Prepify — AI-Driven Mock Interview Platform

Prepify is a production-ready, full-stack mock interview platform that leverages Large Language Models (LLMs) and real-time voice agents to simulate realistic interview scenarios. The platform enables users to practice interviews interactively, receive structured AI-generated feedback, and track performance across sessions.

🔗 Live Demo: https://prepify-mock-interview.vercel.app

Project Overview

Interview preparation tools often lack realism, adaptability, and actionable evaluation. Prepify addresses these limitations by combining modern web architecture with AI-powered reasoning and voice interaction. The system is designed with a scalable, modular architecture, emphasizing clean separation of concerns, maintainability, and extensibility.

Core Capabilities

Authentication & User Management

• Secure user authentication using Supabase Auth
• Session-based access control
• User-specific interview history and metadata persistence

AI-Powered Interview Engine

• Role- and level-specific interview question generation
• Context-aware follow-up questioning using LLMs
• Configurable interview parameters (role, experience level, tech stack)

Real-Time Voice Interaction

• Natural, conversational interview experience via Vapi AI
• Speech-to-text transcription and AI-driven responses
• Event-driven handling of call lifecycle states

Automated Feedback & Evaluation

• AI-generated qualitative feedback on user responses
• Identification of strengths, weaknesses, and improvement areas
• Consistent evaluation logic across interview sessions

Interview History & Progress Tracking

• Persistent storage of completed interviews
• Structured interview records for future analytics
• User dashboard for reviewing past sessions

Technology Stack

Frontend

• Next.js (App Router)
• React
• Tailwind CSS
• shadcn/ui

Backend & Services

• Next.js API Routes
• Supabase (Authentication & Database)
• Groq API (LLM-powered interview question generation and evaluation)
• Vapi AI (Voice agents and real-time conversation)

Deployment

• Vercel

System Architecture

Prepify follows a layered architecture:

• Presentation Layer
  Server and client components rendered via Next.js for optimal performance and SEO.
• Application Layer
  API routes manage interview orchestration, AI service integration, and session handling.
• AI Layer
  LLMs generate interview content and evaluate responses; voice agents manage real-time conversation.
• Data Layer
  Supabase stores user profiles, interview sessions, feedback, and metadata. This architecture ensures scalability, reliability, and ease of future enhancements.

Setup & Installation

Prerequisites

• Node.js ≥ 16
• Supabase project credentials
• Groq API key
• Vapi AI API key

Installation

git clone https://github.com/Madhuri36/Prepify.git
cd Prepify
npm install

Enviroment Configuration

Create a .env.local file

# Supabase (public – safe for frontend)
NEXT_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_public_anon_key

# Supabase (server-only – NEVER expose)
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key

# Base URL
NEXT_PUBLIC_BASE_URL=http://localhost:3000

# AI & Voice (server-only unless explicitly required)
GROQ_API_KEY=your_groq_api_key
NEXT_PUBLIC_VAPI_WEB_TOKEN=your_vapi_web_token
NEXT_PUBLIC_VAPI_WORKFLOW_ID=your_vapi_workflow_id

Run Locally

npm run dev

Application will be locally available at:

http://localhost:3000

Contributions are welcome.
Please open an issue or submit a pull request for improvements or bug fixes.