Structured Data, Explained: The Hidden Labels That Get a Page Quoted by AI

In my teardown of how this site was built to be found by AI, structured data was one move out of eight. It deserves its own post — because it’s the piece that most directly decides whether a machine can quote you by name. So here it is, up close: what it is, exactly what it looks like on a real page, and why each line matters. The worked example is a COLAClear blog post — a live page, not a mock-up.

The one-sentence version

Your reader sees a headline, a byline, a photo. A machine sees none of that as meaning — unless you label it. Structured data is those labels: a small chunk of code, invisible to visitors, whose only job is to say “this is the author,” “this is the publish date,” “this is what the page is about.” Most sites use a format called JSON-LD — a tidy block of labeled facts that sits in the page’s code in one place. Google recommends it, and it’s the easiest kind to add.

What it looks like on a real page

Here is the structured data on that COLAClear post, trimmed to the essentials. Don’t worry about the punctuation — read it like a form the page fills out for machines:

{
  "@type": "Article",
  "headline": "Age Statements on Spirits Labels: When
               \"Time in Oak\" Has to Appear",
  "author":    { "@type": "Person", "name": "Zillah Bahar" },
  "publisher": { "@type": "Organization", "name": "COLAClear" },
  "datePublished": "2026-08-07",
  "description": "When distilled spirits must carry an age
                  statement under 27 CFR 5.74 …",
  "about": [ "Distilled spirits age statement",
             "27 CFR 5.74", "Whisky age statement" ]
}

Now the same block, line by line — and what each line earns you when an AI is deciding whether to trust and quote the page:

• @type: Article — says what this thing is. Not a product, not a bio, not a store page — an article. The machine now knows to treat it as something it can summarize and cite.
• headline — the title, handed over as a clean fact instead of being guessed from the biggest text on the page.
• author and publisher — who stands behind the content, and the brand it belongs to. This is the trust signal AI leans on hardest. A claim from a named author at a named publisher is worth more to a machine than an anonymous page.
• datePublished — proof the page is current. Assistants prefer fresh sources, and this states the date outright rather than making them infer it.
• description — a one-line summary the machine can lift as-is when it needs a short version of the page.
• about — the quiet powerhouse. You list the topics yourself — here, “27 CFR 5.74,” “whisky age statement.” Instead of hoping a machine works out what your page covers, you tell it. When someone asks an AI about whisky age statements, a page that named that topic in its own labels is far easier to match and surface.

None of this changes a word your readers see. It changes what the machine can say about you with confidence.

The markup that actually gets you quoted: FAQ

One kind of structured data does more than describe the page — it makes a piece of it directly quotable. It’s called FAQ markup (FAQPage), and it labels a question and its answer as exactly that. When your answer is marked up this way, it becomes eligible to be lifted straight into a search answer box or an AI reply. Here’s a COLAClear example:

{
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "Does a whisky under four years old need
             an age statement?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Yes. Under 27 CFR 5.74, any whisky aged
        less than four years must carry an age statement,
        unless it is labeled 'bottled in bond,' which
        already means at least four years."
    }
  }]
}

The trick is that the question should be one a real person actually asks, and the answer should be a clean, complete sentence or two — because the machine may quote it word for word. Write the answer you’d be happy to see attributed to you in an AI reply. (This very page uses FAQ markup, too — it’s in the code you can’t see.)

Why this is GEO, not just SEO

Getting cited by an AI is a trust game. The model chooses which sources to surface and name, and it favors the ones it can parse without ambiguity. Structured data hands it exactly that: a plain statement of what your page is, who’s behind it, what it covers, and which parts are quotable. That’s the clean signal a generative engine wants before it puts your name in an answer.

It works. This is part of the same setup that got COLAClear named in Google’s AI Overview with zero paid media — and, more recently, ChatGPT has started sending COLAClear real visitors, landing straight from AI answers. Structured data isn’t the whole reason. But it’s a big part of why the machines can find the page, understand it, and pass it along with a name attached.

What to do (you don’t need to be a developer)

• Put Article (or BlogPosting) schema on every post: headline, author, publisher, date, description — and an honest about list of the real topics.
• Add FAQ markup to any genuine question-and-answer on the page. That’s the piece most likely to be quoted.
• Keep the author and publisher names identical everywhere, so the machine reads one trusted source, not several faint ones.
• Don’t hand-write the punctuation from scratch — a generator gets the format right; your job is to supply true, specific values.

Zillah Bahar is the founder of COLAClear and writes about getting found by AI at GetDiscoverable.io.