Wint announced AI-driven temperature analysis, a new capability that correlates water flow and temperature patterns throughout buildings, giving facility and sustainability teams a deeper understanding of how water behaves across systems.

Temperature is a critical and often overlooked attribute of water in buildings. Roughly 30% of a building’s total energy use is linked to heating and cooling water through systems such as boilers and chilled-water loops. Water temperature stability also affects health and safety, as pathogens like Legionella can develop when water temperature drifts outside regulated ranges. In addition, consistent hot-water supply is vital for occupant satisfaction in facilities such as hotels, hospitals, and multi-tenant buildings.

Wint’s temperature analysis provides a foundation for addressing all of these challenges. Its first implementation focuses on the health of boilers – one of the most energy-intensive systems in commercial and residential buildings alike. 

By correlating water flow and temperature data, Wint’s AI-based Boiler Health Analyzer detects irregular patterns that indicate system inefficiency or malfunction. This allows facility and sustainability teams to identify overheating, which wastes energy and increases costs, as well as underheating or insufficient capacity, which can leave buildings without adequate hot water. With this added insight, organizations such as hotels, hospitals and campuses can prevent service interruptions and costly emergency repairs by detecting problems early and taking action before they escalate.

“Correlating water temperature with flow patterns opens a new dimension in how we understand buildings’ water systems,” said Yaron Dycian, chief product and strategy officer at Wint. “Temperature directly impacts energy efficiency, water safety, and reliability. By introducing boiler health monitoring as the first application of this capability, we’re helping our customers enhance system performance, reduce waste, prevent damage, and advance their sustainability goals.”