The Open Benchmark of AI Brand Recommendations.
AI is no longer just answering. It is shaping brand preference.
This page answers common questions about measuring brand visibility and recommendations in AI-generated answers.
AI Brand Visibility measures how brands and products are cited and recommended inside AI-generated answers from systems like ChatGPT, Gemini and Google AI Overview. It tracks mentions, ranking position, share of AI voice and changes over time.
SEO measures how webpages rank in search engine results. AI Brand Visibility measures how brands are recommended inside AI answers. Search engines rank pages; LLMs generate recommendations by selecting products, arguments and sources that best match the question.
The AI Brand Visibility Score (ABV Score) is an indicator designed to quantify a brand's recommendation performance across AI systems. It helps brands benchmark visibility, ranking position and share of LLM voice versus competitors, and monitor changes over time.
Share of LLM (or Share of AI Voice) is the proportion of AI recommendations a brand receives within a defined category and set of questions. It is comparable to Share of Voice, but computed from AI-generated answers.
LLMs do not rank pages like traditional search engines. They build answers by selecting products, routines, ingredients and proof elements that best fit the user's intent. Recommendation is influenced by narrative coherence, usage specificity, perceived evidence, clarity and source authority.
GEO (Generative Engine Optimization) refers to optimizing content so that AI systems can correctly interpret, cite and recommend brands and products in their generated answers. GEO focuses on how AI retrieves and synthesizes information, not only on webpage ranking.
AI Brand Visibility supports GEO by identifying which sources and Articles AI systems rely on, which arguments they repeat, and why certain products are recommended. This helps brands prioritize content, strengthen expertise signals and improve recommendation outcomes.
LLM Intelligence is the analysis of how AI models describe, compare and recommend brands, turning AI answers into strategic insights. It combines quantitative monitoring (visibility, ranking, share of voice) with qualitative analysis of recommendation narratives and implicit selection criteria.
The public benchmark is built by querying major AI systems with predefined consumer questions across selected categories. Responses are stored over time and analyzed to track brand and product mentions, ranking patterns and the sources referenced by AI answers.
Yes. aibrandviz.com provides free access to a public benchmark on selected categories and questions. Full coverage, custom categories, deeper analytics and expert interpretation are available through Dynvibe's professional offering.