One-week buildJuly 2026 · 6 min read

How to Build a Catering Order App with AI

Catering runs on two numbers that change constantly: how many people, and what that costs per head. Both live in email threads until something goes wrong. An order app that keeps headcount, menu, and cost in one place is not glamorous, but it is the difference between a good month and cooking for forty when sixty arrive.

What a catering order app needs

  • Event order with date, venue, headcount, and a confirm-by deadline
  • Menu builder with per-head cost and automatic order total
  • Headcount change history, so the final number is never disputed
  • Prep list and shopping list generated from the menu and headcount
  • Deposit and balance tracking with a paid or outstanding status

The screens

ScreenWhat it does
OrdersUpcoming events by date with headcount, balance status, and days remaining
Order DetailClient, venue, headcount history, menu, totals, deposit and balance
Menu BuilderPick dishes, set per-head cost and portions, see the live total
Prep ListIngredients scaled to headcount, grouped by supplier or aisle
SettingsDish library, default margin, deposit percentage, export

The data model

  • Order: clientName, contact, eventDate, venue, headcount, confirmByDate, status, depositAmount, depositPaidAt, balanceDueAt
  • Dish: name, portionSize, costPerHead, allergens, supplierNotes
  • OrderItem: orderId, dishId, portionsPerHead, unitCost, lineTotal
  • HeadcountChange: orderId, previousCount, newCount, changedAt, changedBy

The build prompt (copy this)

Paste this into ShipNative and it becomes a working React Native app with these screens, navigable in a live preview, in about two minutes:

An order management app for a small catering business. An order is one event: client name and contact, date, venue, headcount, a confirm-by deadline, a deposit amount, and a balance due date. The caterer builds a menu for the order from a reusable dish library — each dish has a portion size, a cost per head, and allergen tags — and the app shows a live total that updates when the headcount changes. Every headcount change is recorded with the old value, new value, and timestamp, so the final invoice is never a dispute. From the menu and headcount the app generates a prep list of ingredients scaled to the number of guests, grouped for shopping, and an allergen summary for the event. Order status moves through enquiry, confirmed, deposit paid, delivered, and paid in full, and the Orders screen sorts by event date showing days remaining and whether the deposit has landed. Screens: Orders, Order Detail, Menu Builder, Prep List, Settings. Works offline, since venues have bad signal.

Then iterate with small, screen-specific prompts (“make the primary button bigger”, “add an empty state”), preview on your own phone, and export the full Expo project whenever you want. The full method: how to build an app with AI.

How it makes money

Subscription around $12–25 per month for a single caterer, since this app directly touches money and pays for itself on one avoided headcount mistake. Free until the third order is a better trial than a time limit — it lets a caterer through one real event before deciding.

Implementation and the revenue math for every model: how to make money from an app. When you’re ready to ship: App Store and Google Play publishing guides.

Build it now

The prompt above is free to run at shipnative.dev — working catering order app on your phone today, full code export, no credit card.

Frequently Asked Questions

Should the app send quotes and invoices to clients?

A shareable PDF quote is worth building; a full invoicing and payment system is not, at least not first. Caterers already have a way to take money and often an accountant with opinions about it. Generate a clean quote and a final invoice as PDFs, track deposit and balance as statuses, and let the money move wherever it already moves.

How accurate does the per-head costing need to be?

Directionally accurate beats precisely wrong. Ingredient prices move weekly and no caterer will maintain a live price list, so make the per-head cost an editable number they set from experience, with a default margin applied on top. The value is a total that updates when the headcount changes, not a supply chain model.

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