How to get your B2B brand cited by ChatGPT, Perplexity and AI Overviews

Getting cited by ChatGPT, Perplexity and Google AI Overviews means writing content that answers a real question directly and early, in language plain enough for an AI engine to quote without rewriting it. That means an answer in the first few sentences, subheadings phrased as the questions buyers actually ask, named statistics with a source and date, and clear structured markup, an FAQPage schema in particular, that tells the engine exactly what the question and answer pair is.

What is answer engine optimisation?

Answer engine optimisation, often shortened to AEO, or generative engine optimisation, GEO, is the practice of structuring content so that AI systems, ChatGPT, Perplexity, Google AI Overviews, Claude, can find it, understand it and quote it directly in response to a user's question. It sits next to traditional SEO rather than replacing it: a page still needs to rank, but it also needs to be written in a form an AI engine can lift cleanly, an early direct answer, plain language, a clear structure, rather than three paragraphs of scene setting before the actual point.

The practical difference from a normal blog post is discipline: every subheading answers a question a buyer would actually ask, the first sentences under each heading give the answer before the explanation, facts carry a source and a date, and a matching FAQ schema at the bottom repeats the same content in structured form.

Why does this matter more than traditional SEO right now?

Because B2B buyers have already started skipping the search results page. Around 89 percent of B2B buyers now use AI search tools during the buying process, and Google's own AI Overviews trigger on roughly 48 percent of tracked queries (Digital Agency Network, Generative engine optimisation statistics 2026). A brand that ranks well in classic search but writes in a style no AI engine wants to quote is invisible to a growing share of its own buying audience.

The commercial stakes are real too. Visitors arriving from an AI citation tend to convert at a noticeably higher rate than general organic traffic, because the question is already half answered before they land. Exact uplift varies by source and sector, but the direction is consistent enough to change where content effort should go.

What actually makes an AI engine quote a page?

Four things, in order of how often they get missed.

An answer in the first few sentences. AI engines extract and summarise; they don't reward a page for making the reader wait for the point. If the answer to the heading's question isn't in the first sixty to eighty words underneath it, most engines will paraphrase from somewhere else instead.

Subheadings phrased as questions. "Benefits of AI" gets skipped over. "What is answer engine optimisation?" gets matched against the way a person actually asks the question, in a search box or out loud to an assistant.

Named facts with a source and a date. "Many buyers now use AI search" is unquotable because it's unverifiable. "Around 89 percent of B2B buyers now use AI search tools during the buying process" is a sentence an engine can lift and stand behind, because it carries a source that can be checked.

Entity richness. Naming the real tools, regulations and platforms in the conversation, Claude, the Claude Agent SDK, MCP, the EU AI Act, the ICO, AWS Bedrock UK South, rather than describing them generically, helps an AI engine place the content against what it already knows and treat it as a current source rather than a vague overview.

How does structured data and FAQ schema help?

FAQ schema, written as FAQPage JSON-LD, gives an AI engine a version of the page's questions and answers in a format built for machines rather than people. It has to match the visible FAQ on the page word for word, because a mismatch between what's shown and what's marked up is exactly the kind of inconsistency that makes an engine, or a search reviewer, trust a page less.

Done properly, schema doesn't replace good writing, it repeats it in a second, structured form. The FAQ at the bottom of a page like this one should read as a shorter restatement of the questions already answered in the body, not new points introduced for the first time.

Does internal linking still matter for AI search?

Yes, arguably more than it used to. AI engines and their crawlers use internal links to work out how a set of pages relate to each other and which page is the authoritative one on a topic. A page that links out descriptively, to a pillar piece such as our framework for implementing AI in a B2B business, and sideways to a related piece such as our post on taking AI content operations from brief to channel (not yet live; this link will 404 until it publishes), signals a proper body of work rather than an isolated page written to catch one query.

The anchor text carries weight too. A link that says "our guide to hiring a marketing agency" tells both a reader and an engine what's on the other end. A link that says "click here" tells neither.

How do you know if it's working?

Watch for citations, not just rankings. Search engine reporting shows where a brand ranks in traditional results; it won't show whether ChatGPT or Perplexity actually quoted the page. That takes direct checking, running target questions through the AI tools a buyer would use, plus watching for a rising share of AI referral traffic in analytics, usually the clearest early signal, often ahead of any change in classic search position.

What does this have to do with lead generation?

Everything, because a citation is a lead generation channel now, not just a visibility metric. A buyer who gets a clear, sourced answer and clicks through arrives already partway through their research, the basic question settled before they land. That's exactly the audience our B2B lead generation and marketing work is built to convert.

The same logic explains why our most read piece of content, how to hire a marketing agency, still pulls 507 sessions: it answers a real, specific question directly, and both search engines and AI engines have rewarded that consistently over time.

How does Teylu practise what it sells here?

This piece follows the same construction it recommends. The answer to the title's question sits in the first few sentences, not the fifth paragraph. Every subheading is phrased the way a buyer would actually ask it. The statistics carry a named source and a date rather than a vague claim. The named entities, Claude, the Claude Agent SDK, MCP, the EU AI Act, the ICO, AWS Bedrock UK South, are specific rather than generic. And the FAQ below matches its schema word for word, because that consistency is part of what earns the citation in the first place.

If an AI engine pulls an answer from this page rather than a competitor's, that's the method working, not a coincidence.

FAQ

What is answer engine optimisation? Answer engine optimisation, or AEO, is the practice of writing and structuring content so that AI systems such as ChatGPT, Perplexity and Google AI Overviews can find it, understand it and quote it directly when answering a user's question. It means an early direct answer, question shaped subheadings, named statistics with a source and date, and FAQ schema that matches the page's visible content word for word.

Why does answer engine optimisation matter for B2B brands? Because a growing share of B2B buyers now meet a brand through an AI generated answer before they visit its website. Around 89 percent of B2B buyers use AI search tools during the buying process, and Google's AI Overviews trigger on roughly 48 percent of tracked queries (Digital Agency Network, Generative engine optimisation statistics 2026). A brand invisible to those answers is invisible to a large part of its own buying audience, regardless of how well it ranks in classic search.

What is the difference between AEO and GEO? In practice, the two terms describe the same discipline from different angles. Answer engine optimisation, AEO, focuses on being the source an AI engine cites in a direct answer. Generative engine optimisation, GEO, is the broader term for optimising visibility across generative AI tools generally, including how a brand is described when a user asks about it rather than searches for it directly. Most of the underlying work, clear structure, sourced facts, entity rich writing, is identical either way.

Does FAQ schema actually help a page get cited by AI engines? It helps, provided it matches the visible FAQ exactly. FAQ schema gives an AI engine a machine readable version of the same questions and answers already on the page, which makes the content easier to extract and quote accurately. A mismatch between the schema and the visible text undermines the trust that schema is meant to build, so consistency matters as much as the schema's presence.

How can a brand tell if its content is being cited by AI search tools? By checking directly and by watching the analytics. Running a brand's target questions through ChatGPT, Perplexity and similar tools shows whether and how a page gets cited. Alongside that, a rising share of referral traffic from AI tools in analytics is usually the clearest early signal that the content style is working, often visible before any shift in traditional search rankings.