Your customer describes it. The right product comes up.
They type what they want the way they'd say it to a salesperson, and land on the exact product, out of hundreds, in one step. No category tree, no filters, no dead "no results" page.
Your buyer knows what they want. Your catalog makes them hunt for it.
People don't shop by your category tree. They search the way they think: "cosy", "looks like linen", "panels I can fit over tiles". Your filters can't catch that.
And a keyword box misses it too, because the words the buyer uses are nowhere in your product data. AI Search reads the intent behind the sentence, then ranks your real catalog against it.
542
fabrics, found by a sentence
The MyBed catalog: 21 collections, 542 fabrics. Too many to scroll, searchable in one line of plain Polish. A live production figure, checkable.
One engine, two jobs
It always works the same way. The buyer's problem changes.
The AI understands the question, plain code does the ranking. What differs is whether the buyer needs a finder or an advisor.
The finder, for big catalogs.
Hundreds or thousands of variants, and the customer can't browse them all. They describe colour, feel and use in their own words, and AI Search returns the few that match. This is the product variant search, live today.
The complex-catalog advisor.
Not always many products, but each carries consequential differences, and choosing wrong is expensive. Think a wall-panel maker with a different installation technique per range. The understanding-and-ranking engine is here today; the deep advisory layer is what we are building next.
How it works
What one sentence does.
It searches the way people talk.
"Soft grey velvet for a hotel" finds the right fabric even when none of those words are in the spec sheet. The model reads intent; the catalog gets ranked against it.
It can't invent a product.
The AI only ever returns real items from your catalog. A made-up product is impossible by design, because the model picks from your IDs, it doesn't write the answer.
It says "we don't have that."
Ask for something you don't stock, or try to bait it, and it substitutes honestly or points to a human, instead of forcing a confident wrong match. Most search just returns its least-bad guess.
Your team tunes it, not a developer.
Add a tag ("this one reads like denim, even though it isn't") and watch the results move. The people who know your catalog improve the search, by editing labels, not code.
The mechanism
Type it the way you'd say it.
The buyer writes a full sentence. The model turns it into a plan, which colours, which feel, what it's for. Then plain code scores every product against that plan and shows the few that fit, in the buyer's own language.
The AI never touches your catalog directly. It only ever hands the ranking maths a structured plan, so every result is a real product, and you can see exactly why it came up.
query pipeline
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AI PLAN
colour: carameltexture: linen-lookwarm / bright
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Case study · MyBed
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Our shoppers stopped scrolling and started describing. They type what they picture, and the right fabrics come up.
MyBed.pl
Bed manufacturer · semantic fabric search
Challenge
542 fabrics across 21 collections. Polish shoppers describe colour and feel their own way (“autumn leaves”, “looks like linen”), and a filter UI had no box for any of it.
Solution / Result
Search understands colour, feel and use, not just keywords: “looks like jeans” works even though no fabric is denim.
Answers in under 10 seconds, scored on around 50 parameters, technical and abstract.
A back-office panel shows the most searched phrases and the fabrics returned, so MyBed’s own team can tune the fabric tags.
The bed appears in the exact fabric the buyer described.
From a plain search box to one that finds, protects, and learns.
From filters to a live product advisor.
Stop making buyers tick boxes and guess your categories. They say what they want, and the search advises, the way a good salesperson would. A grid becomes a conversation, and feeling understood is what converts.
An AI box that can't be hijacked.
We test it hard against prompt-injection, and it holds. Nobody can turn your search into a free chatbot on your AI bill or make it talk off-brand. It answers about your catalog, full stop.
From vague analytics to your customers' real words.
Every search is logged. Cross-check what buyers searched against what they bought, and you see real demand: what's missing from your range, and what to stock next. A search that found nothing is a gap with a name on it.
What you get
A search that understands, and keeps its promises.
1
Find the variant in the buyer's words.
Colour, texture, formality, use. The buyer describes it however they like and gets the matching products ranked, not a dead "no results" page.
2
Search reads your live catalog.
AI Search pulls the catalog straight from Content Hub (or a partner API). The attributes it matches on are fields you already keep. One source, search is just another reader.
3
Colour that matches how people describe it.
It computes colour from the real pixels, not an unreliable label, so "olive", "powder pink" and "vivid" land on the right shades, down to the individual fabric.
4
Tune relevance by editing tags.
Your content team adds the "looks like / feels like" labels the raw spec data never had. Describing a product better is how you improve the search.
5
Advise on complex products.
For catalogs where the hard part is the fit, not the count, the advisor narrows to the right product and explains why. The finder is live; the deep installation-and-care advisory is in build, and we'll tell you which you're getting.
6
An advisor that never clocks off.
It knows your whole product line and answers the moment a buyer asks, 3pm or 3am. Late-night browsers get a real conversation instead of a closed shop, so leads keep coming while your team sleeps.
A keyword bar matches words your buyers never use. A raw chatbot will happily invent a product you don't sell. AI Search splits the job: the AI understands the sentence, deterministic code ranks your real catalog.
A search that can't guess wrong, and can't make things up.
Where it fits
Solutions powered by AI Search
Help buyers find the right product.
Turn a 500-item grid into a single box the buyer talks to. They describe what they want; the right products come up. Built on the MyBed semantic search.
Fabrics & textiles is a live, named case (MyBed). The rest are where the finder and advisor fit; if your data has some structure to build on, it works well.
FAQ
Questions we actually get asked.
Isn't this just a search bar, or ChatGPT bolted onto our site?
No. A keyword bar matches words; your buyers don't use your words. A raw chatbot will happily invent a product you don't sell. AI Search splits the job: the AI only understands the sentence, then deterministic code ranks your real catalog. It can recommend only things you actually stock, and you can see exactly why each result came up.
Will it ever recommend something we don't sell, or make a product up?
It can't. The model picks from your catalog IDs, it never writes the product, so a made-up item is impossible by design. If you don't stock what the buyer asked for, it either suggests the closest real thing and says so, or points them to a person. It does not fake a match.
People will try to break it, or use it as a free ChatGPT. What stops them?
We test the search hard against prompt-injection, and it holds. "Ignore your instructions", abuse and bait questions are caught before any expensive step runs, and the box can only ever talk about your catalog. So nobody can hijack it into a free chatbot running up your AI bill, pull out how it works, or make it say something off-brand about a supplier's product. Your AI spend stays tied to real shopping, not abuse.
Our product data is thin and a bit messy. Will it still work?
Honestly, the data is the ceiling. Raw spec data says what a product is (polypropylene, 300g); buyers search for what it looks and feels like (linen-look, cosy). We fill that gap by writing the missing "looks like / feels like" labels with AI and then having your team review them. If your catalog has some structure to build on, it works well. If it's pure chaos, shaping it is step one, and we tell you that before you commit.
Can our team improve the results, or are we stuck with what ships?
Your team improves it, and that's where the real gains came on MyBed. You add tags in plain language ("this one reads like denim"), and the results shift. No developer, no deploy. The search is quietly authored by whoever knows the catalog best, which should be you.
Can we see what people are actually searching for?
Yes. Every search is logged, and it's the most honest demand data you have. People tell a survey one thing and buy another; the search box shows what they really want. Cross-check searches against checkouts and you see what's converting, what's missing from your range, and what to stock or enrich next. A search that found nothing is a gap with a name on it.
Does it work in Polish, or only English?
The live MyBed build runs entirely in Polish, including the slang and the idioms ("do hotelu" meaning fit for a hotel, versus "jak w hotelu" meaning just the look). The engine isn't tied to a language; the prompts and labels are set up per deployment, in whatever your buyers actually type.
You mention a "product advisor". Can it really advise on installation or complex specs?
Today it finds the right product and explains, in a line or two, why it fits the request. The deeper advisor, the one that walks a buyer through installation, care or compatibility for a complex catalog, is what we're building now. We'll always tell you what's live versus in build, and we won't sell the roadmap as if it shipped.
What does it cost, and how do we start?
Two ways to work together: AI Search as a product on your catalog, or embedded with our team during a build. Which one fits depends on your catalog and your team, and that's the first thing we work out with you. Tell us what your buyers struggle to find, and we'll tell you honestly whether it fits before anyone talks numbers.
