AI Readiness: Three Steps for Marketplace Sellers

Search used to be the only thing standing between a product and a buyer. Increasingly it is not. Assistants, shopping agents and generative engines now sit in front of the catalogue, and they answer questions rather than return ten links. That changes what a listing has to do: it no longer only has to persuade someone already looking at it, it has to be legible to a system deciding whether to surface it at all.

Nobody outside those companies knows exactly how the ranking works. But the inputs an automated system needs are not mysterious, and they are the same three things a careful buyer needs — evidence the product is real, data clean enough to compare, and enough context to answer a question about it.

1. Expand your multichannel presence

Systems building recommendations lean on corroboration. A product that appears on several trusted marketplaces, with the same identifiers and the same specifications each time, is easier to treat as real than one that exists in a single place. Presence across channels functions as evidence.

The commercial argument runs alongside it and does not depend on any assumption about AI. Sellers operating on two or more marketplaces averaged $10,073,917 in GMV in 2025, against $575,093 for single-channel sellers — a 17.5x difference. Two-thirds of sellers are still on one channel.

Consistency matters more than count. The same EAN, the same brand string, the same specification values everywhere. Contradictory data across marketplaces is worse than one clean listing, because a system evaluating your product finds no stable answer and has no way to choose between them.

2. Standardise your data so a machine can read it

Most product content is written for a person scrolling a page. Attributes are buried in prose, units are implied, and the same property is called three different things across a catalogue. A person copes with that. A parser does not.

Making a catalogue machine-readable is mostly unglamorous work:

• Attributes as structured values, not sentences. “Made from brushed stainless steel” is a sentence. material: stainless steel is a value that can be filtered, compared and matched.
• One vocabulary per property. If colour is “navy” on one channel, “Azul marino” on another and “dark blue” on a third, nothing can group them.
• Identifiers that hold still. A valid EAN or GTIN on every variant, unchanged between syncs. Identifiers are how a system knows two listings describe one product.
• Explicit units and completeness. A dimension without a unit is unusable. An empty required attribute is a product that cannot be compared against its category.

This is the same work marketplaces already demand. Mirakl operators validate exports against a per-category attribute schema, and catalogue quality decides both how fast a listing publishes and how the seller scores. Doing it once, properly, serves both audiences — which is the useful thing about this particular investment.

3. Optimise for generative engines

Generative Engine Optimisation is the part that feels new, and it is mostly a writing problem rather than a technical one. A generative engine assembles an answer from sources it can retrieve and quote. Content that is easy to lift into an answer gets used; content that requires interpretation does not.

In practice:

• Write the context, not just the specification. What the product is for, who it suits, what it does not suit. A specification table tells a system what the product is; context lets it decide whether the product answers a question.
• Structure questions as questions. A heading phrased as the question a buyer asks, answered immediately underneath, is directly quotable. FAQ blocks marked up with FAQPage schema make that machine-readable as well as human-readable.
• Answer first, elaborate second. Lead with the direct answer and expand afterwards. An engine extracting a response takes the first clear statement it finds.
• Be specific enough to be worth quoting. Exact figures, named constraints and real error conditions get cited. Generalities do not, because every competitor has already written them.

Where this starts

Not with the AI. It starts with the catalogue, because all three steps depend on the same underlying data being correct and consistent wherever it appears.

That is the argument for holding product data in one place and letting each channel receive the shape it needs. Sellintu’s PIM maps a Shopify catalogue onto each marketplace’s taxonomy, transforms values into the options that marketplace accepts, and keeps identifiers stable across every sync. It also ships MCP connectors, so an assistant that speaks the Model Context Protocol — Claude or ChatGPT — can read and update that product data directly, without an export in between.

Which is the shortest version of AI readiness available today: if an AI agent can already query your catalogue and get a clean, structured, complete answer, most of the work is done.