Appari is a gen AI try-on app startup that helps shoppers visualise clothing on themselves before buying. I led the end-to-end design of the MVP from user research and strategy through to a live product with 250+ early users and two retail partners signed for pilot testing.
The goal was to reduce purchase uncertainty, lower return rates, and give users confidence in their online fashion choices through a simple, mobile-first experience.
Role
Lead Product Designer
Medium
Mobile App & Website
Tools
Figma, VS Code, ChatGPT, Claude, Vue.Js, Tailwind
App Status
Live with users
Company Status
Startup
Shopping for clothes online is convenient until it isn't. Appari set out to solve common, frustrating problems shoppers face:
Issues
This leads to customer frustration, lower purchase confidence, and costly return rates for retailers.
The challenge: How might we help users see how clothing will look on them before they buy - simply, quickly, and visually?
The goal was to build an MVP that would allow users to upload a selfie and try on clothing virtually using AI. It needed to be intuitive, frictionless, and visually compelling — without requiring complex onboarding or body scanning.
I conducted research with 340+ participants through interviews and surveys, uncovering key insights:
"I usually buy two sizes, try them on, and return one. It's wasteful"
"If I could see how it fits before buying, I'd shop online more often"
"I want to try the dress before ordering"
I analysed existing solutions including Zyler, True Fit, and various AR try-on tools that had been made.
Key gaps identified:
Design a mobile-first, cross-platform solution that works with any retailer.
I prioritised features for the initial launch based on user needs and technical feasibility:
Must-have (Phase 1):
Primary flow:
To create the most accurate digital twin.
a - Scan face & body with camera or upload images
b - Input body measurements for accuracy
Discussion with potential users:
- Too long winded, too many steps. Needs to be simpler
a - Option to input link or screenshot/image of the items.
I added images/screenshots as clothes can be captured from influencers and images friends share.
b - Clothing is remodelled on the user's digital twin.
Feedback:
This is simple and straight forward.
I decided for the initial concept, we should see if people actually can integrate this type of tool into their shopping flow as it's a new way of doing this. I decided to simply the AI model to just a full body selfie as Gen AI is able to work with that.
a - Simple onboarding with Google login
b - Single full body image with instructions for best output.
c - Users can take a photo or upload something from their camera roll
c - Removed url paste for simplicity, added instructions for garments so the gen AI can be more accurate
d - A preview of the outfit is shown so user can change it if they selected the wrong image
e - Garment type is put in so more accurate results can be achived
f - Final output screen
Finding: Users were hesitant to upload personal photos immediately
Solution: Added demo models and outfits to explore first
Finding: AI image generation did not perform to good standard
Solution: Added 'regenerate' option without charging an additional credit
Finding: On first use, people didn't have clothes to try
Solution: Added a gallery of clothing to test with
Finding: Credits were too expensive
Solution: Reduced pricing and increased number of free credits
Goal: People can look at their saved clothing for when they make buying decisions, write notes and share them with others.
Goal: User is able to do basic account tasks and look at their outfit credits, buy more and refer friends.
Based on user feedback and research, the next design priorities include:
Long-term vision: Create an experience where people can visualise themselves in any outfit from any retailer, building confidence and reducing the friction of online fashion shopping.