01
Infrastructure and facilities
Review load history, cooling constraints, reserve policy, topology, and input gaps. A result is scoped to what the model can support.
For AI cloud operators
Evaluate the next workload before committing capacity.
Scope a bounded assessment of a proposed AI workload increment against authorized historical inputs and declared facility conditions. Keep time-to-power, service requirements, and the next commercial decision in view.
Start with one admission question
What workload profile is proposed, at which facility boundary, over which window, and with what minimum service requirement? Name the infrastructure sponsor and operational reviewer before modeling.
A decision package for the right reviewers
Agree the evidence and the review question rather than a target uplift.
02
Service and commercial owners
Compare a requested profile and any modeled revision with the workload's actual service need. Revenue and delivered GPU-hours are not inferred from modeled MW.
Keep investment options separate
Rescheduling, phasing, cooling upgrades, and bridge power may be worth investigating. They remain unassessed until their technical feasibility, cost, timing, and contractual implications are evaluated.
Related operator resources
Continue the proof path
- Scope your next capacity decision.Scope a paid, bounded capacity decision assessment using authorized historical inputs, explicit conditions, model comparisons, and a reviewable brief.
- Define the capacity question before selecting the asset.Scope the evidence needed to investigate bridge power and DER for constrained AI infrastructure, with explicit site and commercial boundaries.
- Evidence informs a decision. Authority stays explicit.See how Shadow Mode, replay, allow / repair / reject decisions, audit logs, and proof packs establish evidence before bounded autonomy.
Proof before autonomy
Start with one capacity question.
Agree the decision, evidence boundary, responsibilities, and paid scope before work begins.