An Asia-Pacific headquartered marketplace runs 80 to 100 interviews biweekly across its India GCC. The panelists are split across two countries and three time zones. The interview styles diverge. Panelists in one region ask differently than panelists in the other. Same role, same rubric, different bar.
This is the calibration problem in its purest form. It is not that one team is better than another. It is that two well intentioned teams converge on different definitions of "strong hire" and never realize it.
The traditional fix is calibration sessions. You get 10 interviewers in a room, watch a recorded interview together, and debate the score. It works. It also costs four hours of senior engineering time per session and you cannot do it weekly.
AI changes the unit economics. Instead of one calibration session per quarter, you get a continuous calibration signal. Every interviewer's scoring distribution is compared against the team average. If one panelist is two standard deviations harsher on system design, that shows up the same week, not in the quarterly review.
The fix is rarely a dramatic intervention. Usually it is a 15 minute conversation where the harsher interviewer realizes they are scoring on production scale problems when the role only requires prototype scale. They recalibrate. The data shows it.
Tools like Mazle treat each interviewer as a measurable instrument. You do not retrain interviewers from scratch. You correct drift early.
Calibration is not an event. It is a maintenance routine.
