Saying it in words, and saying it in structure
Clear, human-readable text is the foundation of AI Discovery. But there is a second layer that reinforces it: structured data — a standardised way of labelling information in a page’s code so that machines can read it explicitly, not just infer it from prose. Used well, it lets an organisation declare, in a form AI crawlers and search systems read directly, exactly what it is and what it does.
Illustrative before
A capability page written only as prose relies entirely on the AI model correctly interpreting sentences. Even good prose leaves room for ambiguity: is “sterile manufacturing” a core capability or a passing mention? Is a certification current or historical? Prose alone cannot always make these distinctions machine-explicit.
Illustrative after
The same page, with structured data added behind the scenes, explicitly labels the organisation type, its location, its certifications, and its service categories in a standardised, machine-readable format — reinforcing, in code, what the visible text says in words.
The visitor sees no difference. But an AI crawler now receives an unambiguous, labelled declaration of the company’s key facts, reducing the room for misinterpretation. Text and structure say the same thing, in two languages: one for people, one for machines.
Why this matters commercially
Structured data does not replace clear content — it strengthens it. For organisations competing to be understood by AI, having both the readable text and the machine-readable declaration aligned is what gives an AI model the greatest confidence to include and describe them accurately. It is a technical detail with a direct commercial payoff.