preeti kundu
Preeti
Preeti
Preeti

Hey there, I'm Preeti, |

Product designer with 4+ years of experience turning complex problems into AI-powered products people love.

Currently at with experience from
reminder: trust the process
scroll to explore
proudly

Selected works

Candidate Journey · Onboarding · Apna

Onboarding
Revamp

Reimagined the candidate onboarding journey, smoothing first-time setup so seekers reach their first meaningful action faster and with less drop-off.

Journey Mapping Flow Design Activation UX
Onboarding
Drop-off
Faster
Time to First
Action
Activation
Rate
01
Candidate Onboarding Journey
Coming Soon
AI Hiring · Employer Platform · Apna

AI Hiring
Assistant

Turned an AI screening SKU into an embedded hiring workflow — making AI explainable evidence recruiters can act on, not a black-box verdict, while keeping them in control of every decision.

UX Strategy Usability Research AI Workflow Design
24×7
Candidate
Calling
~34%
Action on
AI Fit
1.7 min
Time to
Act
02
AI Hiring Assistant
Job Search · UX/UI · Foundit

Job Listing & JD
Page Revamp

Redesigned the job search results page into a clearer, personalized experience with Match Score insights and richer job details.

User Research Competitive Analysis Iterative Design Usability Testing
36M
Job Views
(from 17.8M)
3.63M
Applications
(from 3M)
+2%
SEO Traffic ↑
Job Listing & JD Page Revamp
Profile UX · Job Platform · Foundit

Seeker Profile
Page Revamp

Fixed a fragmented seeker profile experience to improve usability and drive more frequent, seamless profile updates.

Usability Study Survey Design Quantitative Research Visual Hierarchy
6
Research
Participants
Profile Update
Frequency
Drop-off
Rate
Seeker Profile Page Revamp
0→1 Product · Enterprise AI · Apna

OnlyRounds

Designed a source-agnostic, enterprise-grade AI hiring platform that screens and interviews candidates across every sourcing channel — built solo from concept to launch, with humans kept in control of decisions.

0→1 Product AI Workflow Design Enterprise UX
~80%
Connection Rate
(vs ~40% human)
Recruiter
Productivity
0→1
Platform
Built Solo
05
OnlyRounds · Enterprise AI ATS
Coming Soon
What people say about me

Not just my mom thinks I'm good at this.

"Preeti is born curious and has been very crucial in defining and building user experience at Foundit. She has an eye for detail for the work. She is a go-getter and asks the right questions. She is really quick with iteration and absorbing feedback."

Vijayant Kumar
Vijayant Kumar
Product Manager · Foundit

"Preeti began her career at Foundit, successfully transitioning from an intern to a full-time role. Over her two-year journey, I witnessed her remarkable growth, which gave us the confidence to entrust her with multiple key projects. She consistently delivered outstanding results with minimal guidance, handling them single-handedly."

Thilak Bhat
Thilak Bhat
Senior Product Designer · Cooper

"Had the privilege of working with Preeti at the start of her UI/UX design journey. Her rapid growth and dedication were evident from day one. She has evolved into a skilled designer, showcasing a keen eye for user-centric solutions. Her passion and work ethic make her a standout professional in the field."

Mandeep Vadera
Mandeep Vadera
Design Manager · Foundit

"Preeti is one heck of a hardworking youngster in Foundit's Product Design team. I have literally seen her exponential growth curve before my eyes — from intern to full-time, driven by sheer passion, hard work, and curiosity to improve. She constantly comes up with new ideas and never shies away from a healthy debate."

Saurav Mishra
Saurav Mishra
Product Manager · Foundit

"Preeti is one of those people you can trust with a problem and be confident it will be driven to a thoughtful, high-quality outcome. At Apna, she consistently demonstrated strong ownership, handled complex initiatives independently."

Piyush Tyagi
Piyush Tyagi
Design Lead · Skillz (NYSE: SKLZ)

"Worked with Preeti at Apna and honestly she's one of the best designers I've collaborated with. Her design and product thinking is really sharp — she never rushes to solutions. She digs deep into the problem first, asks the right questions, and challenges assumptions before touching Figma."

Sharath S.p
Sharath S.p
Product Lead · Apna

Get to know me?

View the chapter on my interests, stories
and life outside of work.

Preeti by the sea
Preeti in saree
Preeti at the beach
Preeti — portrait sketch

A bit about Myself

the person behind the pixels
Namaste! I'm Preeti ✦

I'm a Product Designer currently designing AI products at Apna, owning end-to-end experiences across candidate and recruiter platforms. I love exploring how design can tell stories, spark emotion, and make everyday moments a little more meaningful.

With 4+ years of experience, my work spans UX audits, research, critique, and prototyping — turning complex problems into intuitive, thoughtful experiences.

My journey started with a love for painting and sketching, and that curiosity still drives how I approach design today — with creativity, empathy, and a genuine interest in people.

Away from the screen, you'll usually find me watching movies, painting, sketching, café hopping, or getting lost in a good series — always collecting little bits of inspiration along the way.

Currently based in Bengaluru, India. If you're building something with heart, let's talk!

Preeti Kundu

My story so far is about learning to see people, patterns, and possibilities, and using design to turn that understanding into something that makes a difference. ✦

User Experience Design
Design Systems
AI-First Design
User Research
Prototyping
UX Audits
UI Design
Usability Testing
Interaction Design

What I bring to the table

Digital experiences that feel effortless, considered, and genuinely useful.

I have worked with

Foundit Apna
3+ years

Where I've Been

May 2025
Present
Product Designer II
Apna · HR Tech
Led AI Screening, AI Matching 2.0, and OnlyRounds — a zero-to-one AI ATS platform built solo from concept to launch.
Nov 2024
Apr 2025
Product Designer I
Apna · HR Tech
Owned recruiter UX — AI mismatch corrections, ECC revamp, WhatsApp Job Invites, database search.
Aug 2022
Oct 2024
UX/UI Designer
Foundit (formerly Monster.com) · Job Platform
Solo designer for Zuno platform, conversion redesigns, and recruiter surfaces driving 10M+ annual visits.
May 2022
Jul 2022
Visual Design Intern
Foundit · Job Platform
Campaign pages and onboarding optimisation — contributed to an 8% increase in user sign-ups.
life outside work

A few
snapshots.

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Case Study · Foundit · 2023

Job Listing &
JD Page Revamp

Redesigned the existing job search results page, where seekers view job listings and job descriptions, for both mobile and desktop platforms.

My Role
UX/UI Designer
Platform
Web, Mobile, App
Company
Foundit
Year
2023
Final Job Search Result Page

About Foundit: Formerly known as Monster, Foundit is a talent management platform that not only facilitates connections between job seekers and companies but also offers services to enhance candidate skills. It also offers comprehensive solutions for businesses, facilitating the discovery and recruitment of top talent.

What wasn't working

Foundit users face several challenges that hinder their job search experience and diminish engagement:

  • Cluttered Layout: The current design is overcrowded with information, making it challenging for users to navigate and find relevant job listings.
  • Lack of Personalization: The job search results are not tailored to individual user preferences, leading to a less engaging and relevant experience.
  • Fragmented User Experience: Users encounter inconsistencies and interruptions throughout the job search process, resulting in a disjointed experience.
  • Lengthy Job Descriptions: Job descriptions are presented as long paragraphs, making it difficult for users to quickly scan and understand the key details.
  • Insufficient Job Information: Current job cards lack comprehensive information, while also including too many elements, making it difficult for users to quickly identify important information. Additionally, there are no smart insights, such as a Match Score, to help users quickly assess job suitability.
  • Ineffective Filter Functionality: The current filters are dysfunctional and provide irrelevant results, hindering users' ability to refine job searches effectively.
What we set out to do

The primary objective of redesigning the job search results page is to create a streamlined, personalized, and user-friendly experience that addresses current issues of cluttered layout, lack of personalization, broken UX, and insufficient job information. By enhancing visual clarity, incorporating smart insights like a Match Score, and ensuring comprehensive and user-friendly job details, we aimed to significantly improve user satisfaction, engagement, and provide more job applications after the revamp.

Drivers:

  • User Feedback: Users expressed the need for more detailed and easily accessible information about jobs and companies directly from the search results.
  • Business Goals: Increase user engagement and application rates through the platform. Enhance brand loyalty by providing a superior user experience.
  • Technical Feasibility: Integrating new data points like company logos, employee count, and estimated salary involves collaboration with data providers and updating backend systems.

Key Metrics:

  • Engagement: Measure application clicks per search and applications per search session as indicators of improved user engagement.
  • Conversion Rate: Track the increase in applications submitted through the revamped job designs.
  • User Satisfaction: Gather user feedback and ratings on the new job card format.
Job Card Redesign

We began by segmenting the job search result page into three parts: job cards, job descriptions (JD), and filters. Our initial focus was on enhancing the job card section.

Current Job Card Analysis — The current job card included: Job Title, Company Name, Job Type, Job Location, Years of Experience, Salary, Skills, Date Posted, Tag (e.g., Great Place to Work), Save Icon, GPTW Banner.

Current Job Card Analysis

Initial Questions and Considerations:

  • Amount of Information: Do we want this much information on the card? Do users actually read so much information?
  • Skills Display: Should the maximum number of skills be minimized to avoid confusion — ideally between 2 to 4 skills?
  • Company Logos: We should use filled logos for quick recognition, better visibility and layout balance.
  • Job Type Visibility: Is it necessary to show the job type upfront? Shouldn't we cover it in the JD?
  • Salary Representation: Should we shorten the salary display by using units like LPA?
  • GPTW Tag Awareness: Are users aware of the banner? Do they value the "Great Place to Work" tag?
What we learned

To understand how we can enhance the job card experience, we conducted comprehensive user research using a mixed-methods approach including Surveys, In-depth Interviews, and Usability Testing.

Key Findings:

  • Experienced Users (6+ years): Prioritize job title, company name, years of experience required, and salary. Value detailed company information and salary expectations.
  • Intermediate Users (3–5 years): Focus on salary, company name, and job title. Often overwhelmed by too much information and prefer simpler job cards.
  • Entry-Level Users (0–2 years): Look primarily at job title, company name, and years of experience required. Tend to bulk apply but appreciate clear and concise job summaries.
Research iteration 1 Research iteration 2
Redesigned Job Card

The revamped job card includes: Job Title, Location, Experience, Estimated Salary, Company Logo, Smart Tags (Early Applicant, Quick Apply, High Match), Job Posting Date, and Save Job Feature.

Redesigned Job Card

After Redesign: The redesigned job card for the same position maintains the essential details but presents them in a more streamlined and visually appealing manner:

  • Simplified Information: The new design prioritizes key information, making it easier to read at a glance.
  • Location and Work Model: Clearly stated, providing candidates with an immediate understanding of the job's flexibility.
  • Visual Hierarchy: Better hierarchy ensuring crucial information like salary range and experience requirements are prominent.
  • Enhanced Applicant Insights: "Early Applicant" and "Strong Match" badges included, offering candidates a sense of their suitability.
  • Improved Call-to-Action: The "Quick Apply" button remains a focal point, complemented by icons indicating early application and strong match.
Job Description Page

After finalizing the job card, we proceeded to redesign the job description (JD) section. Initial analysis revealed job titles lacked prominence, job descriptions were presented in long paragraphs making it challenging for users to scan, and overall UI improvements were needed.

Current JD Redesigned JD

JD Research Key Findings:

  • Challenging Job Descriptions: Job descriptions are presented in long paragraphs, making it difficult for users to scan. Users prefer bullet points for better readability.
  • Lack of Personalization: Users struggle to understand if the job description matches their profile. Implementing a skill match score can indicate a user's fit.
  • Company Identification: Users prefer seeing company logos as they can quickly identify the company posting the job.
  • Missing Perks and Tags: Users search for perks that are currently missing in job descriptions.
Redesigned JD
Filter Redesign

The existing filter functionality was riddled with inconsistencies, creating confusion for users. Many available filters were underutilized by job seekers. Our goal was to redesign the filter functionality to align with industry standards, simplifying the job application process and enhancing user experience.

Current filters Redesigned filters
Final Job Search Result Page
Final Result Page
Desktop Experience Desktop Experience 2
Results
36M
Job Views
(from 17.8M)
3.63M
Job Applications
(from 3M)
24%
SEO Traffic
(from 22%)
User Satisfaction
Significantly Improved
Case Study · Foundit · 2023

Seeker Profile
Page Revamp

Revamp of the existing seeker profile page of Foundit (formerly Monster.com) to improve usability and significantly boost job seeker engagement, encouraging more frequent and seamless profile updates.

My Role
UX/UI Designer
Scope
Usability Testing, Survey, Research
Timeline
1 Week Research
Participants
6 Users
Seeker Profile Cover

About Foundit: Formerly known as Monster, Foundit is a talent management platform that not only facilitates connections between job seekers and companies but also offers services to enhance candidate skills. It also offers comprehensive solutions for businesses, facilitating the discovery and recruitment of top talent.

AIM of the Project

The current seeker profile isn't user-friendly and has a fragmented, broken experience. This issue is adversely affecting the frequency of profile updates.

By revamping the profile page, we aimed to improve usability and significantly boost job seeker engagement, encouraging more frequent and seamless profile updates. Our goal was to create an enhanced and intuitive user experience that increases user interaction and satisfaction.

What success looked like
  • Increase Profile Updates: Drive website traffic to the profile page to boost updates and reduce drop-off caused by poor user experience.
  • Improve Customer Satisfaction: Optimize visual hierarchy, personalization, accessibility, integration, and security to close UX gaps and deliver a cohesive experience.
  • Enhance User Engagement and Minimize Drop-offs: Implement interactive features, encouraging job seekers to spend more time on the platform.
Before Redesign

Current M-site and App experience showing the pain points:

Current App Current M-site
Usability Study

Participants: 6 users, mixed gender, age 22–34 years. We conducted moderated usability testing over 1 week.

Tasks given to participants:

  • Task 1 — Profile Update: "Imagine you want to update your profile information. Can you please walk us through how you would attempt to do that on the current profile page? Share your thoughts out loud as you go through the process."
  • Task 2 — Navigation: "Let's explore the current profile page together. Look for information about your work experience, skills, and education. Tell us how easy or challenging it is to find and update these details."
  • Task 3 — Feedback on Usability: "Share your thoughts on what aspects of the user interface you found user-friendly or challenging? Are there specific features that stand out to you, positively or negatively?"
  • Task 4 — Proposed Changes: "Based on your experience, what improvements would you suggest to make it easier for users to navigate and update their profiles? Are there any features you think should be added or removed?"
What we found
  • Reduce Pop-ups (6 people): Limit the number of pop-ups to minimize user irritation and provide a smoother browsing experience.
  • Emphasise Profile Score (4 people): Highlight the profile score for increased visibility, especially for users returning after a while, to encourage engagement in profile completion.
  • Optimize Resume Upload (4 people): Ensure that users' information is parsed effectively after uploading a resume to streamline the profile completion process.
  • Minimize Banner Prominence (4 people): Decrease the prominence of banners to avoid user confusion with ads, enhancing overall user experience.
  • Improve Mobile Experience (3 people): Address issues with mobile pop-ups and broken mobile number pop-up to enhance the mobile user experience.
  • Prioritize Work Experience (3 people): Recognize the importance of work experience for users; consider adjusting the visual hierarchy to give it more prominence.
  • Enhance Skill Visibility (2 people): Place skills higher in the visual hierarchy and consider asking for skills first to cater to user preferences.
  • Multiple Resumes Option (1 person): Introduce the option for users to maintain multiple resumes, accommodating diverse job applications.
Persona 1 Persona 2
Who we designed for

Priyanka Sharma, 32, Finance Manager — Main Goal: Secure a role with ample growth opportunities. Pain points: Annoyed by excessive pop-ups disrupting smooth profile navigation. Believes the profile score is not easily visible. Finds prominent banners distracting, mistaking them for ads. Finds it difficult to navigate between different sections.

Ansh Thakur, 25, Software Engineer — Main Goal: Obtain a senior engineering position at a leading tech company. Pain points: Frustrated by numerous pop-ups that interfere with seamless profile navigation. Finds the navigation between profile sections cumbersome and time-consuming. Notices inconsistencies in the design that make the user experience less professional.

Current state data

Avg traffic on profile page (Sep 23 – Jan 24):

  • Desktop is used more to visit profile — 2.5M avg; engagement time per session: 1min, 16sec
  • More traffic from emailers — 1M avg; engagement time per session: 1min, 12sec
  • Desktop is more used from emailers; however engagement time is more when user comes from Android — avg engagement time: 2min, 07sec
  • High drop-off rates observed across all platforms
Data insights
Redesign priorities

The user feedback analysis highlights crucial redesign priorities for the job seeker profile page. Streamlining intrusive pop-ups, prioritizing seamless mobile access, and enhancing profile score visibility are focal points. Addressing banner inconsistencies and emphasizing the work experience section align with user preferences. The redesign will integrate robust resume parsing, clear prompts, and early skills placement in the visual hierarchy. Introducing multiple resumes and refining job recommendations aim to accommodate diverse needs. The overarching goal is a user-friendly, visually cohesive profile page tailored to the unique preferences of job seekers in India, fostering a positive and efficient user experience.

The redesign
Redesigned Profile Redesigned Profile 2
Product · Enterprise AI · 0→1

OnlyRounds

A source-agnostic, enterprise-grade AI hiring platform that screens, interviews, and coordinates candidates across every sourcing channel, while keeping humans in control of the final decision. Built solo from concept to launch.

My Role
Product Designer · 0→1
Timeline
2025
Team
Solo Designer
Platform
Enterprise Web · Recruiter Console
🖼️
Hero: OnlyRounds recruiter console
Drop the cover render here (Figma: OnlyRounds dashboard / job lifecycle). Replace this block with an <img>.
About

An AI hiring layer that sits above every sourcing channel.

Enterprise hiring teams handle huge volumes of candidates from many channels at once: job portals, ATS, databases, referrals. OnlyRounds is a source-agnostic, enterprise-grade AI screening and interviewing platform that lets teams screen, interview, and coordinate candidates consistently using AI agents, no matter where those candidates came from.

What it is not: not limited to apna-sourced candidates, not a single static chatbot, and not a hiring-decision engine. Final calls always stay with humans.

Context

Single-channel AI screening couldn't scale to the enterprise.

apna's earlier AI screening product (AISKU) worked well for SMBs because apna was their primary sourcing channel. For enterprises, apna contributed only a fraction of total sourcing, so most candidates still fell back to slow, manual screening and the ROI of automation collapsed.

10-20%
Share of enterprise sourcing that came from apna. The rest was screened manually.
1 channel
Existing AI tools automated only a single channel, leaving partial coverage and poor ROI.
4+ sources
Portals, ATS, databases, referrals, each with its own format and candidate state.
~40%
Human recruiter connection rate. Most outreach never reached the candidate.
The Challenge 🔎

Screen every candidate the same way, no matter where they came from.

Design a platform that unifies all sourcing channels into one pipeline, lets recruiters configure trustworthy AI agents for screening and interviewing, and orchestrates outreach at enterprise scale, all without taking the hiring decision out of human hands.

🧭
Before: fragmented, multi-channel screening
Figma: current-state map of siloed channels.
My Role 🧩

Sole designer, end to end, concept to launch.

01
Area
Job Lifecycle & Setup

JD ingestion, structured job creation, interview-round setup, and evaluation-criteria editing.

Turned a messy JD into a structured source of truth.
02
Area
AI Agent Configuration

Configuring voice/video agents, language, persona, and reviewable scripts, with a test-before-go-live step.

Made complex agent setup reviewable and safe.
03
Area
Candidate Pipeline & Flows

Multiple entry points converging into one pipeline, with distinct inbound and outbound (consent-first) experiences.

One pipeline, two candidate realities handled.
04
Area
Recruiter Dashboard & Reporting

Status tracking, recordings, AI summaries, and credit/usage reporting for the workspace.

Decisions stay with humans, backed by evidence.
Design Goals 🎯

Three questions framed the whole platform.

?
How might we
Unify candidates from every sourcing channel into a single, consistent pipeline?
?
How might we
Let recruiters configure trustworthy AI screening & interview agents without drowning in complexity?
?
How might we
Automate outreach and coordination at scale while keeping humans in control of every decision?
Solution Walkthrough 🎨

Four surfaces, one end-to-end hiring flow.

Surface 01 · Job Setup

From JD to a configured, testable job

Recruiters paste, upload, or AI-generate a JD; an LLM extracts and cleans it into a structured job that becomes the source of truth. They then set up interview rounds (screening, interview, or human-led tasks), edit AI-generated evaluation criteria, configure each agent (voice/video, language, persona), and test the agent before going live.

⚙️
Job setup & round configuration
Figma: JD → structured job → rounds → agent config.
Why this improves UX

A single source-of-truth JD plus editable, AI-generated criteria makes setup fast without giving up recruiter control, and the test step builds trust before a real candidate is ever called.

Surface 02 · Candidate Pipeline

Every source converges into one pipeline

Candidates enter via bulk upload, embedded apply links, direct interview links, or auto-sourcing from ATS and job platforms, and all of them land in a single pipeline per job. Inbound candidates (who have intent) go straight into screening with a short intro; outbound candidates (contacted from a database) get an interest check and give consent first. The screening criteria stay identical across both; only the entry script changes.

📥
Unified candidate pipeline
Bulk upload / links / ATS → one pipeline.
🔀
Inbound vs outbound flows
Consent-first entry for outbound.
Why this improves UX

Keeping evaluation criteria constant while varying only the entry script gives fair, comparable results across sources, and consent-first outbound respects candidates who never asked to be contacted.

Surface 03 · Orchestration

The workflow decides when, how, and what next

An orchestration engine coordinates multi-channel outreach, deciding when to contact a candidate, through which channel (call, WhatsApp, email), for what objective (interest, screening, interview, scheduling), and what to do on response or silence. It optimises for time-of-day and honours retry limits and night-time policies. The design principle: agents decide what task; the workflow decides when and how.

🕸️
Workflow orchestration
Channel · timing · retry logic.
Why this improves UX

Separating "what" (agents) from "when/how" (workflow) kept the system understandable to configure and dramatically lifted reach; automated coordination hit candidates at the right moment on the right channel.

Surface 04 · Evaluation & Dashboard

Evidence in, clear status out

After each interaction, the data is scored by an evaluation agent against the recruiter's criteria and resolved to a clear status: Fit, Not Fit, Partial Fit, Not Interested, No Response, or In-Progress. In the recruiter portal, teams see status at a glance, listen to recordings or watch videos, and read AI-generated summaries, plus credit and usage reporting for the workspace.

📊
Recruiter dashboard & candidate status
Status · recordings · AI summaries.
Why this improves UX

A tight set of well-defined statuses plus one-click access to the recording and summary lets recruiters act in seconds, and keeps the final judgment human, backed by the actual evidence.

Impact 📈

Enterprise AI hiring, orchestrated at scale.

~80%
Connection Rate
(vs ~40% human)
Recruiter
Productivity
All
Sourcing Channels
Unified
0→1
Platform
Built Solo
  • Roughly 2× the connection rate of human recruiters through workflow-driven, multi-channel outreach.
  • 2× recruiter productivity by automating coordination, screening, and first-round interviews.
  • Consistent, unbiased evaluation: the same criteria applied to every candidate, from every source.
  • Extended AI hiring from SMBs to the enterprise by becoming source-agnostic.
Key Learnings ✨

Reflections from building an enterprise AI product 0→1.

1

Separate "what" from "when"

Letting agents own the task and the workflow own timing and channel kept an inherently complex system configurable and legible.

2

Source-agnostic is a design constraint, not just a backend one

Every source has a different candidate state; the UI had to normalise them into one pipeline without hiding what mattered.

3

Trust comes from control + evidence

Editable criteria, test-before-go-live, recordings, and summaries let recruiters hand work to AI while keeping the decision, and the accountability, human.

4

Consent is part of the experience

Outbound candidates never asked to be contacted, so an interest-check-first flow wasn't a nicety; it was the design.

0 to 1 Project

AI Hiring Assistant

Recruiters hiring frontline roles receive hundreds, even thousands, of applications per job. AI Recruiter interviews candidates, evaluates answers against recruiter-defined criteria, summarises every conversation, and recommends qualified candidates before any human interaction. It was never meant to replace recruiters. It removes repetitive first-round screening while recruiters keep complete control of the final decision.

My Role
Product Designer · 3 of 4 Verticals
Timeline
4 Months
Team
PM, Engineering, AI, Business
Platform
Employer Web · Candidate App

The shipped product in action: AI Agent evaluation inside the recruiter dashboard.

AI Calling Agent live in the apna recruiter dashboard
/ 01 Introduction

An AI recruiter that completes the first screening round before you even open the dashboard.

apna is India's largest hiring platform for frontline roles: telecalling, delivery, customer support, sales. Volume is the defining problem. Every candidate still needs manual screening, repetitive qualification checks, and follow-up calls before a recruiter can identify the right fit.

AI Hiring Assistant exists so that hours of daily calling stop delaying hiring. The AI conducts the first screening interview automatically. Recruiters open the dashboard to qualified, explained recommendations instead of a raw pile of applications.

/ 02 My Role

I owned 3 of the 4 verticals, end to end.

AI Screening Setup Employer Candidate Connect Candidate Experience Pricing & Monetization · Monetization team

Pricing and Monetization was owned by the Monetization team. I collaborated closely with them, along with Product Managers, Engineers, AI teams and Business stakeholders, through research, design, validation and launch.

/ 03 Timeline

Four months, research to launch.

ResearchCurrent workflowProblem definitionConcept explorationIteration 1User testingResearch insightsIteration 2LaunchMeasurePhase 2

This chapter covers the journey up to Iteration 2. Launch results and Phase 2 come later.

/ 04 The Problem

Recruiters spend more time filtering than hiring.

Post jobHundreds applyOpen every profileCall individuallyAsk the same questionsFilter manuallySchedule interviews

Recruiters hiring at scale repeatedly told us the same thing:

"I spend more time filtering candidates than actually interviewing them."

4 to 6 hrs
A recruiter calling 50 applicants a day spends this just to find 3 worth interviewing.
48 hrs
The best applicants get offers within two days. Slow screening loses them.
100×
The same five questions, notice period, availability, CTC, asked over and over.
1 lang
English-first tools mis-ranked candidates who were perfectly fluent in Hindi.
The opportunity

Instead of helping recruiters screen candidates faster, what if the first screening round was already completed before recruiters even opened the dashboard? That became the foundation of AI Recruiter.

/ 05 Goals

One product goal. One business goal. Four things in balance.

Product goal
  • Automate the first round of hiring interviews.
  • Configure interviews in minutes, not hours.
  • Keep the candidate experience conversational.
  • Reduce recruiter effort without removing control.
Business goal
  • A premium offering that grows revenue.
  • Differentiate apnaHire from traditional job boards.
  • Success meant balancing four forces: AI adoption, recruiter trust, candidate completion, monetization.
/ 06 Success Metrics

Product-level outcomes, not screen-level wins.

  • Increase AI Recruiter adoption.
  • Reduce manual recruiter screening effort.
  • Improve recruiter confidence in AI recommendations.
  • Increase premium attachment rate.
  • Improve qualified candidate conversion.
/ 07 Problem Statement

Recruiters did not want AI to replace their judgment.

They wanted it to remove repetitive work while giving them enough evidence and control to make the final decision.

How might we

Help recruiters use AI screening to make faster hiring decisions while preserving trust, control, and confidence?

/ 08 The Current Hiring Workflow

We mapped both journeys before designing anything.

The legacy flow, end to end, from checkout to candidate list:

Legacy recruiter and candidate journey on apnaHire

The recruiter journey

Purchase AI RecruiterConfigure screeningAI interviews candidatesReview shortlistedContact qualifiedHire

The candidate journey

ApplyReceive AI interviewComplete interviewAI evaluatesRecruiter reviewsInterview

Mapping this made one thing obvious: AI Recruiter was not a single feature. It touched multiple products across the hiring ecosystem.

/ 09 Product Scope

Four connected touchpoints.

01
Vertical
Pricing & Monetization

Introduce AI Recruiter as an add-on while making its value immediately understandable.

02
Vertical
AI Screening Setup

Help recruiters configure interview criteria with minimal effort.

03
Vertical
Employer Candidate Connect

Present AI recommendations in a way recruiters trust and can confidently act upon.

04
Vertical
Candidate Experience

An interview flow that feels conversational rather than intimidating.

/ 10 Phase 1

We started lightweight on purpose.

Rather than attempting to perfect the experience immediately, we built a set of initial concepts covering all four touchpoints. The goal was not visual polish. The goal was learning, before investing heavily in development.

?
Comprehension
Will recruiters understand AI Recruiter?
?
Trust
Will recruiters trust AI recommendations?
?
Setup
Will recruiters understand screening setup?
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Candidates
Will candidates understand the AI interview?
?
Value
Will recruiters see enough value to pay for it?

These became our first hypotheses. The early explorations below became our Phase 1 prototype.

/ 11 Phase 1 · Iteration 1

Deliberately lightweight. Built to be tested, not shipped.

Iteration 1 · Vertical 01

Pricing & Monetization

Hypothesis: recruiters will pay for AI at checkout if the value is obvious at the moment of posting a job.

We slotted a Smart-AI tier beside Classic and Premium, priced above Premium, with an inline demo so recruiters could experience a screening call before paying. What we expected to learn: whether checkout is where conviction happens.

Iteration 1 pricing: Classic, Premium and Smart-hire tiers with checkout

The first pricing concept: a third AI tier with a demo interview entry, straight into checkout.

Demo interview modal with Monika AI

The demo: recruiters talk to the AI before they buy it.

Iteration 1 · Vertical 02

Screening Question Setup

Problem: configuration effort kills adoption. Assumption: if AI drafts the questions from the job details, recruiters only need to review, mark deal-breakers, and continue.

AI recommended screening questions with deal-breaker toggles

Questions pre-drafted by AI from the job post. Deal-breaker toggles make criteria decisive.

Editing a screening question with answer types

Full control on tap: answer types, preferred answers, remove.

Job live confirmation leading into screening setup

Setup opens right after the job goes live, while intent is high.

Expected behaviour: recruiters accept most AI questions, edit a few, and finish setup in minutes.

Iteration 1 · Vertical 03

Employer Candidate Connect

The riskiest questions lived here. Cards or tables? Depth or speed? Rather than commit, we explored four dashboard concepts and let the trade-offs surface. No final direction yet. The purpose was divergent thinking.

Rich cards with static screening panel

Rich cards: maximum transparency per candidate.

Table view with score summary

Table: maximum scanning speed.

Master detail split view

Master-detail: list beside full AI evidence.

List with filters sidebar

Filter-first: control for high-volume pipelines.

Concept
Trade-off
Rich cards
Clear outcomes per candidate, but tall and repetitive at volume.
Table view
Fastest triage, but scores feel generic without visible proof.
Master-detail
Evidence beside the list, but crowded on small laptops.
Filter-first
Powerful drill-down, but choice overload before value.
Iteration 1 · Vertical 04

Candidate Experience

Approach: conversational, not intimidating. The AI screening call reached candidates right after applying, with a simple in-call layout: duration, mic and volume controls, nothing to learn.

Candidate mobile flow with AI screening call

Iteration 1: the call arrives immediately after applying, while intent is hot.

We wanted completion and comfort. Whether an instant call delivers either was exactly what testing had to answer.

/ 12 User Research & Validation

Then we watched real recruiters work.

With Iteration 1 in front of recruiters, we needed evidence, not opinions. We wanted to understand how recruiters actually screened, what information they trusted, which UI elements they ignored, whether AI influenced decisions, and where friction lived.

Contextual inquiry Shadowing recruiters Task-based usability Observation AI and non-AI recruiters
01 · Trust
Trusted AI, verified anyway
Recruiters re-checked questions and details, and called every candidate regardless of score. Opportunity: make verification effortless instead of fighting it.
02 · Visibility
Hidden insights are ignored insights
Insights two clicks deep were opened 3 times in 10. Opportunity: surface AI evaluation where the decision happens, one click at most.
03 · Speed
Speed beat information density
Lists were scanned in seconds; details opened only when necessary. Opportunity: rapid scan first, depth on demand.
04 · Friction
Manual actions broke the flow
Typing numbers by hand, multi-step status changes, constant callbacks. Opportunity: one-tap call, WhatsApp and status actions.
05 · Signals
Five signals drove every decision
Location, salary, education, experience, communication. Opportunity: pin these to the surface of every candidate.
06 · Proof
Audio was proof, not primary
Recordings were played only when AI conflicted with expectations. Opportunity: keep them one tap away as the tie-breaker.
/ 13 Design Principles

Six rules, straight from the research.

01
Principle
Surface AI insights where decisions happen
02
Principle
Support rapid scanning before deep analysis
03
Principle
Keep recruiters in control
04
Principle
Make AI explainable, not authoritative
05
Principle
Reduce repetitive manual work
06
Principle
Design for high-volume hiring workflows
/ 14 Phase 1 · Iteration 2

Not a new design. An evolution, with evidence behind every change.

Iteration 2 · Pricing

Value before the paywall

Insight: AI was not convincing as a standalone purchase; recruiters wanted to experience it first. Problem: the demo lived too deep in checkout. Change: the AI card moved to the centre with a Recommended tag, value props on the card, and the AI recruiter introduced on the dashboard itself, before posting, with a walkthrough and real voice clips. Expected impact: higher demo engagement and premium attachment.

Centered Smart AI card with Recommended tag

The AI tier moves centre. Framing does the selling.

Smart AI card with waveform and value props

Value props and the voice waveform, on the card itself.

Dashboard banner introducing the AI recruiter

Meet your AI recruiter, before you even post.

Walkthrough popup and voice selection

The walkthrough, plus selectable pre-recorded voices.

Iteration 2 · Setup

Configuring an employee, not filling a form

Insight: recruiters said setup felt like configuring an employee. They wanted to hear the voice and control the company pitch. Problem: Iteration 1 read as a form. Change: voice avatars in English, Hindi and regional languages, an editable company pitch, and AI-generated questions to review. Expected impact: setup completion up, drop-offs down.

Agentic setup with voice avatars Monica, Diya and Tanvi

Pick a voice, shape the pitch, review the questions.

Compiling and success states

Progressive feedback while the agent gets ready.

Iteration 2 · Dashboard

Evidence at the surface

Insight: hidden insights were ignored; five signals drove every decision. Problem: reasoning sat two clicks deep. Change: an AI Agent Fit tab in the pipeline, with the evaluation, criteria checks and key signals visible directly on the candidate, and recordings one tap away as the tie-breaker. Expected impact: faster shortlists, higher trust in Fit.

AI Agent Fit tab with visible evaluation in the dashboard

The direction after testing: a fast list, AI evaluation at the surface, proof one tap away.

Iteration 2 · Candidate

A breath before the interview

Insight: instant calls made candidates anxious; they wanted preparation time. Problem: the interview started the moment they applied. Change: a transition state, "your interview is about to begin", with preparation cues before the AI connects. Expected impact: higher completion, lower drop-off.

📱
Interview transition screen
Screen pending export: the pre-interview preparation state.
Next

Iteration 2 shipped. What follows is what six months of live numbers told us.

The design was shipped after minor back and forth with engineering and PMs
As usual !! :)
Now comes the fun part
Did users actually use it?
/ 15 Launch & Early Results

Phase 1 went live. Then we measured everything, weekly, for six months.

Every number below comes from the live metrics tracker, from launch week through the end of January.

1 in 10
Explorers posted
an AI job (peak 15.8%)
~100%
Of leads screened
automatically
1 in 5
Candidates
tagged FIT
48%
Recruiter action on
AI Fit, launch week
  • Recruiters who explored the AI SKU converted to posting an AI job at roughly 10% on average, peaking at 15.8% in the early weeks.
  • The agent screened a median of 9 to 10 leads per job per day, with screened volume tracking total volume almost exactly: the first round genuinely ran itself.
  • About 20% of screened candidates were tagged FIT, with 12 to 14% Full FIT: a strong, consistent qualification signal.
  • Inconclusive evaluations fell from 27% to about 10% across the period as screening and prompts improved.
  • Jobs hit 88 to 91% of their target lead fulfilment at expiry.
  • Discovery was the real top-of-funnel constraint: only 5 to 6% of recruiters shown the banner went on to explore the AI SKU.
  • Early repeat purchase appeared among existing users, though cohorts were still small and volatile.

And the numbers that told us the truth:

What worked
  • Launch-week engagement was strong: recruiters acted on 48% of AI Fit leads within 3 days.
  • Screening quality kept improving month over month.
What warned us
  • Action on AI Fit leads decayed to about 29 to 31%, with a median 20 hours to first action.
  • Candidate no-response climbed toward 35 to 44% as unknown-number fatigue set in.
  • Shortlist rate stayed at 4 to 6%: recruiters still verified everything themselves.
/ 16 What Phase 1 Taught Us

Every number and complaint pointed at something fixable.

We read the data and the complaints case by case. Five fixes shipped while Phase 1 was still live.

01
Candidate drop-offs
Interviews had no memory

Mid-interview drop-offs died as "inconclusive". Restarting meant repeating everything.

Agent memoryResume where you leftMulti-call interviewsRecording per call
Inconclusive fell 27% to ~10%. And 15 to 22% of Potential FIT upgraded on a later call.
02
Reachability
Unknown numbers get ignored

No-response climbed toward 35 to 44%.

Smart retriesWhatsApp resume linkIn-app nudgeCall me later
The interview no longer depends on one perfect pickup.
03
Multi-job candidates
Same call, different jobs

Candidates with several AI applications could not tell the calls apart.

Job + company introQueued interviews
Every call now announces which job it is for.
04
Screening strictness
One size fits no one

Some recruiters wanted only top matches, others a broad pool.

High PrecisionBalancedBroad Pool
Strictness is editable mid-flight, after watching a few calls.
05
Recruiter trust
Trust needed an escape hatch

Shortlists stayed at 4 to 6%. Recruiters verified everything.

Manual overrideReassessmentRejection reasonsProof of attempts
We built for verification instead of fighting it.
🎧
Multi-call interview timeline
Screen pending export: per-call activity and recordings, call 1 inconclusive, call 2 completed.
Phase 1 validated the product and taught us where it strained. What remained unsolved became Phase 2.