The reactor responds to your load curve
Standard nuclear plants run at constant output because that is what they were designed for. Our AI control layer was designed for something different: continuous, autonomous output adjustment that tracks your computational demand in real time.
Why reactor output control is harder than it looks
Conventional reactor control was engineered for a world of steady industrial loads. Two physical phenomena make real-time adjustment fundamentally difficult.
The system responds slowly to control rod movement
The thermal mass of the reactor pressure vessel, primary coolant, and heat exchanger creates lag between a control input and the resulting change in electrical output. A conventional plant operator moving control rods today may not see the full effect on output for 10 to 20 minutes.
Conventional response time: 10 to 20 minutes
Our design target: under 5 minutes with AI prediction
Approach: anticipate load changes before they happen
Reactor poison builds up when you reduce power
When a reactor reduces output, iodine-135 accumulates in the fuel, which decays to xenon-135, a neutron absorber that further suppresses reactivity. This xenon buildup can prevent the reactor from returning to full power for 12 to 24 hours following a significant power reduction, a phenomenon that has historically limited commercial nuclear load-following.
Xenon recovery time (conventional): 12 to 24 hours
Our approach: model xenon state continuously
Constraint: managed within the operating envelope
The AI dispatch loop
Our control system runs a continuous closed-loop cycle: ingesting sensor data, predicting demand, issuing control signals, and measuring the response. The loop runs every 100 milliseconds. The human operator sees the full state at all times and retains override authority.
Sensor Array
400+ real-time feeds including coolant temperature, neutron flux, turbine parameters, and incoming data center load telemetry from your DCIM system.
ML Prediction Model
Generates a 30-minute rolling load forecast accounting for compute workload patterns, xenon transient state, and thermal inertia. Described as our simulation-environment design.
Control Signal
Automated control rod position and coolant flow adjustments issued within validated safety bounds. Every action is logged. Operator override available at all times.
Reactor Output
Adjusted electrical generation delivered to campus switchgear. Output measured against target at the meter; variance feeds back into the next prediction cycle.
Load Match
Generation output versus data center demand compared in real time. Match quality metric drives continuous model refinement. Loop restarts immediately.
From our internal simulation environment
These characteristics come from our simulation work on the AI control system design. They are not operational data from a deployed plant. We frame them as engineering simulation results and design targets, not proof of achieved performance.
Simulated dispatch response from current operating state to new load target
Output modulation range within the safe operating envelope before xenon management required
Control loop cycle time for sensor ingestion and model update in simulation environment
Plugs into your existing data center management infrastructure
The AI control layer communicates with your data center infrastructure management system through a published API. Your DCIM pushes load telemetry to us; we push generation output status and short-term forecasts back. The integration is designed to work alongside existing UPS and power distribution management software without requiring changes to your control architecture.
We provide a read-only monitoring dashboard accessible through your operations team's browser, showing real-time generation state, 30-minute output forecast, xenon state, and control system status. All data export is available via the same API for integration into your existing NOC tooling.
API Response Sample
{ "unit_id": "AA-UNIT-01", "timestamp_utc": "2026-03-14T09:41:00Z", "output_mw": 8.4, "target_mw": 8.5, "dispatch_state": "FOLLOWING", "forecast_30m": [ 8.5, 8.6, 8.7, 9.1, 9.3 ], "control_mode": "AUTO", "xenon_state": "NOMINAL" }
Talk to our systems team about integration requirements
We discuss AI control architecture and DCIM integration in detail with prospective partners. If you are evaluating how a co-located plant would interact with your infrastructure, we want to understand your operational requirements.
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