Causal, confounder-controlled impact modeling
Applying causal analysis per team, controlling for confounders like seniority and repo complexity, is a genuinely rigorous research approach Tetriz's per-PR cost formula isn't built to replicate.
Tetriz vs Faros AI
Faros AI models AI's delivery effect. Tetriz shows the formula behind its number.
Faros AI's GAINS product applies causal modeling per team, controlling for confounders like seniority and repo complexity, a real step past simple correlation, and its Token Intelligence feature classifies AI token spend as productive, inefficient, or wasteful. Tetriz takes a different, fully disclosed approach: AI ROI is priced from the sessions and merged PRs behind it, cycle-time savings valued at loaded engineering cost, minus the AI Tax and total AI spend.
A modelled effect and an auditable formula answer different questions
Faros AI's core strength is data-platform breadth: over 100 software-delivery connectors feeding a causal-ML model (GAINS) that controls for confounders like seniority and repo complexity to isolate AI's effect on delivery, at the team level. Its Token Intelligence feature also classifies AI token spend as productive, inefficient, or wasteful, tracing it back to the session that produced it. What Faros doesn't publish is the model itself: the causal method behind a given number isn't something a leader can audit line by line. Tetriz takes the other path: AI ROI is a formula, not a model, cycle-time savings per merged PR, valued at loaded engineering cost, minus the AI Tax and total AI spend, computed from the org's own sessions and visible in full.
Where Tetriz wins vs Faros AI. Session-level data, mapped to the pull request it produced.
| Capability | Tetriz | Faros AI |
|---|---|---|
| Methodology transparency | AI ROI is a disclosed formula: cycle-time savings per merged PR, valued at loaded engineering cost, minus the AI Tax and total AI spend, an org-level number a leader can audit end to end. | GAINS applies causal modeling with confounders controlled, a real step past simple correlation, but the model itself isn't published for a leader to audit. |
| Recoverable spend & individual coaching | Flags AI spend that went to abandoned or no-ship sessions as a likely candidate for prompt coaching or seat right-sizing, with a drilldown into the exact sessions and repos behind it, visible to the engineer who ran those sessions and their org's admins. | Token Intelligence classifies token-level waste as productive, inefficient, or wasteful, tracing it to the session that produced it; its public pages don't describe routing a coaching action back to that engineer. |
| Weekly coaching loop | A weekly Session Quality Score, scored across six prompt-quality dimensions, returned directly to the engineer who ran the session, visible only to them and their org's admins, never a cross-engineer ranking. | GAINS and Token Intelligence publish causal-model outputs and token classifications for engineering leaders; there's no published equivalent of an automated score sent directly to the individual engineer. |
| Harness improvement & enforcement | A vetted, pre-filtered base of skills, sub-agents, and rules is curated by a recommendation engine; the Harness Control Plane publishes that baseline to every engineer's machine and tracks each one's install status: installed, pending, or excluded. | Its context-engineering layer (Clara) surfaces org-wide context; it doesn't publish a per-engineer harness baseline or track install status against one. |
Where Faros AI wins. Said plainly, credit where it’s due.
Data-platform breadth & token-level spend classification
100+ software-delivery connectors and Token Intelligence's productive/inefficient/wasteful classification give Faros a broad spend-attribution surface Tetriz doesn't attempt to match at that scope.
Questions teams ask. Comparing Tetriz and Faros AI.
It's a more rigorous signal than most: causal modeling per team, controlling for confounders like seniority and repo complexity, is a real step past simple correlation. What it doesn't publish is the model itself, so a leader sees the output but can't audit how it was built. Tetriz's AI ROI is a disclosed formula instead: cycle-time savings per merged PR, valued at loaded engineering cost, minus the AI Tax and total AI spend, computed from the org's own sessions.
Not usually. Teams running Faros for causal, confounder-controlled delivery-impact research and its data-platform breadth typically keep it, and add Tetriz for the auditable, session-level AI ROI and harness layer underneath.