Elara is particularly well-suited for male fashion shoppers precisely because they don't browse. A shopper who arrives with "I need a wedding guest outfit" gets a direct answer: here is what you wear, here is why. Elara doesn't make him evaluate 20 products to assemble a look himself. It makes the decision for him, with his occasion and size in mind, in one message. That's the interface male shoppers respond to.

Elara answers fit questions from your catalog's product metadata. If your products have specific fit notes — "cut for athletic builds," "runs long in the torso," "slim through the hip and thigh" — Elara uses those to give shopper-specific recommendations. A 6'3" shopper asking about shoulder fit gets a specific answer drawn from the actual catalog data, not a generic size guide redirect.

Yes. "I'm starting from basically nothing — what three pieces should I buy first?" is a question Elara handles. It reasons about which catalog pieces create the most outfit combinations with each other and with common wardrobe staples, and returns a capsule recommendation with reasoning for each choice. Wardrobe-building briefs consistently produce higher AOV than single-outfit briefs because the answer involves multiple pieces.

Yes. Elara understands formal dress codes and the specific requirements of each. A shopper asking for black tie receives different recommendations from one asking for business formal or morning dress. The reasoning in each case includes why specific pieces are appropriate for the dress code — not just what to wear. For brands that sell suits, Elara can also handle configuration questions (two-piece vs three-piece, peak vs notch lapel, which fits work for which body types).

Yes. The SDK works with Hydrogen and any front end that can load a script or call the API.

Your catalog, stock and anonymous on-site behaviour. No personal data is required to style a shopper; nothing is shared across brands or used to train models for anyone else.