Search and recommendations that work on request one. The same model reads your text, looks at your images, and ranks the catalogue — in 60 ms, on the 86% of sessions with no history at all.
Built on the shoulders of giants
One ranker, every surface
The carousel, the listing, the search bar, the agent. One model ranks all of it — no second vendor, no second pipeline.
k = 3 · the rest of the catalogue is invisible here
Same ranker, same model, every surface — no second vendor for "recommendations," no frozen list waiting for next week's re-train.
Zero-shot · per request · per user
Global sorts hand every shopper the same five top picks — and they're the first to sell out. Solenya re-ranks per request, so what surfaces first is what's in *your* size, in stock, right now.
top picks sell out first · same list for everyone
re-rank #N · per request · per anonymous session
Same catalogue, same second — different ranking. No segment, no cookie, no nightly retrain. One click reshapes the next page; the anonymous visitor next to you gets a different one.
The day-zero gap
New stock, new brands, new categories — each one knocks a click-history ranker back down. We rank from request one and don’t flinch when the catalogue moves.
Click-history rankers warm up over weeks — then every new SKU, brand, or category resets the clock. Solenya starts at par on request one and stays there.
More than a wrapper
Seamless hand-off. Agents — but personalised. Eval-led at every step.
{
"query": "warm waterproof boot under £150",
"modality": [
"text",
"image"
],
"top_k": 4
}Why we can do this
Discovery has been stuck in separate jars — text in one vendor, images in another, behaviour in a third, products in a silo. We put them in one model so every surface above runs on the same brain.
Same space. One ranker. Sub-60 ms P50.
Fig 1.0: unified_embedding_space
What that unlocks
One model that reads, sees, and remembers the catalogue means the same query works in every modality — tap a pill to see it on a real listing.
Composed Image
Pair a reference image with a text edit — “like this, but in red” — and retrieve real SKUs from inventory.
Show me what looks like this.
Personalized results from day one: the moment a new user arrives, the moment a new product arrives, the moment a new category is launched, the moment you integrate.
Open-notebook research. Methods, confidence intervals, and the experiments that didn't work.
