CONCEPT UX/UI CASE STUDY · 2026
A faster way to see, style,
and rewear what you own.
WornWell combines closet import, outfit discovery, and body-inclusive previews to make daily outfit decisions easier.
PROJECT TYPE
Wardrobe app concept
ROLE
UX/UI Designer · Solo
TOOLS
Figma · FigJam
MVP SCOPE
iOS first · no retailer or sizing data
TIMELINE
Mar–Jun 2026
OVERVIEW
Research, UX/UI, and prototyping for an inclusive iOS concept.
CORE EXPERIENCE
01
Scan
Add items fast
02
Style
Build outfits
03
Try On
See it on you
DESIGN PROMISE
Less searching. More wearing.
People had plenty to wear, but still struggled to see it, style it, or picture the fit.
01
THE PROBLEM
Clothes disappeared in crowded closets, setup felt tedious, and styling tools offered little help with fit.
02
THE SOLUTION
WornWell imports closet photos, organizes detected items, and previews outfits on more representative bodies.
01
Full closets
Users owned enough, but still felt stuck.
02
Low visibility
Items were forgotten once they left view.
03
Poor representation
Fit previews rarely reflected real bodies.
KEY INSIGHT
Research pattern
The issue was visibility and confidence.
That insight shifted WornWell from inventory tracking to faster outfit decisions.
Three interviews revealed the same pattern: users owned enough clothing, but still lacked visibility, speed, and confidence.
40–50
ITEMS OWNED
3/3
OWNED ENOUGH, STILL FELT STUCK
3/3
WANTED BETTER FIT PREVIEWS
Key theme
Items get forgotten once they leave the front of the closet.
VISIBILITY GAP
User pain point
Outfit decisions take too long on busy days.
DECISION FATIGUE
User pain point
Styling tools rarely show bodies like theirs.
REPRESENTATION GAP
PRODUCT SIGNALS
Research and a quick market scan pointed to the same gap: users didn't need another digital closet. They needed faster setup, a clearer daily decision, and more confidence previewing outfits.
MOBILE EXPERIENCE IN MOTION · WORNWELL
I used the research as a filter: each core feature had to solve a specific user problem or stay out of the MVP.
DECISION FRAMEWORK
The goal wasn't to add features. It was to make each screen earn its place.
Signal
What we learned
Decision
What I chose
Tradeoff
What I accepted
01
USER TASK
SETUP
SIGNAL
Manual entry created too much work before users saw value.
DECISION
Bulk scan first
TRADEOFF
Manual entry stays as a fallback.
02
USER TASK
DAILY USE
SIGNAL
The problem was choosing an outfit, not finding the closet.
DECISION
Today’s Look first
TRADEOFF
Wardrobe supports the decision instead of leading it.
03
USER TASK
PREVIEW
SIGNAL
Users wanted more confidence picturing outfits on bodies like theirs.
DECISION
Body-relevant Try On
TRADEOFF
Visualization only, not exact fit prediction.
04
USER TASK
REWEAR
SIGNAL
Users already owned enough clothing.
DECISION
Keep discovery focused on owned items
TRADEOFF
No shopping feed in the MVP.
I used sketches and lo-fi screens to compare flows, remove unnecessary steps, and choose the shortest path to value.
The key question was simple: how quickly could a user move from closet import to a useful outfit?
I chose the decision-first flow because it surfaced value sooner than a closet-first experience.
The lo-fi pass helped me remove screens that didn't advance import, outfit choice, or preview.
MID-FI FOCUS
The mid-fi pass focused on wardrobe visibility, daily outfit selection, and virtual try-on.
WARDROBE
TRY ON
HOME
The system keeps clothing and decisions—not decoration—at the center.
01
Color
Warm neutrals keep clothing imagery dominant; coral is reserved for status and error.
02
Components
Shared controls reduce relearning across scan, wardrobe, and try-on.
03
Patterns
Repeated card and grid patterns keep dense closet content easy to scan.
Muted neutrals support hierarchy without competing with clothing.
TEXT PRIMARY
#111111
CREAM BACKGROUND
#F7F3EC
SAGE SOFT
#C8D3C1
TAUPE DEEP ACCENT
#B8957F
ERROR SOFT CORAL
#C78375
CORE EXPERIENCE
Three connected screens show the core loop: import clothes, surface a useful look, and preview it on a representative body.
Bulk scan, a daily-look dashboard, and Try On form one decision-first styling flow.
The final flow reflects the four decisions above rather than showcasing every possible feature.
Short onboarding gets users to value before setup starts to feel like work.
WELCOME / FIRST-TIME ENTRY
STYLE PREFERENCES
ADD YOUR CLOTHES
Bulk scan leads; manual entry handles corrections and one-off items.
BULK UPLOAD / AI SCAN
MANUAL ADD ITEM
POST-SCAN REVIEW
The homepage leads with a daily look—not inventory—because the real problem is deciding what to wear.
WARDROBE
ITEM DETAIL / FAVORITE
HOME / DAILY DASHBOARD
Try On supports visualization, not exact fit prediction without garment-level sizing data.
AVATAR FIT SETUP
VIRTUAL TRY-ON
Discovery stays focused on clothes users already own; shopping stays out of scope.
DISCOVER
PROFILE / SAVED LOOKS
01
Participants hesitated around upload and navigation labels.
CHANGE
I strengthened the upload CTA
and simplified labels.
02
The next action became clearer without adding new features.
Simpler language beat more explanation.
03
Broader body types, comfort and mobility needs, screen reader behavior, and whether saved looks actually improve rewear.
Test the full decision loop before expanding recommendations.
INTENTIONAL SCOPE
I left out exact fit prediction, shopping feeds, and social features because none solved the core decision problem.
WornWell was designed to help users feel confident getting dressed through visible closets, realistic previews, and sustainable rewearing.
A UX concept project, not a live product, and not affiliated with any fashion retailer.
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