My Role: Product Designer
Company: Clear Match Talent, tech recruiting agency
The Team: 1 product designer (me), CTO, 2 Co-Founders on conception→0
AI Tools: Claude, Figma Make
Target Users: Agency recruiters that maintain 500+ relationships
My Contribution: Ran a one-week AI experiment that replaced 3 months of planned development and redirected the product roadmap. Led product scoping, designed the priority matrix algorithm, mapped the backend architecture, and built the prototype using vibe coding and Figma Make.
Case Study Timeline: 3 months
“I have hundreds of warm contacts, but I only reach out when there’s a job. It’s hard to keep things warm in between.”
— Agency Recruiter
User Problem
The Struggle to Maintain 100s of Relationships at Once
Warm leads go cold because there's never enough time to stay in touch with everyone. Recruiters manage hundreds of relationships at once, but they naturally gravitate toward whoever they spoke to most recently. Anyone untouched for 3+ months quietly drops off. By the time a job opening comes up, the relationship isn't warm enough to act on.
The intent is there. The bandwidth isn't
How might we help recruiters maintain warm relationships at scale, so no lead goes cold just because there wasn't enough time to reach out?
Mapping the Current State to Highlight Where Leads Go Cold
Leading Early Product Roadmap Discussions: What is This Product?
Before any wireframes, I led early sessions with the co-founders asking the questions the team hadn't asked yet. These conversations shaped what the product was — and what it wasn't.
Strategic Question
Decision
Rationale
Dashboard or Gmail plugin?
Dashboard
A plugin limits what data we can surface. Recruiters need a full view of their pipeline, not a sidebar.
How much detail per contact?
Summary view
Recruiters need to act quickly, not read through long histories. Depth is available on click, not upfront.
Mobile and desktop, or desktop only?
Desktop first
Recruiters work at a desk. Mobile adds scope without solving the core problem. Saved for v2.
Email reminders only?
No
A reminder tells you who to reach out to. It doesn't help you know what to say. Too passive to solve the problem.
Suggest ideas or write the full email?
Write the full email
Speed and ease are the entire value proposition. Suggestions still require work. Full drafts remove the friction entirely.
Should it send on the recruiter's behalf?
No
Ethically ambiguous. Relationship-building requires human intent behind every send. Auto-sending undermines authenticity.
what are the core features and How will it function?
Conceptualizing the Product, From Initial Discussions to First Drafts
Positive Test Findings from 2 Founders and 2 Recruiters
I tested the two wireframes with the co-founders and two recruiters. Sessions were informal, I walked through each screen and asked whether the layout answered the right questions.
Recruiters knew exactly what to do without being told:
The priority matrix gave immediate clarity on who to contact and when — no explanation needed
The AI-drafted message was the most compelling feature. Recruiters wanted to send it with minimal edits
Interaction history was essential — context made the outreach feel personal, not automated
Simpler was always better, cutting out redundancies:
List view removed — recruiters wanted to act, not read through signal details
Alert type labels removed — the priority matrix already communicated urgency without an extra categorization layer
Prompt input field removed — the AI synthesized context well enough on its own. Adding a prompt step created friction instead of removing it
From Concept to High Fidelity: Designing the Priority Matrix and AI Recommended Messages
1
Priority Matrix Dashboard
Summary of prioritized task and timeline of when tasks are due so recruiters act on the most critical items first
Summary of contacts by engagement score so recruiters know which relationships are getting cold
2
AI Recommended Messages
Summary contact card with engagement score breakdown, allowing recruiters to assess relationship health and urgency at a glance; without opening a single conversation thread.
Interaction history timeline for quick recall of past convos
AI recommended message with rationale behind the message
AI-Generated Screens Built with Figma Make
For the AI Recommended Messaging screens, I used Figma Make instead of designing from scratch. I fed it the full context: algorithm logic, data points, profile type examples, user flow, design system, and existing prototype, and let it generate the screens.
The design thinking, constraints, and context were mine. The screen generation was Figma Make's. This freed me up to focus on refining the output rather than producing it.
Figma Make
The Business Conflict
The Prototype Was Ready. Then Came the Hard Question
Does this really need months of dev time to build the backend infrastructure? Or can we leverage existing AI tools to help us achieve the same outcome?
The CTO's plan was to spend 3+ months building custom AI infrastructure from scratch before a single recruiter could touch the product. The founders wanted to move fast but didn't have the technical background to challenge it. So the plan stood. By default.
I didn't think we needed to build any of it.
My hypothesis was that existing AI tools could simulate the entire workflow — relationship analysis, prioritization, personalized outreach — without a single line of custom infrastructure. But a hypothesis isn't an argument. And an argument wasn't going to move anyone.
So I gave myself one week to prove it.
My Experiment
Prooving AI Can Read Relationships Like a Recruiter
In one week, I simulated the core functions of this product using Claude.
My goal was to test whether AI could interpret complex human relationships and take meaningful action, without any custom infrastructure.
The Context I Fed Into Claude
I fed 3 real recruiter contacts' convo histories into Claude, along with the engagement score algorithm, job signal data, and profile type examples.
Engagement Score Algorithm Formula
Kelsey Walker
VP Talent, Rio Finance
Warm Long-term Partner
4-year relationship. Helped Kelsey build recruiting processes at unitT. She now runs her own firm and refers clients to Sam regularly.
Relationship strength
Last contact
Business opportunity
"Mentioned spending Christmas in Quebec City with Silas. Both share Canadian roots — Sam recently moved to Vancouver."
Matt Lange
Controller, Skylo Technologies
Transactional + Recent
Past successful placement of Andy Lai. Matt now needs another hire AND is job-searching himself. Dual role as client and candidate.
Relationship strength
Last contact
Business opportunity
"Shares an interest in US Open tennis. Specifically asked for candidates like Andy Lai — a high bar and a clear brief."
Tyler Phong
Head of Finance, TRM Labs
High-Opportunity, Neglected
43 days since last contact. Actively struggling with hiring. Agreed to refer Sam to his talent leader — but never followed through.
Relationship strength
Last contact
Business opportunity
"Exchanges Chinese New Year greetings with Sam. Based in Austin. Uses emojis — casual rapport despite infrequent contact."
Claude Understood Personal Relationships, Timing, Opportunities
Relationship Understanding
Decoded relationship depth from tone, not just content
Identified Kim as a mutual-value partner, not just a client. Noted reciprocal referrals and multi-year trust without being prompted.
time awareness
Calculated recency gaps and
flagged urgency independently
Surfaced Tony's 43-day silence as the highest-risk gap. Matched timing to open opportunities without explicit instruction.
Opportunity Detection
Mapped relationship warmth to business value
Scored Mark as 25/25 criticality — maximum urgency combined with proven placement history. Identified dual client- candidate dynamic unprompted.
Personal Context Recall
Retained human detail across every output
Referenced Quebec City Christmas, US Open tennis, Andy Lai by name in outreach. Personal details surfaced naturally, not mechanically.
Claude Turned Raw Conversations to Ranked Priorities in Seconds
AI Decision Output — Who to contact first
1
Mark Longiaru
Fresh job posting + proven placement history + active brief. Move immediately.
● High confidence — 25/25
2
Tony Phan
Longest gap. Active hiring need. Referral promise left unresolved.
● Medium confidence — 15/25
3
Kim Walch
Strong relationship, but older posting. Nurture, don't push.
● Moderate — 10/25
Claude Drafted a Message That Felt Human with Personal Reference, Business Hook, Natural Tone.
Proof of depth — AI-drafted outreach for Tyler
90%+ accepted as-is
Hey Tyler,
Hope the
Year of the Dragon
is treating you well and TRM
is off to a strong start!
I've been thinking about our conversation - you were getting
almost no response on that Strategic Finance search,
and I know how
frustrating that is when you have a clear picture of you you need.
I have some thoughts on why top IB + strat fin profiles aren't biting, and a few names who might actually be interested. Worth a quick catch-up?
Personal reference
Business hook
Natural tone
Experimentation Yielded 100% Draft Acceptance Rate
The AI-generated messages were good enough that recruiters barely edited them. The experiment proved we didn't need custom infrastructure.
We scrapped the original plan, redirected the roadmap, and recovered 3 months of development time.
100%
AI Draft message acceptance rate
3 mo
3 months of development time saved
The Best Product Decision I made Wasn't a Design Choice
It was knowing when to challenge a technical assumption before the team spent months building the wrong thing. One week of experimentation replaced 3 months of planned development, and redirected the entire product roadmap.