Adoption isn’t logins. It’s shipped work.
Most tools count logins and tokens. Tetriz measures whether that activity ships as merged code, across every tool, session, and engineer.
From guesswork to ground truth.
- Seat counts say the whole team is “using AI”
- Activity dashboards with no link to shipped work
- Guesswork on who’s moved past autocomplete
- 3 in 5 sessions actually reach a merged PR
- AI’s real share of PRs, LOC and issues closed
- Depth of use and idle seats, per engineer
Adoption, measured where the work happens.
From one engineer to organization-wide clarity.
Tetriz turns private AI coding activity into patterns leaders can act on, without exposing individual work.
Adoption proves AI ships. Impact proves it’s worth it.
Or explore the rest of the platform
Questions leaders ask.
Tetriz reads real coding sessions across every connected tool and measures AI-attributable PRs, commits, LOC and issues, plus effective adoption: the share of sessions that reach a merged PR. Licences are ignored; behaviour is the signal.
Started sessions are easy to inflate. Effective adoption counts only sessions that end in merged code, the honest measure of whether AI activity is actually shipping.
Cursor, Claude Code, GitHub Copilot, Codex and others across repos and project management. One connection reads every tool.
No. One connection reads existing sessions, with nothing to install per engineer and no self-reporting. First insight lands in about 15 minutes.
Per-engineer adoption is private to that engineer by default. Leaders see team- and org-level rollups through RBAC, never individual keystrokes or code.
Never. Tetriz is read-only, session-level data stays in your environment, and nothing is used to train models.