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Agentic Commerce Ready

AI assistants now recommend products Structured data gets you quoted.

WISEPIM publishes your catalog as schema.org Product JSON-LD from Feed Hub, with check-digit validated GTINs and typed specs, so ChatGPT, Gemini, Perplexity and Claude can read it.

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Key features

  • Data AI agents can actually read

    ChatGPT, Gemini, Perplexity, Claude: they read structured data, not marketing copy. Feed Hub ships the same catalog as a schema.org feed, a ChatGPT Shopping feed and a Copilot feed.

    4agents covered

  • Validated GTINs, complete attributes

    Quality Guard checks GTIN check digits, required attributes and spec formats, and can hold back a SKU that fails before it publishes.

  • Built on schema.org Product

    Feed Hub's schema.org feed writes each product as a Product, or a ProductGroup with its variants, with Offer, Brand, GTIN and attributes as JSON-LD: the vocabulary AI agents and search engines both read.

  • Specs agents trust, not sales copy

    AI agents compare on facts: dimensions, materials, compatibility. WISEPIM turns vague descriptions into clean, typed attributes built for machines to read.

Why now

Why agent-ready data matters now, not later

Discovery is moving from a search box to an assistant that reads catalogs directly. Three things change for the catalogs it can read.

  • Discovery is shifting from search to agents

    Consumers are starting to ask ChatGPT, Gemini, and Perplexity for product recommendations directly. The catalogs agents can read and trust will win that channel, the same way fast mobile sites won the smartphone wave.

  • Agents shop differently than humans

    An assistant comparing a shortlist does it on typed attributes such as dimensions, materials and compatibility, not on hero images and brand storytelling. A catalog without machine-readable specs has nothing to compare.

  • Marketplace operators are an early signal

    Marketplace operators are restructuring catalogs to be machine-readable before their sellers are. Brands that sell through those platforms feel the requirement first; direct retailers get the same request later.

Clean Categorization

A category tree an agent can walk

WISEPIM auto-assigns each product to a clean, consistent category tree, so AI shopping agents can browse, filter, and compare your catalog the way a shopper would.

WISEPIM places each product in a consistent category tree that agents can browse and filter.

Side by side

How agents read the same product

The same product, presented two ways. Only one shows up in agent-driven discovery.

How agents read the same product
What an agent looks forMarketing copy onlyAgent-ready data
Structured dataFree-form HTMLschema.org Product / Offer / Brand
Product identifiersSometimes a SKUGTIN / EAN / UPC with a valid check digit
Attribute coverageWhatever the copywriter includedCategory-complete, machine-readable
Image alt textDecorative or empty alt textDescriptive, attribute-bearing alt text
Trust signalsReview ratings (if scraped)Brand, seller, live price and availability

FAQs

Questions before you prioritise this

What agent-ready data actually requires, and what it does not fix.

Still have questions?

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Agentic commerce is where AI shopping agents (ChatGPT, Gemini, Perplexity, Claude, and the next wave) discover, compare, recommend, and increasingly buy products on behalf of consumers. The shift is comparable to the mobile web shift: catalogs that aren't ready for the new interface lose visibility in it.

ChatGPT (with Shopping rolled out late 2024 and expanding through 2025-2026), Gemini, Perplexity (Pro Search and Shopping), and Claude are all surfacing product recommendations in some form. Search engines (Google, Bing) are also starting to embed agent-style answers. The list will keep growing, and the underlying data structure required is largely the same across all of them.

Five things: (1) schema.org Product structure with Offer and Brand; (2) a GTIN/EAN/UPC with a valid check digit; (3) category-specific attribute coverage with no missing required fields; (4) machine-readable specs, including nutrition or ingredient data where your category needs it, as attributes rather than prose; (5) descriptive image alt text. WISEPIM covers all five: Feed Hub's schema.org feed for the structure, Quality Guard for identifiers and required fields, and AI enrichment for specs and alt text.

No. SEO targets keyword-driven search by humans; agentic commerce targets structured data parsing by autonomous agents. There is overlap (schema.org markup helps both), but the optimization goals differ. SEO rewards persuasive copy; agentic commerce rewards complete, accurate, machine-readable structure. Catalogs that are agent-ready are usually better for SEO too, but the reverse isn't true.

Both. Most catalogs are not yet agent-ready, so early movers will see outsized traffic and conversion as agentic commerce grows. Strategically, the data structure WISEPIM produces is also the right one for marketplace operators (Mirakl), modern direct sites, and traditional SEO, so the work is rarely 'just for agents'. We see it as the new baseline, not a bolt-on.

Next step

Make every SKU agent-ready by default

Run a sample of your catalog through WISEPIM and see what changes when the output carries schema.org structure, check-digit validated GTINs and typed specs. The same enrichment feeds your SEO and marketplace exports.

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See the AI Enrichment pillar
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