Guidenco

Consolidates trip planning into a single dashboard where users create, organize, and modify multi-day itineraries without switching between external tools. The system likely uses a document-oriented data model (possibly NoSQL) to store itinerary structures with day-by-day activity slots, allowing real-time updates and collaborative editing through operational transformation or CRDT-based conflict resolution for concurrent user modifications.

Enables users to search, filter, and compare lodging options (hotels, hostels, Airbnb equivalents) within the itinerary context. The platform likely aggregates data from multiple accommodation providers via API partnerships or web scraping, storing results in a searchable index with caching to reduce external API calls. Filtering likely uses faceted search (price range, amenities, location proximity, ratings) with client-side or server-side filtering depending on result set size.

Enables multiple users to simultaneously view and edit a shared itinerary with live synchronization. The system likely implements operational transformation (OT) or conflict-free replicated data types (CRDTs) to handle concurrent edits without requiring explicit locking. Changes are broadcast via WebSocket connections to all connected clients, with a backend state store (possibly Redis for session state + persistent database) maintaining the authoritative itinerary version.

Provides a centralized dashboard to track and manage travel bookings (flights, hotels, activities) made through external platforms. The system likely stores booking references, confirmation numbers, and key details (dates, costs, cancellation policies) in a structured database, with optional email parsing or manual entry to populate booking records. May include reminders for upcoming bookings or check-in deadlines.

Enables users to share itineraries with non-registered users via shareable links or export itineraries to standard formats (PDF, ICS calendar, JSON). Sharing likely uses URL-based access tokens with optional read-only or edit permissions. Export functionality converts the itinerary data structure into portable formats, with PDF generation possibly using a headless browser or server-side rendering library.

Suggests activities, attractions, and points of interest based on itinerary locations and dates. The system likely uses a database of attractions (possibly sourced from Google Places, Wikipedia, or OpenStreetMap) indexed by location and category, with filtering by distance, rating, and user preferences. Recommendations may be rule-based (e.g., 'show museums near hotel') rather than ML-based due to the free tier constraints.

Displays itinerary activities and accommodations on an interactive map with route visualization between locations. The system likely uses a mapping library (Google Maps, Mapbox, or Leaflet) with custom markers for activities and accommodations, and optional route calculation using a routing API (Google Directions, OpenRouteService) to show travel paths between locations. Map state (zoom, center, selected markers) is likely synchronized with itinerary state.

Allows users to log expenses and estimate trip costs by category (accommodation, food, activities, transport). The system likely stores cost data in a structured format linked to itinerary items, with aggregation and categorization logic to compute total trip cost and per-day budgets. May include currency conversion for multi-country trips using real-time exchange rates or cached rates.