AI adoption is accelerating. Advantage is not.
Every org is spending on AI. Only some are pulling ahead. The difference is a curve, and whether you can see where you sit on it.
Where your org is, and how far AI can take you.
The AI-Native Index maps how engineering teams capture value from AI over time. The gap between best-in-class and the rest is widening. Open a level to see what it looks like and how to move up.
- AI use is standard across teams
- You track adoption, not just licenses
- Quality is watched alongside speed
You can’t improve a position you can’t locate.
Every org runs AI. Almost none can locate where they actually stand. Three things keep the picture blurry:
You’re measuring gross, not net
Seats and logins look like progress. They don’t tell you what shipped.
The AI Tax goes uncounted
The gap between what AI produces and what you capture grows, unseen.
There’s no shared baseline
No measured position, so “we’re AI-native” is impossible to prove.
Tetriz locates you, then closes the gap.
Want to see for yourself where your org stands?
Take the 3-minute AI-Native quiz and get your level on the curve. No data connection, no sales call.
But why us, and not another dashboard?
Which is why the comparison isn’t close: everything the others measure, plus the AI-native layer only we do.
| Capability | Tetriz | LinearB | DX | Swarmia |
|---|---|---|---|---|
| Delivery & DORA metrics | ||||
| Cycle time & PR throughput | ||||
| Git & project-tool integrations | ||||
| AI adoption & prompt-to-PR attribution | ||||
| Reads the AI coding sessions | ||||
| AI quality tied to the code that shipped |