Smart talent-matching and project collaboration platform for university communities and innovation hubs
InnoSync connects recruiters (project creators) with talent through AI-powered team formation, detailed profile matching, and integrated collaboration tools.
Experience the seamless team formation and AI-powered matching in action
- ✨ Features
- 🏗️ Architecture
- 🛠️ Tech Stack
- 🚀 Quick Start
- 📖 Detailed Setup
- 🎯 User Roles
- 🤖 Quicksyncing AI
- 🔧 Development
- 📚 API Documentation
- 🤝 Contributing
- 📄 License
- AI-Powered Team Formation - Quicksyncing algorithm matches optimal team members
- Comprehensive Profile System - Detailed skill mapping and experience tracking
- Real-time Collaboration - Integrated chat and communication tools
- Advanced Filtering - Role-based, skill-based, and expertise-level filtering
- Invitation Management - Streamlined application and invitation workflow
- Smart Matching Algorithm - ML-powered candidate recommendation
- Team Synergy Analysis - Compatibility scoring for optimal team formation
- Real-time Notifications - Instant updates on applications and invitations
- File Management - Resume and profile picture uploads
- Role-based Access Control - Secure authentication and authorization
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Frontend │ │ Backend │ │ ML Service │
│ (Next.js) │◄──►│ (Spring Boot) │◄──►│ (FastAPI) │
│ Port: 3000 │ │ Port: 8080 │ │ Port: 8000 │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
▼
┌─────────────────┐
│ PostgreSQL │
│ Port: 5440 │
└─────────────────┘
- Framework: Next.js 14 with React 18
- Language: TypeScript
- Styling: CSS Modules
- State Management: React Hooks
- UI/UX: Custom design system
- Framework: Spring Boot 3.x
- Language: Java 21
- Database: PostgreSQL 15
- Authentication: JWT with Spring Security
- API Documentation: Swagger/OpenAPI
- Framework: FastAPI
- Language: Python 3.11
- ML Libraries: Pandas, NumPy, Scikit-learn
- Algorithms: Team synergy analysis, skill matching
- Containerization: Docker & Docker Compose
- CI/CD: GitHub Actions
- Web Server: Nginx
- Monitoring: ELK Stack (Elasticsearch, Logstash, Kibana)
- Docker and Docker Compose
- Git
- Node.js 18+ (for local development)
- Java 21+ (for local development)
# Clone the repository
git clone https://github.com/IU-Capstone-Project-2025/InnoSync.git
cd InnoSync
# Start all services
docker-compose up --build- Frontend: http://localhost:3000
- Backend API: http://localhost:8080
- ML Service: http://localhost:8000
- Database: localhost:5440
- API Documentation: http://localhost:8080/swagger-ui/index.html
Create .env file in the backend directory:
# Database Configuration
DB_URL=jdbc:postgresql://postgres:5432/registration
DB_USERNAME=myuser
DB_PASSWORD=mypassThe PostgreSQL database will be automatically initialized with the required schema when the containers start.
# Check if all services are running
docker-compose ps
# View logs
docker-compose logs -f
# Test API endpoints
curl http://localhost:8080/api/health
curl http://localhost:8000/health- Profile Creation: Comprehensive skill and experience mapping
- Project Discovery: Advanced filtering and search capabilities
- Application Management: Direct project applications
- Invitation Handling: Accept/decline team invitations
- Project Management: Create and manage project requirements
- Talent Discovery: Manual search with advanced filters
- Quicksyncing: AI-powered automatic team matching
- Team Formation: Invitation and team finalization
- Recruiter Opt-in: Enable Quicksyncing for project
- AI Analysis: ML algorithms analyze requirements and candidate profiles
- Team Recommendation: Optimal team composition suggestions
- Review & Revoll: Recruiters can review and request better matches
- Batch Invitations: Send invitations to recommended team members
- Skill Compatibility Scoring: Advanced matching algorithms
- Team Synergy Analysis: Experience variance and skill overlap
- Role-Specific Matching: Expertise level and technology alignment
- Performance Optimization: Continuous learning from user feedback
cd backend
./mvnw spring-boot:runcd frontend/innosync
npm install
npm run devcd ML
pip install -r requirements.txt
python -m uvicorn app:app --reload --host 0.0.0.0 --port 8000# Backend tests
cd backend
./mvnw test
# Frontend tests
cd frontend/innosync
npm test
# ML service tests
cd ML
python -m pytest- Backend: Maven with Spring Boot conventions
- Frontend: ESLint, Prettier, TypeScript strict mode
- ML: Black, isort, mypy for Python code quality
- Swagger UI: http://localhost:8080/swagger-ui/index.html
- OpenAPI Spec: http://localhost:8080/v3/api-docs
- Authentication:
/api/auth/* - Profiles:
/api/profile/* - Projects:
/api/projects/* - Applications:
/api/applications/* - Invitations:
/api/invitations/* - ML Recommendations:
/api/recommendations/*
- 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
- Follow existing code style and conventions
- Write comprehensive tests for new features
- Update documentation for API changes
- Ensure all tests pass before submitting PR
- Team Lead: Baha
- Frontend: Baha & Anvar
- Backend: Yusuf & Asgat
- ML/AI: Gurban
- DevOps: Aibek
- CustDev: Egor
This project is licensed under the MIT License - see the LICENSE file for details.
For technical support or questions:
- Telegram: @east511
- Issues: GitHub Issues
Built with ❤️ by the InnoSync Team
Innopolis University Capstone Project 2025