A language model's memory of your brand is nothing more than the sum of how the web writes about you, filed under your name. Which makes two unglamorous things, what you are called and how consistently you are described, into infrastructure.
Here is how machine memory forms around a name, the canonical-sentence system that shapes it, and the audit that shows what the models currently believe.
The name is the filing key
Every mention you earn, the currency of the memory door, accrues to whatever string it was written under. Three naming situations decide how well that filing works.
| Situation | What happens in machine memory |
|---|---|
| Distinctive name | Mentions accumulate cleanly into one association |
| Generic dictionary word | Your mentions dissolve into the word's ordinary meaning |
| Name written many ways | The memory splits across variants, each weaker |
Founders naming things today should weigh machine disambiguation as a real criterion. Everyone else plays the hand they have, and the consistency system below is how a non-ideal name still accumulates one memory instead of several fragments.
One canonical sentence, everywhere
The core tool is embarrassingly simple: one sentence, brand plus category plus differentiator, written once and repeated verbatim across everything you control.
This formalizes the co-occurrence insight: the sentence teaches brand, category and reason together, and every surface repeating it verbatim multiplies the identical lesson. Boilerplate is not lazy writing here; it is deliberate signal engineering.
Spell the name one way everywhere while you are at it: one casing, one spacing, the domain only when it is the name. Variants feel harmless and quietly split the file.
The entity plumbing
Underneath the words, wire the identity: Organization markup with sameAs links tying your site to your real profiles, per the schema priorities, and an about page that states the canonical facts plainly, because about pages are exactly what assistants fetch when asked "what is X".
Treat the about page as your machine-facing fact sheet: the sentence, the founding facts, what the product does, in plain HTML. Most companies write it for investors; the more frequent reader now is a retrieval system deciding what to say about you.
The audit: ask the machines what they believe
Quarterly, cold, no context: "what is [name]?" across the major assistants, plus "what is [name] known for?" and your category's recommendation prompt. The sampling rules apply, so ask a few times each.
Score what comes back: correct and confident, correct but stale, confused with a similarly-named thing, or blank. Each has its fix: stale points at refreshing the sources, confusion points at sharpening the differentiator everywhere it appears, blank points at the whole ladder, and wrong facts point upstream, at correcting the pages the error lives on, because models restate the web's consensus and the consensus is editable.
The rename warning
Machine memory makes renames more expensive than they look: associations do not transfer to the new string on their own, and the rebuild runs on training-cycle time. If a rename is unavoidable, run the longest "formerly known as" overlap you can stand, on your site, your profiles and especially the surfaces models read most, so the two strings get welded together in the text the machines learn from.
The one-line takeaway: AI remembers you as the web spells and describes you, so pick a distinctive name, write the one canonical sentence, repeat it verbatim everywhere with the entity markup wired, and audit quarterly by asking the machines cold what they believe.