Ruoxuan Pan, Junjie Tang and Huixiang Chen 嘅作品 Closet AI 二手轉售商務平台

由設計師 Ruoxuan Pan, Junjie Tang and Huixiang Chen 創作嘅 Closet AI 二手轉售商務平台係備受推崇嘅 2026 A' Design Award 鐵獎得獎作品，獲獎類別為流動設計。

鐵流動· 2026

Closet AI 二手轉售商務平台

由 Ruoxuan Pan, Junjie Tang and Huixiang Chen

流動類別嘅 A' Design Award 鐵獎得獎者

5,797

經驗證嘅獲獎者

驗證設計 #182338 嘅獲獎狀態

喺 A' Design Award 查看新聞稿獎項徽章

色彩

設計

關於 Closet AI 二手轉售商務平台

Closet AI 係一個二手服裝嘅流動轉售平台。用戶上載服裝相片,應用程式就會根據轉售數據自動生成分類、描述同建議價格。喺議價過程中,系統會草擬回覆建議,用戶可以編輯或者批准,確保最終決定權留喺賣家手上。獎勵系統透過任務、徽章同里程碑追蹤用戶進度。呢個平台係基於轉售行為研究而開發,目的係減少令人卻步嘅時間同精力成本,鼓勵更多人參與循環時尚。

榮譽認可

關於A' Design Award 鐵獎

Closet AI 二手轉售商務平台是 A' Design Award 鐵獎得主，這是授予滿足人們對設計精良的創作所期待的專業與工業要求的優良設計的聲望認可。鐵獎得主展現出其創作者的高超技藝、高度專業化、專家級的理解力和創造能力。預計Closet AI 二手轉售商務平台將成為一件備受推崇、令人欽佩且被珍愛的創作，既實用又創新。這件贏得 A' Design Award 鐵獎的設計整合了行業最佳實踐，並擁有與專業知識和才華相結合的稱職技術特性。Closet AI 二手轉售商務平台因其嚴謹而受到尊重，旨在提供有助於讓世界變得更美好的生活品質改善。

設計背後

新聞資料包

由設計師提供嘅製作人員名單、團隊成員同技術詳情。

獨特屬性: 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.
尺寸同包裝: 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.
標籤: 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
生產技術: 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.
設計挑戰: 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.
研究: 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.
靈感: 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.
項目時長: The project began in Dec 2025 in Austin and is currently in advanced prototype development.
操作流程: 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.
團隊成員: Ruoxuan Pan
Junjie Tang
Huixiang Chen
圖片來源: 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