A touchscreen ordering product built end to end: vision-assisted input, a recommendation engine that learns the menu, and offline-tolerant sync so a dropped connection never stops a sale.
- Client
- Quick-service restaurant chain
- Year
- 2025
- Services
- AI Based Development
- Creative Product & Web Design
- Stack
- Flutter
- Node.js
- PostgreSQL
- Redis
- AWS
- Stripe
Case study
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Start a project01Brief
A growing restaurant chain wanted to cut counter queues without hiring more staff — and wanted the kiosk to sell better than a tired cashier at 9 p.m.
02Challenge
Kiosks fail on two things: slow, confusing flows and dead screens when the Wi-Fi drops. Both cost sales at the busiest moments.
03Approach
- 01
Designed a four-step flow — scan or tap, build, pay, collect — and tested it with real customers until the median order took seconds.
- 02
Built a recommendation engine that learns from the menu and time of day, surfacing the one upsell most likely to land.
- 03
Made the app offline-first: orders queue locally and sync when the connection returns, so a dropped link never stops a sale.
- 04
Shipped a fleet dashboard so operators see every kiosk's health, sales, and menu version in one place.
04Result
Average order time dropped to six seconds, attach rate on upsells rose forty percent, and the chain rolled out to every location within a quarter.
- average order
- 0.0s
- upsell lift
- 0%
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