Product data
Complete details per variant, correct prices including tax and shipping, real availability rather than a default value.
Agentic commerce for online shops
When your shop has more than human visitors.
AI agents research products, compare prices and prepare purchases. For that to work on your side, range, prices and availability have to be machine readable and current. That is what we work on.
Context
Two questions worth separating.
Status
15 Aug 2026
- Preparing product data possible today
- Visibility in comparisons measurable
- Purchase closed by agents still moving
- Reference project none published yet
The first part is craft and pays off now. The last one is open.
The first question: can an agent find your product, understand price, variant and availability, and compare it sensibly with others? That is measurable today, and the work behind it is solid craft on product data and markup.
The second question: can an agent also buy from you without a human closing the transaction? Here protocols, payment routes and the question of liability are all in motion. That is open and will settle over the coming months.
We work on the first question and watch the second. Getting the groundwork right now means no catching up later, and the benefit arrives before that anyway: the same data helps in classic comparisons and in generated answers.
What we work on
The part that counts today.
Markup
Product, Offer and AggregateRating set cleanly and matching what the page actually says.
Comparability
Attributes an agent can line up against each other: dimensions, material, performance, compatibility. Not just prose.
Reachability
Serving product pages without unnecessary hurdles. Anything behind a script an agent does not execute does not exist for it.
Measurement
Running questions like “which retailer has X in size Y in stock” against the systems and recording who gets named.
Watching the protocols
We follow the emerging standards for agent-driven purchases and get in touch when something concrete applies to your system.
As of 15 August 2026. Several competing approaches to agent-driven checkout exist and no settled practice. We have published no reference project for it yet and therefore claim no experience with it.
Common questions
About agentic commerce.
What is agentic commerce?
Commerce where an AI agent takes over parts of the purchase: researching, comparing, configuring and in some models ordering. For shops that shifts a portion of the audience from humans to software.
Do agents already buy things?
Research and comparison happen and are measurable. Completed purchases without human confirmation are in testing and depend on protocols and payment routes that are not settled. So we keep the two questions apart deliberately.
What does a shop need for this?
Complete, current product data per variant, correct prices with tax and shipping, real availability and clean markup. Product pages have to be readable without script execution. This is hygiene, not a future project.
What is an agentic commerce protocol?
A proposal for how an agent and a shop talk to each other: query products, check availability, trigger a purchase. Several providers are working on such standards. Which one prevails is open, so we do not build any of them in prematurely.
Does this only affect large shops?
No, rather the opposite. Small ranges can be cleaned up faster. And for comparison questions, completeness of the details counts more than size.
What changes in our product feed?
Less than most people expect. A well-maintained feed for shopping ads is a good starting point. What usually gets added are attributes that make comparison possible, and honest availability instead of a default value.
How does this relate to GEO?
The same data works in both places. Appearing in an answer to a product question requires machine-readable details. In that sense this work is a special case of general visibility work.
Who is liable if an agent orders wrongly?
That is not conclusively settled in law and depends on contract design, payment route and the protocol in use. We are not legal advisers and explicitly recommend clarifying it before any test operation.
What do you deliver today and what not?
We deliver product data review, markup, comparability and measurement. We do not deliver a connection to a purchase protocol or support for an agent-driven checkout, because we have not done that in any project.
What can we start with now?
A review of your range: how complete the data is per variant, whether prices and availability hold, whether the product page is readable without script. That takes a few days and pays in regardless of how the rest develops.
Difference
How others do this, and how we do.
Not a judgement on individual agencies, but a description of two approaches. The right column is checkable, because this website itself is built that way.
| Difference | Usual agency approach | agenticsearch.at |
|---|---|---|
| Starting point | A proposal after a first call | A measurement with a date, before anything gets recommended |
| Basis | Keywords from classic search | A question set of real prompts, clustered and prioritised |
| Proof of success | Rankings and a visibility index | Mentions per question and system, with the raw data |
| Reporting | A monthly dashboard with curves | The same measurement repeated, change called only after three runs |
| When unsure | Try it and see what happens | We say what is not evidenced, and write it on the page |
| Tooling | Agency licences, access ends with the contract | The question set and the data are yours, afterwards too |
| Scope | As many channels as possible from one supplier | A capped number of concurrent projects, otherwise we turn it down |
Your guides
You play the lead.
You know your business, your customers and your offer better than any agency does. What is usually missing is solid numbers on how AI systems talk about it, and an order in which to tackle that.
Who is behind this
two people
- Florian Hieß measurement, technology, structure
- Romana Hieß day to day support and implementation
- Operated by Digital Wings GmbH, founded 2010
- Client projects 100+
The number of concurrent projects is capped. When a piece of work needs more capacity than exists, we turn it down rather than take it on.
01
You know where you stand
It starts with a measurement, not a proposal. You see in black and white which AI answers name your company and which do not, with a date and a question set.
02
You know what comes next
Instead of a list of everything, you get an order. Every step has a result you can verify yourself, including without us.
03
You keep the decisions
Your offer and your positioning stay yours. We tell you what stands in the way of visibility and take on the technical part of it.
Next step
Let us find out where you stand in AI answers.
One conversation, 30 minutes, no sales pressure. You leave with an honest read on whether this is worth the effort for you.
