Gopher

My project is an AI powered itinerary make for live events. Users enter their destination, dates, and preferences and the app combines AI with real world APIs to generate a personalized itineraries.

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About the Project

Inspiration

This project was inspired by how frustrating it is to plan an entire day around a live event. While buying tickets is simple, figuring out what to do before and after the event, where to eat, how far things are, and how everything fits together usually requires switching between multiple apps. I wanted to build something that treats a live event as the anchor of the day and automatically builds a realistic plan around it.

What I Learned

Through this project, I learned how to design and build a full stack application that integrates several real world services. I gained experience working with the Ticketmaster API for live event discovery, the Google Maps API for location based recommendations and routing, and large language models from Groq to generate structured and personalized itineraries. I also learned how to use Neon as a cloud hosted database and how to implement Google authentication using Google Cloud.

In addition, I learned how to manage API keys securely, handle inconsistent external data, and structure AI outputs so they could be reliably stored and reused.

How I Built the Project

The project begins by fetching live events from the Ticketmaster API based on user input such as location, date, and event type. Once a user selects an event, it becomes the central point of the itinerary.

Nearby restaurants, attractions, and activities are discovered using the Google Maps API. AI models from Groq are then used to intelligently select and organize these places into a coherent plan that fits naturally around the event. User accounts and saved itineraries are stored in a Neon database, allowing users to return to and modify their plans later.

Authentication is handled through Google login using Google Cloud, which provides a secure and familiar sign in experience while simplifying user management.

Challenges Faced

One of the biggest challenges was coordinating data from multiple APIs that were not designed to work together. Event times, location data, and place details often differed in structure and reliability, requiring careful validation and error handling. Another challenge was prompt design for the AI models, where small changes could significantly impact the quality and consistency of the generated itineraries.

Balancing automation with user control was also challenging. The goal was to make planning effortless while still giving users the ability to customize their experience. Overcoming these challenges helped me better understand how AI can be combined with real world data to build practical, user focused applications.

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