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Interviewbit Platform

AI-powered interview practice platform for realistic mock interviews, voice-based answering, and structured AI feedback.

Component Documentation

Project Overview

Interviewbit helps candidates practice interview rounds in a guided flow, get immediate rubric-based feedback, and track progress over time. The platform combines a React frontend, FastAPI backend, AI evaluation services, and analytics to simulate an end-to-end interview experience.

Main Features Offered

  • Adaptive interview flow with one-question-at-a-time progression
  • Voice and text answer support
  • AI-generated evaluation with strengths and improvement areas
  • Session summaries and per-question detailed feedback
  • Preparation and suggestion views before interviews
  • Analytics dashboard for tracking performance trends

Interview Pipeline

1. Preparation

Users begin by selecting interview mode/preferences and preparing for the target role.

Preparation

2. Interview Center

The interview setup screen organizes session start options and interview configuration.

Interview Center

3. Live Interview Session

Candidates answer generated questions (voice/text) in a structured session flow.

Interview Session

4. Evaluation

After submission, AI evaluates responses using rubric-based criteria.

Evaluation

5. Detailed Explanation

The platform provides detailed explanation-level feedback for each answer.

Detailed Explanation

Detailed Explanation 2

6. Strengths from the Interview

Users receive highlighted strengths from the completed interview.

Interview Strengths

7. Dashboard and Analytics

Performance summaries and trends are visualized in dashboard and analytics views.

Dashboard

Dashboard Light

Analytics

System Workflow

High-level workflow of the overall platform:

System Workflow

Architecture

  • Frontend: React + Vite + Zustand + Tailwind
  • Backend: FastAPI with modular routes and interview state engine
  • Data Layer: DynamoDB access abstraction (Firestore-compatible structure in code)
  • AI Services: AWS Bedrock for question/evaluation assistance + STT adapter
  • Deployment: GitHub Actions pipeline to S3 + CloudFront for frontend delivery

API Surface (Main)

  • POST /api/v1/sessions create session
  • POST /api/v1/sessions/{session_id}/start generate first question
  • POST /api/v1/sessions/{session_id}/questions/{question_id}/answer submit answer
  • POST /api/v1/sessions/{session_id}/complete complete interview
  • GET /api/v1/sessions list user sessions
  • GET /api/v1/analytics/* performance insights

Local Run

Backend

cd server
pip install -r requirements.txt
uvicorn src.main:app --reload --port 8000

Frontend

cd client
npm install
npm run dev