Connect AI spending to ownership and useful work.

Create an accountable financial view

We help define the data and ownership needed to understand cloud and AI spending together. Allocation should reflect how your organization makes decisions, with coverage gaps and shared costs made visible.

• Allocation by department, application, project, model, agent, or business unit
• Usage and cost reporting across supported providers
• Forecasting, budgets, and spend thresholds
• Showback and chargeback rules appropriate to the organization

Connect financial signals to governed action

A warning, an approval request, and an enforced limit have different operational consequences. We help define when each is appropriate, who owns it, and how critical workloads are protected.

• Spend anomaly detection and investigation workflows
• Policy-driven guardrails and human approval controls
• Exception handling, escalation, and reporting cadence
• Continuous optimization tied to business priorities

Measure useful outcomes as well as tokens

Compare cost per accepted document, resolved request, or other agreed successful outcome. Include model charges and the relevant cloud, retrieval, tooling, platform, and operating costs. A lower token count can be poor value if it creates errors or rework.

Start with a FinOps for AI Review

Establish the reporting baseline, ownership model, and highest-priority decisions. Then select targeted workload improvements or evaluate financial-management platforms and specialist resources where needed.