An intelligent full-stack web application that provides structured, personalized, and role-specific interview preparation through AI-driven mock interviews and real-time feedback.
Track&Prep empowers job seekers with a comprehensive interview preparation experience tailored to their target roles and experience levels. By leveraging AI technology, the platform generates customized practice modules, conducts realistic mock interviews, and provides actionable feedback to help candidates succeed in their career goals.
Most candidates struggle with:
- 📚 Unstructured and scattered preparation resources
- 🔄 Lack of personalized feedback and progress tracking
- 🎭 Absence of realistic mock interview experiences
Track&Prep addresses these challenges by delivering AI-guided, data-driven, and personalized preparation in one integrated platform.
- Role-Based Content: Questions tailored to specific job roles
- Experience-Level Matching: Content difficulty adjusted to user's experience
- Dynamic Adaptation: AI adapts to user's strengths and weaknesses
- Realistic Interview Flow: Simulates actual interview scenarios
- Timed Responses: Practice under real interview conditions
- Automated Evaluation: Instant AI-based assessment of answers
AI evaluates responses for:
- ✅ Content quality and depth
- ✅ Relevance to the question
- ✅ Clarity and communication skills
Provides comprehensive feedback including:
- 📈 Numerical scores
- 💪 Identified strengths
- 🎯 Targeted improvement suggestions
- Complete interview history
- Performance trends over time
- Skill-wise improvement insights
- Analytics visualization
- User authentication and authorization
- Protected routes and API endpoints
- Isolated user data management
| Layer | Technologies |
|---|---|
| Frontend |  React.js |
| Backend |  Node.js +  Express.js |
| Database |  MongoDB |
| AI Integration |  Google Gemini API |
┌─────────────────────────────────────────────────────────────┐
│ Frontend (React.js) │
│ ┌─────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Dashboard │ │ Mock Interview│ │ Analytics │ │
│ └─────────────┘ └──────────────┘ └──────────────┘ │
└────────────────────────────┬────────────────────────────────┘
│ REST APIs
┌────────────────────────────┴────────────────────────────────┐
│ Backend (Node.js + Express.js) │
│ ┌──────────────┐ ┌────────────┐ ┌───────────────┐ │
│ │ Auth Service │ │ Business │ │ AI Integration│ │
│ │ │ │ Logic │ │ (Gemini API) │ │
│ └──────────────┘ └────────────┘ └───────────────┘ │
└────────────────────────────┬────────────────────────────────┘
│
┌────────────────────────────┴────────────────────────────────┐
│ Database (MongoDB) │
│ ┌──────────┐ ┌─────────────┐ ┌──────────┐ ┌─────────┐ │
│ │ Users │ │ Interviews │ │ Feedback │ │ Progress│ │
│ └──────────┘ └─────────────┘ └──────────┘ └─────────┘ │
└─────────────────────────────────────────────────────────────┘
- Node.js (v14 or higher)
- MongoDB (v4.4 or higher)
- Google Gemini API Key
-
Clone the repository
git clone https://github.com/Kajalmeshram11/tracknprep.git cd tracknprep -
Install dependencies
# Install backend dependencies cd backend npm install # Install frontend dependencies cd ../frontend npm install
-
Configure environment variables
Create a
.envfile in the backend directory:PORT=5000 MONGODB_URI=your_mongodb_connection_string JWT_SECRET=your_jwt_secret GEMINI_API_KEY=your_gemini_api_key
-
Start the application
# Start backend server cd backend npm start # Start frontend (in a new terminal) cd frontend npm start
-
Access the application
- Frontend:
http://localhost:3000 - Backend:
http://localhost:5000
- Frontend:
1. User Registration/Login
↓
2. Select Target Role & Experience Level
↓
3. AI Generates Custom Practice Modules
↓
4. User Takes Mock Interview
↓
5. AI Evaluates Responses in Real-Time
↓
6. Detailed Feedback & Scoring Provided
↓
7. Progress Tracked in Dashboard
↓
8. Continuous Improvement Loop
{
name: String,
email: String,
password: String (hashed),
targetRole: String,
experienceLevel: String,
createdAt: Date
}{
userId: ObjectId,
role: String,
questions: Array,
answers: Array,
scores: Array,
feedback: Array,
overallScore: Number,
completedAt: Date
}Through building Track&Prep, I gained hands-on experience in:
- ✅ Full-Stack Development: End-to-end MERN stack application development
- ✅ Scalable Architecture: Designing modular and maintainable backend systems
- ✅ AI Integration: Implementing Google Gemini API for intelligent features
- ✅ Real-Time Systems: Building evaluation and feedback mechanisms
- ✅ Authentication & Security: Implementing JWT-based secure authentication
- ✅ Data Visualization: Creating interactive analytics dashboards
- ✅ RESTful APIs: Designing and documenting clean API endpoints
- 🎤 Voice-Based Interviews: Speech-to-text integration for verbal practice
- 📄 Resume Analysis: Generate questions based on uploaded resumes
- 🌐 Multi-Language Support: Conduct interviews in multiple languages
- 🏢 Company-Specific Tracks: Specialized preparation for top companies
- 👥 Peer Mock Interviews: Connect with other users for practice
- 📱 Mobile Application: Native iOS and Android apps
- 🎥 Video Interview Practice: Record and analyze video responses
- Lines of Code: ~10,000+
- Development Time: 8 weeks
- API Endpoints: 15+
- AI Models Used: Google Gemini Pro
Contributions, issues, and feature requests are welcome! Feel free to check the issues page.
- Fork the project
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Kajal Meshram
B.Tech in Computer Science & Engineering
Full-Stack Developer | AI Enthusiast
- Google Gemini API for AI capabilities
- MongoDB Atlas for database hosting
- React community for excellent documentation
- All open-source contributors