Grok Interview

1 jobs
2 candidates
3 interviews
4 upload candidate resume for AI context
5 interview HR prescreening
6 interview Performance report
7 live coding with AI
8 live coding problems passed
9 Interview report
10 detailed AI feedback

Inspiration

Traditional interviews are time-consuming and inconsistent. We wanted to scale hiring at X and xAI while keeping the human touch. When we saw how natural AI voice assistants had become, we realized we could leverage this for structured, fair interviews at scale.

What it does

Grok Interview conducts multi-round AI interviews through natural voice conversations. It runs three rounds: HR screening for cultural fit, technical assessment with adaptive difficulty, and live coding with a full IDE. The platform automatically parses resumes, scores candidates across multiple competencies, and provides analytics dashboards for hiring decisions.

How we built it

Built with Next.js, using X.AI's Grok Realtime API for voice conversations. We integrated Monaco Editor for the coding IDE, created custom prompt templates for each interview round, and built a resume parsing pipeline using unpdf and mammoth. State management uses React hooks, localStorage, and server-side stores with custom audio hooks.

Challenges we ran into

Making the AI interviewer feel genuinely human was the hardest part - getting natural pauses, turn-taking, and conversation flow right took extensive prompt engineering. Each interview round needed distinct personalities (probing technical vs supportive coding), which required tons of iteration.

Accomplishments that we're proud of

The AI interviewers actually feel human - natural pauses, realistic flow, the whole thing. The adaptive difficulty system that adjusts questions in real-time based on performance is pretty cool. The Monaco Editor integration with test execution rivals professional coding platforms. Our analytics dashboard with transcripts and competency scores helps hiring managers make informed decisions.

What we learned

Prompt engineering is an art - creating realistic AI personas requires extensive iteration. Managing state across client, server, and persistence layers taught us when to use each approach. Monaco Editor integration and resume parsing from unstructured formats were both deep dives.