Busier is not faster. Tetriz shows which one AI delivered.
Cycle time, throughput, review and quality with AI inside the delivery picture—not sitting in a separate dashboard.
From delivery metrics beside AI, to delivery metrics that include it.
- Delivery metrics cannot identify AI work
- Sprint health read from standups
- Coding speed measured; the rest invisible
- Cycle time, throughput and quality by team
- DORA split AI against non-AI
- Focus tax and knowledge risk beside speed
One cycle-time number hides the problem.
Split into three stages, it shows whether work is waiting, looping in review, or stalled after approval.
Open to review
Time waiting for the first review.
Review in progress
Review duration and iteration cycles.
Approval to merge
The final tail before work ships.
Open to review
Time waiting for the first review.
Review in progress
Review duration and iteration cycles.
Approval to merge
The final tail before work ships.
The full engineering picture, in one place.
From one week to company throughput.
Roll individual delivery, focus and review signals into the organization view each role should see.
Everything you can measure, in one place.
Productivity connects delivery health to the rest of the AI measurement layer.
Questions leaders ask.
Cycle time, throughput, review turnaround, quality and the four DORA metrics, available by team, repository and engineer.
Into open-to-review, review-in-progress and approval-to-merge, so queueing, review and shipping delays are not collapsed into one number.
The cost of meetings, interruptions, handoffs and waiting that fragments engineering time even when coding itself becomes faster.
Areas where critical knowledge is concentrated in one or two people, making delivery fragile when those engineers are unavailable.
Access follows role-based controls. Engineers see their own detail first; managers and leaders see the appropriate team or organization resolution.