AI doesn't just find your store — it buys from it.
First we make your product data machine-readable, so you get found and recommended. On top of that we build agentic commerce: a shopping assistant on your site, and the option for a customer's own AI to buy directly.
Your store has two cockpits
You run the store with an agent from the inside. The customer's own AI shops from the outside. Both read the same product data, and neither works without it.
approved field → shortlisted → bought
Same product, different data per buyer
A single rain jacket can carry hundreds of datapoints. No shopper needs them all, but each one is decisive for someone. You shouldn't list them for a human. The AI needs them.
Waterproof outdoor jacket for everyday and trail.
{
"price": 189.00
"availability": "InStock"
"cuffClosure": puuttuu
"hoodPeakStiffened": puuttuu
"returnPolicy": puuttuu
}Try it on your own product
The rain jacket is our example. Here is yours: paste this into your own Claude or ChatGPT and see how many of the questions your product page actually answers.
I sell this in my online store: . Act as a shopper who uses you to buy things. List 20 questions a shopper would ask you before choosing this product. Include the precise, odd ones that are decisive for a single buyer segment only. After each question, note what product data you would need in order to answer it with confidence. Do not guess the product's properties — if you wouldn't know, say you wouldn't know.
Most of those questions aren't answered on your product page — and they shouldn't be, not for a human reader. Filling that gap in machine-readable form is the product-data deepening, and it is the most valuable part of the work. See what it costs
AI became a buying channel
AI-referred shoppers convert about 2× better than other search traffic.
of Google searches end without a click. Discovery is moving to AI answers.
of the world's online stores earned an A for AI readability.
A measured result, not a promise
- Before
- 37 / D — price and availability were missing from structured data
- After the fix
- 96 / A — the agent reads the whole catalogue
- Rich Results
- 14 errors → 0
- Customer-facing page
- no visible change at all
A step-by-step model
The price driver is data depth and content gap, not product count. The start is fixed and easy. The biggest value is created in the product-data deepening, always tailored case by case.
start here · see your AI visibility0 €
fixed · assessment + basic data layer, same for a large catalogue2 000 €
tailored · all relevant data + expert content, scales with the catalogueQuoted
setup · shopping assistant + agentic checkout, on the customer's own Claude key1 500–3 000 €
most common start · agent-readiness package