AI Equipment Lifecycle Management
The Problem
“You’re running buildings reactively—failures hit tenants before your team sees them coming”
Organizations face these key challenges:
Emergency work orders and after-hours callouts spike because issues are caught only after alarms or complaints
BMS/IoT data, CMMS work orders, and vendor notes aren’t connected—root cause analysis is slow and inconsistent
Preventative maintenance is calendar-based, so teams over-service some assets while missing early failures on others
Energy and comfort drift (bad setpoints, stuck dampers/valves, failing sensors) persists for weeks unnoticed
Impact When Solved
Real-World Use Cases
AI Predictive Maintenance for Commercial Buildings
This is like giving a commercial building a smart “check engine light” that looks at all the sensor data (HVAC, elevators, lighting, water systems) and warns you before something breaks, instead of after tenants complain or systems fail.
AI for Building Operations in Assisted and Independent Living Facilities
Think of this as a smart autopilot for senior living buildings: software that constantly watches heating, cooling, lighting and equipment data, then quietly tweaks settings and flags issues so the building runs cheaper, safer, and more comfortably without staff having to babysit it.