AI Optimization
Machine learning models analyse live and historical operational data to identify inefficiencies and recommend energy, cooling and workload improvements.
Solutions
AI‑powered optimization for data centre energy, cooling and carbon performance.
Swizet provides a software intelligence layer that connects existing systems, learns from operational data and recommends efficiency improvements.
Cooling Analytics
Thermal and environmental signals are monitored to forecast temperature spikes, reduce overcooling and improve airflow management.
Predictive Alerts
Anomaly detection and forecasting help operators act before inefficiencies become downtime, disruption or equipment stress.
Carbon Tracking
A climate intelligence layer calculates emissions, tracks PUE and supports ESG‑aligned reporting with measurable sustainability indicators.
Energy Insights
Energy meters, workload metrics and cooling data are unified to reveal usage patterns, cost pressure and practical optimization actions.
How Swizet creates value
From fragmented monitoring to coordinated optimization.
The platform collects infrastructure data, processes it in real time, identifies inefficiencies and produces actionable recommendations for cooling setpoints, workload balancing, airflow and power utilization.
Reduce energy waste
Recognise abnormal consumption spikes, inefficient usage patterns and underutilised or overloaded resources.
Improve reliability
Predict thermal and infrastructure risks early so operators can act before downtime or equipment stress.
Track sustainability
Connect operational efficiency with carbon, PUE and ESG reporting support.
Software‑based deployment