Data-Center Cooling Intelligence
On-Prem5-12% cooling cost reduction, commissioned in seconds on a single CPU, inside your facility
Trained in seconds on a CPU, on-prem, with calibrated bounds your operators can audit. The AI buildout has made every megawatt count, and we make learned cooling optimization something any facility can commission without a data-science team.
The Problem
Fixed worst-case setpoints
Most facilities run static cooling setpoints sized for the worst case, burning energy on every normal day
AI control demands trust and time
Deep-RL cooling control works, but requires months of per-site onboarding and black-box trust
No visibility into drivers
Operators cannot see which pumps, temperatures, or schedules actually drive total consumption
Data sensitivity
Colocation and edge operators cannot send facility telemetry to the cloud for analysis
Bottom line: AI cooling control demonstrably works (Google proved it in production), but the deep-RL route keeps the savings on the table for everyone else.
The Product: An Advisory Cooling Optimizer
Connects to existing BMS, BACnet, and DCIM systems, with no new sensors and no data-pipeline project
A learned thermal-power model is fit in seconds on a single CPU, fully on-prem
Every 15 minutes the system recommends the setpoint that minimizes energy and carbon
Every recommendation carries a calibrated confidence interval and stays inside the thermal envelope
Operators keep final authority; closed-loop only after savings are measured
What-if analysis: operators can drag any lever and see the predicted KPI impact before acting
Results from a Live Facility Analysis
Anonymized findings from a real data-center deployment, with site details withheld for client privacy.
Operational Insights Delivered to the Team (Examples)
Example insights from this analysis: not a black box, but a concrete, prioritized action list operators can execute today.
Prioritize the dominant pump load driver: peak-load reduction and load balancing across units
Widen the supply-temperature setpoint band gradually to prevent overcooling
Align operating schedules: early/late starts and night loads reviewed against actual demand
Verify auxiliary pumps only run when needed, avoiding premature staging
Tune chiller water inlet temperature toward the efficiency sweet spot
Start With a Read-Only Savings Estimate
No pilot budget, no installation, nothing touching your plant. Send historical telemetry and get back the same analysis shown above for your own facility. The model itself fits in seconds.
A Data Twin of Your Facility at Affordable Compute
We build a data twin of your facility from its own telemetry: a learned model you can test setpoints against before touching the plant. Deep reinforcement learning is the approach hyperscalers use for cooling. Google's DeepMind, acquired for around $500M in 2014, cut data-center cooling energy by up to 40% with it. It also needs a research team, a training cluster, and a retraining cycle. We match published deep-RL results on the same open benchmark, using their code and their metrics, on hardware that already sits in your facility.
Savings vs fixed-setpoint baseline
SustainDC benchmark, 5 held-out months (% saved)
Commissioning compute
Hardware needed to train the controller (CPUs)
Compared on the SustainDC data-center benchmark (HP Labs, NeurIPS 2024): a data twin of a facility, evaluated over 5 simulated months of workload and weather not used for fitting. Published deep-RL numbers are cited from that paper, not rerun. All results are from the data twin, at affordable compute, and a real-site advisory pilot is the next step. We report PUE, never target it; optimizing the ratio alone can inflate IT power.
On-Prem by Construction
Telemetry never leaves
All learning and inference happen inside the facility.
No GPU, no cloud
A single CPU is enough, with no data-pipeline project required.
Deterministic & auditable
Calibrated bounds instead of black-box trust; every recommendation is inspectable.
Ready to See Results in Your Data Center?
Book a free demo and we'll show you exactly how IoTGPT can improve your KPIs — using your own operational data.