Every org is adopting AI. Almost none are compounding it.
Adoption is a purchase. Compounding is a system — one that gets better at using AI every time AI is used. Tetriz measures whether yours does, and closes the gap if it doesn’t.
Tetriz gets you up the curve in ½ the time
Every org climbs the same five levels, from gated experiments to compounding. What decides how fast is a self-improving loop: Tetriz scores how well your teams direct AI at each stage, from spec to deploy, then feeds the outcome back, so every merged PR makes the next one better. The gap between the two curves is the AI Tax — quarters of AI spend without the payoff — and the loop is what turns those years into quarters.
How Tetriz moves you up the curve
Piloting
AI is in the building, one engineer at a time. Nothing is rolled out, and nothing is measured.
What Tetriz does hereIllustrative views — your numbers come from your own tools.
- Live in 15 minutes, read-only: The coding tools Tetriz reads — Cursor, Claude Code, GitHub Copilot, Codex, Kiro, Antigravity and Confluence — connected read-only
- AI share of output: AI share of output: 61% of pull requests, 59% of lines of code and 87% of issues
- Who's actually using it: Active use per engineer: eight daily, four weekly, two seats never opened
- A baseline you can defend later: The pilot-week baseline: 14% AI share of PRs, 41% effective sessions, $63 AI spend per PR
- Nothing to mandate: Who sees what, and when: the engineer's own sessions first, aggregate team numbers next, never a mandate or a leaderboard
Deploying
Licences are out across every team, but usage is uneven and the impact is still anecdotal.
What Tetriz does hereIllustrative views — your numbers come from your own tools.
Outcome funnel · this quarter
41% of sessions reach a merged PR. That is the adoption number that tracks with output.
- Effective adoption: Outcome funnel: 1,284 sessions run, 873 produced code, 693 matched a PR, 526 verified
- Idle-licence reclaim: Seats you are paying for: 47 shipping code against 18 idle for thirty days, worth $4,320 a quarter
- Spend split by tool: Spend by tool and tier: Claude Code, Cursor and Codex with seats and cost side by side
- Where adoption stalled, and why: Adoption by team with the blocker named: Payments on review drag, Checkout on context gaps, Platform without repo rules, Data with seats unused
- A renewal case that holds up: The renewal case: $430,000 paid against $1,830,000 produced, less $190,000 lost to rework — $1.21M net
Integrating
AI is standard in the workflow and adoption is tracked, but the link between a session and what shipped is missing.
What Tetriz does hereIllustrative views — your numbers come from your own tools.
Lifecycle stage × autonomy level
Levels up in Code and Review, barely off zero in Plan and Deploy.
- AIDLC Maturity Map: Nine lifecycle stages against five autonomy levels, the reached levels filled — deep in Code and Review, empty in Plan
- Prompt-to-PR attribution: Three sessions each linked to the pull request they merged, confidence-scored at merge
- Session quality scoring: Session quality across six dimensions: clarity, specificity, context, actionability, completeness and efficiency, each scored out of ten
- Outcome Efficiency Score: Outcome efficiency: 1.8 active agent-hours per merged PR against an org median of 3.4
- Early warning on AI-generated debt: Four early-warning counters on AI-heavy code: rework, bug fix rate, revert and review burden
Reinventing
Work is being rebuilt around agents, which makes what those agents are configured with the variable that decides output.
What Tetriz does hereIllustrative views — your numbers come from your own tools.
What your agents are configured with
24
Skills
9 active
8
Sub-agents
5 active
12
Plugins
10 active
16
Rules & hooks
2 conflicts
Counted across 214 machines — including two rules files that contradict each other.
- Harness inventory: Configuration counted across 214 machines: 24 skills, 8 sub-agents, 12 plugins, 16 rules and hooks files with two conflicts
- Ranked recommendations: Three recommendations ranked for this repo, each scored with the evidence behind it
- Harness control plane: The org baseline pushed to 214 machines: 18 skills and 6 rules files shipped, two risky plugins blocked at install
- Loop engineering: Three recurring loops with their runs and cost, one flagged as not earning it
- Weekly prompt retros: A private weekly retro: prompt quality 8.4, up 1.6, with one concrete thing to try next
Compounding
Every merged PR makes the next one better, and the lead widens each quarter instead of flattening.
What Tetriz does hereIllustrative views — your numbers come from your own tools.
Config changes · this week
Outcome data decides the baseline: what correlates with shipped work stays.
- A harness that improves itself: This week's config changes: two promoted on outcome data, one retired for no observed effect
- Engineers who get better at directing agents: Three engineers' agent-direction scores rising over time, each with what improved
- Enterprise memory: Org memory feeding an agent the conventions, past decisions and codebase reasoning a task needs
- AI ROI: Return quarter over quarter: 1.4×, 2.1×, 2.8×, 3.2× as value created rises against flat spend
- The gap closes, quarter by quarter: What moved this quarter: shipping time 3.9 to 1.8 days, wasted spend $310K to $74K, rework 15% to 8.2%
You can’t improve a position you can’t locate. 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.
Know where you stand
The AI-Native Index locates your level on the curve, without a data connection or a sales call.
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 | LinearBDXSwarmia | ||
|---|---|---|---|---|---|---|---|
| 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 | |||||||
| View the full comparison | View the full comparison | ||||||