Plato
Mentorship that meets you where you are
An AI-powered mentorship matching platform
that connects professionals with the right mentors:
transparently, and with low-commitment first steps.
Kevin Gao
Product Designer
THE PROBLEM
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Most professionals want mentorship.
Few know how to find the right one.
Cold start anxiety
Reaching out to a stranger for career advice feels high-risk. What if you waste their time? What if it's awkward?
Poor match quality
Existing platforms match on industry and title only, not communication fit, shared experience, or mentoring style.
No follow-through
Even when matched, the first conversation never happens. The gap between finding someone and booking feels too big.
PROJECT OVERVIEW
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Scope & approach
Role
Product Designer (solo project)
Timeline
8-week sprint — research through hi-fi prototype
Scope
End-to-end mentee experience: onboarding → matching → booking → feedback
Research
Behavioral observation · Journey mapping · Heuristic evaluation
Out of scope
Mentor-side experience, payment system, mobile
RESEARCH
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Behavioral observation
01
Trust starts with people, not platforms
Everyone started by asking friends for introductions. Personal trust consistently outweighed platform credibility.
02
Browsing leads to paralysis
LinkedIn browsing sessions averaged 20+ minutes but ended without action — too many options, no confidence in any single choice.
03
First message anxiety
When someone finally chose a mentor, they agonized over the first message — drafting and deleting for 20+ minutes, afraid of wasting the mentor's time.
People trust recommendations over search results. The platform should feel like a thoughtful friend making an introduction.
RESEARCH
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Mentee journey map
1
Awareness
"I'm stuck"
Motivated
→
2
Exploration
Browsing, asking
Overwhelmed
→
3
Selection
Picking someone
Anxious
→
4
First contact
Sending message
Vulnerable
→
5
Ongoing
Regular sessions
Fulfilled
THE TRUST GAP — biggest drop-off between finding someone and actually talking to them
Stage 5 insight: Even when relationships form, mentees describe sessions as "scattered conversations" — no sense of accumulation or progress tracking.
RESEARCH
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Heuristic evaluation
HIGH
Onboarding friction
All 3 require 5+ steps before showing any mentors. MentorCruise asks for payment info before you've seen a single match.
HIGH
Match opacity
None explain why a mentor appears in your results. Users are left guessing whether a match is relevant to their needs.
MED
High commitment first step
First interaction on all platforms is a 30-60 min session. No low-stakes way to test chemistry before committing.
0 of 3 platforms explain match reasoning. 0 of 3 offer low-commitment first interactions.
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DESIGN STRATEGY
Research → Design opportunities
1
Reduce the matching burden
Don't make users evaluate 50 profiles.
Do the matching. Explain the reasoning.
2
Make the "why" visible
Show match rationale on dimensions users
care about — not just industry and title.
3
Lower the first-step commitment
A 15-min intro call feels safer than a
60-min mentoring session.
DESIGN STRATEGY
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Design principles
"Guided, not gated"
Onboarding should feel like a conversation, not a form. Reduce decisions, increase clarity.
"Show the why"
Every AI recommendation comes with a human-readable explanation. Transparency builds trust.
"Low floor, high ceiling"
First interaction is lightweight. Depth grows over time. Don't front-load commitment.
DESIGN STRATEGY
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Information architecture
Onboarding
3 steps
→
Matches
AI-curated
→
Profile
Trust signals
→
Book
15 min intro
→
Session
AI prep + notes
→
Feedback
Dimensional
→
Dashboard
Progress
Core loop
Matches → Profile → Book → Session → Feedback → Improved matches. Each session's feedback refines the AI matching for the next recommendation.
CORE DESIGN
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Onboarding: goal selection
Step 1 of 3 — "What brings you to Plato?"
Selectable cards, not a dropdown, each has context
Progress indicator shows 3 total steps upfront
No sign-up wall before value preview
Tutorialization: explains why each step matters
CORE DESIGN
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Onboarding: style & availability
Steps 2–3 — communication fit and scheduling
Spectrum slider for feedback style — not a binary choice
Session frequency, day/time preferences — flexible options
CORE DESIGN
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AI match results
The hero screen — curated recommendations with transparent reasoning
3 curated matches, not 50 search results
Match % as intentional AI transparency design choice
Dimensional tags: goal, style, schedule alignment
Natural language "why this match" explanation
Trust signals: sessions completed, ratings
CORE DESIGN
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Mentor profile
Full profile with trust signals and mentoring style data
Mentoring style bars based on mentee feedback, not self-reported
Testimonials from past mentees for social proof
Experience timeline showing career trajectory
CTA: "Book an intro call · 15 min“, low commitment
93% match breakdown: goal, style, availability
CORE DESIGN
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Session booking
Calendar + AI-suggested topics to reduce first-message anxiety
15-min intro format, low commitment, high signal
AI-suggested talking points based on stated goals
Session summary sidebar for confirmation clarity
Calendar integration for frictionless scheduling
CORE DESIGN
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Session hub
Pre-session prep transforms scattered conversations into structured growth
AI-generated prep questions prime deeper conversation
Session notes area for in-session capture
Topic and time info at a glance
Research anchor: journey map Stage 5 sense of accumulation
CORE DESIGN
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Session feedback
Dimensional feedback that feeds back into AI matching quality
"Did this session help with your goal?" not star ratings
Communication style spectrum, "too direct" to "too cautious"
Open text for qualitative improvement ideas
"This improves your future matches" which closes the loop
CORE DESIGN
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Progress dashboard
Modular layout, the sense of accumulation mentees need
Goals progress tied back to onboarding selections
Session history creates visible growth narrative
Active mentors and upcoming sessions at a glance
Clean typography-driven layout, generous whitespace
ITERATION
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Usability testing
3 participants · 20-minute moderated remote sessions
Task: Complete onboarding → review matches → book first session
P1 — Sarah
Junior designer, 2 yrs experience. Recently started looking for career guidance.
Focus: Goal selection clarity
P2 — James
Mid-level PM pivoting to design. Actively browsing mentorship platforms.
Focus: Match comprehension
P3 — Ava
Senior designer, 5 yrs. Wants leadership mentorship for first management role.
Focus: Booking confidence
ITERATION
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Finding: goal selection friction
2/3
participants hesitated for 30+ seconds on goal selection (Step 1)
Categories were too broad — "Career transition" and "Career growth" felt overlapping.
"Am I doing a career transition or career growth? It's kind of both..."
— P1 (Sarah), after 40 seconds of deliberation
"I don't want to pick wrong and get matched with the wrong person."
— P2 (James), hovering between two options
Root cause: Predefined categories force users into boxes. Anxiety of choosing the wrong.
ITERATION
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Iteration: conversational goal setting
BEFORE
AFTER
OUTCOMES
Closing the loop
3/3
Cold start anxiety → resolved
test participants described first contact as "low-pressure." AI-suggested talking points + 15-min format eliminated first-message drafting anxiety.
3/3
Poor match quality → resolved
participants could articulate why a mentor was recommended. Dimensional match reasoning on every card, vs. 0/3 existing platforms.
3/3
No follow-through → resolved
participants completed the full flow from onboarding to booking. 15-min intro + AI prep questions + session hub provided structural support.
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REFLECTION
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With more time...
Design the mentor-side experience — onboarding, session management, earnings
Test match quality longitudinally — do matched pairs actually continue meeting?
Explore group mentorship features — 1-to-many sessions for scalable guidance
Validate AI matching dimensions — are goal alignment and communication style the right axes?
Honest acknowledgments
· Current testing validates usability, not match effectiveness
· Match percentage is a design hypothesis — real implementation would need validation
· Business model (pricing, mentor incentives) deliberately out of scope