Wardrobe capture
Review a jacket photograph with AI garment tags awaiting confirmation.
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- Personal wardrobe
- AI mobile concept
Drapelyt
A personal wardrobe app for visual clothing organization, explained outfit suggestions and capsule packing.
Designed around4 AI workflows
Project overview
Drapelyt explores personal wardrobe through a mobile experience built around an outfit collage with swipeable garment cards. A personal wardrobe app for visual clothing organization, explained outfit suggestions and capsule packing. The proposed journey connects wardrobe capture, outfit studio, packing edit, closet library. Four original phone showcases establish the visual direction, while the case study defines the supporting feature set and a proposed React Native architecture.
The challenge
A wardrobe assistant needs reliable item records and user control over style suggestions, especially when photographs are incomplete. On a phone, the person also needs to capture information quickly, understand where an answer came from and return to the original material without navigating through a dense desktop interface.
- Keep wardrobe capture and outfit studio connected within a short mobile flow.
- Make garment recognition correctable when the model misreads the input.
- Explain visual similarity through visible references or user-selected preferences.
- Keep changes, sharing and data retention under explicit user control.
Our solution
The design begins with an outfit collage with swipeable garment cards, using large touch targets, short task sequences and contextual sheets. Suggest category and color tags from clothing photographs. Combine saved items around the user's stated occasion. The companion views keep drafts, original material and accepted decisions distinct. A plum, pale pink and charcoal visual system gives this app its own character within the collection.
- Suggest category and color tags from clothing photographs.
- Combine saved items around the user's stated occasion.
- Retrieve alternatives from the user's own wardrobe.
- Draft a packing list from chosen days and repeat-wear preferences.
Key features
Inside the platform
See more possibilities in what you own.
Original mobile interface concepts with fictional content. AI responses illustrate proposed interactions; they are not live model results.
Proposed technology stack
Proposed iOS & Android architecture using React Native and TypeScript. AI processing, storage and synchronization are design proposals for this concept, not integrations implemented by this portfolio.
- React Native
- TypeScript
- SQLite
- AI retrieval
The results
The concept defines four mobile views, four AI workflows and twelve feature areas. The next validation would ask a user to complete wardrobe capture, inspect the proposed outfit studio output and correct an intentional extraction or suggestion error. Evaluation would focus on source traceability, correction effort and task completion. No usability study or business-impact measurement is claimed.
4Original mobile showcase views
4Proposed AI workflows
12Designed feature areas
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