AI Janitorial Scheduling
The Problem
“Your cleaning schedules are static while building usage changes hourly—so you pay more and miss SLAs”
Organizations face these key challenges:
Fixed routes cause over-cleaning in low-traffic areas and missed hotspots (lobbies, restrooms, food courts) during peaks
Supervisors spend hours reshuffling shifts and dispatching last-minute tasks across sites and contractors
Cleanliness quality is inconsistent—depends on which supervisor is on duty and how quickly complaints are seen
SLA reporting is manual and late: you find out after tenant complaints, audits, or penalties
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.