In practice, structured data is a small script block of JSON-LD sitting in a page's HTML, invisible to visitors but unambiguous to machines. Each block declares a schema.org type and its properties: an Organization block names the company behind the site, an Article block states the headline, author, and publish date, an FAQPage block lists question-and-answer pairs verbatim, and a LocalBusiness block pins down address, phone number, and opening hours. Search engines have used this markup for years to power rich results and to disambiguate entities — knowing that "Mercury" on your page means the company, not the planet. The markup doesn't change what humans see; it changes how confidently machines can restate what you said.
Structured data matters more now that AI answer engines, not just blue-link search, decide who gets cited. A model assembling an answer works fastest with facts that are already labeled: a date marked as datePublished can't be confused with a date mentioned in passing, and an FAQPage block hands over self-contained question-and-answer pairs in exactly the shape an answer engine wants to quote. That's why structured data shows up in every serious answer engine optimization checklist, alongside its siblings generative engine optimization and llms.txt — different mechanisms, same goal of making your pages effortless for machines to read accurately. Pages that force engines to guess tend to get paraphrased loosely or skipped; pages that state their facts in markup get restated correctly.
If AI findability is the goal, markup is one of the first levers to check — and it's part of how Brohns approaches the problem. Brohns' GEO auditor agent scans how visible your site is in AI answers like ChatGPT and Perplexity, benchmarks you against competitors, and generates concrete fixes for the gaps it finds. True to how the rest of the platform works, those fixes arrive as proposals you read and apply on your own terms — an agent never rewrites your public pages while you're not looking. The same transparency applies everywhere: you can watch the auditor's actual reasoning on its timeline as it works, so you know why each fix was suggested, not just that it was.