How do you get your business cited by AI search?

To get cited by AI search, make your site the easiest source to quote, which is mostly a matter of disciplined writing: answer real customer questions directly in the first paragraph of a page, keep your business facts identical everywhere they appear online, structure pages so a machine can extract them cleanly, and publish an llms.txt file that summarises the site.

This guide is for business owners and marketing managers watching search behaviour shift and wondering what to do about their website. It's the same playbook we run on our own site, this page included, so you can judge the method by the page you're reading.

What actually changed about search?

A growing share of your customers now ask an assistant a question instead of scanning ten blue links. The assistant reads a handful of sources, assembles one answer and cites where it came from. If your site is one of the citations, you're in front of someone at the moment they're deciding. If it isn't, a competitor may be, and the customer may never see a results page at all.

Two honest caveats before any tactics. First, nobody outside the AI companies knows the full selection recipe, and it keeps changing, so what follows is the observable pattern of what gets cited rather than a guarantee. Second, traditional search hasn't died, and the same structure that earns citations tends to earn featured snippets too, so the work pays off in both.

How do AI systems choose what to cite?

Cited pages tend to work like good reference books:

• They answer a specific question directly, near the top of the page, in plain language a system can lift whole.
• They state facts unambiguously: who, where, what it costs and who it's for. A weaker page says "contact us to find out".
• They agree with the rest of the web. When your site, your Google profile and the directories all say the same thing, confidence rises, and conflicts lower it.
• They're technically boring: clean HTML, fast to load, crawlable, with nothing important locked behind scripts or images of text.

There's no keyword-stuffing equivalent worth chasing here. These systems are built to find the clearest source, so the work is mostly writing and consistency, which suits small businesses because it takes effort more than budget.

What is answer-first structure?

Answer-first structure means building each page around a real question and answering it before anything else:

• The H1 is the question as a customer would phrase it, rather than a clever headline.
• The first paragraph answers it completely in roughly 40 to 60 words, so it still works when quoted alone.
• The subheadings are the follow-up questions people genuinely ask next.
• Anything list-shaped becomes a list. Steps, checklists and tables are far easier to extract, and to read, than paragraphs doing the same job.
• FAQ answers lead with the answer and explain afterwards.

You're reading the pattern right now: this page opens with a direct answer under a question, and every heading is a question. We build client sites the same way.

Why do consistent entity facts matter?

Before an AI assistant cites or describes a business, it cross-references. If your website says one suburb and your Google profile says another, the assistant's confidence in you drops, and the same happens when five directories describe your services five different ways. Low confidence means you get skipped, or worse, described inaccurately in an answer you never see.

The fix is unglamorous. Choose one canonical set of facts: business name, location, what you do, who you do it for, and prices if you publish them. Then repeat that set, word for word wherever possible, across your site and about page, your Google Business Profile, directory listings and social bios.

Then audit it quarterly, because facts drift when nobody owns them: an old address in a directory, or a service you stopped offering still listed in a bio.

Give those facts a proper home, too. Write an about page in plain declarative sentences, which are easier for machines to quote, and add structured data (schema markup) for your organisation, location and services if your platform supports it. Schema has been standard search practice for years, and it backs up the written facts by handing machines the same information in a format built for them.

What is llms.txt and is it worth adding?

llms.txt is a plain text file, written in markdown, that sits at yoursite.com/llms.txt and gives AI systems a structured summary of your business and pointers to your most useful pages. Think of it as a reading guide for machines, pointing them to who you are and to the pages that answer real questions.

It's an emerging convention that not every system reads today, and nobody can promise it will be decisive. Still, it takes about an hour with no real downside, and writing your canonical facts and best pages into one clear document is useful even if the file never gets read. We publish one on our own site for that reason.

What should you do this quarter?

A sequence you can run without new tools:

1. List ten real questions customers ask before buying from you. Pull them from sales calls, enquiry emails and quote conversations, not from keyword tools alone.
2. Give each question a page or section with answer-first structure: question as heading, complete answer in the first paragraph, follow-ups underneath.
3. Audit your entity facts everywhere they appear and fix every conflict against your canonical set.
4. Publish llms.txt with your facts and your best answer pages.
5. Keep facts current, prices especially, because a stale price quoted in an AI answer makes for an awkward first conversation with a prospect.
6. Measure it manually. Once a month, ask the major assistants your ten questions and record whether you're cited and how you're described. Also watch your analytics for referrals from assistant domains.

A citation only gets you the introduction, and the page it leads to still has to convince a person. That's why we treat websites as part of a sales ecosystem rather than a brochure. We cover how to measure that whole system in measuring content, and the content that earns citations in the first place comes from a steady content engine.