Data Input & Integration
Connect APIs, sensors, energy meters, cooling systems, server utilisation and monitoring tools without hardware modifications.
Technology
A cloud‑native AI architecture for real‑time infrastructure intelligence.
Swizet uses a structured Google Cloud data pipeline to transform raw data centre signals into predictive recommendations, dashboards and sustainability metrics.
Core stack
Google Cloud, streaming data and Vertex AI models.
The platform is designed to handle large volumes of streaming data from complex data centre environments while delivering actionable insights with minimal latency.
It combines data engineering, machine learning and cloud services to create a unified intelligence layer for real‑time optimization strategies.
System workflow
Six stages from infrastructure input to climate impact intelligence.
Real‑Time Streaming
Cloud Pub/Sub moves infrastructure data through a reliable, low‑latency messaging pipeline.
Data Processing
Dataflow prepares raw signals by removing noise, standardising formats and enriching datasets.
AI Prediction & Optimization
Vertex AI models forecast energy demand, temperature spikes, anomalies and optimisation actions.
Dashboards & Alerts
Operators see energy, cooling, recommendations, alerts and historical trends in a clear interface.
Climate Impact Tracking
Carbon emissions, PUE, sustainability indicators and ESG reporting support complete the operational loop.
InputEnergy, cooling, sensors, workloads
ModelForecasting and anomaly detection
ActionOptimization recommendations
Closed‑loop intelligence
Designed to move from recommendations toward autonomous optimization.
Through continuous learning and feedback loops, Swizet can evolve from recommendation‑based support to semi‑autonomous and fully autonomous optimization, reducing manual intervention and improving efficiency over time.