Wattr
Wattr is building a predictive intelligence layer for data center operations, using spiking neural networks and digital twin technology to forecast thermal conditions before they become operational problems. The platform connects workload behavior, facility telemetry, cooling dynamics, and environmental conditions to help operators move from reactive overcooling to proactive, efficient cooling management.
- Artificial Intelligence
- Clean Technology
- Energy
- Data & Analytics
- Software Only
- Internet of Things
Funding
Founders
Founder details are not available yet.
Product
Problem
Data centers face growing pressure to reduce energy consumption and carbon footprints, yet cooling systems—which account for a significant share of facility energy use—are often managed reactively. Without accurate predictions of thermal load and cooling demand, operators either over-provision cooling (wasting energy) or risk equipment overheating and downtime.
Solution
Wattr provides a data center intelligence platform that combines spiking neural networks with digital twin technology to forecast and optimize thermal conditions in real time. By creating a virtual replica of the facility's physical infrastructure, the platform simulates how heat moves through servers, aisles, and cooling units, then uses neuromorphic computing techniques to predict future thermal load and cooling requirements with high efficiency. This enables operators to adjust cooling dynamically, matching supply to actual demand rather than running equipment at constant, worst-case levels. The platform integrates historical data to calibrate models and improve prediction accuracy over time, giving operators a proactive tool for managing facilities.
Target Audience
Primary customers are data center operators, facility managers, and colocation providers seeking to reduce energy costs and meet sustainability targets.
Features
- Spiking neural network engine for energy-efficient, event-driven thermal prediction
- Digital twin modeling of facility layout, airflow, and cooling equipment for realistic simulation
- Real-time optimization of cooling setpoints and equipment scheduling based on forecasted load
- Historical data integration to calibrate models and improve prediction accuracy over time
- Read-only digital twin forecasting for rising rack temperatures, cooling inefficiency, water-use risk, workload-driven hotspots, and capacity limits