PrepScholar

For PrepScholar, we developed a multi-application ed-tech platform powered by AI-driven adaptive learning algorithms. The system personalizes test preparation paths for thousands of students worldwide, using machine learning to continuously optimize content delivery based on individual performance patterns. We built a scalable, compliant infrastructure supporting multiple specialized applications under one unified ecosystem.
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Service’s
AI Strategy & Consulting
Client
PrepScholar

Technologies Used

Frontend:
Django Server-Side Rendering, Backbone.js, React.js, Responsive UI Design

Backend:
Django 3.2, Python 3.8, RESTful APIs, Nginx Reverse Proxy, Gunicorn, MySQL

Infrastructure:
AWS ECS + Fargate (Containerized Microservices), CloudFlare (CDN & Security), Docker, CircleCI (CI/CD Pipelines)

Security:
SSL/TLS Encryption, Role-Based Access Control, GPG & Blackbox Secret Management

Development Environment:
Dockerized setup, Automated Linting & Testing, Tmpfs-mounted database for speed, Version-controlled seeds

Project Goals

  • Build a scalable and maintainable multi-application platform.
  • Deliver a seamless and consistent user experience across all products.
  • Ensure secure handling of sensitive student data.
  • Provide high availability and performance to handle concurrent users globally.
  • Enable future expansion with minimal operational overhead.

Deliverables

  • Fully functional educational technology platform with multiple applications.
  • Robust content management system and real-time tracking systems.
  • Comprehensive documentation, CI/CD pipelines, and automated testing.
  • Secure, scalable, and containerized infrastructure.

Impact

  • 99.9% uptime and sub-second response times.
  • Improved student engagement and streamlined educational content delivery.
  • Simplified deployment and scaling using containerized architecture.
  • Future-ready platform supporting growth and additional features.

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