The taste layer for
how the world shops
Try-on is becoming free. The hard part — and the defensible part — is knowing what actually suits someone. Zaya is building the AI that turns an infinite catalog into personal taste.
How Zaya compounds
One product, three reinforcing layers — each one feeds the next.
The Hook
AI try-on is the most shareable moment in fashion — see yourself in anything, instantly. It's how users arrive, and how they bring their friends. Acquisition, not moat.
The Engine
Personalized styling is the reason they come back. Zaya learns body, taste, and budget, then curates looks like a stylist who actually knows you. This is the product.
The Moat
Every try-on, save, and purchase teaches Zaya what actually suits each person. That taste signal is proprietary, compounds with use, and can't be cloned by wrapping someone else's model.
Why this wins now
Generative AI just crossed the line where virtual try-on and personalized styling are production-ready on a phone. The capability is no longer the question.
That means try-on itself is commoditizing — a dozen apps now wrap the same models behind paywalls. The durable advantage isn’t the try-on. It’s the taste layer on top.
Target Market
Style-conscious shoppers aged 18–35 who buy online constantly but lack confidence in fit and what suits them. They’d never hire a $300 stylist — that’s the opening.
The Growth Loop
The try-on moment is built to be shared — every share is a free, high-intent install. Organic distribution is designed into the product, not bolted on.
From taste to transaction
Zaya monetizes by owning the moment between wanting something and buying it.
Now — Affiliate GMV
Every recommendation is shoppable. Zaya earns on what users buy through it.
Next — Brand Partnerships
As taste data scales, brands pay to reach the right shopper at the moment of intent.
Later — The Marketplace
Owning discovery and try-on positions Zaya to own the transaction itself.
Built by people who ship
Two disciplines most consumer apps never combine in one room.
Engineering
The founder builds infrastructure for AI agents at Meta and previously built large-scale equity data systems at Bloomberg. That systems depth is why Zaya’s recommender, taste model, and data pipeline are built in-house — not stitched together from vendor APIs.
Brand
Creative direction comes from a finance and brand background — giving Zaya editorial polish, not just engineering.
Interested in learning more?
We’re raising to bring this to market. If you believe AI will reshape how people discover and buy fashion, let’s talk.