to shipped outcomes. Compare Claude Code against Cursor and Copilot on the same repos, benchmark against your own history with engineering metrics, and roll it into the wider measure workflow.

You get one place to answer the question every CTO is now asking: are our AI coding tools making us faster without making us fragile? Explore the full guide on measuring AI coding tools.