Dresy

AI wardrobe personal stylist that uses what you ALREADY own.

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Problem: Market gap – an AI wardrobe personal stylist that uses what you ALREADY own. 1.Outfit apps → usually offer to buy items from stores, but not style the clothes you already have. 2.Style apps → generate outfits randomly and don’t recognize your actual clothes. 3.Fast-fashion push → encourages you to buy more, instead of using what you already own.

Users who face these problems: 1.People who struggle with hormonal imbalance or stubborn weight, where diets don’t work anymore — but they still want to feel good in their bodies. 2.Women in postpartum who still want to feel attractive. 3.People preparing for important events, when they usually don’t know what to wear. 4.Young adults who go on dates.

The solution: Dresy is the AI Personal Stylist that "Shops Your Closet." Dresy is a hyper-personalized mobile application that digitizes the user's existing wardrobe to plan their life ahead. It replaces guesswork with data.

Digitization: Uses AI to instantly recognize and categorize real clothing items from photos (no manual data entry).

The "Good Outfit" Algorithm: Recommendations are not random; they are based on a 4-pillar system: Meteorology: Real-time weather integration (temperature, precipitation, humidity). Color Science: Automatic color analysis based on skin tone and item harmony. Trend Analysis: Scans current global fashion data to ensure relevance. Personal Context: Tailors suggestions to specific user needs (e.g., "hiding a bloated tummy," "first date confidence," "power suit for an interview").

Tools & technologies used: Cursor & Claude code; Google Gemini; Nano Banano; Rembg; Supabase; Figma; Canva; Screen Studio; Adobe Premiere Pro.; Next.js; Expo Go.

Why Dresy Will Grow into a Profitable Startup: Dresy is positioned to become a high-value asset because it addresses the three pillars of a unicorn consumer app: Emotional Necessity, Sustainability, and Scalable Monetization.

A. Solving Deep-Seated Emotional Pain Points (Market Stickiness) Unlike generic fashion apps, Dresy targets high-retention user groups by solving specific anxieties: The "Body Confidence" Segment: For users struggling with stubborn weight, hormonal imbalances, or postpartum changes, Dresy acts as a confidence booster, curating outfits that flatter their current body, not the body they "wish" they had. The "Social Climber" Segment: For Gen Z and young adults, dating and social signaling are crucial. Dresy removes the anxiety of "looking weird" by validating outfits against trends and astrology-based confidence boosters (a massive niche market). Effect: This creates an emotional dependency on the app, significantly reducing churn.

B. Capitalizing on the "Anti-Consumption" Wave The global market is shifting away from Fast Fashion. Consumers are feeling guilty about overconsumption. Dresy positions itself as the ethical alternative: "Look better without spending a dollar." This makes the app highly marketable to the eco-conscious generation.

C. Validated Monetization Roadmap The financial trajectory moves from high-margin SaaS to a massive platform economy. Phase 1: Validation & recurring Revenue (Year 1) Model: Freemium + Premium Subscription (€7.99 - €14.99/month). Target: Aggressive acquisition of 100,000 active users. Conversion: Conservative estimate of 4-6% conversion to paid (4k-6k subs). Projected Revenue: €400,000 – €550,000. Phase 2: Ecosystem & Marketplace (Year 5) Evolution: Once the user's closet is digitized, Dresy knows what they have and what they need. Marketplace Integration: Suggesting one perfect item to complete 5 outfits (affiliate revenue) or facilitating peer-to-peer reselling of unused items. Personalization Upgrades: AI coaching for body types. Projected Revenue: €6,000,000 – €10,000,000.

The app was built using Cursor and CloudCode. It uses a Supabase backend to store the images and do the user authentication as well as call APIs such as Google Gemini and RemBG to remove the backgrounds of images. and finally: NanaBanana.

The biggest challenge of ours was the time planning ahead, which we first didn't recognize as crucial for the success of the project.

It's fair to say our biggest accomplishment was the app build itself: this is our first time successfully building an app in such a fast-paced environment, and we are really proud of achieving the result we did with the app.

The biggest lesson we are going to take away from this hackathon will be the crucial matter of planning ahead (the biggest challenge). While we did, there was definitely room left for improvement, and next time we will make sure to list all the tasks in our Notion workspace ahead of starting the development itself.

Next for Dresy: Further development of the application and the social pages. The app will improve and get additional features soon, mainly related to the business partners we got a collaboration with. The development will definitely continue after the 28th. 😉

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