Compos Resto
Cross-platform restaurant management app for .COMPOS — tables, orders, kitchen flow, and POS

Compos Resto is a restaurant management mobile app built for .COMPOS, aimed at restaurant and fast-food operators who need to run service from the floor and the pass: table state, orders, kitchen progress, and payments wired into a point-of-sale (POS) context.
It was developed as employer work for the company; the codebase and distribution are not public, so there is no repository link or public app listing here.

Running a busy restaurant with spreadsheets or disconnected tools slows down service. Staff need a single place to see which tables are live, what is cooking, and how to close the bill—without fighting the POS.
I designed and developed a cross-platform app using React Native and Expo so the same codebase ships to the devices kitchens and front-of-house already use. The product focuses on operational clarity: table lifecycle, ordering with modifiers, kitchen status, and in-app payment flows aligned with POS expectations.
Core capabilities
- Table management — track tables as reserved, available, outdoor, in service, and related states staff rely on during service
- Reservations and ordering — create orders with quantities and extra options where the menu allows add-ons
- Kitchen pipeline — follow preparation from in kitchen through ready to delivered
- Payments — handle payments inside the app as part of the service workflow
- Live tracking — order and table status surfaced so hosts, servers, and the pass stay aligned
- Staff-first UI — layouts tuned for quick scanning and touch under real shift conditions
The app is built on Expo for streamlined builds and device workflows, with TypeScript across the UI layer for safer refactors as service rules evolve. State and navigation are structured around real restaurant flows (floor → order → kitchen → payment) rather than generic CRUD screens.
Shipping tools for restaurant staff means optimizing for speed and mistakes, not pixel-perfect marketing pages. Table and order models have to stay honest under concurrency—multiple people updating the same service—and the UI has to stay legible when the room is loud and moving fast.
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