A UAE residential operator managed 12 luxury towers with consistently high occupancy and a demanding tenant mix. Multi-channel service intake created duplicate tickets and blind spots while costs rose, especially during summer AC peaks and weekend spikes. Operations grew complex and costly even as the mandate remained a premium tenant experience with lean OPEX. The business intent was to restore tenant trust, stabilize SLAs, and protect NOI. Machine Learning has become a transformative technology across industries, empowering businesses to automate processes, predict outcomes, and solve complex problems with data-driven insights.
Fragmented intake across reception, email, app, and WhatsApp created duplicate tickets and blind spots.
Weekend spikes and summer AC issues routinely overwhelmed dispatchers. Managers lacked a single, auditable view of SLA performance.
Slow acknowledgments and repeat faults drove reopens and delays.
Rising churn and negative review sentiment threatened NOI. Target: restore tenant trust, stabilize SLAs, and protect NOI.
Adapt and improve over time through machine learning. Models learn from seasonal loads, asset behavior, and ticket patterns so triage, dispatch, and communications recommendations get better over time.
Within 12 towers over 5 weeks, Advisoryn delivered a low-disruption pilot that proved outcomes and set the path to scale:
Works with what you have: integrates on top of CMMS/BMS/ERP and vendor portals—no rip-and-replace.
Operator-led, audit-ready: property ops, data, and finance expertise combined so fixes are practical, traceable, and board-friendly.
Fast pilot to scale: focused deployment that demonstrates value first, then expands portfolio-wide.
Improves itself: rules and models adapt to seasons, loads, and behaviors for continuous gains.
Governance by design: UAE-hosted, role-based access, full audit trails, and reporting aligned to RERA/owner and finance requirements.
Change that sticks: training, playbooks, and adoption support so teams keep the gains after go-live.