Interview9.ai
Structured interview platform — rubric-driven evaluation, bias-aware scoring, and AI-assisted synthesis of candidate feedback across panels.
Why I Built This
Most interview debriefs are a group of smart people averaging their gut reactions. That's a great way to hire for comfort and a bad way to hire for role fit. Interview9 forces the conversation back to the rubric, keeps it there, and then lets AI synthesize the panel's evidence into a decision memo that survives a hiring manager's scrutiny.
The Problem
Unstructured interviews are low-signal, inconsistently calibrated across panelists, and prone to bias. Rubrics exist but usually live in a doc nobody opens mid-interview, and debrief meetings collapse into whoever speaks first.
How It Works
- Rubric-driven scoring interface that panelists actually use during the interview, not after
- Bias-aware prompts and scoring patterns designed to surface evidence rather than vibes
- AI-assisted synthesis that turns individual panelist notes into a structured candidate memo
- Calibration views across roles and panelists — highlights drift and outliers for hiring-bar consistency
The Impact
Hiring decisions grounded in rubric evidence, not the loudest voice in the debrief
Faster time-to-decision — synthesis memos ready for hiring committee in minutes
Calibration feedback loops make panelists measurably more consistent over time
Audit-friendly trail for every hire, which matters for regulated and enterprise buyers