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.
See also
GEO (Generative Engine Optimization)
The discipline of making a brand or product surface in AI-generated content - mentioned, cited, or recommended by generative AI models. Broader than AEO by including AI-generated recommendations, not just answer citations.
Citation Rank
Tru Commerce's flagship AI Share-of-Voice score - measures how often and where a brand appears when AI agents answer category-relevant shopper questions.
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