Fan Predictor & +60-Second Vision

+60-Second Vision: Predicts pressure and temperature a full minute into the future by streaming real-time data and then forecasting the control variables using our patent-pending deep learning model.

Proactive PID Nudging: Gently nudges the PID controller ahead of upcoming thermal events to eliminate headroom and overshoot, whilst preserving the existing control philosophy.

Intelligent Confidence Scoring: Cooling AI assigns a confidence score to its predictions based on historical learning. If it encounters an unfamiliar scenario, the score drops, ensuring it never makes an unsafe operational change. If low confidence persists, the model can be rapidly retrained.

Calibration and bias handling Improves forecast consistency by correcting slow offsets without learning transient spikes as permanent bias.

Shadow and staged authority modes: Allows safe deployment: observe first, then gradually increase bounded authority once validated.

Energy analytics: Supports customer conversations with measured performance comparison rather than generic efficiency claims.

Why fan optimization matters:

Fan power scales approximately with the cube of fan speed. That means small reductions in unnecessary speed can create disproportionate reductions in fan energy.

A data hall does not need dramatic changes to benefit; it needs better timing, less headroom, fewer overshoots and smoother transitions across a fleet of units running every hour of the year.

The Predictive Fan Optimizer targets the energy hidden in conservative control behavior. Reactive loops move after the process variable has already changed. Cooling AI™ gives the loop foresight, helping reduce the area under the fan-speed curve while maintaining pressure-control intent.

DCIM Digital Twin of Your Data Hall

Gain unprecedented visibility into your white space

By building a comprehensive digital twin of your facility, our system allows you to visualize airflow paths from fan wall units directly to your servers, with live telemetry, historic playback, predictive views and structured what-if simulation.

It is designed to help operators, engineers and customers understand how cooling behaviour, rack conditions, fan wall operation and pressure distribution interact across the hall, with three headline views that allow you to see live data, near-future prediction and historic data – all with the option of scenario modelling – at the touch of a button.

Features and benefits:

Identify Heat Spots: Instantly spot thermal inefficiencies before they impact IT hardware.

Zero-Risk “What If” Scenarios: Need to switch a fan wall off for maintenance? Run it in the digital twin first to see the exact impact on your operation without putting your live environment at risk.

Rack and zone context: Shows where thermal or pressure issues sit within the physical hall, not just on a trend chart.

Live heat and pressure overlays: Makes invisible cooling behaviour visible so operators can spot imbalance faster.

Compare and communicate: Improves cross-team communication by turning complex data into a visual story that management and customers can understand

Leak Detection

Advanced monitoring to ensure the continuous safety and integrity of your critical cooling infrastructure.

Refrigerant leaks rarely begin as a single obvious alarm. They often appear as a pattern of subtle changes across pressures, temperatures, compressor behaviour, valve position, cooling output and circuit performance. CoolingAI™ Refrigerant Leak Detection is designed to make those patterns visible.

It continuously monitors chiller sensor telemetry using four anomaly detection algorithms. Each algorithm looks for abnormal behaviour from a different perspective. When the system sees a sustained pattern of anomalies consistent with the symptoms of a refrigerant leak, it raises a leak insight and reports how confident it is.

Four anomaly detection algorithms: Improves robustness by avoiding reliance on a single model or one fixed alarm threshold.

Continuous chiller telemetry monitoring: Tracks asset behaviour over time rather than relying only on manual checks or reactive alarms.

Pattern-based leak symptoms Looks for combinations of abnormal behaviour that are more meaningful than isolated noisy readings.

Confidence scoring Helps operators prioritize investigation based on how strongly the evidence matches a possible leak pattern.

Chiller or circuit-level focus Where data allows, guides service teams toward the asset or circuit most likely to require inspection.

Maintenance evidence trail Supports service handover with anomaly trends, confidence history and affected telemetry points.

Why Upgrade to Cooling AI?

Cooling AI™ uses every watt of cooling power more intelligently. It helps reduce unnecessary fan energy, visualize how the hall is behaving, simulate operational changes before they are made and detect early warning patterns in chiller behaviour. The result is a practical route from traditional monitoring to intelligent cooling operation.