Closet AI リセールコマースプラットフォーム by Ruoxuan Pan, Junjie Tang and Huixiang Chen is a highly esteemed winner of the very prestigious 2026 A' Design Award 鉄賞 in モバイル技術・アプリケーション・ソフトウェアデザイン Competition Category.
鉄モバイル· 2026
Closet AI リセールコマースプラットフォーム
by Ruoxuan Pan, Junjie Tang and Huixiang Chen
A' Design Award 鉄賞 Winner in モバイル Category
Verified Award Winner
Verify Award Status for #182338The Design
About Closet AI リセールコマースプラットフォーム
Closet AIは、中古衣類のためのモバイルリセールプラットフォームです。ユーザーが衣類の画像をアップロードすると、アプリケーションがリセールデータに基づいてカテゴリー、商品説明、推奨価格を生成します。交渉時には、システムが返信の提案文を作成し、ユーザーはそれを編集または承認できるため、最終的な判断は常に出品者に委ねられます。リワードシステムは、クエスト、バッジ、マイルストーンを通じて進捗を記録します。本プラットフォームは、リセール行動に関する調査に基づいて開発されました。その目的は、サーキュラーファッションへの参加を妨げる時間と手間を軽減することにあります。
The Recognition
About the A' Design Award 鉄賞
Closet AI リセールコマースプラットフォームは、よく設計された創造物に期待される専門的・産業的要件を満たす優れたデザインに授与される、権威ある評価である A' Design Award 鉄賞の受賞作品です。鉄賞の受賞作品は、制作者の高い技量、高度な専門性、専門家としての理解、創造的な能力を示しています。Closet AI リセールコマースプラットフォームは、高く評価され、称賛され、愛される、実用的で革新的な創造物であることが期待されます。この A' Design Award 鉄賞を受賞したデザインは、業界のベストプラクティスを統合し、ノウハウと才能に結びついた有能な技術的特性を備えています。Closet AI リセールコマースプラットフォームは、その徹底ぶりで尊敬され、世界をより良い場所にするための生活の質の向上をもたらすよう設計されています。
Behind the Design
Press Kit
Credits, team members and technical details provided by the designer.
- Unique Properties
- Closet AI is an AI powered resale platform designed to remove friction from circular fashion. It automates listing creation, pricing suggestions and negotiation support, transforming second hand selling into a streamlined experience. By integrating intelligent automation with a reward system that tracks impact and milestones, Closet AI reduces emotional barriers and encourages sustainable participation, making fashion circulation measurable and efficient.
- Dimensions & Packaging
- Closet AI is a mobile first digital platform prototyped in Figma at 402 x 874 pixels aligned with iPhone interface standards. The system follows a modular and scalable architecture supporting real time feedback and adaptive interaction flows. A structured design system ensures visual consistency accessibility compliance and responsive layout behavior across future multi platform extensions.
- Tags
- AI powered resale platform, UX/UI design, circular fashion system, sustainable commerce design, intelligent negotiation support, automated listing generation, user centered interaction design, behavioral incentive system, mobile marketplace experience, fashion sustainability innovation
- Production Technology
- Closet AI was developed through a human centered design process informed by user research and resale behavior analysis. Insights into pricing uncertainty and negotiation stress shaped the interaction framework. Computer vision, natural language generation and data driven recommendations were integrated as supportive technologies. Iterative prototyping refined the system into an assisted workflow that reduces cognitive load while preserving user agency.
- Design Challenge
- The major challenge was balancing automation with user autonomy. Fully automated systems risk removing agency, while manual systems maintain friction. Closet AI introduces assisted intelligence, where AI drafts and recommends actions but users retain final control. Another challenge was integrating sustainability incentives without overwhelming users. The solution implements subtle impact indicators and milestone badges that reinforce behavior without creating gamified complexity.
- Research
- Type of Research: Qualitative behavioral research and workflow analysis. Research Objectives: To identify barriers preventing users from participating in clothing resale despite sustainability awareness. Methodology: User interviews journey mapping and competitive benchmarking. Data Collection: Structured interviews usability testing and prototyping. Participants: Gen Z and Millennial users. Results: Identified negotiation fatigue and listing complexity as key friction points. Insights and Impact: Automation reduces participation barriers and supports circular fashion adoption.
- Inspiration
- Closet AI was inspired by the gap between sustainable intention and practical behavior. While many Gen Z and Millennial users want to reduce waste, resale platforms often demand time and effort. Listing creation, price negotiation and cross platform management introduce friction that discourages participation. The project explores how artificial intelligence can reduce this friction and connect resale with meaningful environmental impact.
- Project Duration
- The project began in Dec 2025 in Austin and is currently in advanced prototype development.
- Operation Flow
- Users upload clothing images to generate automatic categories, descriptions, and smart pricing suggestions based on resale data. They publish listings, track engagement insights, and receive recommendations such as price adjustments or repost timing. Users complete quests, earn badges, and unlock milestones as they list, optimize, and close deals. During negotiations, AI drafts reply suggestions that users can edit or approve, keeping full control.
- Team Members
- Ruoxuan Pan
Junjie Tang
Huixiang Chen - Image Credits
- Main Image: Creator Junjie Tang, Huixiang Chen, Closet AI Home page and Swipe flow, 2026
Image #1: Creator Huixiang Chen, Item Scan, Item Swipe and Select, Item Editing, and Item Posting page, 2026
Image #2: Creator Ruoxuan Pan, AI listing and message assistant flow, 2026
Image #3: Creator Junjie Tang, reward and badge pages, 2026
Image #4: Creator Ruoxuan Pan, Huixiang Chen, Junjie Tang, Closet AI interface overview, 2026
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