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Development of a Comprehensive Travel Services Platform
  1. case
  2. Development of a Comprehensive Travel Services Platform

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Development of a Comprehensive Travel Services Platform

elinext.com
Hospitality & leisure
eCommerce
Information technology

Challenges Identified in Travel Services

Fragmented booking systems across platforms, lack of personalized travel recommendations, inconsistent user experiences on mobile/desktop, and limited integration with real-time travel data (e.g., flight/hotel availability, pricing).

About the Client

A travel services company providing booking solutions and itinerary planning for global travelers.

Project Objectives

  • Create a unified digital platform for seamless travel bookings
  • Implement AI-driven personalization for tailored travel suggestions
  • Integrate real-time data feeds for accurate pricing and availability
  • Enhance cross-device user experience with responsive design

Core Functional Requirements

  • Multi-provider booking engine (flights, hotels, activities)
  • AI-powered recommendation engine
  • Dynamic itinerary builder with drag-and-drop functionality
  • Real-time notifications for price changes/alerts
  • Integrated payment gateway with multi-currency support

Technology Stack Preferences

React.js
Node.js
AWS Lambda
MongoDB
GraphQL

Critical System Integrations

  • Amadeus/Travelport API for flight data
  • Hotel booking APIs (e.g., Booking.com, Expedia)
  • Stripe/PayPal payment gateways
  • Google Maps API for location services
  • Twilio for SMS notifications

Non-Functional Requirements

  • 99.95% system availability (SLA)
  • Sub-2-second page load times for 90% of requests
  • PCI-DSS compliance for payment processing
  • Horizontal scalability for peak travel seasons
  • Role-based access control (RBAC) for user data

Expected Business Impact

Anticipated 40% increase in booking conversion rates, 30% reduction in customer service inquiries through self-service features, and 25% growth in repeat customers via personalized recommendations. Operational efficiency gains expected to reduce backend processing costs by 20%.

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