Stylebot co.,ltd.
Fashion
- Website : http://www.stylebot.co.kr
- Location : #410 465, Dongdaegu-ro, Dong-gu, Daegu, Korea
O Company Overview
- Led by a CEO with over 20 years of experience as a fashion designer and supported by fashion experts with over 10 years of experience, along with specialized AI/SW development personnel.
- Addresses consumer styling concerns using recommendation algorithms and generative AI technology to create real-time outfit images.
- Provides new value to fashion consumers and the online fashion commerce industry.
O Technology Development and Business Expansion
- Established a foundation for technology development and business expansion through participation in various government-supported projects.
- Actively pursuing collaboration and business expansion in the global market.
- Collaborating with leading companies such as Samsung Electronics, Hyundai Department Store Group's Handsome, Hyundai Construction, CJ ENM, and Shinsegae Group's W Concept.
- Advancing the fashion industry and creating new value through innovative and unique fashion AI technology.
O Product and Service Overview
- Provides a service that allows users to easily mix and match their wardrobe using styling recommendation algorithms and AI clothing image generation technology.
- Creates various styles of looks from simply taking pictures of the wardrobe, offering a personal stylist experience and enhancing wardrobe utilization.
- During online shopping, suggests numerous stylings by combining the selected product with other items on the site, increasing product exposure and extending customer engagement time, ultimately boosting sales.
O Key Features
- AI Technology: Utilizes AI image recognition technology to classify product images into over 160 detailed categories and automatically store them in the database.
- Coordination Recommendations: Automatically generates fashion coordination matching user and brand preferences, weather, and latest trends through the proprietary FSTA recommendation algorithm.
- Virtual Fitting: Uses generative image AI technology to provide real-time virtual try-on images of clothing on avatars.
- Customization/Personalization: Differentiates recommended coordination images and products based on user and brand fashion preferences.
- Real-Time Image Generation Technology: Creates styling images in real-time with avatars wearing recommended items.
O Service Differentiation and Applications
- Provides a service that creates editorial-like content by mixing and matching products for set sales in online fashion shopping.
- Differentiates from the studio-shot content creation and labeling-based coordination recommendation methods used by large fashion platforms such as Musinsa.
- Applied in online fashion commerce sectors including women's clothing, men's clothing, golf/leisure wear, and children's clothing.
- Led by a CEO with over 20 years of experience as a fashion designer and supported by fashion experts with over 10 years of experience, along with specialized AI/SW development personnel.
- Addresses consumer styling concerns using recommendation algorithms and generative AI technology to create real-time outfit images.
- Provides new value to fashion consumers and the online fashion commerce industry.
O Technology Development and Business Expansion
- Established a foundation for technology development and business expansion through participation in various government-supported projects.
- Actively pursuing collaboration and business expansion in the global market.
- Collaborating with leading companies such as Samsung Electronics, Hyundai Department Store Group's Handsome, Hyundai Construction, CJ ENM, and Shinsegae Group's W Concept.
- Advancing the fashion industry and creating new value through innovative and unique fashion AI technology.
O Product and Service Overview
- Provides a service that allows users to easily mix and match their wardrobe using styling recommendation algorithms and AI clothing image generation technology.
- Creates various styles of looks from simply taking pictures of the wardrobe, offering a personal stylist experience and enhancing wardrobe utilization.
- During online shopping, suggests numerous stylings by combining the selected product with other items on the site, increasing product exposure and extending customer engagement time, ultimately boosting sales.
O Key Features
- AI Technology: Utilizes AI image recognition technology to classify product images into over 160 detailed categories and automatically store them in the database.
- Coordination Recommendations: Automatically generates fashion coordination matching user and brand preferences, weather, and latest trends through the proprietary FSTA recommendation algorithm.
- Virtual Fitting: Uses generative image AI technology to provide real-time virtual try-on images of clothing on avatars.
- Customization/Personalization: Differentiates recommended coordination images and products based on user and brand fashion preferences.
- Real-Time Image Generation Technology: Creates styling images in real-time with avatars wearing recommended items.
O Service Differentiation and Applications
- Provides a service that creates editorial-like content by mixing and matching products for set sales in online fashion shopping.
- Differentiates from the studio-shot content creation and labeling-based coordination recommendation methods used by large fashion platforms such as Musinsa.
- Applied in online fashion commerce sectors including women's clothing, men's clothing, golf/leisure wear, and children's clothing.
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Business Description
O Major Milestones
- 2019.01: Incorporated StyleBot Co., Ltd.
- 2019~2024: Engaged in various activities including government-supported projects, fundraising, establishment of an in-house R&D center, and participation in global exhibitions.
O Awards and Certifications
- 2019~2023: Recognized as an Excellent StyleTech Company, certified as a Venture Company, awarded by the Startup Promotion Agency, and won the ABB Startup Competition Grand Prize.
O Project History
- 2023~2024: Selected for IBK Changgong, Samsung Electronics C-Lab Outside, and the Content Promotion Agency's overseas expansion program.
O Revenue Models
- B2B Revenue Model Verification: Provided styling recommendation services using generative AI technology to online fashion sellers (e-commerce sites, brands, platforms, etc.) and monetized through a B2B SaaS model.
- B2C Business Expansion Infrastructure: Secured a stable network for future styling recommendation-based fashion platform services by leveraging the foundation built from numerous B2B clients.
O Key Technology Achievements
- User Preference Analysis Technology: Developed the FSTA process to analyze fashion preference groups by distinguishing factors that determine users' fashion preferences.
- Clothing Item Classification System: Established a classification system with 160 categories and over 1,000 subcategories for clothing items.
- Coordination Recommendation Algorithm: Recommended optimal outfits based on user wardrobe data and preferences.
- Clothing Image Recognition and Processing Technology: Developed technology to detect image similarity, recognize objects, and remove backgrounds.
- Virtual Fashion Fitting Technology: Used MaskRCNN to create natural virtual fitting images.
- Generative AI Technology: Reconstructed flat clothing images into natural fitting images using diffusion AI technology.
O Data Acquisition
- Fashion Item Classification Dataset: Over 1 million records (200,000 refined).
- User Clothing Photo Images: Approximately 40,000 images (20,000 refined).
- Virtual Fitting Image Data: About 10,000 images.
- AI Keypoint Processed Data: About 10,000 records.
O Market Entry Status
- Cafe24: Applied fitting service linked with the API of 1.2 million fashion shopping malls nationwide.
- Handsome Corp.: Supplied fitting room service to The Handsome.com’s Style Live service, generating revenue since 2023.
- Sejung: Supplied styling service for the planning stage of fashion products, leading to revenue generation and discussions for expansion to in-store styling services.
- Jprimo: Applied styling recommendation service, generating revenue from product registration and recommendation fees.
- Samsung Electronics: Completed POC contract, developing linkage with SmartThings.
- Hyundai Construction: Supplied digital wardrobe prototypes to the Apgujeong model house showroom.
- OLIVELA: Developing API-linked styling service for a luxury fashion online shopping mall in the UK.
- JBLIN: Developing linked styling service for a domestic women's casual online shopping mall.
- CJ ENM: Supplied styling recommendation service to CJ OnStyle’s fashion brand, pursuing a subscription fee model.
- LVMH: Proposed a collaboration model for global luxury brand stores after a meeting with LVMH Lab; aiming for VivaTech submission and innovation award.
O Investment Achievements
- Successfully raised funds seven times, totaling 1.5 billion KRW.
- 2019.01: Incorporated StyleBot Co., Ltd.
- 2019~2024: Engaged in various activities including government-supported projects, fundraising, establishment of an in-house R&D center, and participation in global exhibitions.
O Awards and Certifications
- 2019~2023: Recognized as an Excellent StyleTech Company, certified as a Venture Company, awarded by the Startup Promotion Agency, and won the ABB Startup Competition Grand Prize.
O Project History
- 2023~2024: Selected for IBK Changgong, Samsung Electronics C-Lab Outside, and the Content Promotion Agency's overseas expansion program.
O Revenue Models
- B2B Revenue Model Verification: Provided styling recommendation services using generative AI technology to online fashion sellers (e-commerce sites, brands, platforms, etc.) and monetized through a B2B SaaS model.
- B2C Business Expansion Infrastructure: Secured a stable network for future styling recommendation-based fashion platform services by leveraging the foundation built from numerous B2B clients.
O Key Technology Achievements
- User Preference Analysis Technology: Developed the FSTA process to analyze fashion preference groups by distinguishing factors that determine users' fashion preferences.
- Clothing Item Classification System: Established a classification system with 160 categories and over 1,000 subcategories for clothing items.
- Coordination Recommendation Algorithm: Recommended optimal outfits based on user wardrobe data and preferences.
- Clothing Image Recognition and Processing Technology: Developed technology to detect image similarity, recognize objects, and remove backgrounds.
- Virtual Fashion Fitting Technology: Used MaskRCNN to create natural virtual fitting images.
- Generative AI Technology: Reconstructed flat clothing images into natural fitting images using diffusion AI technology.
O Data Acquisition
- Fashion Item Classification Dataset: Over 1 million records (200,000 refined).
- User Clothing Photo Images: Approximately 40,000 images (20,000 refined).
- Virtual Fitting Image Data: About 10,000 images.
- AI Keypoint Processed Data: About 10,000 records.
O Market Entry Status
- Cafe24: Applied fitting service linked with the API of 1.2 million fashion shopping malls nationwide.
- Handsome Corp.: Supplied fitting room service to The Handsome.com’s Style Live service, generating revenue since 2023.
- Sejung: Supplied styling service for the planning stage of fashion products, leading to revenue generation and discussions for expansion to in-store styling services.
- Jprimo: Applied styling recommendation service, generating revenue from product registration and recommendation fees.
- Samsung Electronics: Completed POC contract, developing linkage with SmartThings.
- Hyundai Construction: Supplied digital wardrobe prototypes to the Apgujeong model house showroom.
- OLIVELA: Developing API-linked styling service for a luxury fashion online shopping mall in the UK.
- JBLIN: Developing linked styling service for a domestic women's casual online shopping mall.
- CJ ENM: Supplied styling recommendation service to CJ OnStyle’s fashion brand, pursuing a subscription fee model.
- LVMH: Proposed a collaboration model for global luxury brand stores after a meeting with LVMH Lab; aiming for VivaTech submission and innovation award.
O Investment Achievements
- Successfully raised funds seven times, totaling 1.5 billion KRW.
