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AI Optimization for Data Centers

Cooler, moreresilient. Operator-led.

BMS, DCIM, SCADA, power, cooling and security telemetry turned into predictive insight and operator-approved actions.

Request a demoSee the workflow
  • Read-only OT ingestion
  • Human-in-the-loop
  • On-premise or edge
  • Digital twin
DataCenterAIILLUSTRATIVEHALL A · THERMAL MAPCOOLING LOAD · FORECASTNOWRECOMMENDATIONRebalance coolingin rows 2–4OPERATOR APPROVAL REQUIREDApproveHoldNo change reaches the facilityuntil an operator approves it.

01 — Capabilities

Every capability tied to a decision on shift.

Forecast, detect, simulate — then a person approves.

  1. Predictive analyticsThermal stress, cooling demand, power draw and SLA risk, before they become incidents.
  2. Anomaly detectionDrift across BMS, DCIM, SCADA, meters, CRAC, chillers, pumps and UPS.
  3. Cooling normalisationSetpoints and airflow that follow live load, not static rules.
  4. Energy and capacity forecastingMWh, PUE and rack-level headroom for growth planning.
  5. Digital twin scenariosTest load, weather and setpoint changes before acting.
  6. Incident summariesAuto-triage tickets and predict escalation risk.

02 — Where it works

Built for the halls operators already run.

Read-only telemetry from the facility you have — no new hardware to begin.

The data hall
The data hallCooling, airflow and thermal headroom across rows and racks.
The operators
The operatorsRecommendations land with the people on shift, who approve what happens next.
The equipment
The equipmentBMS, DCIM, SCADA, power and cooling telemetry, ingested read-only.

Representative photography, public domain. Not a customer facility.

03 — Workflow

From telemetry to measured value.

  1. Ingest telemetry

    BMS, DCIM, SCADA, meters, cooling plant, UPS, cameras, access.

    Read-only pathways
  2. Model baseline

    PUE, thermal limits, safe operating limits and success KPIs.

    Before any recommendation
  3. Generate insights

    Forecast behaviour, detect anomalies, simulate cooling actions.

    Forecast, detect, simulate
  4. Approve actions

    Operators review recommendations and optional automation.

    Human-in-the-loop by default
  5. Measure value

    Cooling savings, PUE movement and reliability impact.

    Against the agreed baseline

Operational safety

AI recommends the action. Operators lead the decision.

Rule-based guardrails protect safety envelopes and compliance. Closed-loop automation runs only when explicitly approved.

04 — Governance

Operational control with audit-ready safeguards.

OT isolation

Controlled, read-only data pathways.

Approval workflow

Recommendations reviewed before execution.

Override logic

Safety envelopes and rule-based controls stay active.

Audit evidence

Lineage, approvals, outputs and escalation context.

05 — Engagement

Start with one hall. Scale from evidence.

Start with one data hall, scale to managed operations, or align the model to verified savings.

01

Quick Start

Validate one data hall over 6–8 weeks with baseline KPIs.

  • Telemetry readiness review
  • Cooling and PUE baseline
  • Initial anomaly and savings insights

02

Operational Scale

Ongoing optimisation for cooling, reliability, capacity and SLA risk.

  • Operator-approved recommendations
  • Incident summaries and escalation risk
  • Digital twin scenario testing

03

Shared Value

Commercial outcomes aligned to verified energy and reliability impact.

  • 15–30% cooling energy target
  • 10–15% PUE improvement target
  • $0.7M–$1.4M illustrative annual savings

The savings figures are illustrative targets used to frame a pilot, not a measured result on your facility. A pilot is what turns them into numbers.

06 — FAQ

Questions buyers ask before a pilot

Does DataCenterAI replace our DCIM or BMS?

No. DataCenterAI augments existing DCIM and BMS infrastructure by adding predictive analytics, optimization recommendations, and system-level intelligence.

Can it run without closed-loop automation?

Yes. Human-in-the-loop review is the default. Closed-loop automation is optional and activated only when explicitly approved within safety limits.

What data sources are supported?

BMS, DCIM, SCADA, power meters, CRAC units, chillers, pumps, UPS telemetry, cameras, access systems, and approved OT and cyber signals can be evaluated during integration planning.

What outcomes can a pilot validate?

A pilot can validate cooling savings, PUE improvement, anomaly detection, incident prevention, telemetry quality, and scale-out readiness.

Where can DataCenterAI be deployed?

Deployment can be on-premise, at the edge, or in a customer-approved cloud environment depending on security and operating requirements.

07 — Insights

Written for the people who make it stick.

All insights

AI Optimization for Data Centers

See DataCenterAI against your own telemetry.

Request a demoScope a pilot