Published findings from controlled tests on how AI systems interact with structured data. Every blueprint in the AIFDS library is built from this research. 20 articles across field-level findings, industry analysis, page-type breakdowns, and validation methodology.
The pillar article. Isolated test findings on which structured data fields AI systems parse vs ignore — contact information, hours, pricing, and why each one matters. The evidence behind every field in every AIFDS blueprint. Every other article on this page builds on these findings.
Read the findings →Social links, reviews, founding date, employee count, awards — the fields conventional wisdom says to include that did not change AI citation behavior. Includes the controlled experiment where a site with zero traditional signals earned AI citations in 33 days.
Read →Does linking entities with @graph change how AI treats a page? How sameAs linking affects citation in competitive vs non-competitive markets. Why AIFDS blueprints use the connected approach.
Read →The three structured data formats compared for AI readability. Why JSON-LD reduces crawler workload and why AIFDS uses it exclusively.
Read →How the core findings apply to specific business types. Each article explains which schema fields matter most for that industry and links to the corresponding blueprint family.
Contractors, agencies, law firms, accountants. Why service area, business classification, and individual service listings matter more here than any other industry. Routes to 120+ blueprints.
Read →Restaurants, salons, gyms, hotels, auto shops. Why physical address, hours, and third-party directory alignment are critical when the customer comes to you. Routes to 130+ blueprints.
Read →Dentists, clinics, pharmacies, therapists. Why provider specialty, credentials, and accepted insurance matter in a high-trust industry where AI is cautious about recommendations. Routes to 60+ blueprints.
Read →NGOs, schools, research organizations. Why mission, programs, and involvement pathways matter when the transaction loop is donation or volunteering, not purchase. Routes to 35+ blueprints.
Read →Storefronts, product pages, collections. Why product details, availability, and shipping matter when AI is matching users to specific items. How to fill the gaps platform-generated schema leaves. Routes to 8 blueprints.
Read →Pricing, features, integrations, changelog. Why SaaS is the industry where AI comparison queries are most common and how structured pricing data determines who wins. Routes to 9 blueprints.
Read →Blogs, publishers, media companies. Why publisher identity, author credentials, and article metadata determine whether AI uses your content or cites your content. Routes to 55+ blueprints.
Read →What AI needs from each page type, regardless of industry. Each article links across to relevant blueprints in every family.
The single highest-impact page for AI visibility. Why the homepage is the identity anchor every other page references, and the common problems that make homepages invisible to AI.
Read →How AI matches specific user queries to specific services. Why one service per page with individual schema outperforms a single services page with everything on it.
Read →Making team credentials, provider expertise, and organizational background matchable by AI. Why about pages are content-rich and schema-poor.
Read →Why most FAQ schema is built for Google rich results, not for AI. How to write FAQ schema that gives AI operational context instead of keyword-stuffed questions.
Read →The difference between AI using your content and AI citing your content. What to mark up (author, publisher, topic) and what to skip (comment counts, reading time).
Read →How transparent pricing in schema lets AI pre-qualify users and why that drives 6–10x higher conversion rates. Covers SaaS tiers, service ranges, and ecommerce positioning.
Read →Check what AI systems can actually see on your site. Every article in this section routes to the AIFDS validator.
Step-by-step audit process: check what exists, identify what is missing, send the results to your developer. The Google Business Profile analogy that makes schema click for business owners.
Read →The gap between what business owners think AI sees and what AI actually sees. Includes the finding where AI served a contact email that only existed in JSON-LD schema — not anywhere visible on the page.
Read →Valid schema is not the same as complete schema. Why a page can pass Google's Rich Results Test with a green checkmark and still be invisible to AI. The gap the AIFDS validator fills.
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