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AI-powered Resume Screening for High-Volume Pipelines
Introducing AI into MokaHR’s existing recruiting workflow through an iterative rollout strategy — delivering immediate value while learning from real-world usage data to shape phase 2 directions.
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.
45% trial-to-paid
The 3-week interim launch converted 45% of trial users to paying customers — the fastest AI-feature-to-revenue validation MokaHR had run.
Problem
As candidate volume grows, HR teams spend days manually reviewing resumes for each role — slowing hiring cycles and risking overlooking strong candidates.
Solution Overview
An AI-assisted screening workflow that surfaces top-match candidates, validates key resume information, and hands recruiters prioritized lists.
My Contribution
Discovery
- Collaborated with data analytics to track user behavior.
- Conducted user interviews 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.
Context
Background
MokaHR is an enterprise HR SaaS serving 2K+ mid-to-large clients. Three forces drove the decision to integrate AI into current hiring products.
Business Opportunity
Unlock usage-based revenue
Per-seat pricing plateaus with fixed HR users — AI features priced on usage scale with hiring volume instead.
Technical Advantage
Leverage domain expertise
Moka's edge is specialized AI trained on recruitment workflows — a stronger differentiator than competitors' general-purpose models.
User Need
Accelerate hiring cycles
AI-assisted screening enables precise analysis of candidate qualifications, shortening the path from application to interview.
AI Integration Vision Plan
I partnered with PM to map AI opportunities across the full hiring journey. Screening was prioritized — it accounts for 35-40% of recruiter time and concentrates the most pain points.
Problem Framing
Problem
Using traditional ATS tools for resume screening is time consuming — recruiters spend on average 3 days per opening.
Root Causes
1
It’s hard to surface strong candidates with keyword search only
2
It’s time-consuming to manually find evidence of candidate qualifications
Problem
Three jobs-to-be-done synthesized from research guided design scope.
Design Process
Design Challenge 1
How should AI be introduced into an established screening workflow?
Phase 1: Search bar as a quick win
While the AI chatbot was still in development, I launched a search bar with pre-defined prompts like "Top match" — delivering AI value immediately while learning recruiter behavior in practice.
Discovering the recruiter decision tree
Query analysis and user interviews revealed a consistent pattern: after reviewing top matches, recruiters either raise the bar to narrow down or relax criteria to widen the pool. This decision tree guided Phase 2 design.
Phase 2: Tiered candidate list
Pre-prompt of “top match”
Tiered candidate list
After the universal chatbot launched, AI-assisted screening 2.0 introduced structured tiers — auto-categorizing candidates from strongest to weakest match, aligning with the decision tree recruiters were already following.
Early chatbot testing revealed
Free-form chat responses were too long to scan. Recruiters needed actionable results, not paragraphs.
Improving the chatbot response: on-demand list
The chatbot now returns a candidate list as an actionable package — a working document that scopes to the recruiter's current screening step.
Design Challenge 2
How should the system communicate candidate-requirement match at different stages of the screening process?
Early prototype testing insights
Prototype testing with 3 recruiters revealed uncertainties about AI evaluation:
- Match percentages felt arbitrary — minor differences between candidates carried no meaningful signal without a visible scoring logic.
- Requirement-level reasoning at scan level was overwhelming — recruiters wanted speed, not depth.
Phase 2 response: progressive disclosure of requirement match
1
quantity at list level
2
match level on demand
3
reasoning & evidence at individual level
- Scanning: AI summary communicates strengths, differentiators, and gaps at list level.
- Evaluating: Expanded card shows match levels per requirement, ordered by priority.
- Validating: Full AI reasoning maps resume lines to requirements — recruiters verify the system's judgment before deciding.
Candidate List
Candidate Profile
Solution Overview
The screening system shifts candidate evaluation from manual keyword filtering to AI-driven match assessment — surfacing who's strongest, why, and helping recruiters narrow down with confidence.
Surface Top Match
Top candidates are grouped into the highest tier upfront, with lower tiers visible to give recruiters an at-a-glance read on overall applicant quality.
1
Tiered candidate list
Evaluate Candidate Fit
Candidate-requirement fit is revealed progressively — from match indicators at scan level, to AI summaries for evaluation, to cited evidence for validation.
1
Match level indicators
2
Candidate summary
3
Match reasoning per requirement
4
Resume evidence citations
Refine the Shortlist
An AI chatbot supports both free-form queries and pre-prompted shortcuts, returning candidate lists for refinement from any angle.
1
AI chatbot with pre-prompts
2
Free-form prompting
3
On-demand list
4
Quick filters
Impact
Phase 1 automation features reduced manual workload and improved conversion across the hiring journey.
25%
reduction in time-to-interview**
45%
trial-to-paid conversion on AI recruitment tools**
30%
increase in AI trust rating*
* From early prototype usability testing with 3 recruiters
** Phase 1 MVP launch across screening, outreach, and scheduling features among 60 seed users within 3 weeks.
Let’s Connect!
© 2025 Yiqing Wang
AI-powered Resume Screening for High-Volume Pipelines
Introducing AI into MokaHR’s existing recruiting workflow through an iterative rollout strategy — delivering immediate value while learning from real-world usage data to shape phase 2 directions.
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.
45% trial-to-paid
The 3-week interim launch converted 45% of trial users to paying customers — the fastest AI-feature-to-revenue validation MokaHR had run.
Problem
As candidate volume grows, HR teams spend days manually reviewing resumes for each role — slowing hiring cycles and risking overlooking strong candidates.
Solution Overview
An AI-assisted screening workflow that surfaces top-match candidates, validates key resume information, and hands recruiters prioritized lists.
My Contribution
Discovery
- Collaborated with data analytics to track user behavior.
- Conducted user interviews 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.
Context
Background
MokaHR is an enterprise HR SaaS serving 2K+ mid-to-large clients. Three forces drove the decision to integrate AI into current hiring products.
Business Opportunity
Unlock usage-based revenue
Per-seat pricing plateaus with fixed HR users — AI features priced on usage scale with hiring volume instead.
Technical Advantage
Leverage domain expertise
Moka's edge is specialized AI trained on recruitment workflows — a stronger differentiator than competitors' general-purpose models.
User Need
Accelerate hiring cycles
AI-assisted screening enables precise analysis of candidate qualifications, shortening the path from application to interview.
AI Integration Vision Plan
I partnered with PM to map AI opportunities across the full hiring journey. Screening was prioritized — it accounts for 35–40% of recruiter time and concentrates the most pain points.
Problem Framing
Problem
Using traditional ATS tools for resume screening is time consuming — recruiters spend on average 3 days per opening.
Root Causes
1
It’s hard to surface strong candidates with keyword search only
2
It’s time-consuming to manually find evidence of candidate qualifications
Problem
Three jobs-to-be-done synthesized from research guided design scope.
Design Process
Design Challenge 1
How should AI be introduced into an established screening workflow?
Phase 1: Search bar as a quick win
While the AI chatbot was still in development, I launched a search bar with pre-defined prompts like "Top match" — delivering AI value immediately while learning recruiter behavior in practice.
Discovering the recruiter decision tree
Query analysis and user interviews revealed a consistent pattern: after reviewing top matches, recruiters either raise the bar to narrow down or relax criteria to widen the pool. This decision tree guided Phase 2 design.
Phase 2: Tiered candidate list
Pre-prompt of “top match”
Tiered candidate list
After the universal chatbot launched, AI-assisted screening 2.0 introduced structured tiers — auto-categorizing candidates from strongest to weakest match, aligning with the decision tree recruiters were already following.
Early chatbot testing revealed
Free-form chat responses were too long to scan. Recruiters needed actionable results, not paragraphs.
Improving the chatbot response: on-demand list
The chatbot now returns a candidate list as an actionable package — a working document that scopes to the recruiter's current screening step.
Design Challenge 2
How should the system communicate candidate-requirement match at different stages of the screening process?
Early prototype testing insights
Prototype testing with 3 recruiters revealed uncertainties about AI evaluation:
- Match percentages felt arbitrary — minor differences between candidates carried no meaningful signal without a visible scoring logic.
- Requirement-level reasoning at scan level was overwhelming — recruiters wanted speed, not depth.
Phase 2 response: progressive disclosure of requirement match
1
quantity at list level
2
match level on demand
3
reasoning & evidence at individual level
- Scanning: AI summary communicates strengths, differentiators, and gaps at list level.
- Evaluating: Expanded card shows match levels per requirement, ordered by priority.
- Validating: Full AI reasoning maps resume lines to requirements — recruiters verify the system's judgment before deciding.
Candidate List
Candidate Profile
Solution Overview
The screening system shifts candidate evaluation from manual keyword filtering to AI-driven match assessment — surfacing who's strongest, why, and helping recruiters narrow down with confidence.
Surface Top Match
Top candidates are grouped into the highest tier upfront, with lower tiers visible to give recruiters an at-a-glance read on overall applicant quality.
1
Tiered candidate list
Evaluate Candidate Fit
Candidate-requirement fit is revealed progressively — from match indicators at scan level, to AI summaries for evaluation, to cited evidence for validation.
1
Match level indicators
2
Candidate summary
3
Match reasoning per requirement
4
Resume evidence citations
Refine the Shortlist
An AI chatbot supports both free-form queries and pre-prompted shortcuts, returning candidate lists for refinement from any angle.
1
AI chatbot with pre-prompts
2
Free-form prompting
3
On-demand list
4
Quick filters
Impact
Phase 1 automation features reduced manual workload and improved conversion across the hiring journey.
30%
increase in AI trust rating*
25%
reduction in time-to-interview**
45%
trial-to-paid conversion on AI recruitment tools**
* From early prototype usability testing with 3 recruiters
** Phase 1 MVP launch across screening, outreach, and scheduling features among 60 seed users within 3 weeks.
Let’s Connect!
© 2026 Yiqing Wang
Work
Resume
About
AI-powered Resume Screening for High-Volume Pipelines
Introducing AI into MokaHR’s existing recruiting workflow through an iterative rollout strategy — delivering immediate value while learning from real-world usage data to shape phase 2 directions.
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.
45% trial-to-paid
The 3-week interim launch converted 45% of trial users to paying customers — the fastest AI-feature-to-revenue validation MokaHR had run.
Problem
As candidate volume grows, HR teams spend days manually reviewing resumes for each role — slowing hiring cycles and risking overlooking strong candidates.
Solution Overview
An AI-assisted screening workflow that surfaces top-match candidates, validates key resume information, and hands recruiters prioritized lists.
My Contribution
Discovery
- Collaborated with data analytics to track user behavior.
- Conducted user interviews 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.
Context
Background
MokaHR is an HR SaaS serving 2K+ enterprise clients. Three forces drove AI integration.
Business Opportunity
Unlock usage-based revenue
Per-seat pricing plateaus with fixed HR users — AI features priced on usage scale with hiring volume instead.
Technical Advantage
Leverage domain expertise
Moka's edge is specialized AI trained on recruitment workflows — a stronger differentiator than competitors' general-purpose models.
User Need
Accelerate hiring cycles
AI-assisted screening enables precise analysis of candidate qualifications, shortening the path from application to interview.
AI Integration Vision Plan
I partnered with PM to map AI opportunities across the full hiring journey. Screening was prioritized — it accounts for 35-40% of recruiter time and concentrates the most pain points.
Problem Framing
Problem
Using traditional ATS tools for resume screening is time consuming — recruiters spend on average 3 days per opening.
Root Causes
1
It’s hard to surface strong candidates with keyword search only
2
It’s time-consuming to manually find evidence of candidate qualifications
Jobs-to-be-done
Three jobs-to-be-done synthesized from research guided design scope.
Design Process
Design Challenge 1
How should AI be introduced into an established screening workflow?
Phase 1: Search bar as a quick win
While the AI chatbot was still in development, I launched a search bar with pre-defined prompts like "Top match" — delivering AI value immediately while learning recruiter behavior in practice.
Discovering the recruiter decision tree
Query analysis and user interviews revealed a consistent pattern: after reviewing top matches, recruiters either raise the bar to narrow down or relax criteria to widen the pool. This decision tree guided Phase 2 design.
Phase 2: Tiered candidate list
Pre-prompt of “top match”
Tiered candidate list
After the universal chatbot launched, AI-assisted screening 2.0 introduced structured tiers — auto-categorizing candidates from strongest to weakest match, aligning with the decision tree recruiters were already following.
Early chatbot testing revealed
Free-form chat responses were too long to scan. Recruiters needed actionable results, not paragraphs.
Improving the chatbot response: on-demand list
The chatbot now returns a candidate list as an actionable package — a working document that scopes to the recruiter's current screening step.
Design Challenge 2
How should the system communicate candidate-requirement match at different stages of the screening process?
Early prototype testing insights
Prototype testing with 3 recruiters revealed uncertainties about AI evaluation:
- Match percentages felt arbitrary — minor differences between candidates carried no meaningful signal without a visible scoring logic.
- Requirement-level reasoning at scan level was overwhelming — recruiters wanted speed, not depth.
Phase 2 response: progressive disclosure of requirement match
1
quantity at list level
2
match level on demand
3
reasoning & evidence at individual level
- Scanning: AI summary communicates strengths, differentiators, and gaps at list level.
- Evaluating: Expanded card shows match levels per requirement, ordered by priority.
- Validating: Full AI reasoning maps resume lines to requirements — recruiters verify the system's judgment before deciding.
Candidate List
Candidate Profile
Final Design
The screening system shifts candidate evaluation from manual keyword filtering to AI-driven match assessment — surfacing who's strongest, why, and helping recruiters narrow down with confidence.
Surface Top Match
Top candidates are grouped into the highest tier upfront, with lower tiers visible to give recruiters an at-a-glance read on overall applicant quality.
1
Tiered candidate list
Evaluate Candidate Fit
Candidate-requirement fit is revealed progressively — from match indicators at scan level, to AI summaries for evaluation, to cited evidence for validation.
1
Match level indicators
2
Candidate summary
3
Match reasoning per requirement
4
Resume evidence citations
Refine the Shortlist
An AI chatbot supports both free-form queries and pre-prompted shortcuts, returning candidate lists for refinement from any angle.
1
AI chatbot with pre-prompts
2
Free-form prompting
3
On-demand list
4
Quick filters
Impact
Phase 1 automation features reduced manual workload and improved conversion across the hiring journey.
30%
increase in AI trust rating*
25%
reduction in time-to-interview**
45%
trial-to-paid conversion on AI recruitment tools**
* From early prototype usability testing with 3 recruiters
** Phase 1 MVP launch across screening, outreach, and scheduling features among 60 seed users within 3 weeks.
Let’s Connect!
© 2026 Yiqing Wang