Zelena AI provides an AI‑driven platform that continuously models and optimizes data‑center infrastructure to improve energy efficiency and operational resilience. By ingesting real‑time telemetry from power, cooling, and compute systems and applying reinforcement‑learning algorithms, it automatically adjusts HVAC, power distribution, and workload placement to lower PUE and carbon intensity for hyperscale and enterprise data‑center operators.
Funding
Funding not disclosed
Founders
Product
Problem
Data centers that support large‑scale AI workloads consume significant electricity and generate high operational costs, while traditional design and management tools lack the granularity to optimize energy use in real time. This results in lower PUE (Power Usage Effectiveness) scores and limits the sustainability of expanding compute demand.
Solution
Zelena AI offers an AI‑driven platform that continuously models and optimizes data‑center infrastructure to improve energy efficiency and operational resilience. The system ingests telemetry from power, cooling, and compute subsystems, then applies machine‑learning algorithms to predict load patterns and recommend hardware placement, cooling set‑points, and workload distribution. Optimizations are executed via an automated control layer that adjusts HVAC, power distribution, and server allocation without manual intervention. Results are presented through a web‑based dashboard that visualizes real‑time PUE, carbon intensity, and cost metrics, enabling operators to meet sustainability targets while maintaining AI performance.
Target Audience
The primary customers are hyperscale cloud providers, colocation operators, and enterprise data‑center managers who run high‑performance AI workloads and need to reduce energy costs while meeting sustainability commitments.
Features
- Real‑time data ingestion from power meters, environmental sensors, and workload schedulers via a unified API
- Predictive analytics engine using reinforcement learning to forecast demand spikes and recommend proactive cooling adjustments
- Dynamic workload placement optimizer that balances compute density against thermal headroom to minimize hot‑spot formation
- Digital‑twin simulation of the entire facility for scenario testing and capacity planning
- Automated control loops that interface with HVAC, UPS, and PDUs to execute energy‑saving actions on the fly
- KPI dashboard with customizable visualizations for PUE, carbon‑footprint, and cost per compute unit
- Integration hooks for major cloud management platforms (e.g., OpenStack, VMware) and support for industry standards such as IEC 61850