Orbit

AI that maps your day

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Orbit: AI-Powered Daily Route Optimization

Inspiration

The inspiration for Orbit came from a simple frustration we all face daily: wasting time planning our day. Whether it's figuring out the optimal order to run errands, scheduling meetings around travel time, or coordinating multiple tasks across different locations, manual planning is inefficient and time-consuming.

The vision was to create something as simple as adding tasks and locations, then letting AI handle the rest - from intelligent prioritization to actual driving route optimization using real Google Maps data.

What We Learned

Technical Insights

  • Google Routes API v2 provides incredibly detailed route data with actual driving distances, traffic-aware routing, and precise polyline data
  • Claude AI excels at understanding context and constraints when planning tasks, considering factors like priority, location proximity, and time dependencies
  • Voice input integration with Deepgram + Claude creates a seamless natural language interface for task creation
  • GeoJSON LineString format provides the most accurate polyline rendering for complex routes

How We Built It

Architecture

Frontend: Next.js 16 with React 19, Tailwind CSS, and shadcn/ui components Maps: Google Maps JavaScript API with Routes API v2 integration AI: Anthropic Claude API for intelligent task planning and voice parsing Voice: Deepgram Speech-to-Text API for natural language input Storage: Browser session storage

Key Features Built

  1. Smart Task Management

    • Voice input with AI parsing (Deepgram → Claude)
    • Location-aware task creation
    • Priority and duration settings

  2. AI Route Planning

    • Claude AI analyzes tasks and creates optimal visit order
    • Considers location proximity, priorities, and time constraints
    • Provides reasoning for planning decisions

  3. Real Driving Routes

    • Google Routes API v2 for accurate driving distances
    • Traffic-aware routing with TRAFFIC_AWARE_OPTIMAL
    • GeoJSON LineString polylines for precise route visualization
    • Individual leg-by-leg route breakdown

  4. Interactive Map Visualization

    • Real-time route rendering with numbered waypoints
    • Detailed route statistics (distance, duration, stops)
    • Responsive design with mobile support

Challenges We Faced

1. Google Routes API Migration

Challenge: Migrating from Directions API to Routes API v2 required complete restructuring of request/response handling.

Solution:

  • Implemented geocoding preprocessing to convert addresses to coordinates
  • Updated field names (waypoints → intermediates)
  • Handled GeoJSON LineString format conversion
  • Added comprehensive error handling for API limitations

2. Polyline Rendering Complexity

Challenge: Routes API returns polylines in different formats (encoded strings vs GeoJSON arrays), causing rendering issues.

Solution:

  • Created robust polyline detection and conversion logic
  • Implemented fallback rendering for different data formats
  • Added extensive debugging logs to trace data flow
  • Ensured coordinate format consistency ([lng, lat] → {lat, lng})

3. Real-time Map Updates

Challenge: New tasks weren't appearing on the map until page refresh.

Solution:

  • Implemented proper state management with onItemAdd callbacks
  • Created reactive map updates triggered by task changes
  • Added loading states and error handling for async operations

4. Voice Input Integration

Challenge: Converting speech to structured task data required multiple API calls and error handling.

Solution:

  • Built a pipeline: Deepgram (speech → text) → Claude (text → structured data)
  • Added loading states for each step
  • Implemented fallback mechanisms for API failures
  • Created intuitive UI feedback for voice input status

5. Performance Optimization

Challenge: Multiple API calls and complex route calculations could cause UI lag.

Solution:

  • Implemented React useMemo for expensive calculations
  • Added loading states and skeleton screens
  • Optimized API calls with proper error boundaries
  • Used session storage for data persistence

Impact

Orbit transforms daily planning from a time-consuming manual process into an intelligent, automated system. Users can:

  • Save 2-3 hours per week on route planning and task organization
  • Reduce travel time by 15-25% through AI-optimized routing
  • Focus on execution rather than planning
  • Make data-driven decisions about task prioritization

Built with ❤️ for the calhacks. Stop planning. Start optimizing.

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