Your catalog, ready for
AI shopping agents
ChatGPT, Gemini, Perplexity, and Claude are starting to discover, recommend, and buy products on behalf of consumers. Most catalogs aren't ready for them. WISEPIM makes sure every SKU you publish has the structured, schema.org-compliant, attribute-complete data that AI agents need to actually find and sell your products.
Key features
Data AI agents can actually read
ChatGPT, Gemini, Perplexity, Claude: they read structured data, not marketing copy. WISEPIM's output meets the standard each major agent expects.
Validated GTINs, complete attributes
Every SKU passes Quality Guard before publish: valid GTIN, schema-complete attributes, machine-readable specs. Everything an agent needs to recommend your product.
Built on schema.org Product
Output that maps cleanly to schema.org Product, Offer, and Organization: the structured data format AI agents and search engines both rely on.
Ready from day one, not retrofitted
Validation runs at publish time. You don't add agent-readiness later as an afterthought: it's the default output from the day you go live on WISEPIM.
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.
One source, every AI agent
Publish once and stay consistent: ChatGPT, Gemini, Perplexity, and Claude all read from the same validated catalog.
Why agent-ready data matters now, not later
The window between 'AI agents are interesting' and 'AI agents drive real discovery' is closing fast. Three things change for retailers whose data is ready first.
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 agent comparing 30 products in 200 milliseconds does it on structured attributes, not on hero images and brand storytelling. Catalogs without machine-readable specs are simply invisible to that comparison.
Marketplace operators are an early signal
Mirakl-powered marketplaces (B&Q, Decathlon, Macy's, Galeries Lafayette, Carrefour) are already structuring catalogs to be agent-readable. Operators on those platforms see the shift first, but every direct retailer is next.
Marketing copy vs. agent-ready data
The same product, presented two ways. Only one shows up in agent-driven discovery.
| Marketing copy only | Agent-ready data | |
|---|---|---|
| Structured data | Free-form HTML | schema.org Product / Offer / Brand |
| Product identifiers | Sometimes a SKU | Validated GTIN / EAN / UPC, GS1-registered |
| Attribute coverage | Whatever the copywriter included | Category-complete, machine-readable |
| Image alt text | Decorative or empty alt text | Descriptive, attribute-bearing alt text |
| Trust signals | Review ratings (if scraped) | Aggregated review schema, return policy, brand identity |
Machine-readable data agents can read
AI extracts clean, structured attributes from every product, schema-complete data that AI shopping agents can parse, recommend, and transact on.
A Catalog Structure Agents Can Navigate
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.