Case study · LaundryPoint
A laundry network, run entirely from a phone.
End-to-end digital product for a self-service laundry network in Latvia: cloud control of every machine, native and web apps, wallet payments, real-time operations and the go-to-market. Designed, engineered and launched by one team.
- Client
- LaundryPoint
- Location
- Riga, Latvia
- Industry
- Consumer services
- Year
- 2026
The challenge
Self-service laundries still run on coins, card terminals and staff on site. LaundryPoint wanted a fully digital laundry: any machine, at any location, paid for and started from a phone, even from home.
Our solution
We engineered the complete stack, from the cloud layer that commands physical machines to the apps customers use and the dashboard the business runs on, then took it to market.
Scan. Pay. Wash.
- 01
Scan
QR code on every machine opens the app instantly.
- 02
Choose
Live map, live machine status, program and temperature.
- 03
Pay
Apple Pay, Google Pay or card. The machine starts itself.
- 04
Done
Live timer, remote start and smart notifications.
Engineering
Under the hood.
- React Native
- Next.js
- TypeScript
- PostgreSQL
- Realtime
- Edge functions
- Stripe
- Apple Pay & Google Pay
- IoT cloud API
- Push notifications
IoT cloud control
Event-driven integration with the manufacturer’s cloud: payment-triggered start commands and real-time machine telemetry streamed to every client.
Fail-safe payment orchestration
Server-side pricing, idempotent start commands and automatic refunds if a machine doesn’t respond. Money and machines can never drift out of sync.
One codebase, three platforms
iOS, Android and web from a single React Native codebase, with over-the-air updates.
Real-time operations
Live revenue, utilisation, peak hours and anomaly alerts in an owner dashboard, plus a rules-based pricing, subscription and promotions engine.
Outcome
What it delivers.
100% cashless
No coins, terminals or staff required on site.
Remote start
Pay and start a wash from anywhere.
Live business view
Revenue and machine health in real time.
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Planning something similar?
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