Drip Deck (Prototype)

Men don’t track their wardrobe properly. Clothes get buried, repeated outfits happen, and half the items in the closet are unused because there’s no system. Shopping becomes guesswork, leading to mismatched pieces and wasted money. Existing apps are either fashion-blog junk or cluttered with ads. No platform gives a clean, structured, AI-driven wardrobe that actually understands the user’s style, needs, and gaps. The project solves this with a wardrobe manager that auto-organises every item. Users upload images or paste online links and the system detects the item, tags it, and places it in the right category. The platform covers all clothing categories including shoes and accessories, giving a complete top-to-bottom wardrobe view. AI (Gemini) generates daily outfits using the user’s own clothes. The system analyses weather, occasion, color harmony, and style patterns to create practical fits instead of Instagram fantasy looks. It also identifies missing pieces and recommends only Myntra links. No random sites, no spam, only curated additions that genuinely improve the wardrobe. The app rates the user’s wardrobe based on versatility, color balance, and category coverage. It shows weaknesses like “too many casual tops,” “missing neutral trousers,” or “no matching shoes for formal wear.” Insights are simple, actionable, and based on actual data. A leaderboard adds motivation by comparing wardrobe scores with other users. It creates a competitive but fun environment to refine style. Users can see how their wardrobe evolves and climb ranks as they build a more cohesive collection. The interface uses a liquid-glass design: clean, translucent, smooth animations, zero clutter. The entire experience focuses on clarity, aesthetics, and usability. In short, the project fixes disorganised wardrobes, bad outfit choices, wasted shopping money, and inconsistent style by combining structured categorisation, powerful AI reasoning, and curated recommendations — all wrapped in a clean, modern UI built for Gen-Z users.

Built With

• base44