AI Trip Planner with Day-Wise Itineraries

Project overview

We built an AI trip planner for a travel startup that wanted to replace hours of research across blogs and booking sites with a single planning app. Travellers enter a destination, dates, budget, pace and interests, and the app drafts a day-by-day itinerary they can rearrange, share and book from.

The engagement covered discovery, UX, Flutter mobile apps, a Node.js back end, the OpenAI integration and a console for the client's content team. It builds on our travel app development work in the travel and hospitality sector.

The client and the challenge

Early experiments by the client had simply asked a language model for an itinerary. The results read well but were unreliable: attractions that had closed, restaurants on the wrong side of a city, days that could not physically be walked, and no link to anything bookable.

The product needed the flexibility of generated plans with the reliability of real place data, plans that respected opening hours and travel time, and a way to keep generation costs predictable as usage grew.

Solution architecture & delivery

We treated the model as a planner over a curated catalogue rather than a source of facts. The Node.js back end first retrieves candidate places for the destination from the client's catalogue, filtered by the traveller's interests, budget band and dates, including opening hours and coordinates. Only those candidates are passed to OpenAI with a structured prompt, and the model must return JSON that references place IDs, day numbers and time blocks.

The response is validated before the traveller sees it. A rules layer rejects unknown IDs, checks opening hours, and estimates travel time between consecutive stops; days that are too long or zig-zag across the city are re-ordered or sent back for a targeted regeneration of that day only. This pattern follows our approach to production retrieval-augmented generation.

In the Flutter app, each day appears as a timeline and a map. Travellers can drag stops between days, swap a stop for a similar suggestion, lock items they want to keep and ask for the rest of the day to be regenerated around them. Saved itineraries are cached on the device so they work offline abroad.

Bookable items such as tours, transfers and hotels link to partner inventory through the back end, which records the referral against the itinerary. To control cost, generated plans are cached by destination and preference profile, and small edits are handled by the rules layer without calling the model at all. The content console lets the client's editors add and retire places, correct opening hours, tag seasonal closures and review itineraries that travellers flagged as unrealistic. Flagged days are stored with the prompt, candidate list and model output, so the team can see whether a problem came from missing catalogue data, a validation gap or the model's choices, and fix it at the right layer rather than adjusting prompts by guesswork.

Key features we built

• Trip set-up by destination, dates, budget, pace and interests
• AI-drafted day-wise itineraries grounded in a curated place catalogue
• Opening-hours and travel-time checks on every generated day
• Drag-and-drop editing, locking and single-day regeneration
• Map view of each day's route with walking and transit estimates
• Offline access to saved itineraries and tickets
• Shared trips for groups with comments on individual stops
• Partner bookings for tours, transfers and stays from within the plan

How we delivered it

1. Discovery. Reviewing the client's earlier prompt experiments, defining what made an itinerary usable, and agreeing the catalogue data each place needed.
2. Prototype. A quick evaluation harness comparing generated days against hand-written itineraries for sample destinations, used to shape the prompt and validation rules.
3. Build. Two-week sprints on the Flutter apps, generation pipeline and content console, with every prompt change checked against the evaluation set before release.
4. Beta. A closed beta with invited travellers, reviewing edited and abandoned itineraries to find where plans were unrealistic or too generic.
5. Support. Ongoing catalogue expansion, model and prompt updates, and new booking partners added under a support agreement.

Technology choices

• Flutter — one codebase for iOS and Android with smooth timeline, drag-and-drop and map interactions and reliable offline storage.
• OpenAI — an LLM that produces structured itinerary JSON from a shortlist of real places, handling preferences that are hard to encode as fixed rules.
• Node.js — orchestrates retrieval, model calls, validation, caching and partner booking APIs behind a single app-facing API.

Outcomes

• Travellers get a workable day-by-day plan in the app instead of assembling one from many websites
• Every suggested stop comes from the verified catalogue, so plans no longer include places that do not exist
• Days respect opening hours and realistic travel time between stops
• Editing is interactive, with the model only called when a day needs regenerating
• Bookable activities are attached to the itinerary, giving the client a referral revenue stream

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