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Canonicalization

Also known as: Product canonicalization

The process by which an AI agent decides which brand's version of a product is the 'canonical' one to recommend when multiple brands sell similar SKUs. Built over time via content depth + review + external authority.

When two or more brands sell what looks like the same product, agents pick one to recommend by default. That preferred brand — the "canonical" version — captures most of the recommendation traffic. Losing canonicalization to a competitor is losing default rank for the category.

Canonicalization signals include: longest and deepest product page for the SKU, most first-party reviews with syndicated schema, most external citations (press, expert coverage, comparison sites), earliest product introduction (agents weight incumbency).

You cannot fake canonicalization by submitting more feeds or spending more on ads. You build it over months by being genuinely the most thoroughly-described version of your product on the internet. It's the closest thing agentic commerce has to a durable moat.

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