CASE STUDY

AI Travel Itinerary Generator Platform

Confidential travel startup · Travel & Tourism

Challenge

A travel startup wanted to generate personalised multi-day itineraries based on user preferences, budget, and destination — but manual itinerary creation was slow, inconsistent, and did not scale for growing user demand.

Solution

Astro Dream built an AI-powered itinerary platform using Laravel with OpenAI integration. Users input preferences and receive structured day-by-day plans with activities, dining suggestions, and transport notes — refined by prompt engineering and validation layers.

Technologies used

Laravel
OpenAI API
Vue.js
PostgreSQL
Redis
Queue Jobs
Prompt Engineering

Outcomes achieved

90% faster generation

Itineraries that took 30+ minutes to create manually are generated in under 30 seconds.

Consistent quality

Structured output templates ensure every itinerary includes transport, dining, and activity balance.

Cost-controlled AI

Token usage monitoring and caching reduce API costs while maintaining response quality.

The product vision

Travellers increasingly expect personalised recommendations, not generic "top 10 things to do" lists. The client wanted a platform where users describe their travel style — adventure, relaxation, food-focused, family-friendly — and receive a complete day-by-day itinerary with realistic timing, neighbourhood context, and budget awareness.

AI architecture

We designed a multi-step AI pipeline rather than a single prompt, because raw LLM output is unpredictable for structured travel plans.

Step 1: Preference extraction

User inputs (destination, dates, budget, interests, pace) are validated and normalised into a structured preference object before any AI call.

Step 2: Itinerary generation

A carefully engineered prompt instructs the model to return JSON with day-by-day activities, including:

Morning, afternoon, and evening blocks
Estimated duration and travel time between locations
Dining suggestions matched to budget tier
Rainy-day alternatives for outdoor activities

Step 3: Validation and enrichment

Server-side validation checks the AI response for completeness, removes duplicate suggestions, and enriches entries with stored venue data where available.

Step 4: User editing

Generated itineraries are editable — users can swap activities, regenerate single days, or adjust pace without re-running the full pipeline.

Cost and reliability controls

AI API costs can spiral without guardrails. We implemented:

Token budgets per user tier with daily limits
Response caching for popular destination + preference combinations
Fallback templates when the API is unavailable or returns invalid JSON
Async generation via queue jobs so users see a progress indicator rather than a timeout

UX decisions

The frontend uses Vue.js for interactive itinerary editing with drag-and-drop day reordering. Users can share itineraries via unique URLs and export to PDF for offline access during travel.

Outcome

The platform transformed itinerary creation from a manual service bottleneck into a self-serve product feature. Users generate personalised plans in seconds, the startup scales without hiring more travel planners, and the AI layer improves continuously as prompt templates are refined based on user feedback.

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