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AI-powered Resume Screening
MokaHR provides end-to-end recruitment solutions for 2K+ enterprise clients. I led the design of AI-powered resume screening workflows through phased rollout. The goal was to accelerate hiring cycle to encourage AI feature conversion from recruiters.
Why it matters
0→1 AI launch
The first AI feature introduced into an established recruiting workflow — where I improved user trust on AI recommendation with 4.2/5 rating.
35% trial-to-paid
The AI hiring assistant converted 35% of trial users to paying customers after 2 months of launch — the fastest feature-to-revenue validation MokaHR had run.
Timeline
Phase 1: Sep 2025Phase 2: Nov 2025-Jan 2026
My Role
Sole designer on end-to-end screening workflow
Team
5-person pod inside a 10-person product team
My Contribution
User research
Collaborated with data analytics to track user behavior. Conducted user interviews and testings to synthesize user goals and pain points.
Project scoping
Partnered with PM on phasing, feature prioritization, and user stories.
Translated research findings into design requirements and negotiated scope tradeoffs between phases.
Interaction design
Defined how recruiters interact with LLM-generated outputs — from match indicators and evidence citations to a context-adaptive AI chatbot.
Business context
MokaHR is pivoting from a rigid ATS with static workflows into an adaptive AI-native platform, aiming to achieve 2 goals.
Increase User Efficiency
Deploying AI to remove frictions throughout the hiring process, reducing time-to-hire to attract more enterprise clients.
Drive New Revenue Stream
Adding AI-assistant features as an add-on package that users can activate per job opening.
finding the problem to prioritize
I partnered with PM and data analyst to define areas of focus.
The user Problem
User research uncovers 3 user goals and core pain points associated.
I conducted query analysis from current search, and 5 user interviews to discover recruiters’ current workflow and roadblocks.
Recruiters want to focus on the best 10-30 candidates
but boolean search takes so many iterations to narrow it down.
Recruiters want to find specific info
but the old search is not sophisticated enough to return what they want
Recruiters want to validate candidates’ qualifications thoroughly
but manually confirming if resume evidence matches every qualification is time-consuming
Solution
AI-driven application review follows well defined criteria to quickly surfaces top match, helping recruiters review candidates with high consistency, efficiency and confidence.
User need 1
“I need to focus on reviewing the best fit candidates who meet the most important job requirements with strongest signals.”
Solution: Top match shortlist
An AI-generated shortlist with fit level indicators and candidate summary
User need 2
“I need to find specific info, such as experts with experience in a specific area, or candidates from direct competitors.”
Solution: suggested searches
AI-generated prompts guide recruiters to explore relevant candidate groups
User need 3
“I need to validate candidates’ qualifications on their resume and evaluate how well they match job requirements.”
Solution: qualification summary + evidence
AI-generated qualification summaries map each requirement to supporting evidence highlighted in the original resume
Design Study
Design Challenge
How might we communicate candidate-requirement fit?
I explored 2 options to display the evaluation of candidate-requirement fit. Through concept testing with 8 users, I landed on the UI that best fit recruiters’ mental model and information needs.
Option 1
The overall score feels arbitrary.
option 2: final pick ✅
Level of fit indicator for each requirement enables fast scanning.
Impact
The AI-assistant features reduced manual workload and archived conversion.
25%
reduction in time-to-interview**
35%
trial-to-paid conversion**
30%
increase in AI trust rating*
*Measured from phase 1 MVP usability testing to post-launch usability testing
**Measured across combined AI launch (screening, outreach, interview)
Let’s Connect!
© 2025 Yiqing Wang
Work
About
AI-powered Resume Screening
MokaHR provides end-to-end recruitment solutions for 2K+ enterprise clients. I led the design of AI-powered resume screening workflows through phased rollout. The goal was to accelerate hiring cycle to encourage AI feature conversion from recruiters.
Why it matters
0→1 AI launch
The first AI feature introduced into an established recruiting workflow — where I improved user trust on AI recommendation with 4.2/5 rating.
35% trial-to-paid
The AI hiring assistant converted 35% of trial users to paying customers after 2 months of launch — the fastest feature-to-revenue validation MokaHR had run.
Timeline
Phase 1: Sep 2025Phase 2: Nov 2025-Jan 2026
My Role
Sole designer on end-to-end screening workflow
Team
5-person pod inside a 10-person product team
My Contribution
User research
Collaborated with data analytics to track user behavior. Conducted user interviews and testings to synthesize user goals and pain points.
Project scoping
Partnered with PM on phasing, feature prioritization, and user stories.
Translated research findings into design requirements and negotiated scope tradeoffs between phases.
Interaction design
Defined how recruiters interact with LLM-generated outputs — from match indicators and evidence citations to a context-adaptive AI chatbot.
Business context
MokaHR is pivoting from a rigid ATS with static workflows into an adaptive AI-native platform, aiming to achieve 2 goals.
Increase User Efficiency
Deploying AI to remove frictions throughout the hiring process, reducing time-to-hire to attract more enterprise clients.
Drive New Revenue Stream
Adding AI-assistant features as an add-on package that users can activate per job opening.
finding the problem to prioritize
I partnered with PM and data analyst to define areas of focus.
The User Problem
User research uncovers 3 user goals and core pain points associated.
I conducted query analysis from current search, and 5 user interviews to discover recruiters’ current workflow and roadblocks.
User Goal
Pain Point
1
Recruiters want to focus on the best 10-30 candidates
but boolean search takes so many iterations to narrow it down.
2
Recruiters want to find specific info
but the old search is not sophisticated enough to return what they want
3
Recruiters want to validate candidates’ qualifications thoroughly
but manually confirming if resume evidence matches every qualification is time-consuming
Solution
AI-driven application review follows well defined criteria to quickly surfaces top match, helping recruiters review candidates with high consistency, efficiency and confidence.
User need 1
“I need to focus on reviewing the best fit candidates who meet the most important job requirements with strongest signals.”
Solution: Top match shortlist
An AI-generated shortlist with fit level indicators and candidate summary
User need 2
“I need to find specific info, such as experts with experience in a specific area, or candidates from direct competitors.”
Solution: suggested searches
AI-generated prompts guide recruiters to explore relevant candidate groups
User need 3
“I need to validate candidates’ qualifications on their resume and evaluate how well they match job requirements.”
Solution: qualification summary + evidence
AI-generated qualification summaries map each requirement to supporting evidence highlighted in the original resume
Design Study
Design Challenge
How might we communicate candidate-requirement fit?
I explored 2 options to display the evaluation of candidate-requirement fit. Through concept testing with 8 users, I landed on the UI that best fit recruiters’ mental model and information needs.
Option 1
The overall score feels arbitrary.
option 2: final pick ✅
Level of fit indicator for each requirement enables fast scanning.
Impact
The AI-assistant features reduced manual workload and archived conversion.
30%
increase in AI trust rating*
25%
reduction in time-to-interview**
35%
trial-to-paid conversion**
*Measured from phase 1 MVP usability testing to post-launch usability testing
**Measured across combined AI launch (screening, outreach, interview)
Let’s Connect!
© 2026 Yiqing Wang
Work
About
AI-powered Resume Screening
MokaHR provides end-to-end recruitment solutions for 2K+ enterprise clients. I led the design of AI-powered resume screening workflows through phased rollout. The goal was to accelerate hiring cycle to encourage AI feature conversion from recruiters.
Timeline
Phase 1: Sep 2025Phase 2: Nov 2025-Jan 2026
My Role
Sole designer on end-to-end screening workflow
Team
5-person pod inside a 10-person product team
Why it matters
0→1 AI launch
The first AI feature introduced into an established recruiting workflow — where I improved user trust on AI recommendation with 4.2/5 rating.
35% trial-to-paid
The AI hiring assistant converted 35% of trial users to paying customers after 2 months of launch — the fastest feature-to-revenue validation MokaHR had run.
My Contribution
User research
Collaborated with data analytics to track user behavior. Conducted user interviews and testings to synthesize user goals and pain points.
Project scoping
Partnered with PM on phasing, feature prioritization, and user stories.
Translated research findings into design requirements and negotiated scope tradeoffs between phases.
Interaction design
Defined how recruiters interact with LLM-generated outputs — from match indicators and evidence citations to a context-adaptive AI chatbot.
Business context
MokaHR is pivoting from a rigid ATS with static workflows into an adaptive AI-native platform, aiming to achieve 2 goals.
Increase User Efficiency
Deploying AI to remove frictions throughout the hiring process, reducing time-to-hire to attract more enterprise clients.
Drive New Revenue Stream
Adding AI-assistant features as an add-on package that users can activate per job opening.
finding the problem to prioritize
I partnered with PM and data analyst to define areas of focus.
The User Problem
User research uncovers 3 user goals and core pain points associated.
I conducted query analysis from current search, and 5 user interviews to discover recruiters’ current workflow and roadblocks.
User Goal
Pain Point
1
Recruiters want to focus on the best 10-30 candidates
but boolean search takes so many iterations to narrow it down.
2
Recruiters want to find specific info
but the old search is not sophisticated enough to return what they want
3
Recruiters want to validate candidates’ qualifications thoroughly
but manually confirming if resume evidence matches every qualification is time-consuming
Solution
AI-driven application review follows well defined criteria to quickly surfaces top match, helping recruiters review candidates with high consistency, efficiency and confidence.
User need 1
“I need to focus on reviewing the best fit candidates who meet the most important job requirements with strongest signals.”
Solution: Top match shortlist
An AI-generated shortlist with fit level indicators and candidate summary
User need 2
“I need to find specific info, such as experts with experience in a specific area, or candidates from direct competitors.”
Solution: suggested searches
AI-generated prompts guide recruiters to explore relevant candidate groups
User need 3
“I need to validate candidates’ qualifications on their resume and evaluate how well they match job requirements.”
Solution: qualification summary + evidence
AI-generated qualification summaries map each requirement to supporting evidence highlighted in the original resume
Design Study
Design Challenge
How might we communicate candidate-requirement fit?
I explored 2 options to display the evaluation of candidate-requirement fit. Through concept testing with 8 users, I landed on the UI that best fit recruiters’ mental model and information needs.
Option 1
The overall score feels arbitrary.
option 2: final pick ✅
Level of fit indicator for each requirement enables fast scanning.
Impact
The AI-assistant features reduced manual workload and archived conversion.
30%
increase in AI trust rating*
25%
reduction in time-to-interview**
35%
trial-to-paid conversion**
*Measured from phase 1 MVP usability testing to post-launch usability testing
**Measured across combined AI launch (screening, outreach, interview)
Let’s Connect!
© 2026 Yiqing Wang