EndcapGemini and Copilot: the same catalog, different front doors
Merchants keep asking how to optimise for Gemini, then separately for Copilot, then separately again for the next one. The answer is unsatisfying and worth internalising: they read the same kind of structured product data, and Shopify syndicates Catalog to AI channels rather than maintaining a bespoke feed per assistant.
In short
- Shopify Catalog is the mechanism; there is no per-assistant submission for a merchant to complete.
- A product excluded for a missing image is missing from all of them at once, and fixing it fixes all of them at once.
- What varies between assistants is presentation and phrasing, not whether your data is complete.
One feed, several readers
Shopify Catalog is the mechanism. Products that meet its requirements are included automatically, and that inclusion is what makes them available to the assistants Shopify syndicates to. There is no per-assistant submission step for a merchant to complete.
So a product excluded for a missing image is missing from all of them at once, and fixing the image fixes all of them at once.
What actually varies
Presentation and phrasing vary a great deal. One assistant will name five brands, another will name two and hedge. One leans on editorial reviews, another on feed attributes. What does not vary much is the underlying question of whether your product data is complete enough to be a candidate.
The tactic that does not exist
There is no Gemini-specific field, no Copilot tag, no per-assistant keyword. If a tool implies otherwise, ask which documented mechanism it is using. The absence of an answer is the answer.
What to do instead
Get included, complete the fields Shopify names, publish policies, and then measure across more than one assistant so you are not tuning to the quirks of a single one.
Why per-assistant tooling keeps being sold anyway
If the mechanism is shared, the obvious question is why so many tools are marketed per assistant. The honest answer is that a dashboard with a tab for each one demonstrates effort, and effort is easier to sell than the unglamorous truth that a single feed feeds all of them.
There is a real version of per-assistant work, and it is measurement rather than optimisation. Assistants genuinely differ in who they name, so checking more than one tells you whether a result is a pattern or a quirk. That is worth doing. Maintaining separate optimisation programmes for each is not, because there is no separate lever to pull.
How Endcap handles this
Endcap treats the catalog as the unit of work rather than the channel, which is why it audits against Shopify Catalog requirements instead of promising per-assistant rankings. Fixing an exclusion improves your standing everywhere at once, and that is a property of the feed, not of the tool.
For measurement it asks unbranded buying questions derived from your own product types and records which brands came back. There is no ranking position in the output, because none is published by any of these assistants, and inventing one would make the number look more precise while making it less true.
Common questions
Is there a Gemini-specific field or tag?
No. If a tool implies otherwise, ask which documented mechanism it uses. The absence of an answer is the answer.
Should I optimise separately for each assistant?
Not for optimisation. For measurement, yes: checking more than one tells you whether a result is a pattern or a quirk of a single system.
Why do assistants give different answers about my store?
They weight sources differently and phrase things differently. The shared input is your product data, which is the part you control.
Check your own catalog
Endcap runs every check in this guide and shows you which products fail, and why.
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