Teams look busy. Token meters spin. Output does not keep pace, and some of that spend never touches production. R8dor.io shows quality per token: who earns their credits, who burns them, and where budget goes to waste.
Engineering org · June 2026
Token spend vs dev velocity
AI spend (MTD)
$18,420
+220% vs last month
Merged PRs
47
−2% vs last month
Unaccounted
23%
Non-work / no hook
| Engineer | Tokens | Output | Signal |
|---|---|---|---|
Alex M. High burn, flat output | 4.2M | 12 PRs | Review |
Sam K. Spend up, velocity flat | 3.8M | 11 PRs | Review |
Jordan L. Low tokens, strong output | 0.4M | 14 PRs | Healthy |
Casey R. Personal / off-scope use | 2.1M | 3 PRs | Waste |
“Your AI spend is up 3×.
What's the ROI?”This is the conversation in the boardroom.
R8dor.io gives you the numbers before someone else asks.
The Problem
AI generates volume that looks impressive in standups but does not ship better software unless prompted. Consumption rises. Reviews pile up. Outcomes stall. You are funding motion, not progress.
Enterprise credits are finite. Some engineers burn them on personal tasks and off-scope work while delivery stays flat. Does every PR meet your standards for clarity, maintainability, and functionality AT SCALE?
What R8dor.io does
Connect Cursor and your stack. See who spent credits, on which model, and for what. No more org-wide totals that hide individual behaviour.
Lay token consumption beside merged PRs, shipped work, and project attribution. See where activity outruns real progress.
Flag high spend with flat output, non-work usage, and missing attribution before the budget review.
R8DOR analyses AI interactions to generate governance insights without retaining your prompts or uploaded files.
Connect your coding apps, attribute spend to real people, and stop paying for the illusion.