Verrus builds purpose‑built data center facilities with an intelligent energy management platform that coordinates power, cooling, and compute. The system monitors grid conditions, carbon intensity, and workload demands to dynamically balance energy use, delivering up to 99.999% availability while reducing peak demand and carbon emissions. It provides modular power distribution, AI‑driven thermal management, and real‑time telemetry APIs for hyperscale cloud providers and AI enterprises.
Funding
Funding not disclosed
Founders
Product
Problem
Current data center designs are optimized for legacy workloads and rely on inefficient backup power, leading to excessive grid stress, high carbon emissions, and limited flexibility for bursty AI compute demands. This results in reduced sustainability, constrained scalability, and difficulty meeting stringent uptime requirements for modern high‑performance applications.
Solution
Verrus delivers purpose‑built data center facilities that integrate an intelligent energy management platform across power, cooling, and compute layers. The platform continuously monitors grid conditions, carbon intensity, and workload characteristics to dynamically balance energy consumption, minimizing grid impact while honoring customer availability SLAs of up to 99.999%. By replacing underutilized legacy power assets with modular, compute‑aware power distribution, Verrus enables flexible, high‑density AI workloads to scale without over‑provisioning. Real‑time telemetry and predictive analytics feed a cloud‑based dashboard, giving operators visibility into carbon footprints, grid usage, and performance metrics, supporting both sustainability goals and operational efficiency.
Target Audience
The primary customers are hyperscale cloud providers, enterprise AI platforms, and large‑scale compute‑intensive enterprises seeking high‑availability, low‑carbon infrastructure for AI and high‑performance workloads.
Features
- Grid‑aware power flow controller that shifts load in response to real‑time grid congestion signals, reducing peak demand charges and grid stress.
- Carbon‑aware optimization engine that routes workloads to periods of low regional emissions, providing transparent carbon accounting for customers.
- Compute‑aware power allocation that offers up‑to‑99.999% availability for latency‑critical services while granting flexible, batched power for AI training jobs.
- Modular electrical and cooling architecture with AI‑driven thermal management to maintain optimal PUE (Power Usage Effectiveness) across variable load profiles.
- Integrated telemetry stack delivering per‑rack power, temperature, and carbon intensity data via RESTful APIs for automated orchestration.
- Predictive maintenance analytics that forecast equipment wear and schedule interventions without disrupting service.
- Support for renewable energy integration and on‑site energy storage to further reduce reliance on grid imports.