Why travel planning is a structured-output AI problem

A travel app that generates a full itinerary looks impressive and is actually simple. One LLM call with the destination, dates, budget, and interests returns a structured day-by-day plan. The part that feels magical is fast because the LLM does the work. The part that is work is everything around the call: caching, editing, sharing, offline access.

I have not shipped a travel app, but the one-shot structured itinerary is the same shape as the recipe meal plan, which I have tested in practice.

What you actually need to build

• Trip creation form: Destination, dates, budget tier, interests (food, museums, outdoors). Five fields, not twenty.
• One-shot AI itinerary: One LLM call with structured output (days → activities). 2-3 activities per day. Cap at 7 days.
• Manual edit per activity: Tap any activity to swap, remove, or add notes. You need this because the LLM will always get one thing wrong.
• Offline itinerary: Cache the trip locally. Travel apps must work on a plane.
• Share trip: Export as a shareable link or PDF so travel partners can view it.
• Place details on tap: Link out to Google Maps or Apple Maps. Do not build your own map layer.

No booking integration in v1. Booking is a different product.

The stack I use

• React Native + Expo.
• Supabase — trips, activities, user preferences.
• NestJS — the itinerary endpoint. Structured output mode. Cached on (destination, days, budget_tier, interests_hash) so a second user asking for "3 days Tokyo food budget" gets the cached trip instantly.
• expo-sqlite for offline cache.
• react-native-pdf-lib for the share PDF.
• Claude Code + 11 AI agents — scaffold the form, itinerary view, and edit screens.

Real build time

With the boilerplate, 3 weekends.

• Trip form + submission: ~4 hours.
• Itinerary endpoint + structured output parsing: ~6 hours.
• Day-by-day view + swap UI: ~8 hours.
• Offline cache: ~4 hours.
• Share PDF + map link-outs: ~4 hours.
• Store submission: ~4 hours.

About 30 hours.

Where people get stuck

• LLM hallucinating restaurants that do not exist: The model confidently returns made-up places. Ground the response — pass a Google Places API lookup for each activity before the user sees it. Drop any activity that fails validation.
• Cost per trip on uncached generation: A 5-day itinerary is ~2000 output tokens. Without caching, every user request costs real money. The input-hash cache hits on roughly 40% of requests in testing because travel queries cluster hard around top destinations.
• Timezone offsets for day boundaries: When a user flies overnight, "day 3" needs to respect the destination timezone. Store activities as local_date in the destination timezone, not UTC.

Skip the setup

Structured output itinerary endpoint with cache, Supabase schema, offline sync, share PDF template — pre-wired. The 11 AI agents scaffold the form and day-by-day screens.

See pricing