# 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.

## Key facts

- **Domain:** REAL ESTATE
- **Status:** Built
- **Use when:** Property managers running large portfolios need to shift maintenance from reactive to predictive.
- **Reference engagement:** Available for new engagements

## The problem

Property portfolios drown in reactive maintenance. Costs are unpredictable, tenant satisfaction erodes, and the same equipment fails the same way every quarter.

## 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.

## 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.

## 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

## Tech stack

- Python
- TensorFlow
- React
- Node.js
- MongoDB
- AWS
- IoT Sensors

## 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

## Related

- [All accelerators](https://levent.ai/accelerators/)
- [Next: Demand Forecasting and Inventory Optimiser](https://levent.ai/accelerators/demand-forecasting/)

Metrics on this page are estimated and expected improvements describing the design intent of the accelerator. Real-client delivered metrics stay in private decks; see https://levent.ai/ai-content-policy/ for the abstraction policy.

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**Canonical URL:** https://levent.ai/accelerators/mauza/