Every AI buck traced to its source
Three practice areas built around one premise: AI spend becomes controllable only when every charge maps to a decision, a team, and a business reason.
Structured around cost accountability
AI Cost Visibility
AI Spend Governance
Engineering-Finance Alignment
GPU compute, inference endpoints, and training runs carry the same accountability requirements as any cloud resource. We build the framework before the bill arrives.
We tag, trace, and attribute every line of your cloud bill to the team and workload that generated it. Untagged spend becomes the exception, not the rule.
We translate provisioning decisions into financial language and financial targets into engineering constraints — closing the gap that creates surprise invoices.
Phase 1 — Spend Audit
Full attribution pass: every AI SKU, every team, every workload. Untraced spend identified and flagged within two weeks.
Spending on purpose, not spending less
Phase 2 — Accountability Model
Cost ownership assigned to engineering teams with agreed thresholds. Finance receives a live allocation view, not a monthly surprise.
Every engagement begins with a spend audit — mapping current charges to the decisions behind them. From there, we build accountability loops that keep cost and velocity in sync.
Phase 3 — Continuous Loop
Recurring review cadence aligns engineering roadmap decisions with their forward cost implications before resources are provisioned.
See the methodology behind the service
The engagement structure is designed to be auditable. Walk through exactly how we move from a raw cloud bill to a traceable cost model.