A curated index of the primary studies, large-scale data analyses, and peer-reviewed research on how AI engines retrieve, cite, and recommend brands. Organised by research question. Updated as new evidence emerges.
The AI search visibility space has a significant noise problem. Many widely cited statistics come from vendors with a commercial interest in the findings, thin sample sizes, or secondary reporting that has drifted from the original source. This index distinguishes between primary research, vendor-commissioned studies, and claims that lack reliable sourcing.
The strongest evidence in this space comes from a small number of large-scale studies with clear methodology and no direct vendor incentive. The Princeton KDD paper, the Ahrefs brand correlation studies, and the Seer Interactive citation analysis are the three most methodologically rigorous sources available as of mid-2026.
The most important question in the field. The answer reshapes how visibility strategy should be built — and it differs significantly from what traditional SEO optimises for.
The only controlled experiment available in this space. Everything else is correlational. The Princeton findings are the closest thing to causal evidence for on-page optimisation.
Understanding volatility is essential before measuring anything. Position tracking is largely meaningless in a probabilistic system. These studies establish the baseline for why frequency-based measurement is necessary.
Freshness preference varies significantly between engines. Perplexity is the most recency-biased. Google AI Overviews are the exception — they favour slightly older, more authoritative content.
Understanding the size of the opportunity and the downstream commercial effects of AI visibility is essential context for any brand investment in this area.
The free scanner applies the signal framework from this research to your specific brand and category — returning your AI Appearance Rate, technical signal audit, and a view of which competitors are appearing instead.