INTERNSHIP
FAIshion.AI
Web
Mobile
Chrome Extension
TEAM
2 Designers •
1 Product Manager
2 Engineers
DURATION
Sept - Dec 2025, 13 Weeks
CONTRIBUTIONS
Wireframing
Interaction design
Usability testing
Vibe coding
CONTEXT
FAISHION.AI is an AI styling tool that allows users to visually "try on" clothing items online, mix and match outfits, and get personalized feedback from a virtual stylist.
I worked with my team to design their recent feature release, Mix & Match. This feature allows users to combine different items of their choosing, and generates a realistic preview of the full look on the user. I designed the UI from scratch and conducted research to create a flow that is cohesive to the Try-On function and easy for the user to navigate through. This release was anticipated by 78% of the 800 total participants in a user feedback survey.
THE INITIAL BRIEF
Initially, we only wanted to create an outfit builder centered around the Recommendations page.
FIG. 1
INITIAL ANALYSIS FROM FIRST WEEK
• THE PROBLEM
Upon further research, we decided to take it up another notch and evolve the concept by integrating AI into the feature.
Our previous approach relied heavily on users’ ability to visualize the final look. However, it's difficult to coordinate and plan outfits without trying everything on. This can lead to users experiencing these different pain points:
AMBIGUITY
What you picture in your head can be completely different to what it actually looks like when you try it on.
ACCURACY
Clothing items come from sites with different models, lighting, and styles, making it hard to guess if anything matches.
EFFORT COST
Coordinating outfits requires you to physically swap through clothing items, which can become redundant and tiring.
This friction can be enough for users to avoid experimenting with different combinations and delay purchase decisions if they are unsure about the results.
• THE PROCESS
Visualizing the Journey
Through research online, I noticed that users approach outfit creation through 2 distinct pathways:
Intentional styling, where they actively build an outfit.
Passive discovery, where they save or collect items while browsing.
Based on the two pathways, 2 Mix & Match entry points will be integrated that the user can access interchangeably:
The "Shopping Basket" will be accessed through already existing pages.
The standalone page will have the same functions, but can be accessed through the navigation menu.
FIG. 2
FLOW CHART VISUALIZATION, CREATED TO MAKE THE DIGITAL PROCESS FEEL MORE TANGIBLE FOR THE TEAM.
RESEARCH
I audited different layouts from outfit builders and generative AI tools.
FIG. 3
OUR COMPETITIVE ANALYSIS
Comparing them side to side highlighted three core needs that need to be prioritized: visibility, flexibility, and control. This led to the first design iteration, created using Gemini AI to decide on the initial flow. After some tweaks, our team had a good idea about what to build and how we can integrate the feature into the site.
Standalone
"Shopping Basket"
Feature Priorities of the Map
MAIN FEATURES
EDIT TITLE NAME
ADD ITEMS
WRITE PROMPT
SUBMIT/GENERATE
ENTRY POINTS
STANDALONE PAGE
"SHOPPING BASKET"
•
THE SOLUTION
MY REFLECTIONS
The freedom of not starting from scratch
Choosing to work inside an existing design system was admittedly difficult, but it gave me a newfound perspective on how to justify my design decisions that led to more thinking outside the box.
AI shortens the loop, and taste closes it
Being able to vibe-code different layouts quickly gave me a much clearer picture of the overall structure. I could see five versions of an idea instead of imagining one. However, AI didn't replace the design thinking; it simply allowed me to explore more options and iterations.
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