AI that gets cheaper as it scales.
Cost engineering as a discipline: routing, caching, and right-sized models, measured in cost per outcome.
- 55% lower run-cost (illustrative)
- 4× cheaper per task with routing
- $/task the unit we optimize, not $/token
What ships.
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Model routing
The right model per task: frontier models where judgment matters, small models where they win, benchmarked on your data.
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Caching & reuse
Prompt and retrieval caching that turns repeated work into near-zero marginal cost.
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Cost budgets & alerts
Per-workflow spend budgets with alerting, so cost regressions surface like quality regressions.
The lens we use.
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Cost per outcome
We report the cost of a resolved ticket or processed claim, not a token bill nobody can act on.
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Compounding savings
Routing and caching improve with usage data; run-cost falls as volume grows.
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No lock-in economics
Model-agnostic architecture means price drops in the market become your savings, not your vendor's margin.