Definitions for the terms that show up across StyleCopilot features and our blog. Bookmark for reference.
A small, intentional collection of clothing (typically 30–40 pieces) that mix and match to produce dozens of coordinated outfits. The goal is versatility and minimal waste, not austerity.
A personal colour palette category (Spring, Summer, Autumn, Winter, plus modifiers) determined by your skin undertone, hair colour, and contrast level. Guides which clothing colours flatter your natural colouring.
A short text description of your fashion preferences (e.g., 'earth tones', 'no crop tops', 'professional but modern') that an AI stylist uses to personalise outfit suggestions.
Purchase price divided by number of times worn. The most honest shopping metric: a $200 item worn 100 times costs $2/wear; a $50 item worn twice costs $25/wear.
An AI feature that overlays clothing on a photo of you, allowing you to preview how an outfit looks without physically wearing it.
A log of what you actually wore each day, enabling cost-per-wear tracking, outfit-repetition avoidance, and seasonal wardrobe analytics.
A digitized inventory of your wardrobe — each garment photographed and auto-categorized by an AI so it can be searched, filtered, and combined into outfits programmatically.
An AI feature that takes a single garment as input and proposes coordinating pieces from your closet to complete an outfit around it.
A review of your wardrobe to identify categories or colours that are under-represented relative to your lifestyle needs. Used to prioritise future purchases.
The underlying warm or cool cast of your skin, independent of its depth. Warm undertones lean golden/peachy; cool undertones lean pink/blue; neutral undertones show a mix.
How a material falls and flows on the body. Silk drapes fluidly; linen drapes stiffly; wool drapes with structure. Drape affects how a garment reads visually.
Combining multiple garments (e.g., tee + cardigan + blazer) to extend the usable temperature range of a wardrobe and add visual interest.
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