Giyiyo
An AI wardrobe assistant: the user digitises their closet and the app suggests outfits based on the weather, the day's plan and personal style. Adding items through a live camera, a premium subscription and an admin panel make it an end-to-end mobile product.
What the problem was.
Morning decision fatigue
The target user loses time every morning on 'what do I wear'; most of the closet sits unused.
The wardrobe is not digital
The clothing inventory is not recorded anywhere, so there is no database to generate suggestions from. If adding an item is not effortless, the product gets abandoned.
The revenue model has to be there from day one
For the product to be sustainable, the subscription and payment infrastructure has to sit in the core from the MVP onwards.
How we solved it.
Discovery & design
UX flows, wireframes, technical architecture and the database schema were settled in a single week.
Core development
Sign-up and sign-in, wardrobe inventory, adding items from a photo and the core live camera flow.
AI & subscription
The outfit engine, weather and calendar context, style learning through swipes and the premium subscription infrastructure.
Delivery & maturing
First MVP delivery through QA and TestFlight; camera recognition quality and bulk upload from video go deeper in the next sprints.
The building blocks.
Wardrobe inventory
Adding items from a photo, categorisation and item detail.
Live camera
Instant item capture; background removal for clean inventory images.
AI stylist
Three daily outfit suggestions based on colour harmony and personal taste.
Dynamic planner
An outfit plan for the day from weather plus calendar.
Packing assistant
Minimum items, maximum outfits for the destination city and trip length.
Subscription & panel
Premium subscription, payment infrastructure, backend API and admin panel.
Tell us about
the project.
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