Admit the next AI workload
Compare the requested profile with modeled constraints before a service or revenue commitment. For AI cloud infrastructure and operations teams.
Explore the decision →Capacity decisions · proof before autonomy
Make your next capacity commitment with confidence.
Scope a paid, bounded capacity decision assessment using authorized historical inputs. Understand the constraints, test a workload profile, and give decision-makers a reviewable basis for the next commitment.
A decision assessment. No live connection or equipment control.
Synthetic example · DEMO-01 · Fixture B
Model screen: REPAIR
A smaller commitment needs review
Requested increment
7.0 MW
Modeled eligible increment
5.8 MW
Proposed revised increment
5.8 MW
Additional load above the same 20.0 MW reference. 2026-09-22 · 00:00–01:00 UTC.
The proposed 7.0 MW increment exceeds the fixture’s modeled 5.8 MW increment. A reduced workload profile requires review.
Decision still to make
Would the reduced 5.8 MW profile still meet the service and commercial requirement?
Economics unestimated. Operator acceptance and delivered capacity: not applicable. This synthetic model screen provides no operational authority.
Start with the decision
What are you ready to commit?
Constrained AI infrastructure makes each capacity commitment consequential. Start with one defined question and the person accountable for it.
Commit colocation capacity
Review a proposed tenant increment against the declared facility conditions before commercial commitment. For colocation operators and infrastructure executives.
Explore the decision →Considering cooling investment or bridge power and on-site generation? These are investigation options until a separate scope evaluates their feasibility and economics.
Worked example · synthetic
A smaller profile is only useful if it meets the business need.
The example separates the requested increment, modeled limit, and unresolved commercial decision. It is not a customer result or permission to operate.
Synthetic example · DEMO-01 · Fixture B
Model screen: REPAIR
A smaller commitment needs review
Requested increment
7.0 MW
Modeled eligible increment
5.8 MW
Proposed revised increment
5.8 MW
Additional load above the same 20.0 MW reference. 2026-09-22 · 00:00–01:00 UTC.
The proposed 7.0 MW increment exceeds the fixture’s modeled 5.8 MW increment. A reduced workload profile requires review.
Decision still to make
Would the reduced 5.8 MW profile still meet the service and commercial requirement?
The proposed revision is 1.2 MW below the request. No minimum commercially viable workload is specified for this fixture.
Economics unestimated. Operator acceptance and delivered capacity: not applicable. This synthetic model screen provides no operational authority.
The brief carries the conditions with the conclusion, so the next reviewer can see what remains unproven. See what belongs in the review package.
The assessment
A defined question. A reviewable answer.
Agree the deliverables before work begins. A useful finding can support a conditional path, explain why a request does not fit, or identify evidence that is still missing.
01
Decision contract
One capacity question, facility boundary, historical window, agreed alternatives, and acceptance criteria for the report.
02
Readiness and constraint findings
An input inventory, assumptions, binding constraints, and evidence gaps that limit what can be concluded.
03
Bounded model comparison
Requested and revised workload profiles assessed against the agreed conditions. Unassessed alternatives remain separate.
04
Review package
A concise decision brief with the supporting model record, limitations, unresolved questions, and agreed review rounds.
Inputs and responsibilities
Agree the evidence boundary first.
The assessment uses authorized historical inputs. Data readiness and review responsibilities are part of scoping.
01
Customer responsibilities
Name the decision owner and operational reviewer. Confirm permission to share inputs, the facility boundary, workload requirements, and applicable operating constraints.
02
Historical inputs
Agree the relevant load and cooling history, capacity commitments, topology, reserve policies, and workload profiles. Readiness is reviewed before modeling is scoped.
03
GridNinja responsibilities
Document assumptions and gaps, perform the agreed analysis, distinguish modeled findings from operating permission, and deliver the contracted review package.
What is demonstrated
Inspect the work and its limits.
The public assessment example is synthetic software and explanatory material. It does not establish performance on an operating site.
GridNinja is developing an AI Data Center Virtual Capacity Control Plane: a runtime-assured virtual capacity engine for inside-the-fence orchestration. That development direction is separate from the current assessment offer.
Named delivery responsibilities, relevant experience, and reviewer availability must be established during scoping. Public team credentials and customer outcomes are not yet published.
Your next capacity decision
Bring the question before the commitment.
Start with fit and data readiness. A paid assessment proceeds only after scope, responsibilities, price, and deliverables are agreed.
Related operator resources
Continue the proof path
- A control-plane direction, with evidence before authority.Explore the intended virtual capacity control-plane architecture, its evidence and authority boundaries, and the current bounded assessment offer.
- 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.
- Scope your next capacity decision.Scope a paid, bounded capacity decision assessment using authorized historical inputs, explicit conditions, model comparisons, and a reviewable brief.