Get your brand into AI answers.

How an AI tool decides who to name

An AI tool can only mention you through one of three routes, and every tactic in this guide maps to one of them. Diagnose the route before prescribing a fix — most wasted GEO effort is the right work aimed at the wrong channel.

The two clocks — you have to win both

A brand that wins only retrieval disappears the moment a fetch fails or the model answers from memory. A brand that wins only parametric memory is frozen at the last training cutoff and wrong the day its specs change. The slow clock rewards ubiquity, consistency and corroboration; the fast clock rewards crawlability, structure and freshness.

Indexation makes you eligible. It does not make you chosen. Large-sample citation studies keep finding that only a minority of sources cited in AI answers rank in the organic top ten — what wins the citation is being the clearest self-contained passage on the exact question.

Entities, not keywords

Old search matched strings. Models reason over entities and the relationships between them: a product connected to its category, its maker, its alternatives, the standards it meets. This is why entity work is decisive in niche and B2B categories — where there is little competing signal, whoever establishes the canonical facts first effectively writes what every model believes.

Diagnostic: the AI tool says something wrong about us

The visibility pyramid

Work bottom-up. Lower layers are prerequisites. Effort spent high is wasted while something low is broken. This ordering is the most useful thing in the guide, because it prevents the most common failure in the field: polishing structured data on a page no crawler can read.

Definition of done

A subject is not finished until every one of these is true. This list works well pasted straight into a ticket.

• One canonical URL — the undisputed home, not three competing pages.
• A one-paragraph canonical definition in plain language, reused word for word across the site, the datasheet and the knowledge graph.
• A machine-readable spec table — a real HTML table, with units, ranges and standards.
• Structured data on the canonical page, with sameAs to authoritative profiles.
• Crawlable to the AI agents you care about — or a deliberate, documented decision otherwise.
• An HTML version of every datasheet's key facts.
• A visible question-and-answer block in question → direct-answer form.
• At least three independent sources stating the same category and the same numbers.
• A named owner and a review date.

Can a crawler actually read it?

Most AI crawlers do not execute JavaScript. They read the raw HTML your server sends. Every fact you want cited has to exist as text in view-source — not only in an image, a chart, a PDF, or content that loads on click.

The evidence is Vercel and MERJ's December 2024 measurement, the only large one we found: the OpenAI, Anthropic, Meta, ByteDance and Perplexity crawlers did not execute JavaScript; Google's (Gemini, through Googlebot) and Apple's did. Design for the strictest reader, not for the most capable one. A client-rendered single-page app is close to invisible to AI tools however good its content is.

Verify it — do not assume it

• View source with JavaScript off and search the raw HTML for the definition sentence, each headline number, each answer, each comparison value.
• Check the status code and that nothing sits behind a consent wall, geo-block or login for the crawler.
• Fetch robots.txt and enumerate which AI agents are allowed, and whether a Sitemap: directive exists.
• Confirm the sitemap is current and lists the canonical URLs.
• Confirm the canonical link, and that variants point at the hub.
• Confirm the page is indexed in the underlying search engines — live retrieval depends on it.

Our free AI crawler access checker does the robots.txt parse and the live per-agent probes for you, including the CDN and firewall blocks that robots.txt alone will not reveal.

The robots.txt decision is two decisions

Conflating them is the most expensive mistake in this section.

Two further rules worth holding: robots.txt is a request, honoured by reputable crawlers, not an enforcement mechanism — if you need actual prevention, that is authentication or edge rules. And blocking is hard to reverse in effect, because lost presence takes time to rebuild. Ready-made rules are in the templates pack.

The entity foundation

The model has to be able to answer, with no contradiction: what is it, what category, who makes it, what is it not, and what is it the same as. This is the highest-leverage durable work available, and it is mostly unglamorous.

• State the full name, category and maker in the first sentence, everywhere. The pattern is "Name is a category made by Maker that does Y for Z". This single repeated sentence is the most valuable string you control.
• Resolve legacy and alias names explicitly. After a rebrand, models trained across the transition hold conflicting facts. Record the legacy name as an alias and an alternateName, and let the machine-readable layer carry the bridge, and the running copy stays clean.
• Handle name collisions. Search your name in isolation. If unrelated things share it, your canonical text needs disambiguating context to keep entity linking from merging you with them.
• One spelling and casing, consistently, with the variants recorded as aliases.

The open knowledge graph, in order of effort

Wikidata first — structured, editable, and it feeds both training corpora and grounding systems. Give every statement a reference; unreferenced statements are low-trust and prunable. Add multilingual labels, because one item serves every language. Wikipedia second, and slowly: a standalone article needs notability established by independent sources you do not control, and a promotional page will be removed and can backfire. Build the independent-source base first.

Do not make covert promotional edits, cite your own marketing as a notability source, or create an article before independent coverage exists. Disclose conflicts of interest and propose sourced edits transparently. This is one of the few places in GEO where the shortcut has a real downside.

sameAs — wiring the entity together

In the canonical page's structured data, sameAs should point at every authoritative profile of the same entity: Wikipedia, Wikidata, official social profiles, industry registries, standards listings. It is the literal machine instruction "these all refer to the same thing". Cheap, durable, and left out more often than anything else on this page. Our schema generator emits it correctly.

Structure and extractability

Engines retrieve passages, not pages. A page is a container; the unit that earns a citation is a paragraph that makes sense on its own.

• Self-contained sections of roughly 50–150 words, each answering exactly one question under a descriptive heading.
• No orphan pronouns. A retrieved paragraph must make sense with zero surrounding context — repeat the subject's name instead of "it" or "the platform".
• One idea per paragraph, one claim per sentence, conclusion first, support after.
• Put the direct answer immediately under the question heading, then elaborate.
• Front-load the page. A disproportionate share of citations come from the top, and the top is what survives truncation. The definition goes above the narrative.

Self-contained, not fragmented. There is no requirement to chop content into tiny pieces, and doing it causes cannibalisation — a supporting page out-ranking and out-citing the page it supports. The target is complete and self-contained.

Semantic HTML

One clear <h1> carrying the canonical name and category, with a logical heading hierarchy phrased as real user questions. Real tables for specs and comparisons, lists for steps, definition lists for term-and-definition pairs. Valid HTML, a set lang, a descriptive title and an answer-first meta description of about 155 characters. Native <details> for progressive disclosure — never a JavaScript accordion that fetches its own text.

Structured data

Structured data is the most reliable way to hand a machine unambiguous facts. It is not a ranking lever — Google states it is not required for AI features — but it is cheap, durable and it disambiguates. Mark up the primary entity, the organisation behind it, the breadcrumb trail, and any visible question-and-answer block, and make sure every marked-up fact appears in the visible text. Structured data that contradicts the page is worse than none.

Media, PDFs and datasheets

PDFs are weakly retrievable: often poorly chunked, sometimes not crawled at all, with the real numbers trapped in tables and figures. This matters enormously in B2B, where the facts live in datasheets.

• Mirror every datasheet's key facts in HTML. The PDF can stay the formal artifact; the HTML is what gets retrieved.
• Never put a number only in an image. Restate it in adjacent text, or use inline SVG with real text plus a caption carrying the values.
• If a PDF must stand alone, give it selectable text, a descriptive filename, a title, headings, a stable URL and an HTML landing page that summarises and links it.
• Express every spec with its units and qualifiers inline. "20,000 mm, tested to ISO 811" beats "fully waterproof", and models reproduce the qualifier when it sits beside the number.

llms.txt — the honest verdict

A proposed convention: a Markdown file at the site root pointing at your most citable pages. Google says Search does not use it, no engine documents reading it, and Chrome's experimental Lighthouse audit checks for one; it may standardise or fade. Cheap and harmless, not shown to change citations. Ship it if it costs an hour, and never let it substitute for robots.txt, a sitemap, structured data or HTML hygiene. Our llms.txt generator builds one from your sitemap.

Hub and spoke

The canonical page is the hub; focused spokes each own one question and link both ways. Engines read the internal-link graph to decide which page is the citation-worthy source. Route links and authority toward the hub with descriptive, varied anchor text — never "read more" alone. Contextual in-body links carry more signal than the same link in a related block; do both, but do the in-body one. Roughly three to five internal links per thousand words, key pages within about three clicks, no orphans.

Off-site corroboration

A fact stated only by you is a claim. The same fact stated by independent sources is knowledge.

Corroboration is how a claim enters parametric memory and earns retrieval trust. In specialised categories it is decisive, because whoever seeds consistent independent facts first owns the model's understanding. Ordered by trust:

1. Standards bodies and official registers — classifications, metrology institutes, regulators. Highest authority, durable, hard to fake.
2. Peer-reviewed and preprint literature that names the subject. For technical products this is the dominant channel and it ages extremely well.
3. Independent technical press and vertical trade publications.
4. Review and directory platforms — accurate third-party profiles raise citation probability.
5. Communities and forums, which some engines cite disproportionately. You cannot astroturf this; you can be genuinely present.

The aggregate charts are easy to misread. Published analyses disagree on which sources lead: some put Reddit, Wikipedia and YouTube near the top, and Profound's 2026 analysis of 11.84 billion citations found brand-owned sites the largest source overall. The mix differs by engine and moves month to month, and none of it is a statement about your category. For a specialised B2B subject the cited sources skew heavily to standards, literature and vendor documentation. The actionable reading is about format: what those platforms have in common is that they are independent, question-shaped, plainly written and densely factual. That is a description of what to make your own content look like.

The durable principle is consistency. Every corroborating source should repeat the same definition and the same numbers. Divergent numbers across sources are the leading cause of model hedging on technical subjects.

Freshness and decay

• Keep dateModified accurate and show a visible "last updated" date.
• Date your facts in the prose — "as of 2026, the current model is…" — and a stale claim is self-evident.
• Do not fake freshness. Bumping a date with no substantive change is a low-trust signal, and it destroys your own ability to read measurement deltas afterwards.
• Run a propagation pass on the triggers: a new model or spec, a rebrand or acquisition, a new engine launch, or a measured accuracy drop. Update the facts, then push them to the site, the structured data, the knowledge graph and your partners within one sprint.

Deciding what to write

A beautifully structured page that answers the wrong question will not get cited. Models cite the page that best matches the intent behind a prompt. If nobody mapped the prompts, the team is guessing at intent — and whoever did map it gets cited instead.

Two governing principles. Content is evidence, not persuasion: buyers in considered purchases verify rather than get sold, and a page of adjectives loses to a page of numbers and standards. And route by stage — learn questions to explainer pages, decide questions to the product page, act questions to the contact route. A page serving all three usually serves none.