Framing the Problem

What is the problem you are trying to solve? Who does it affect?

Students and professionals of all backgrounds must prepare for interviews, a process that can be stressful, time consuming, and uncertain.

Interviewees often spend hours researching companies, roles, and possible questions, yet still struggle to find practice opportunities or personalized feedback.

This problem affects anyone seeking to improve their interview performance, especially those without access to mentors, recruiters, or mock interview resources.

Idea Explanation

What is your idea? How does it fix the problem?

Our team’s idea is an AI-centered interview preparation platform designed to simulate realistic, personalized interview experiences and preparation.

The project uses AI to:

• Generate personalized interview questions based on the user’s résumé and target role.

• Analyze résumés to provide section-specific feedback and create questions around identified gaps.

• Provide company summaries that include culture, values, and example interview questions.

• Simulate mock interviews, allowing users to answer questions in real time and receive AI-driven feedback on posture, tone, and confidence.

This helps users prepare smarter, focus their preparation efforts, and gain confidence before real interviews.

Implementation

How do all the pieces fit together? Does your frontend make requests to your backend? Where does your database fit in?

Frontend: React, TypeScript, Vite, and Tailwind CSS

The frontend handles all user interactions, including uploading résumés, selecting interview preferences, and beginning mock interviews.

It consists of multiple pages:

• Home Page: Project title, summary, and a “Get Started” button.

• Interview Start Page: Collects user information such as name, company, role, résumé, and interview type (HR, Technical, or Manager).

• Interview Prep Page: Analyzes the résumé or company data, displaying feedback or company details.

• Interview Page: Displays AI-generated questions and includes webcam features for real-time interaction.

• Technical Interview Page: Uses a Monaco Editor for live coding challenges and technical question responses.

Backend (FastAPI + OpenAI API)

The backend powers the AI logic and résumé analysis:

• Extracts and reads through the resume content

• Calls the OpenAI model to structure resume

• Generates custom interview questions

Integration

• Frontend makes request to API endpoints

• Backend returns structured data and questions

Challenges

What did you struggle with? How did you overcome it?

Challenges that appeared were:

• Frontend–Backend Integration:
  Connecting the React frontend with FastAPI required careful setup of API endpoints and CORS configuration. The team resolved this by testing each endpoint individually using Postman before connecting to the frontend.

• Creating the AI bot in the backend to create custom questions, to overcome the team implemented the OpenAi's features to ensure reliable outputs

Accomplishments

What did you learn? What did you accomplish?

Through this project, our team:

• Successfully built a full-stack AI interviewer integrating FastAPI, OpenAI, and React.

• Learned how to design structured prompts for reliable JSON responses from GPT models.

• Gained hands-on experience with webcam and editor integrations in frontend applications.

• Created a functional prototype capable of parsing résumés, generating feedback, and simulating interview scenarios.

Next Steps

What are the next steps for your project? How can you improve it?

Our team would like to

• Implement a database (PostgreSQL or Firebase) to store user data, interview progress, and analytics.

• Creating the audio for the AI bot when asking questions

• Add user authentication for returning candidates.

• Improve interview feedback with confidence scoring.

• Deploy the platform for public access.