Case Study

Interview.AI

AI Hiring Platform

Interview.AI needed to eliminate subjective interview drift and help recruiting teams make faster, higher-confidence decisions at scale.

AI PlatformVoice IntelligenceHiring Ops
Interview.AI hero mockup

The Problem

  • Recruiters were spending too much time consolidating feedback across disconnected tools and notes.
  • Decision quality varied heavily by interviewer style, creating inconsistent candidate scoring.
  • Leadership lacked real-time visibility into hiring pipeline quality and drop-off behavior.

Our Process

Discovery

Mapped recruiter and hiring-manager workflows to identify where bias, latency, and context loss occurred.

AI Product Design

Structured competency rubrics, interview timeline intelligence, and transcript highlight layers for faster evaluation.

Platform Build

Built a scalable dashboard system for pipeline analytics, recommendation confidence, and role-level benchmarking.

Rollout

Calibrated scoring thresholds and feedback loops with recruiting leads to improve model trust and decision adoption.

Impact Metrics

Measured outcomes after launch

Interview Review Time

-46%

Time-to-Decision

-31%

Scoring Consistency

+52%

Recruiter Throughput

+37%

Mockup Gallery

Interface snapshots and product direction

Interview.AI mockup 1
Interview.AI mockup 2
Interview.AI mockup 3

Outcomes

What changed for the business

  • Hiring teams now run structured and repeatable interview evaluations across every role.
  • Recruiters gained a single source of truth for playback, highlights, and decision-ready candidate context.
  • Leadership can monitor funnel health and quality in real time, improving staffing predictability.
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