Faster is half the answer. Quality is the other half.
Velocity charts show speed. None show whether it cost you quality. Tetriz measures both, against your own baseline.
From it feels faster, to speed with the quality proof beside it.
- Velocity dashboards with no quality check beside them
- It feels faster, with nothing to compare it against
- AI spend with no cost per outcome
- Cycle time and review turnaround, benchmarked against the non-AI baseline
- First-pass merge, rework and defects on AI-authored code
- Dollar cost per merged PR, and the spend lost to rework loops
Only one quadrant is worth being in.
Fast and fragile is the trap. Two axes are the honest read, and Impact reports both.
Where compounding happens.
Safe, but leaves value on the table.
Looks good, breaks down later.
Hard to improve from here.
of engineering leaders report net-positive speed from AI
cite code review as the single biggest burden AI creates
are unsure they’re capturing the full impact of AI
growth in duplicated code
Velocity, quality and cost, on the same page.
From a single PR to company throughput.
How each engineer's AI-assisted work compares to their own baseline, rolled up to velocity and quality across every team, with the places quality slips surfaced early.
Impact proves it’s worth it. ROI proves it in dollars.
Or explore the rest of the platform
Questions leaders ask.
Your own pre-AI historical window, plus non-AI PRs shipped in the same period. Every impact number is relative to how your org actually worked, not an industry average.
DORA stops at deploy frequency and lead time. Impact adds first-pass merge rate, rework rate, and defects traced to specific AI sessions: the quality half DORA never captured.
OES is active agent-hours per merged PR, with rework cycles included, so a session that finished fast but needed three rewrites doesn’t read as efficient.
From outcomes, not source: first-pass merge rate, rework windows, defect linkage, and review turnaround. Tetriz stays read-only and never inspects your code.
Yes. Session-to-PR attribution links merged code back to the AI sessions that produced it, so AI and non-AI work are compared cleanly.
Impact includes per-unit economics: dollar cost per PR and token waste. The aggregate return lives on the AI ROI page.