AI shopping agents recommend products they can read with confidence. In practice that means complete, consistent, machine-readable product data: accurate attributes and specs, correct variants and availability, descriptions that state what the product is and who it's for, and structured signals a model can extract without guessing. Where that data is missing or inconsistent, the agent hedges — or leaves you out.
1. Attributes and specifications
This is the foundation. Material, size, fit, dimensions, ingredients, compatibility, power, capacity — whatever matters in your category. Agents use attributes to match products to specific questions ("waterproof", "under 2kg", "fits an iPhone"). Vague or missing attributes mean you can't be matched to the query, full stop.
- Name the attributes that matter in your category, explicitly
- Be consistent — the same attribute, named the same way, everywhere
- Prefer structured fields over burying specs in prose
2. Variants and availability
Agents need to understand what's actually buyable: which variants exist, how they differ, and what's in stock. A model that can't tell whether the blue, large version is available will avoid recommending it rather than risk being wrong.
3. Descriptions that answer "what is this and who is it for"
Marketing copy written purely to sound good often fails the readability test. Agents extract meaning: what the product is, the problem it solves, who it suits, how it differs. You can keep your voice and still be explicit about those four things — and you should.
Rule of thumb
If a careful human skim-reading your product page couldn't confidently say what it is, who it's for, and how it differs, an AI agent can't either.
4. Machine-readable structure
Finally, all of that needs to be exposed in a structured, extractable way — clean markup, consistent fields, signals that let a model parse your catalog programmatically rather than scraping prose. This is the layer most stores never touch, and it's a big part of what separates a catalog that gets cited from one that gets skipped.
Auditing all four layers across a whole catalog by hand is slow, which is exactly why most stores don't. Our free AI Shopping Visibility Audit does it for you and ranks what to fix first; the done-for-you engagement closes the gaps and verifies the result.
