InnoSync 🚀

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.

🎬 Demo

Experience the seamless team formation and AI-powered matching in action

📋 Table of Contents

• ✨ Features
• 🏗️ Architecture
• 🛠️ Tech Stack
• 🚀 Quick Start
• 📖 Detailed Setup
• 🎯 User Roles
• 🤖 Quicksyncing AI
• 🔧 Development
• 📚 API Documentation
• 🤝 Contributing
• 📄 License

✨ Features

🎯 Core Features

• 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

🚀 Advanced Capabilities

• 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

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │    Backend      │    │   ML Service    │
│   (Next.js)     │◄──►│  (Spring Boot)  │◄──►│   (FastAPI)     │
│   Port: 3000    │    │   Port: 8080    │    │   Port: 8000    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                              │
                              ▼
                       ┌─────────────────┐
                       │   PostgreSQL    │
                       │   Port: 5440    │
                       └─────────────────┘

🛠️ Tech Stack

Frontend

• Framework: Next.js 14 with React 18
• Language: TypeScript
• Styling: CSS Modules
• State Management: React Hooks
• UI/UX: Custom design system

Backend

• Framework: Spring Boot 3.x
• Language: Java 21
• Database: PostgreSQL 15
• Authentication: JWT with Spring Security
• API Documentation: Swagger/OpenAPI

Machine Learning

• Framework: FastAPI
• Language: Python 3.11
• ML Libraries: Pandas, NumPy, Scikit-learn
• Algorithms: Team synergy analysis, skill matching

DevOps & Infrastructure

• Containerization: Docker & Docker Compose
• CI/CD: GitHub Actions
• Web Server: Nginx
• Monitoring: ELK Stack (Elasticsearch, Logstash, Kibana)

🚀 Quick Start

Prerequisites

• Docker and Docker Compose
• Git
• Node.js 18+ (for local development)
• Java 21+ (for local development)

One-Command Setup

# Clone the repository
git clone https://github.com/IU-Capstone-Project-2025/InnoSync.git
cd InnoSync

# Start all services
docker-compose up --build

Access Points

• 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

📖 Detailed Setup

1. Environment Configuration

Backend Configuration

Create .env file in the backend directory:

# Database Configuration
DB_URL=jdbc:postgresql://postgres:5432/registration
DB_USERNAME=myuser
DB_PASSWORD=mypass

2. Database Setup

The PostgreSQL database will be automatically initialized with the required schema when the containers start.

3. Service Verification

# 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

🎯 User Roles

👤 Recruitee (Talent/Applicant)

• 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

👥 Recruiter (Project Creator)

• 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

🤖 Quicksyncing AI

How It Works

1. Recruiter Opt-in: Enable Quicksyncing for project
2. AI Analysis: ML algorithms analyze requirements and candidate profiles
3. Team Recommendation: Optimal team composition suggestions
4. Review & Revoll: Recruiters can review and request better matches
5. Batch Invitations: Send invitations to recommended team members

ML Features

• 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

🔧 Development

Local Development Setup

Backend Development

cd backend
./mvnw spring-boot:run

Frontend Development

cd frontend/innosync
npm install
npm run dev

ML Service Development

cd ML
pip install -r requirements.txt
python -m uvicorn app:app --reload --host 0.0.0.0 --port 8000

Testing

# Backend tests
cd backend
./mvnw test

# Frontend tests
cd frontend/innosync
npm test

# ML service tests
cd ML
python -m pytest

Code Quality

• Backend: Maven with Spring Boot conventions
• Frontend: ESLint, Prettier, TypeScript strict mode
• ML: Black, isort, mypy for Python code quality

📚 API Documentation

Interactive Documentation

• Swagger UI: http://localhost:8080/swagger-ui/index.html
• OpenAPI Spec: http://localhost:8080/v3/api-docs

Key Endpoints

• Authentication: /api/auth/*
• Profiles: /api/profile/*
• Projects: /api/projects/*
• Applications: /api/applications/*
• Invitations: /api/invitations/*
• ML Recommendations: /api/recommendations/*

🤝 Contributing

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Code Standards

• 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 Structure

• Team Lead: Baha
• Frontend: Asgat & Anvar
• Backend: Yusuf & Egor
• ML/AI: Gurban
• DevOps: Aibek

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Support

For technical support or questions:

• Telegram: @abdugafforzoda
• Issues: GitHub Issues

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Built with ❤️ by the InnoSync Team

Innopolis University Capstone Project 2025