Align the operating data
Start by assessing telemetry availability, units, timestamps, and equipment relationships. A shared operating picture depends on data that can be compared across systems.
Coordinate the entire AI factory as one system.
Connect facility infrastructure, compute capacity, and workload demand to reveal constraints, evaluate tradeoffs, and coordinate the use of available resources.
A thermal constraint changes compute allocation and workload placement. This diagram explains the approach; it is not live product telemetry.
- 01 / Physical infrastructure
Cooling headroom tightens
Power, cooling, water, and heat define the available operating envelope.
- 02 / PhysaFlow decision
Trace the dependency
Relate the thermal constraint to affected servers and the workloads using them.
- 03 / IT infrastructure → AI workloads
Reassign demand
Evaluate moving eligible demand from constrained compute to servers with available power and cooling. Check network and storage dependencies.
- 04 / Operator review
Validate available capacity
Compare usable MW and supported workload demand before and after the proposed move, within approved operating limits.
Reassess facility conditions ← updated compute allocation ← training and inference placement
Turn signals into decisions operators can evaluate.
Model the dependencies
Relate workload demand to compute, power, and thermal constraints. Evaluate where a change in one layer creates or consumes headroom in another.
Evaluate reassignment
Compare potential workload and resource allocations against facility limits. Make the expected capacity outcome and its assumptions reviewable before action.
From fragmented signals to coordinated capacity.
Connect
Bring telemetry and operating context into a shared picture. Confirm available data sources during assessment.
Understand
Relate capacity, constraints, and workload demand to reveal dependencies across systems.
Optimize
Evaluate opportunities to reallocate power, cooling, and compute within operating limits.
Coordinate
Review recommendations with operators and define which actions may progress to approved automation.
See the opportunity before changing anything.
ShadowMode is the observation-first adoption path: evaluate constraints and potential interventions before giving the platform operational control.
Observe first. Recommend second. Automate when you’re ready.
- Begin without changes to production controls.
- Review recommendations and their reasoning with your team.
- Validate the capacity opportunity against operating conditions.
- Define safety boundaries before progressing to approved automation.
Define the boundaries before enabling action.
The adoption model starts with observation and recommendations. Operators validate the reasoning and define which actions, if any, can progress to automation.
- Agree on the systems in scope and the quality of available data.
- Document power, thermal, reliability, and workload constraints.
- Review proposed changes and the expected capacity effect.
- Agree on approval, measurement, and recovery procedures before operational changes.
Deployment scope, supported actions, and control requirements are assessed for each environment.
Add intelligence—not another infrastructure overhaul.
The approach starts with the infrastructure and controls already in your facility. Assess available telemetry, system access, and operating practices to define a practical scope for coordination.
Connection requirements and supported actions must be confirmed for your environment during assessment.
Start with a scope you can validate.
Identify the facility’s primary constraint, confirm accessible data, and agree on an observation scope. Review the resulting opportunity against operating evidence before considering broader deployment or automation.
How much AI capacity is already inside your facility?
Start with a modeled capacity assessment. Explore how your facility inputs affect the estimated opportunity, then use the assumptions to guide deeper measurement.
