Agent-Ready Product Data
14+
150+
$1B+
Machines compare facts. Give them yours.
Agents don't see your photography. They read your data. When an agent compares your product against two competitors, it's comparing structured facts: dimensions, materials, compatibility, price, availability, shipping. If those facts live in a lifestyle paragraph or a spec-sheet PDF, you lose the comparison by forfeit.
The same failure hits AI search. Category questions cite brands, but product questions cite products, and engines can only cite what they can parse. A catalog described entirely in prose is a catalog that never appears in an answer.
Agent-ready product data fixes this at the source: model the facts once, render them everywhere a machine looks.
What we build
Attribute modelling
The decision layer: which facts matter for your category, where they live, how variants behave. Modelled once in metafields, Sanity or your PIM, then enforced everywhere.
Product schema that's actually complete
Product markup with real offers, availability, GTINs and reviews, rendered server-side per template. Most stores ship half-empty Product schema; agents notice the gaps even when validators don't.
Variant and availability truth
Agents abandon on ambiguity. Size ranges that resolve to real SKUs, stock that means stock, delivered pricing that doesn't surprise at checkout.
Feeds and sync
The same clean data pushed to every surface that needs it, kept in sync from your source systems through systems and integrations so accuracy doesn't decay.
Shopify or Die
Insights into the current and future state of Shopify Plus commerce. Headless architecture, agentic commerce, integration strategy, and the engineering decisions behind stores that scale.
One dataset, three payoffs
Clean product data pays three times. It gets your catalog into AI answers through AI search. It powers product discovery on your own store, because filters and search are only as good as the attributes underneath. And it's the entry ticket to agentic commerce, where a purchase can complete without a human ever seeing the page.
Structure work like this sits inside content architecture for the rest of the site; products are just where the stakes are highest.
Frequently Asked Questions
What does agent-ready actually mean?
A product an agent can fully evaluate without guessing: structured attributes, accurate availability, clear pricing, honest variants. If a machine can answer "does this fit, is it in stock, what does it cost delivered" from your data alone, you're agent-ready.
Is this just schema markup?
No. Markup is the last step. The real work is upstream: deciding where product facts live (metafields, Sanity, or your PIM), modelling them consistently, and keeping them accurate. Schema rendered from bad source data is just well-formatted wrong answers.
We have thousands of SKUs. How does this scale?
Systematically. We design the attribute model once, then migrate programmatically: bulk operations, automated extraction from existing descriptions where it's reliable, human review where it isn't. Nobody retypes a catalog by hand.
Where should product data live?
Depends on your stack. Shopify metafields work for most. Sanity suits content-heavy catalogs on headless builds. A PIM earns its keep at multi-channel scale. We'll recommend the simplest option that survives your growth, and connect it properly through systems and integrations.