Intervyo is an AI-driven interview preparation and evaluation platform designed to simulate real-world technical and HR interviews.
It helps candidates practice interviews, receive structured, criteria-based feedback, and improve performance through AI analysis instead of vague human opinions.
This is not a generic βchat with AIβ project.
Intervyo is built for realism, accountability, and measurable improvement.
- Why Intervyo Exists
- Core Features
- Advanced Multi-Company Features (NEW)
- Tech Stack
- System Architecture
- Installation & Setup
- Docker (Optional)
- Current Status
- Use Cases
- Design Philosophy
- Contributing
- Code of Conduct
Most interview preparation platforms fail because they:
- Ask generic questions
- Give fluffy, non-actionable feedback
- Do not simulate real interview pressure
Intervyo fixes this by:
- Running structured interviews
- Evaluating responses against defined criteria
- Giving actionable feedback, not motivational nonsense
If it doesnβt help you perform better in a real interview, it doesnβt belong here.
- Technical, behavioral, and mixed interview modes
- Timed questions to simulate real interview pressure
- Adaptive follow-up questions based on candidate responses
- New: Real-time Body Language Coach (Eye contact & Posture tracking) ποΈ
- Communication clarity analysis
- Technical correctness scoring
- Confidence & structure assessment
- Strengths, weaknesses, and improvement suggestions
- Live Confidence HUD during interviews π―
- Store past interviews
- Compare performance over time
- Identify recurring weaknesses
- Authentication & authorization
- Private interview data
- Secure API handling
- AI-powered analysis of your interview performance
- Personalized company recommendations based on skill level
- Success probability calculation for each company
- Gap analysis with improvement suggestions
- Route:
/advanced-featuresor/api/recommendations
- Track upcoming interview dates with countdown timers
- Automatically generated preparation milestones
- Daily practice recommendations based on days remaining
- Progress tracking and readiness score
- Route:
/api/calendar
- Crowdsourced real interview questions from actual interviews
- Voting system (upvote/downvote) for question quality
- Question verification workflow
- Frequency tracking (how often questions are asked)
- Search and filter by company, difficulty, type
- Trending questions feature
- Route:
/api/questions
- Find compatible study partners preparing for same companies
- Compatibility algorithm based on target companies and skill level
- 1-on-1 buddy connections with mock interview scheduling
- Study group creation and management
- Route:
/api/buddy
- Enhanced company profiles with hiring bar benchmarks
- Success thresholds for each interview type
- Difficulty ratings and acceptance rates
- Historical performance statistics
- Real-time speech-to-text using Web Speech API
- Live metrics: words, WPM, average sentence length, filler words
- Coaching tips for pace and clarity
- Save sessions locally for quick review (no backend required)
- Route:
/practice-lab - Requires microphone permission in the browser (Chrome recommended)
- Full Playback: Review completed interviews with complete conversation history
- Timestamped Notes: Add personal notes at any point with categorization (improvement, strength, mistake, learning)
- Smart Bookmarks: Quick-jump to important moments in the interview
- Resume Functionality: Pick up where you left off during review sessions
- Global Search: Search across all notes and bookmarks from all interviews
- View Analytics: Track how often you review each interview and total watch time
- Secure Sharing: Generate share links to get feedback from mentors or study buddies
- Self-Reflection: Identify patterns and track improvement over time
- Route:
/api/replay - Perfect for: Post-interview analysis, mentor feedback, peer review, progress tracking
- Predictive Intelligence: Analyzes your last 20 interviews to predict where you'll fail BEFORE your next interview
- Personalized Attack Plans: 3-phase improvement strategy (Emergency Fixes β Strengthen Core β Polish & Perfect)
- Micro-Challenges: 15 bite-sized, actionable tasks targeting your specific weaknesses (30-90 min each)
- Success Probability: Get real probability scores for easy/medium/hard interviews and specific companies
- Real-Time Progress Tracking: Improvement score, completion percentage, trend analysis (improving/declining/stable)
- AI Insights: Hidden strengths, blind spots, quick wins, peer comparison, long-term goals
- Weakness Categories: Tracks 10 areas (technical-depth, system-design, coding-efficiency, communication-clarity, etc.)
- Severity Levels: Critical (urgent), High (significant), Medium (polish needed), Low (strengths)
- Route:
/api/attack-plan - Unique value: Proactive vs Reactive - Know your failure points before they happen, not after
- React
- Tailwind CSS
- Responsive UI (desktop + mobile)
- Node.js
- Express.js
- MongoDB
- REST APIs
- LLM-based interview logic
- Prompt-engineered evaluation criteria
- Structured scoring system (not random text output)
User
β Frontend (React)
β Backend (Express API)
β AI Evaluation Engine
β Database (MongoDB)
β Feedback & Analytics
Simple, scalable, and not overengineered.
- Node.js (v18+ recommended)
- MongoDB
- Git
git clone https://github.com/santanu-atta03/Intervyo
cd intervyo
cd backend
npm install
npm run dev
This project currently uses React 19.
Some dependencies do not yet officially support React 19.
As a result, running npm install may fail with an ERESOLVE peer dependency error.
Until full React 19 support is available across dependencies, install frontend packages using:
npm install --legacy-peer-deps
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### π» Frontend Setup
cd frontend
npm install
npm run dev
---
### π Environment Variables
Create a `.env` file in the backend directory:
PORT=5000
MONGO_URI=your_mongodb_connection_string
AI_API_KEY=your_ai_api_key
---
## Docker (Optional)
This setup is for local development only and does not change the default workflows.
1) Create any needed backend env values (optional). The Docker Compose config uses
`Backend/.env.example` by default and overrides the MongoDB host.
2) Start the stack:docker compose up --build
Frontend: http://localhost:5173
Backend: http://localhost:5000
If you want to point the frontend to a different API URL, set
`VITE_API_BASE_URL` before building.
---
For a deeper walkthrough and rationale, see `docker_guide.md`.
## π¦ Current Status
- Core interview flow implemented
- AI-based evaluation logic working
- User authentication
- Advanced analytics (in progress)
- Multi-role interview templates (planned)
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## π― Use Cases
- Students preparing for placements
- Developers preparing for technical interviews
- Self-assessment before real interviews
- Mock interview practice without human bias
---
## π§ Design Philosophy
- Realism over gimmicks
- Feedback over praise
- Skill improvement over vanity metrics
This platform is built to expose weaknesses, not hide them.
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## π€ Contributing
Please read [CONTRIBUTING.md](CONTRIBUTING.md) before opening a pull request.
Low-effort, spam, or cosmetic-only contributions will be closed.
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## π Code of Conduct
This project follows the Contributor Covenant Code of Conduct.
Please read CODE_OF_CONDUCT.md before contributing.