Vertiv Holdings Co (NYSE: VRT), a global leader in critical digital infrastructure, today announced it has acquired Waylay NV, a Belgium-based leader in hyperautomation and generative AI software platforms, as part of its continued investment in AI-driven monitoring and control technologies for its power and cooling systems. Vertiv expects this investment to strengthen its ability to enable customers around the world to increase uptime, optimise energy usage, and enhance operational intelligence across their critical digital infrastructure.

As AI workloads continue to drive strong growth in data centre demand, the need for intelligent, adaptive infrastructure continues to grow. AI-based digital services for power and cooling systems are essential to support the high-density, high-performance computing environments powering generative AI. These services support real-time monitoring, predictive maintenance, and dynamic optimisation of power and thermal performance. With the integration of Waylay’s advanced technology, Vertiv is positioned to deliver these advanced capabilities at scale, helping customers meet the evolving demands of critical digital operations. 

“With the addition of Waylay’s technology and software-focused team, Vertiv will accelerate its vision of intelligent infrastructure — data-driven, proactive, and optimised for the world’s most demanding environments,” said Vertiv’s CEO, Giordano Albertazzi. “We are excited about the operational efficiencies and the resilience that Vertiv’s further augmented predictive capabilities can provide for our customers.”

Founded in 2014, Waylay has developed an AI-driven hyperautomation software platform that enables workflow-based automation, system integration, and orchestration across a wide range of connected assets, enterprise applications, and cloud services. Among its capabilities are data analytics and orchestration scenarios that operate with unprecedented speed, significantly cutting the cost of integration and product deployment. The software can analyse real-time machine data, identify operational trends, and propose predictive actions that minimise downtime and improve system performance.