Buyers describe what they want the way they'd tell a salesperson. Give them a search box that understands the sentence, and answers with the products you actually sell.
We built this for MyBed: buyers ask for a fabric in their own words and the right ones come back, from a range no filter list could make browsable.
Live in production since August 2026, answering real buyer Polish every day.
MyBed's full range across 21 collections, found by phrases like "soft grey velvet for a hotel". (Source: MyBed deployment.)
Your buyer knows the look they want. "Cozy." "Like linen." "Warm autumn tones." Your filters offer material and price. Your keyword search needs the buyer's words to appear in your product data, and "linen look" is nowhere in "polypropylene, 300 g". So they scroll a grid of hundreds, and most quit before they find it.
Turn "I'll know it when I see it" into a sale.
The AI reads the buyer's sentence and turns it into a plan: what must be true, what would be nice, which colour, what to sort by. Then plain code scores every item in your catalog against that plan.
No black box. Every result can be traced to the points that put it there, query by query.
Finding is half the sale. A buyer who typed "dark green weave, tall headboard" wants to see that bed, in that fabric.
With the variant matrix prerendered, the exact combination is already an image, so the find appears at page speed.
Searched-but-not-found and searched-but-not-bought are gaps with names on them: the fabric everyone asks for and nobody buys, the phrase your catalog has no answer to. Your people tag the catalog and watch results move.
Type the sentence, get the right products, see the exact variant. The buyer who knew what they wanted finally gets to say it.
The search box answers like our best salesperson.
Our customers don't know our collection names. They know 'soft grey velvet'. Now they type that, and the right fabrics come back.
Put 542 fabrics across 21 collections within reach of a buyer who searches in plain Polish, on a store where paid traffic was already arriving.
One honest requirement: a structured catalog.
Thin data searches badly, and enriching it is part of the job.
Two fields, then it's an email thread with the people who built it. No demo sequence, no drip campaign. We reply by email.
