Why recipes are an underrated AI niche

Recipe apps look simple but the good ones solve three problems, not one: find a recipe, build a shopping list from it, and plan the week. AI helps most with the third, and the third is also the most common reason people uninstall food apps. They open it, cook once, then forget.

I have not shipped a recipe app, but I have built structured-output LLM flows inside two of my apps and the meal plan use case is exactly that shape.

What you actually need to build

• Recipe list + detail: Name, image, ingredients, steps. Start with a seed of 200 recipes imported from a public dataset. Do not generate recipes on the fly — nobody trusts that yet.
• Shopping list: Tap ingredients to add to a running list. Deduplicate when the same ingredient comes from two recipes. Group by aisle.
• Weekly meal plan from one AI call: User taps "plan my week", backend sends one LLM call with pantry + preferences + dietary constraints, gets back 7 recipes with a combined shopping list. One call, not seven.
• Save + favorite: Standard table, two screens.
• Serving size scaling: 2 people vs 4 people. Trivial math but the #1 requested feature in reviews.

No AI-generated photos in v1. You will regret it.

The stack I use

• React Native + Expo.
• Supabase — recipes, ingredients, shopping list, user pantry.
• NestJS — the meal plan endpoint. One function, structured output response format, cached on (pantry_hash, preferences_hash) so two users with identical inputs share a plan.
• Seed data: an import script to load a CSV of recipes on first deploy. The AI agents can draft this script from a prompt.

Real build time

With the boilerplate and a seeded recipe set, this is a 3-weekend category.

• Recipe import + list + detail: ~6 hours.
• Shopping list with aisle grouping: ~5 hours.
• Meal plan endpoint + structured output parsing: ~6 hours.
• Serving scaling + favorites: ~4 hours.
• Copyright check on imported recipes + store submission: ~6 hours.

About 27 hours.

Where people get stuck

• Ingredient dedup: "2 cups flour" and "1 cup flour" from two different recipes need to merge into "3 cups flour" on the shopping list. This is a parsing job. Store ingredients in structured form ({amount, unit, item}), not as free text.
• Imported recipe copyright: Scraping Allrecipes or NYT Cooking will get you rejected and sued. Use a public dataset (Recipe1M+, TheMealDB) or write original recipes. Include attribution.
• Meal plan LLM cost spikes: Without caching, a weekly plan is ~3000 output tokens. Cache on input hash and you pay once per unique request. Costs drop about 80% in real use.

Skip the setup

Supabase schema, structured-output meal plan endpoint with caching, shopping list dedup helper — pre-wired. The 11 AI agents scaffold the recipe list, detail, and meal plan screens.

See pricing