Product Design Case Study

Making AI hiring decisions explainable

How I designed a unified AI screening system for IIIP that turned opaque candidate filtering into explainable, criteria-backed decisions, reducing screening drop-off from 90% to 40%.

RoleProduct Designer CompanyIntervue.io ProductIIIP Platform ScopeEnd-to-End

Scroll

IIIP pipeline board, Applied, Resume screening, HR screening, Tech round 1, with candidate cards and screening report links

01 - Context

Background - Intervue

Intervue started as an interview outsourcing platform, like Uber for tech interviews. Companies with engineer bandwidth issues would send requests, and we'd assign anonymous external interviewers. But the problem wasn't just interviews. It was the entire hiring infrastructure.

The Origin

Companies were stitching together 6–8 tools just to hire one person, ATS, job boards, assessment platforms, video tools, calendars, spreadsheets. Fragmented data, inconsistent evaluation, slow cycles.

The Evolution

Intervue evolved into IIIP, an all-in-one hiring platform covering AI sourcing, AI screening, interviews, analytics, and offers. I designed every screen across this entire ecosystem.

The Positioning

Where traditional ATS tools make companies switch between platforms, IIIP consolidates the entire pipeline. One job, one platform, one source of truth, from sourcing to offer letter.

The Scale

Currently in beta with 4 enterprise customers using the platform for various hiring needs, from high-volume BPO hiring to specialized tech roles.

02 - Problem

The Hiring Funnel is Broken at the Top

Massive candidate volume enters the funnel, but without structured screening, most drop-offs happen at the wrong stages, wasting interviewer time and losing good candidates.

Hiring flow sketch showing 300 applied → 50 first interview → 5 second round → 2 cultural fitment → 1 offered, with a 90% drop-off at the screening step — Mapped during early discovery with 6 TA leaders, the same shape showed up with two beta customers. Screening is where the funnel breaks: **~90% drop-off** between "Applied" and "First interview round," mostly from manual or tool-assisted review.

90%

of candidates are filtered out at screening with no structured criteria, good candidates get lost.

6 sec

average time a recruiter spends per resume. Gut-feel decisions at scale lead to inconsistent evaluation.

90%

of interview drop-offs happen because unqualified candidates passed weak screening, wasting engineer time.

03 - Discovery & Ideation

From recruiter pain points to solutions

By shadowing recruiters and studying where candidates dropped off, I found four places where screening broke down. For each one I asked a How might we question, and the solution is my answer to it.

Problem 01

No shared rubric

Two recruiters, same resume, opposite decisions.

How might we help every recruiter judge the same way?

Write it in plain English

Requirements typed as simple sentences, applied to every applicant.

Problem 02

Keyword blindness

Good people rejected over missing buzzwords.

How might we show why someone fits?

See why someone fits

Each requirement marked met or not met.

Problem 03

No early soft signals

Communication only shows up in late phone screens.

How might we hear from candidates before the first call?

Hear from candidates first

A short video answer sits next to the resume.

Problem 04

Screening fatigue

200+ resumes per role wear reviewers down.

How might we surface the strongest applicants first?

Spot the best matches

Every applicant tagged Most fit or Least fit.

Screening was a filter, not an evaluation. It removed people, but didn’t actually assess them.

The Core Insight

04 - Final Design

The 4 Solutions in the Interface

What each solution looks like in the product, from setting up a job to reviewing the people who applied.

Solution 01 Job setup

Write what you want, in plain English

While creating a job, the recruiter types requirements as simple sentences, like “3+ years of JavaScript, TypeScript or Python”. Every applicant is then checked against that same list, so two recruiters can no longer reach opposite verdicts on the same resume.

Recruiter typing screening requirements as plain sentences, with optional keyword and job-title filters

Hiring pipeline setup showing the requirements turned into rules that all 1,243 applicants will be screened against

Yes/No screening question with an auto-reject toggle

Job settings page where screening questions, team members and the careers page are managed

Step 1: the recruiter lists what the role needs, one plain sentence per line.

Solution 02 Screening report

See exactly why someone fits

Each applicant gets a report that goes through the requirements one by one and marks them as met or not met. Recruiters see the reasoning, not just a score, and can open the candidate’s full profile to see their summary and progress.

Screening report for Natalie Craig: three requirements matched, three not matched

Natalie meets 3 of the 6 requirements. Matched ones get a green tick, missing ones a red cross.

Solution 03 Video introduction

Hear from candidates before the first call

Alongside the resume, recruiters can ask for a short video answer such as “Please introduce yourself”. Applicants with a video are shown to companies first, so most record one, and recruiters can judge how someone communicates before booking a phone screen.

Recruiter setting up a one-minute video question, what to look for, and an auto-reject score

Job page banner telling the applicant their video answer is still pending

Post-application screen explaining why recording a video helps

Application form where the candidate records or uploads a video answer

Recruiter side: set the question, the time limit, what to look for, and the score below which applicants are turned away.

Solution 04 Candidate pipeline

Spot the best matches at a glance

Once screening runs, every applicant is tagged Most fit or Least fit with a match score and a link to their report. Recruiters start with the strongest candidates instead of reading hundreds of resumes in arrival order.

Pipeline board where the Resume screening column groups candidates into Most fit and Least fit

All Candidates table with each applicant’s fit tag, match score and report link

Pipeline view: the screening column splits candidates into Most fit and Least fit, and the criteria can be edited right there.

05 - Impact

Design Outcomes

Impact measured through design system adoption, workflow efficiency, and structural improvements to the hiring pipeline.

90%→40%

Drop-off at screening reduced by making AI evaluation criteria explainable and actionable

100%

of screening decisions now backed by structured criteria, zero gut-feel

3→1

Evaluation surfaces unified under one criteria system, resume, video, sourcing

beta enterprise customers closed and actively using the screening module in production