AI-Work Interview Room flow

The session opens with a real ticket brief: what is broken, what success looks like, which files are likely relevant, and which constraints matter. The candidate can ask clarifying questions before touching the code. The interviewer can answer as a teammate would, not as a quizmaster.

This changes the posture of the interview. The candidate is not performing memorized knowledge. They are entering a work situation, building context, and deciding where to spend limited time.

AI use is not a side channel. The room keeps the assistant workflow visible so the team can see how the candidate prompts, verifies, rejects, and edits generated help. The transcript is not used to penalize tool use. It is used to understand judgment.

A useful candidate asks specific questions, checks output against the codebase, and stays accountable for the final diff. A risky candidate asks the assistant to solve the whole task, accepts code they cannot explain, or loses sight of the ticket.

At the end, the interviewer does not just ask whether the task works. They review the diff, test choices, assumptions, and transcript with the candidate. This is where seniority shows up: how someone explains tradeoffs, what they would do with more time, and what risks they intentionally left alone.

That discussion is often more valuable than the raw completion result. It shows whether a candidate can communicate like a teammate after making a change, which is the behavior the company actually needs.

The room produces a single evidence packet: ticket brief, repo state, assistant transcript, final diff, tests, interviewer notes, and scorecard. Hiring teams can calibrate on the same artifacts instead of debating vibes from separate calls.

For founders, this means fewer interviews, cleaner signal, and a candidate experience that feels like the company understands modern engineering work.