Version 3.0.0 | Status: Production Ready | Last Updated: November 2024
IvyLevel is a comprehensive, data-driven college preparation platform that combines AI-powered insights with personalized coaching to maximize students' chances of admission to top-tier universities.
- Unified Entry Portal - Single sign-on for students, coaches, and admins
- AI-Driven Assessments - Real-time Ivy+ readiness scoring (Four Pillars)
- Netflix-Style Video Platform - S3-integrated coaching sessions with smart features
- Smart Coaching Platform - Multi-student view for coaches
- Personalized Game Plans - AI-generated strategies for each student
- Real Data Integration - 23 students and 30 coaches with complete profiles
# 1. Start Backend (Terminal 1)
cd backend/api
source venv/bin/activate
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
python main_s3_simple.py
# 2. Start Frontend (Terminal 2)
cd frontend/apps/unified-app
npm install # First time only
npm run dev
# 3. Open Browser
# Go to http://localhost:5173Demo Login:
- Student:
hudasir4j@gmail.com/Welcome123! - Coach:
jennyduan@ivymentors.co/Welcome123! - Admin:
admin@ivylevel.com/admin123
For detailed setup instructions, see QUICK_START.md
ivylevel_one/
├── backend/
│ ├── api/ # FastAPI backend with all services
│ │ ├── main_production.py # Production server entry point
│ │ ├── src/ # Source code
│ │ │ ├── agents/ # AI agents (ProfileAssessor, GamePlan, etc.)
│ │ │ ├── api/ # API endpoints
│ │ │ ├── auth/ # Authentication (AWS Cognito)
│ │ │ ├── models/ # Database models
│ │ │ └── services/ # Business logic
│ │ └── requirements.txt # Python dependencies
│ │
├── frontend/
│ └── apps/
│ ├── unified-app/ # Main React + TypeScript + Vite app
│ │ ├── src/
│ │ │ ├── components/
│ │ │ │ ├── student/ # Student dashboard components
│ │ │ │ ├── coach/ # Coach platform components
│ │ │ │ └── admin/ # Admin tools
│ │ │ └── services/ # API services
│ │ └── package.json
│ │
├── data/
│ ├── input/ # Source CSV files
│ │ ├── studentscomprehensive.csv
│ │ └── coaches.csv
│ └── output/ # Generated files
│
├── docs/ # Documentation
│ ├── API_DOCUMENTATION.md # Complete API reference
│ ├── ARCHITECTURE_OVERVIEW.md
│ └── archive/ # Historical documentation
│
├── scripts/ # Utility scripts
│ ├── start_production_2users.sh
│ ├── import_real_users.py
│ └── quick_import_users.py
│
└── QUICK_START.md # Getting started guide
- Framework: FastAPI (Python 3.11+)
- Database: PostgreSQL + SQLAlchemy
- Authentication: AWS Cognito + JWT
- AI/ML: OpenAI GPT-4, scikit-learn
- Task Queue: Celery + Redis
- Real-time: WebSockets
- Framework: React 18 + TypeScript
- Build Tool: Vite
- Styling: Tailwind CSS
- State Management: React Query + Context
- UI Components: Custom design system
- Charts: Recharts for visualizations
- Container: Docker + Docker Compose
- Cloud: AWS (S3, SES, Cognito)
- Monitoring: OpenTelemetry
- CI/CD: GitHub Actions ready
- Personal dashboard with Ivy+ readiness score visualization
- Four Pillars assessment (Identity, Aptitude, Passion, Service)
- AI-generated game plans with priority-based tasks
- Netflix-style video sessions with smart start time
- Progress tracking and analytics
- College recommendations based on profile
- Netflix-style sessions view for all assigned students
- Filter by student name instead of coach name
- Smart onboarding system with video training
- Comprehensive knowledge base (316+ sessions)
- AI-powered coaching insights
- Student portfolio management
- Comprehensive admin dashboard with 9 key modules
- User management with CSV bulk import
- Platform analytics and metrics visualization
- System health monitoring and alerts
- Content management for guides and resources
- Billing and subscription management
- Email campaign tools
- Audit logs and compliance tracking
- Platform-wide settings configuration
The platform includes:
- 23 Real Students with complete profiles, grades, and activities
- 30 Real Coaches with specializations and assignments
- 9,051 Processed Emails with behavioral insights
- Historical Data from 2023-2025 coaching sessions
- Node.js 18+ and npm
- Python 3.11+ and pip
- PostgreSQL 14+
- Redis 6+
# Backend
cd backend/api
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# Frontend
cd frontend/apps/unified-app
npm install# Backend tests
cd backend/api
pytest
# Frontend tests
cd frontend/apps/unified-app
npm test- QUICK_START.md - Get up and running in 5 minutes
- DOCUMENTATION_INDEX.md - Complete documentation index
- Features Overview - Comprehensive features documentation
- Implementation Guide - Video platform implementation
- Deployment Guide - Production deployment
- API Documentation - Complete API reference
- Folder Organization - Project structure guide
- Production-grade authentication with AWS Cognito
- JWT token-based authorization
- Rate limiting and DDoS protection
- Input validation and sanitization
- Encrypted data at rest and in transit
For production deployment:
# Use the production deployment script
./scripts/deploy_production.sh
# Or deploy to specific environment
./scripts/aws-setup-prod-1000users.sh # For 1000 users
./scripts/aws-setup-staging-20users.sh # For stagingSee DEPLOYMENT_GUIDE.md for detailed instructions.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is proprietary and confidential. All rights reserved.
- Issues: Create an issue in the repository
- Documentation: Check the
/docsdirectory - API Issues: See API logs at
logs/backend_production.log - UI Issues: Check browser console and
logs/unified-app.log
Built with ❤️ by the IvyLevel Team