Objectives.

The platform aims to modernise and enhance the reward seat search experience for users. The objectives include:

  • System Enhancement: Update the front end with React for user experience and decouple it from RoR to NodeJS microservices for scalability.
  • Flexible Content Management: Implement Strapi CMS for adaptable backend management.
  • Enhance Data Collection:  Develop a tool gathering availability data from multiple airlines.
  • Predictive Analytics Integration: Incorporate a predictive ML model to forecast seat availability.
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Our Solutions.

To achieve the objectives, Systango meticulously executed the following tasks:

  • Frontend Revamp

    Redesigned the entire web application frontend using React, creating a faster, more user-friendly single-page application.

  • Backend Transition & Microservices

    Migrated backend to NodeJS from RoR, implementing microservices for scalability.

  • CMS Implementation

    Integrated Strapi for enhanced flexibility and scalability in content management.

  • Bots Orchestrated

    Developed new bots and integrated them with a Bot Orchestrator to collect real-time seat availability data from multiple airlines like Avios, Air Miles, Flyer Miles, etc.

  • Predictive ML Model

    Integrated a machine learning model to predict seat availability based on routes, dates, passenger counts and cabin classes.

  • Data Warehouse

    Set up a data warehouse to store historical data for analysis and model improvement.

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Key Benefits.

  • Enhanced user experience with a faster interface
  • 25% scalability improvement
  • Increased data accuracy with predictive analytics
  • Improved efficiency

Tech Stack.

Next Cases.

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