160 AI responses. 110 cited brands. Every one audited. The pattern is structural — and replicable.
By William Bouch · Last updated September 27, 2026
In February 2026, 40 real industry queries were submitted across ChatGPT, Claude, Gemini, and Perplexity — four verticals, four platforms, 160 responses. Every cited brand was audited. No surveys. No proxies. Direct observation of what 110 cited brand websites had in common.
97.3% of cited brands had Schema.org structured data. Brands with Schema were cited 35.67 times more often than brands without it. Only 3 of 110 cited brands lacked Schema—all were small local businesses cited solely by Perplexity through live web search.
What this means: Without Schema.org JSON-LD, you are structurally invisible to the citation layer of AI answer engines. Three exceptions exist across 110 brands — all were Perplexity live-search citations of local businesses. For everyone else, Schema is the entry requirement. See our Schema Markup for AEO guide →
109 of 110 cited brands had customer reviews or testimonials visible on their site or on third-party platforms. The single exception was a small law firm cited once by Perplexity. AI engines use review presence as a trust and social proof signal when deciding which brands to recommend.
Implication: Actively collect and display customer reviews. Google reviews, testimonials on your site, and third-party review platforms all count.
63.6% of cited brands had dedicated FAQ pages or FAQ sections with structured FAQPage schema. Brands with FAQ content were cited 1.75 times more often than those without. FAQ content gives AI engines pre-formatted Q&A pairs that are easy to extract and cite directly.
91.8% of cited brands had high domain authority scores. AI engines heavily weight established, credible domains. However, this is a trailing indicator—you build domain authority through the other signals (Schema, reviews, quality content), not independently.
All four AI platforms agreed on the top-cited brand in only 2.5% of queries (1 out of 40). Each engine has its own data sources, ranking logic, and preferences. This means optimizing for just one AI engine is insufficient—you need a strategy that works across all four.
Key insight: Claude cited brands 97.5% of the time (highest), but Perplexity was the only platform providing clickable source URLs. ChatGPT and Gemini mention brands by name without linking. Each engine delivers value differently.
B2B SaaS had the highest citation rate at 95%, with AI engines citing specific brand names and linking to service pages. Local service queries (HVAC, plumbing, legal) were different: AI engines primarily cited directory platforms like Yelp, Google Maps, and Angi (48.2% of all citations) rather than individual business websites.
For local businesses: Being listed and well-reviewed on major directories is as important as optimizing your own site. For B2B: your own site's AEO matters most.
This is the priority sequence. As you implement, start at Tier 1 and confirm each prerequisite before moving to accelerators and differentiators:
Schema.org JSON-LD markup (97.3%) • Customer reviews (99.1%) • High domain authority (91.8%)
FAQ pages with FAQPage schema (63.6%, 1.75x lift) • Pricing transparency • AI crawler access (robots.txt)
GIST algorithm alignment • Information gain density • Multi-engine optimization • llms.txt / ai.txt
In our first case study, AEOfix achieved 70% AI visibility in 6 days using comprehensive Schema markup, FAQ-first content, and E-E-A-T signals. This 110-brand audit independently confirms that exact approach: the top three signals we implemented (Schema, FAQ content, authority signals) are the same three signals that 88% of all AI-cited brands share.
160 responses, per-engine breakdowns, industry analysis, and raw AI response data. A free AI visibility check applies the same lens to your brand — so you know exactly where you currently stand before deciding whether you need the Source Map Report's full 150-query depth.