AI Occupancy Analytics
The Problem
“You’re making rent, capex, and ops decisions with stale occupancy data.”
Organizations face these key challenges:
Occupancy/utilization data is scattered across PMS/lease systems, badge/Wi‑Fi/BMS sensors, and spreadsheets—no single source of truth
Rent and concession decisions lag the market because analysis is manual and monthly/quarterly, not continuous
Maintenance and staffing are scheduled by calendar, not actual utilization—leading to wasted spend or tenant-impacting failures
Early churn signals (reduced foot traffic, after-hours drop-off, service complaints) are missed until renewal is already at risk
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-Enhanced Property Management Decision Support
Imagine every building and lease you manage came with a super-analyst who never sleeps, reads every report, compares market data, and then suggests what rents to set, which repairs to prioritize, and which tenants might churn—before it happens. That’s what AI-augmented property management is aiming to do.