Features

Measure AI Impact

Are your AI investments paying off?
Track adoption across 50+ AI tools and correlate activity to delivery outcomes. Prove what’s working and scale what matters.
AI Analytics: Cycle Time comparison

Trusted by the teams transforming software delivery

Track AI adoption across workflows, tools, teams

Complete visibility into your AI tool landscape
Adoption by workflows, team, repo, and tool

Understand how AI impacts delivery

Correlate AI activity to outcomes

Compare cycle time, throughput, and quality metrics to analyze impact.

Segment by tool, workflow, repo, and team

Know where AI is accelerating delivery and where it’s shifting work into review and rework.

Measure, refine, and repeat

Run data-driven evaluations based on actual effect on team productivity.

Operationalize with the APEX framework

Evaluate and scale AI impact without sacrificing predictability or developer health. Establish a weekly, monthly, and quarterly cadence that turns metrics into action.

Explore APEX
Cover and pages of the APEX framework guide.

Workflow automation has had a cascading impact on improving business outcomes while enabling my team to focus on solving the correct problems. Tracking DORA metrics helped us pinpoint areas of flow that could be enhanced, dramatically boosting team morale and engagement.

Craig W.
Head of Engineering, Flipdish

Go beyond measuring with automations

See automations library

Assign an expert reviewer

Automatically identify and assign the best reviewer for every pull request.

Auto-merge safe changes

Define what safe changes mean to you (docs, tests, images) and automatically approve them.

Label missing Jira info

Automatically label PRs that don't reference a Jira ticket in the title or description.

Review sensitive files

Define a custom list of files and directories that trigger additional reviews.

Quantify AI impact to maximize ROI.

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