Predictive Appliance Care

For

Property managers with rental units

Solves

Appliances break unexpectedly causing operational disruption and repair costs.

Delivers

Automated maintenance schedules, replacement orders, technician bookings

Built in

about 3 weeks of creation time, MVP in 3 days

Investment

$6,000 for the MVP, $19,500 for the full product

Run it

Inside your business, or as part of your offer to clients

For property managers, turn appliance telemetry and usage data into automated maintenance schedules and replacement orders.

1. Monitor sensor telemetry and usage patterns.
2. Cross-reference anomalies with technical manuals.
3. Check parts availability and service history.
4. Book technician slots automatically.
5. Order replacement parts.
6. Dispatch replacement units.

What goes in, what comes out

The workflow

1. In
   Start with

   Appliance telemetry, usage patterns, manufacturer manuals, parts databases

2. 1

   Connect sensors

3. 2

   Ingest data

4. 3

   Detect anomaly

5. 4

   Check history

6. 5

   Book repair

7. 6

   Order parts

8. 7

   Notify customer

9. Out
   Finish with

   Automated maintenance schedules, replacement orders, technician bookings

AI does the heavy lifting, people stay in charge

Use machine learning models to analyse sensor data against normal operating patterns. Cross-reference anomalies with technical manuals and parts databases. Use vision models to interpret customer photos of faults. A human supervisor confirms critical actions before execution.

What your team sees

Key screens: Asset dashboard, anomaly alert, technician booking. Use a central dashboard for all connected units, a detailed alert view for specific faults, and a booking interface for service scheduling. Display real-time sensor data and historical trends. In this product, the first view is the asset dashboard, followed by anomaly alert and technician booking.

Accounts and administration

Asset ownership, sensor status, maintenance history, booking confirmations, audit trail, approval states, version control for maintenance logs.

Integrations and data access

IoT device APIs, parts supplier databases, technician scheduling software.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   3 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   4 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   6 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will property managers with rental units use it to solve "appliances break unexpectedly causing operational disruption and repair costs"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Prove it by comparing the monthly cost of this service against the historical cost of emergency repairs for a set of 50 units.
4. Measure, then decide. Track reduction in emergency repair calls and reduction in tenant complaints. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. First cut: one buyer (property managers), one use case (washing machines), first two modules (monitoring and anomaly detection), manual review of alerts.

After the MVP. Automated parts ordering, voice agent customer notifications, vision model integration for customer-reported faults.

What the build depends on. Connected appliance hardware with telemetry capabilities.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

$50 per appliance per month

Message to test

Stop paying for emergency repairs and start paying for predictable appliance uptime.

Where to find buyers

Property management software forums, real estate trade shows, commercial cleaning suppliers.

Lead magnet

Free 30-day trial of the monitoring dashboard for one property.

The first 30 days

1. Week 1: Install sensors on a small batch of washing machines.
2. Week 2: Train the model on normal usage patterns.
3. Week 3: Review flagged anomalies with a human operator.
4. Week 4: Confirm the system can predict failures.

Paid pilot

Prove it by comparing the monthly cost of this service against the historical cost of emergency repairs for a set of 50 units. The decision is whether the savings outweigh the subscription fee.

Success metrics

Reduction in emergency repair calls, reduction in tenant complaints.

Retention and expansion

Renew monthly subscription based on continued uptime and satisfaction.

Why clients would pick it

Accumulated data on appliance failure patterns creates a proprietary model that improves accuracy over time.

Alternatives and positioning

Manual inspection, reactive repair calls, standard warranties.

Main delivery costs

Sensor hardware costs, technician dispatch fees, parts inventory holding.

Limit automated spending to pre-approved budgets, require human approval for part orders over a certain value.