AI styling that knows your body, your colours, your moment.
One photo. Two seconds. Fifteen outfits a shopper will actually buy — picked against the retailer's live, in-stock catalog. Never stale. Never out of stock.
Fashion ecom is bleeding money in three places.
Personalisation hasn't fixed any of them.
Most "AI personalisation" tools recommend what other shoppers bought. That's collaborative filtering — useful for groceries, useless for fashion, because fashion is about this body, this colour, this occasion. The data shows up everywhere on the brand's P&L:
of online fashion orders are returned
Industry average is 25-30% in apparel — vs ~8% for non-apparel ecom. Shipping, restocking, and processing returns destroys margin. The #1 reason cited: "It didn't fit / suit me."
average ecom conversion rate
97 out of 100 shoppers leave without buying. Most personalisation tools shift this by <1 percentage point. Persona Engine is showing 38% click-through to product pages — 4× category baseline — because the answer "will this look right on me?" gets answered at the moment of decision.
CAC rise in 5 years
Customer acquisition cost has tripled. Brands can no longer afford a 2.7% conversion rate. Every visitor needs to count — which means the experience has to actually help them buy, not just track them.
Persona Engine answers "will this work on me?" from a single photo. Before the shopper has to imagine, before they have to ask a friend, before they leave to check YouTube for styling advice.
No behavioural history needed. No invasive tracking. Works on first-time visitors. Plus-size-aware. Hourly catalog sync — never recommends a sold-out item.
Six parameters. One photo. Recommendations a shopper trusts.
Every product in the retailer's live catalog is scored against the shopper's profile across six axes. Hard filters first (gender, stock), then weighted scoring across the rest.
Body shape
Detected from the photo's silhouette using pose landmarks + segmentation mask. 14 shape classifications including plus-size-aware logic that defeats shapewear-induced false cinch.
Skin-tone palette
Fitzpatrick scale mapped to a seasonal palette (Light/True/Deep × Spring/Summer/Autumn/Winter). Products in-palette score higher per overlapping colour.
Style archetype
Streetwear / Smart Casual / Classic Elegant / Feminine Romantic / Bohemian / Minimalist / Urban Minimal — tag-overlap scoring.
Gender
Detected or shopper-overridden. Hard filter — never leaks into results.
Live stock
Hourly sync from the retailer's Storefront/Typesense API. Sold-out and delisted products are removed from eligibility — every recommendation is buyable right now.
Feedback loop
Shoppers tap "this isn't right" on body shape; recommendations re-rank in real time. Corrections also improve the model over time.