Content structure for AI: the pages and hierarchy that get retrieved.
In short
AI retrieves from sites that are well-organized and semantically clear: one clear topic per page, proper heading hierarchy, and dedicated pages for the questions buyers actually ask (specs, comparisons, FAQs). Structure tells AI what each page is for.
Why it matters for AI
Retrieval systems chunk and index pages by structure. Clean headings and focused pages produce clean, confident chunks the model can match to a query. A sprawling page that tries to cover everything matches nothing well.
The pages product-and-quote B2B usually needs
- Spec pages. One per product or family, with structured attributes (dimensions, materials, tolerances, certifications, MOQs, lead times).
- Comparison pages. "X vs Y" and "best supplier for [use case]," answering shortlist questions directly.
- FAQ pages or blocks. Real buyer questions with direct answers, and FAQ schema (see the structured-data guide).
- Capability and trust pages. Certifications, compliance, export experience, case studies, as retrievable proof.
Hierarchy rules
- One
<h1>per page that names the topic;<h2>and<h3>for sub-points in order. - Don't bury answers in PDFs or images, keep them as real HTML text.
- Link related pages to each other so the topic cluster is obvious.
Common questions
How many pages is too many?
Prefer a focused page per buyer question over one mega-page. Depth per topic beats breadth per page.
Do PDFs work?
Treat them as secondary. Put the answer in HTML; offer the PDF as a download, not the only source.