InterviewIQ
Inspiration
Students like us constantly find ourselves in interviews — for student organizations, internships, and full-time jobs. We created InterviewIQ because when it matters, you should be ready for the interview.
What it does
InterviewIQ is an AI-powered mock interview platform that generates adaptive interview questions based on a user’s target role and job description.
The system evaluates responses in real time, adjusts difficulty based on performance, and tracks long-term progress across sessions. Users can practice with voice input, hear AI interviewer speech, and view performance analytics on a live dashboard.
How we built it
- Frontend: Next.js + TypeScript + TailWind CSS for the interactive interview experience
- Backend: Next.js API routes for session logic and scoring
- AI: OpenAI API for question generation, evaluation, and feedback
- Memory: Supermemory for long-term performance tracking
- Voice: OpenAI Whisper (speech-to-text) + ElevenLabs (text-to-speech)
- Deployment: Vercel
Challenges we ran into
- Designing adaptive difficulty that feels natural
- Managing real-time voice input/output latency
- Maintaining consistent session state across the interview flow
- Structuring long-term memory for meaningful cross-interview insights
Accomplishments that we're proud of
- Successfully integrated ElevenLabs voice interviewer
- Built a fully adaptive interview engine
- Implemented cross-interview memory tracking
- Deployed a working end-to-end product during the hackathon
What we learned
- How to design adaptive AI feedback loops
- Best practices for real-time voice applications
- Prompt engineering for structured interview evaluation
- Full-stack coordination under hackathon time pressure
What's next for InterviewIQ
- Real-time AI interviewer characters
- Tone and confidence analysis
- More role-specific interview tracks
- Improved analytics and progress insights
- Multiplayer/mock panel interview mode
Built With
- elevenlabs
- next.js
- openaiapi
- supermemory
- tailwindcss
- typescript
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