Fluixpro provides an autonomous AI platform, A.I.M.I., that continuously optimizes set points and load balancing across fragmented HVAC, lighting, water, and server systems in data centers. By integrating these systems through an agnostic layer and delivering real‑time analytics, the platform reduces HVAC energy consumption by up to 65% and enables up to 40% more compute output per unit of energy, while safely increasing capacity for additional workloads.
Funding
Funding not disclosed
Founders
Product
Problem
Data centers consume large amounts of energy for cooling and power distribution, and many facilities operate with fragmented HVAC, lighting, water, and server control systems that require manual tuning and analysis. This leads to inefficient resource use, higher operating costs, and limited capacity to safely add additional compute workloads.
Solution
Fluixpro offers an autonomous AI platform, A.I.M.I., that continuously optimizes environmental set points and load balancing across disparate data center systems. The AI self‑learns to identify the most efficient operating parameters, reducing HVAC energy consumption while maintaining safe temperatures for equipment. By providing an agnostic integration layer, A.I.M.I. enables HVAC, lighting, water, and server controls to communicate and coordinate automatically. Real‑time data analytics surface bottlenecks and predict safe capacity for additional IT load, allowing operators to make faster changes with minimal manual analysis. The result is up to 40% more compute output per unit of energy and significant reductions in overall facility energy use.
Target Audience
Primary customers are data center operators, facility managers, and IT infrastructure teams responsible for maintaining high‑performance, energy‑efficient environments.
Features
- Autonomous, self‑learning AI engine that continuously adjusts set points and balances loads for optimal energy efficiency
- Agnostic integration layer that connects fragmented HVAC, lighting, water, and server control systems into a unified control network
- Real‑time monitoring and analytics to identify bottlenecks, model system changes, and forecast safe additional compute capacity
- Dynamic load‑balancing controls that reduce HVAC energy consumption by up to 65% while preserving equipment reliability
- Dashboard and alerting tools that enable operators to implement changes quickly without extensive manual analysis