[ REAL ESTATE ]
Mauza
Property Inventory and Prospects Platform
IoT sensor telemetry plus historical maintenance data, fused into a rolling predictive maintenance schedule. Work orders fire before equipment fails.
Domain
REAL ESTATE
Status
Built
Use when
Property managers running large portfolios need to shift maintenance from reactive to predictive.
Reference
Available for new engagements
01. The problem
Property portfolios drown in reactive maintenance. Costs are unpredictable, tenant satisfaction erodes, and the same equipment fails the same way every quarter.
02. The approach
Survival analysis and anomaly detection on IoT sensor telemetry, trained against historical maintenance records, generating a rolling 30-day predictive maintenance schedule per asset.
03. The architecture
Python + TensorFlow for the models. Node.js + MongoDB for the work-order system. React for the operations dashboard. Sensor ingestion runs on AWS. Vendor matching, tenant communication, and SLA tracking integrate around the predictive schedule.
04. The outcome
Maintenance shifts from reactive firefighting to scheduled prevention. Operations teams catch most failures before tenants notice them.
[ ATI SHAPE ]
Predictive intelligence
Survival analysis + anomaly detection per asset
Agentic execution
Automated work-order dispatch with vendor matching
Secure data
Tenant data and IoT telemetry stay inside the property estate
Actionable outcomes
Scheduled work orders before failure, not after
[ IMPACT ]
Rolling 30-day
Predictive maintenance horizon per asset
Auto-dispatch
Work orders + vendor match without human triage
SLA-tracked
Resolution measured from report to sign-off
[ NEXT STEP ]
See Mauza in your data.
[ READY FOR YOUR STORY? ]