Faros AI applies causal, per-team modeling to isolate AI's effect on delivery, a real methodological step past simple correlation, but it doesn't publish the model itself. Becoming AI-native also needs a number a leader can audit end to end. Tetriz builds that number bottom-up: AI ROI, from real sessions and merged PRs, priced by a disclosed formula.
Faros AI's GAINS product applies causal modeling per team, controlling for confounders like seniority and repo complexity, a genuinely rigorous approach whose underlying method isn't published for a leader to audit. Tetriz's AI ROI is built the other way: cycle-time savings per merged PR, valued at loaded engineering cost, minus the AI Tax and total AI spend, a formula visible end to end.
Faros AI's causal-model outputs and Token Intelligence classifications are signals engineering leaders use for planning, not an automated score delivered to the individual engineer. Every engineer using Tetriz gets a personal, weekly Session Quality Score directly, visible only to them and their org's admins, never a ranking.
Faros AI's strength is stitching together 100+ software-delivery data sources into one model, an advantage for org-wide reporting. Tetriz needs far less to start: the Desktop App on an engineer's machine, plus one admin-authorised connector for pull-request correlation.
Not usually. Teams that value Faros AI's causal, confounder-controlled delivery-impact research and its data-platform breadth tend to keep it, and add Tetriz for the auditable, session-level AI ROI layer underneath.
The Tetriz Desktop App on each engineer's machine, plus admin-authorised access to GitHub, GitLab, or Bitbucket for pull-request correlation. No 100-connector data platform required for a personal coaching signal from day one; the line-item ROI figure builds up as enough comparable sessions accrue.
It's a genuinely rigorous signal: causal modeling per team, controlling for confounders like seniority and repo complexity, is a real step past simple correlation. What a switching team can't do is audit the model itself, only see its output. Tetriz's AI ROI is a disclosed formula instead, computed from the org's own sessions and merged PRs.