About
Built by operators who have already carried infrastructure risk.
The product point of view comes from environments where trust boundaries, cost ownership, and operational accountability were not optional: Amazon, Cruise, Salesforce, Oxford, and the European Space Agency.
- Today
- Working with a limited number of design partners on enterprise AI spend visibility and intervention.
- Point of view
- AI cost governance should happen at the request path, not after the invoice lands.
- Constraint
- The system has to fit real customer environments without broadening operational risk.
Why Now
The spend problem shows up before the ownership graph does.
Beliefs
What shapes the product.
From totals to traceability
Cloud bills show totals. We show which team called which model, from which service, for what purpose, and at what cost.
Decision-time intervention
We act at the moment of inference: routing to cheaper equivalent models, enforcing budgets, flagging waste before money is spent.
Built for imperfect data
Enterprise data is fragmented and inconsistent. The attribution layer should surface confidence instead of pretending the graph is clean.
Inside the customer trust boundary
Read-only connectors and fail-open design matter because the product has to fit the environments where AI spend governance actually matters.
Design Partners
Bring the release, route, or budget problem that is already bothering someone.
The best conversations start with a concrete spend spike, approval bottleneck, or routing choice that is currently expensive but still not accountable.