“A dessert for ten people” is not the name of any product: it's how your customers ask.
Conventional search
0 results
«a dessert for ten people»
The product exists. That sale was just lost.
AI Search
3 of 214 products
Sample catalog from a bakery, the real case in the proposal. The queries are the ones in the document.
Companies already working with Hellomatik
The customer can't find a product the store actually sells. And that sale is lost in silence.
How it searches
Literal text matching
By meaning and intent
“chocolate cake for a birthday”
0 results if the name doesn't match
Chocolate and celebration cakes
Typos
Break the search
Corrected without losing precision
What isn't found
Lost in silence
Logged, and feeds the next training
Over time
Same as day one
More precise over time
Your customers ask in their own words; your model answers with your products. And you see everything: what they search, what they can't find, and how it improves.
User query
Intent interpretation
dessert + about ten servings
Search by meaning
in the catalog's vector database
Relevant products
even when the words don't match
Your customer types “chocolate cake for a birthday” and the search bar recognizes the celebration behind it: it shows them your chocolate cakes and your birthday cakes, step by step before your eyes.
Tolerates typos
«chocolat brwonie» → chocolate brownie
Your brand's synonyms
«palmier» → puff-pastry palmier · elephant ear
Real catalog attributes
It suggests as they type, corrects “carot cake with wallnuts” without anyone noticing and filters by what really matters: gluten free, lactose free, the real attributes of your catalog.
Your catalog
Your store's model
BERT architecture: it understands whole phrases
Training on your products…
On Hellomatik infrastructure
Periodic retraining on what isn't found
It is not generic AI: we train a proprietary language model (BERT architecture) only on your products, on Hellomatik infrastructure. That's why it knows what a “palmier” means in your store, and gets it right where a general-purpose model falls short.
We don't just hand you the search bar: you get the place to see how it's doing — what your customers look for, what they can't find and how it improves every week.
This exact panel, on your store's real searches, is the diagnostic: ask for it and see what slips away today.
Know your customer
What they search in your store, right now
Searching now
Most searched this week
Zero-result searches
16%
0zero results → into the next training round
Sample searches on the proposal's catalog; your panel will have the real ones.
Customers find catalog products that, because the text didn't match, had remained invisible until now.
You know what your customers look for and what they can't find, as a basis for catalog and business decisions.
It operates exclusively on your real products. Over time it understands your customers better and finds products with greater accuracy.
A few days. What takes time is training the model on your catalog, and that's on us.
With Shopify and WooCommerce, out of the box. If your platform is custom, we build it alongside your tech team.
No. No lock-in: you stay as long as it pays off.
With a diagnostic of your current search: we measure how many searches end with zero results today and what your customers really ask for. The proposal comes from there.
Yes. It interprets intent even when the text doesn't match: typos, synonyms and the way your customers actually ask. That's what the demo above shows, with real queries.
Bring us your catalog and see it working on your real products before you decide. We also measure your current search: how many queries end with zero results today and what your customers really ask for.
