TasteTrails

AI-powered travel recommendations based on your entertainment preferences

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Inspiration

TasteTrails was born from a frustration I've experienced countless times: "Should I even visit this city? Nothing there seems to fit my taste." Too often, I found myself disappointed by destinations because I was seeing them through generic tourist recommendations, missing all the hidden gems that would actually resonate with who I am as a person.

I realized that our entertainment choices - the movies we love, the actors we admire, the books we devour - reveal so much about our personality and what we'd truly enjoy in the real world. Having the opportunity to use the Google Maps Platform seemed like the perfect chance to dive into this challenge and create something truly innovative.

What it does

TasteTrails is your AI travel companion that actually understands you. Input your favorite actors, movies, books, brands, video games, TV shows, podcasts, and people - then watch as it creates personalized activities for itineraries that feel like they were designed specifically for your soul.

The platform combines Qloo's cultural intelligence, Claude AI's reasoning, and Google Maps Platform's comprehensive APIs (Places, Geocoding, Weather, Air Quality, Pollen) to generate activities that do more than just fill your schedule - they create experiences that genuinely excite you because they align with your cultural DNA and current environmental conditions.

How I built it

I designed TasteTrails as a microservices architecture with three core components:

  • React + Vite Frontend: Delivers a seamless, intuitive user experience with Google Maps JavaScript SDK integration
  • Spring Boot Backend: Connected to PostgreSQL for rock-solid reliability, security, and data persistence
  • FastAPI AI Service: Built with client-service-controller architecture, orchestrating external APIs (Qloo, Claude, Google Maps Platform) for intelligent recommendation generation

Each service runs in Docker containers, connected through nginx for production-ready deployment on DigitalOcean.

Challenges I ran into

The biggest learning curve was mastering Google Maps Platform APIs efficiently. I had to figure out how to integrate weather data, air quality, pollen levels, geocoding, and places data - each with their own unknowns and rate limits.

I went through the same cycle repeatedly: research the API, then implement basic integration, then optimize for performance, then add intelligent caching, then handle edge cases. By the end, I felt like I had genuinely mastered the Google Maps ecosystem.

Another major challenge was leveraging Google Maps Platform to its fullest potential - integrating not just basic mapping, but weather forecasting, air quality monitoring, pollen tracking, and places discovery into a cohesive environmental intelligence system that Claude AI could use for contextual recommendations.

Accomplishments that I'm proud of

What makes me most proud is the reaction I get when I demo this to people. They're genuinely speechless when they see how deeply the AI understands their preferences and connects their entertainment choices to real-world experiences they'd actually love.

I'm particularly proud of creating the first travel platform that combines Google Maps Platform's environmental APIs with cultural intelligence - using weather, air quality, and pollen data to optimize activity timing while maintaining cultural relevance.

I've created something that innovates on multiple levels - cultural intelligence, environmental optimization, AI reasoning, and user experience design. It feels like a true "work of art" in the technical sense, and that level of innovation just inspires me to keep pushing boundaries.

What I learned

  • Google Maps Platform mastery: Deep integration across multiple APIs with intelligent caching strategies
  • Prompt engineering: Crafting Claude AI prompts that consistently deliver high-quality, contextual recommendations
  • Cultural intelligence: Understanding how Qloo's cross-domain affinities can create unexpected but meaningful connections
  • Microservices orchestration: Coordinating multiple services with different technologies seamlessly
  • Production deployment: Taking a complex multi-service application from development to live production on DigitalOcean

What's next for TasteTrails

I'm honest about TasteTrails' current weaknesses. The application lacks comprehensive testing, which puts it at risk of breaking when new features are added since so many components depend on each other.

Immediate priorities:

  • Comprehensive testing suite: Unit tests, integration tests, and end-to-end testing
  • CI/CD pipeline: Automated testing and deployment for safer feature additions
  • Performance optimization: Database query optimization and API response caching improvements

Future vision:

  • Social features: Share itineraries and discover friends with similar cultural DNA or share an itinerary and contribute to activities together
  • Routes API Integration: Improve activity generation by checking the time needed to travel from one activity to the next using Google Maps routing
  • Mobile app: Native iOS/Android experience with offline capabilities
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