Agentic Commerce Readiness

Will AI find your products
or your competitor's?

Increasingly, it's no longer the human who searches first, but their AI assistant. It compares, selects and prepares the inquiry. The only question is: does it recommend you — or the competition?

Start the readiness check Download the whitepaper See how it looks →

An ordinary Tuesday

Your customer used to call. Today they ask their AI.

A maintenance manager urgently needs a spare part. He'd have searched the catalogue or called your sales team. Today he types this:

Inquiry to the AI

“Find the right spare part for machine type MX-300, year 2019, serial number 84729. Check compatibility, whether there's an approved alternative part — and prepare an inquiry to the supplier.”

What the AI needs now

It won't patiently read PDF catalogues and won't call when in doubt. It needs clear, retrievable answers: Which part fits? Which standard applies? Which alternative part is permitted?

If your answers are stuck in PDFs and in your sales team's heads, the AI can't reach them. The result: you don't make the list — and you don't even notice.

Two vendors, one difference

It's not about “more AI”. It's about clean data.

Both have similar products. Only one gets recommended by the AI.

Vendor A

Online-visible — but not AI-ready

Product data maintained inconsistently
Compatibilities buried in PDF tables and prose
Spare parts not cleanly linked to machines
Inquiry only via a classic contact form

→ Doesn't make the shortlist. The inquiry never comes.

Vendor B

Prepared — the AI understands it

Product data structured and unambiguous
Variants, standards and dimensions machine-readable
Spare parts linked to machines & serial numbers
A clear, controlled inquiry process

→ Found, correctly matched, and recommended.

Four silent revenue risks

The tricky part: you can't see lost inquiries.

You never learn which AI research never reached you. That's exactly what makes these four risks so dangerous.

Not found

Your products don't even appear in the AI's research. You're not in the running.

Mismatched

Unclear data leads to wrong recommendations — or the AI doesn't bother to ask at all.

Variant chaos

Variants and compatibilities live in PDFs and people's heads. Not reliable for the AI.

No control

Without rules, approvals and an audit trail, every AI access becomes a black box — a real risk.

Four pillars of Agentic Commerce Readiness

How we make your products AI-ready.

Four building blocks decide whether an AI agent finds you, understands you, and brings you into a process under control.

Data & findability

Unambiguous product data: variants, standards, dimensions, operating limits — clean and findable.

Correctness & rules

Compatibility and configuration rules become explicit — instead of living in Excel and people's heads.

Control & approvals

The AI may read, check, prepare — a human always approves the critical steps.

Logging & audit

Every access and action documented and traceable. Governance as a success factor.

Our stance: No black-box agent. We prepare your data so that AI workflows stay controlled, compliant and traceable at all times.

From check to rollout

Three packages for a focused start.

You don't have to solve everything at once. We begin with one focused first step.

Start here 2 weeks

Audit Sprint

Where do you stand: how AI-ready is your product data today? You get a prioritised roadmap and concrete quick wins.

2–6 weeks

Use-Case Sprint

One concrete, commercially relevant use case is made AI-ready — from data through rules to controlled access.

4–12 weeks · optional

Rollout & Enablement

Scaling across product groups — including governance, audit trail and enabling your teams.

Clarify the right step in the readiness check

2-minute readiness check

How AI-ready are you today?

Five short questions. You get an instant assessment — and it reaches us with your inquiry, so the first call is concrete from the start.

Question 1 / 5 Score 0/100

Free intro call