vHive is extending its WorldTwin AI™ platform into the data center market as operators face growing pressure to maintain accurate visibility across increasingly complex physical infrastructure. The move takes technology the company has developed across telecommunications and renewable energy into an industry where AI-driven demand is rapidly changing facility configurations, equipment requirements and deployment cycles. vHive says its platform is designed to close a persistent gap between the physical infrastructure inside facilities and the digital systems used to manage it. The company has already digitized more than 120,000 critical infrastructure assets globally, giving it an established base for applying its approach to data center environments.

AI infrastructure growth is adding another layer of complexity to facilities that already contain dense combinations of servers, racks, power equipment and cooling systems. Traditional Data Center Infrastructure Management platforms can provide visibility into network and logical environments, but the physical layer can remain fragmented across spreadsheets, databases, drawings and manual site checks. vHive’s approach creates a structured digital representation spanning facility floors, racks and individual equipment, giving operators and colocation tenants a common view of physical assets. As a result, the company is positioning digital twins as an operational layer that can connect what exists on the floor with what facility software says should exist.

Physical Assets Become Part Of The Digital Model

“Data centers are the backbone of the modern AI economy, yet many operators struggle with an ‘inventory visibility gap’ because their digital systems are disconnected from the physical assets on the floor,” said Yariv Geller, CEO and Co-Founder of vHive. “By applying the same Physical AI principles we perfected in the telecom and renewable energy sectors, we are enabling data center operators to move beyond static, manual spreadsheets to a dynamic, always current, structured understanding of their facilities. We are creating a bridge where the physical asset, the digital model, and the management software act as one.”

The WorldTwin AI™ engine automates the collection of field and facility information through autonomous technologies, turning physical environments into structured Digital Twins. Operators can use those models to catalog equipment, map infrastructure, assess rack density and identify available floor or rack capacity without relying solely on manual inventory processes. The system also tracks physical changes over time, which matters as data center configurations evolve faster under AI and high-performance computing workloads. This gives facility teams a mechanism to maintain a current representation of infrastructure rather than repeatedly rebuilding asset records through individual site inspections.

World Models Target Space And Onboarding Challenges

“vHive’s extendible WorldTwin AI™ engine uses our proprietary World Model that enables an understanding of the facility in its entirety, enabling operators to simulate changes, spot available space to accelerate customer onboarding , and verify the build status against planning,” said Tomer Daniel, CTO and Co-Founder of vHive. “Expanding from telecom and renewable energy to Data Centers is a natural evolution for us. In fact, some of our customers provide both services. Whether it is a radio tower or a server rack, the core challenge remains the same: the need for accurate understanding of the infrastructure through an automated, repeatable workflow that ensures that digital databases match physical sites.”

The expansion comes as data center operators contend with both hyperscale growth and shortages of specialized infrastructure talent. Automating physical asset digitization could reduce the administrative workload associated with data entry, inventory reconciliation and site verification, allowing teams to spend more time on facility performance and uptime. Meanwhile, a continuously updated physical model could support planning decisions around capacity, customer onboarding and construction progress. For operators managing rapidly changing AI infrastructure, the larger proposition is not simply visualization but a tighter operational connection between physical assets and the software used to manage them.