The practical entity, schema and content moves that make LLMs quote you as a source — not your competitor.
Why AI citations are the new page one
Six in ten searches now end without a click. Your buyers ask ChatGPT, Perplexity, Claude and Gemini a question, read the synthesized answer, and act on it — often without ever visiting a website. In that world, the brands that win aren’t the ones ranking #1 on a results page nobody scrolls. They’re the ones the model quotes inside the answer.
We call this share-of-answer, and it’s quickly becoming the metric that matters most. If an LLM names you as a source, you get the click, the trust, and the conversion. If it names a competitor, you’re invisible — no matter how good your classic SEO is.
How LLMs actually pick their sources
Large language models don’t ‘rank’ pages the way Google does. When they generate an answer, retrieval-augmented systems pull from a blend of their training data, a live web index, and — increasingly — real-time search. To be pulled in, your content has to be unambiguous, well-structured, and strongly associated with the entities and questions being asked.
In practice that means three things: the model has to be able to find you, parse you cleanly, and trust you enough to attribute the claim. Miss any one of those and you don’t get cited.
Step 1 — Engineer your entities
LLMs reason about the world in entities — people, companies, products, concepts — and the relationships between them. If your brand isn’t a well-defined entity with consistent attributes across the web, the model has nothing solid to attach a citation to.
Lock down a consistent name, description, and category everywhere you appear. Build out your Wikidata, Crunchbase, and LinkedIn presence. Make sure your ‘About’ and founder pages state plainly who you are, what you do, and who you serve — in language a model can lift verbatim.
Step 2 — Make your content machine-readable
Schema markup is how you hand a machine a clean, labelled version of your page. Article, FAQ, HowTo, Organization and Author schema all help models (and the search indexes that feed them) understand exactly what each block of content is and who stands behind it.
Pair structured data with a clean content structure: clear H2/H3 headings phrased as the questions people actually ask, short declarative answer paragraphs near the top of each section, and tables or lists where they fit. LLMs love extractable, self-contained answers.
Step 3 — Write answers, not just articles
The pages that get cited tend to lead with the answer, then explain. Open each section with a direct, quotable sentence that resolves the question — the kind of line a model can drop into its response with attribution. Then add the depth, examples and nuance underneath for the humans who do click through.
Originality matters too. Models prefer to cite primary sources: your own data, your case studies, your specific point of view. Generic content that restates what’s already everywhere gives the model no reason to pick you over the original.
Step 4 — Measure your share-of-answer
You can’t improve what you don’t track. Build a list of the prompts your buyers actually ask, then run them across ChatGPT, Perplexity, Claude and Gemini on a schedule. Record whether you’re cited, where, and against whom.
Treat that like a rank-tracker for the AI era. As you ship entity, schema and content work, your citation rate should climb — and you’ll see exactly which prompts you own and which still belong to competitors.
The takeaway
Getting cited inside ChatGPT and Perplexity isn’t a hack — it’s classic SEO discipline pointed at a new surface. Define your entities, structure your content for machines, lead with quotable answers, and measure relentlessly. Do it consistently and you stop competing for a blue link nobody clicks, and start being the answer itself.
