How we're building defensible moats through proprietary AI and data infrastructure
Built specifically for extended sizes
We use GenAI to automatically tag and categorize inventory, maintaining accuracy across sizing, color, inseams, and style types without fear of LLM hallucination or mislabeling.
Processing time vs. hours with OpenAI API
Reduction in operational costs
Accuracy in extended-size classification
The future of personalized extended-size discovery
Generative AI models analyze purchasing patterns, user searches, and external trend data to predict emerging demand in extended-size fashion.
Generative AI models analyze user searches, preferences, saved products, sizing, and style data to deliver hyper-personalized recommendations.
Higher repurchase rates with personalization
Increase in customer lifetime value
More effective brand partnerships
McKinsey Consumer Report
Why our AI advantage compounds over time
100+ extended-size brands mapped and standardized. The more brands we add, the stronger our data network becomes.
Personalized fit recommendations create switching costs. Users invest time in our platform, making it harder to leave.
Deep integrations and data partnerships create mutual dependency. Brands rely on our insights and customer targeting.
As we add more users, brands, and data points, our AI becomes smarter, our recommendations more accurate, and our moat deeper. This creates a flywheel effect in the overlooked extended-size market where we're building first-mover advantage.