Inspiration

Many candidates struggle to practice interviews realistically and often do not receive personalized feedback. We wanted to create an AI-powered interview experience that helps candidates practice anytime and understand where they can improve.

What it does

AI Interview Coach simulates a real interview using AI. Users can select a job role, experience level, interview type, and difficulty. The AI asks adaptive questions, analyzes answers, provides scores and personalized feedback, and generates a final performance report. Users can answer by typing or using voice input.

How we built it

We built the frontend using Next.js, React, TypeScript, and Tailwind CSS. The backend uses Node.js, Express.js, and TypeScript. MongoDB with Mongoose is used to store user and interview data. Gemini AI is used for interview question generation, answer evaluation, adaptive questioning, and feedback.

Challenges we ran into

The main challenges were designing an adaptive interview flow, connecting the frontend with backend APIs, handling AI-generated responses reliably, managing interview state, and making voice input work smoothly across different browsers.

Accomplishments that we're proud of

We are proud of building an interactive AI interview experience instead of a simple question-and-answer system. The platform can adapt questions based on previous answers, provide personalized feedback, support both text and voice responses, and track interview performance.

What we learned

We learned how to integrate AI APIs into a full-stack application, connect React/Next.js with an Express backend, work with MongoDB and Mongoose, design REST APIs, manage application state, handle AI responses, and build a more realistic conversational user experience.

Built With

  • agents
  • api
  • call-e
  • calling
  • calls
  • conversational
  • css
  • fullstack
  • language
  • natural
  • next.js
  • node.js
  • outbound
  • phone
  • procrssing
  • react
  • rest
  • tailwind
  • typescript
  • voice
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