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Technical Stack & Architecture

1. Frontend (UI):

• Framework: React or Angular (both have large ecosystems, good for dynamic UI and real-time interactions)
• UI Library: Tailwind CSS or Material UI for styling components quickly and responsively
• Voice Input: Web Speech API (native browser support) or third-party like Google Cloud Speech-to-Text for better accuracy
• State Management: Redux or Context API (React), or NgRx (Angular) for managing user session, questions, responses, and feedback
• Real-time Updates: WebSockets or libraries like Socket.IO if you want real-time feedback during the interview

2. Backend (API):

• Framework: Node.js with Express or NestJS (TypeScript recommended for scalability and maintainability)
• Interview Logic & Question Engine: Modular service that handles question selection, adaptive flow, and feedback generation
• Speech Analysis: Integration with Speech-to-Text APIs (Google, AWS Transcribe, Azure Speech Services) for voice response processing
• Natural Language Processing (NLP): Use ML models or services (like OpenAI GPT or custom NLP pipelines) to analyze answer quality, detect keywords, and generate feedback
• Authentication: JWT-based authentication or OAuth2 for user accounts and progress tracking
• API Documentation: OpenAPI / Swagger for clear contract with frontend

3. Database:

• Primary DB: PostgreSQL or MongoDB (PostgreSQL if you want relational data like user profiles, interview sessions; MongoDB if more flexible schema needed for question sets and logs)
• Caching: Redis for session management, fast retrieval of frequent question sets, or real-time scoring state
• Blob Storage: AWS S3 or Azure Blob Storage for storing voice recordings if needed

4. DevOps / Hosting:

• Frontend Hosting: Vercel, Netlify, or Azure Static Web Apps (fast CDN, auto-deploy from Git)
• Backend Hosting: AWS Elastic Beanstalk, Azure App Service, or containerized with Kubernetes / Docker on AWS EKS / Azure AKS
• CI/CD: GitHub Actions or GitLab CI for automated build, test, and deployment pipelines

5. Additional Tools:

• Analytics: Google Analytics or Mixpanel to track user engagement and identify drop-off points
• Monitoring: Sentry or Datadog for error tracking and performance monitoring
• Logging: Winston (Node.js) or built-in logging with integrations to ELK stack (Elastic, Logstash, Kibana)

How it all fits together

• User opens UI, chooses interview type, industry, and level.
• UI calls backend API to get first questions, passes user answers (text or speech converted to text).
• Backend applies adaptive logic, runs NLP to evaluate answers, stores results in DB, and returns feedback & next questions.
• Feedback shows on UI instantly; progress and session info saved for later review.
• Voice responses recorded optionally and processed asynchronously for deeper feedback.
• User can review past sessions via frontend calls to API that queries the DB.