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Study Abroad Admissions Platform

Full-stack admissions platform — multi-role dashboards (student/counselor/admin/superadmin), university management, applications, documents, AI search, messaging, and commission policies.

  • React 18
  • Vite
  • Redux
  • Tailwind
  • Express
  • MongoDB
  • Socket.IO
  • Redis
  • RabbitMQ
  • OpenAI
  • AWS S3
  • JWT

01

Problem

Education service teams needed a unified platform for applicant admissions, counselor workflows, university/course management, and commission tracking across multiple stakeholder roles.

02

Solution

Built a multi-role admissions platform with role-based dashboards, approval workflows, AI-powered search, document vault, real-time chat/notifications, and Excel import/export for university data.

03

Architecture

React 18 + Vite + Redux + Tailwind frontend. Express + MongoDB backend with Socket.IO, Redis, RabbitMQ workers, OpenAI integration, AWS S3 document storage, and Swagger API.

04

Business impact

Unified study-abroad operations from lead capture through application approval, enabling data-driven counselor workflows and commission management.

Decisions

Technical decisions

  1. 01RabbitMQ for async notification and cron job workers
  2. 02Redis for session caching and rate limiting
  3. 03OpenAI integration for intelligent course/university search

Features

Feature breakdown

  • Multi-Role Dashboards
  • University Management
  • Application Workflows
  • Document Vault
  • AI Search
  • Real-time Chat
  • Commission Policies

Responsibilities

  • Built multi-role dashboards for students, counselors, admins, and super-admins
  • Implemented approval workflow routing and document vault with PDF/Office viewers
  • Integrated AI search, real-time chat, and notification workers via RabbitMQ
  • Developed university/course management with Excel import/export and commission policies

Challenges

  • Designing approval workflows for diverse application stages across universities
  • Scaling real-time notifications and AI search across large course/university datasets

Performance considerations

  • Redis caching for frequently accessed university data
  • Async workers for notification and AI processing

Lessons learned

  • Message queue workers (RabbitMQ) decouple notification dispatch from API request cycles
  • Multi-role dashboards require strict RBAC at both API and UI levels

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