Dressing Room
Find your style. Find your fit. Find your confidence.
Introduction
Shoppers scroll endlessly only to find styles that miss the mark. We are an AI fashion discovery engine that solves this through occasion-based search, peer-to-peer styling advice, and personalized AI recommendations.
Problem
We are solving the problem that style discovery is very difficult, specifically:
- Low Style Confidence: Shoppers don't know what looks good on them or how to combine items into an outfit, so they constantly second-guess their choices.
- Inability to Search: People know what they like when they see it, but they can't put it into words. You can't search for a "vibe", so they can't find what they want.
- Endless Scrolling: Finding the right item takes way too long. Filters are too basic (just size/color), forcing users to dig through thousands of products to find one that fits their occasion.
- Can't Visualize Fit: Shoppers can't tell if an item will look good on their body versus the professional model, which stops them from buying.
Opportunity
We are building an AI fashion discovery platform that moves online shopping from "keyword search" to "visual context."
- Occasion-Based Discovery: We replace endless scrolling with intent-based shopping. Users search by occasion (e.g., "Outdoor Wedding," "Tech Interview") rather than generic keywords, allowing our AI to surface items that match the specific "vibe" they couldn't articulate.
- Instant Virtual Try-On (VTO): We solve the visualization gap by instantly dressing a personalized avatar in recommendations. Users see exactly how the cut and fit look on their specific body type, not a model’s.
- Peer Validation: We bridge the confidence gap by connecting users with peers of similar body shapes. Shoppers can share their VTO looks to get "gut checks" and advice from real people who fit the same profile.