Business Value Simulator

Model what an AI code governance layer is worth against your own headcount, deploy frequency, and incident load. The simulator below is built on the same three published targets Tomosu holds itself to:

3× faster review throughput once AI-generated PRs clear the governance lane — measured against human-only review queues.
~40% drop in repeat escalation clusters once production incidents feed back into the lane as guardrails — pilot target, day 90.
A board-ready Production Reliability Index trendline in 90 days — from read-only connection to first executive review.

These are targets, not guarantees: every number the simulator returns is a projection from the inputs you enter. For how the underlying indexes are calculated, see the Tomosu indexes; for how the governance lane produces them, see how it works.

Platform ROI Engine

Tomosu Business Value Simulator

Operational Parameters

SOFTWARE ENGINEERS

AVG. BURDENED ANNUAL COST

ANNUAL SUPPORT SPEND

TOMOSU ANNUAL COST

Total Spend Without Tomosu

Combined engineering payroll and standard support desk overhead.

Total Savings With Tomosu

ROI: 0%

Engineering capacity recovered from governance overhead + support deflection savings, net of platform cost.

Projected Spend (Target Efficiency)

Optimized runtime budget including platform licensing fees.

Projected SWE Headcount Equivalency

Headcount capacity required to deliver identical roadmaps under Tomosu governance.

0 SWEs

Functional Savings Attribution

Velocity Return

Recovers ~10% of eng capacity lost to governance rework & review churn — equivalent to 0 FTEs redirected to features.

Support Optimization

Deflects ~25% of support spend via automated routing and proactive governance signals before tickets escalate.

*Modeled estimates based on standard benchmarks — not a contractual guarantee.