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SEO Vendor Releases First Reproducible Study on How AI Models Recommend Brands

Henderson, NV -- SEO Vendor today announced the publication of their first-of-a-kind, fully reproducible, open-source study designed to measure how large language models recommend brands, websites, and entities. The study introduces a statistically grounded methodology for evaluating AI-driven brand visibility across models, categories, and geographic locales.

As AI assistants increasingly influence consumer discovery, the ability to measure how brands surface inside large language models has become strategically important. Until now, most analyses of AI brand visibility relied on isolated prompts or anecdotal testing. SEO Vendor's research replaces that approach with large-scale sampling and mathematical consensus modeling.

The study, titled Entity-Conditioned Probing with Resampling: Validity and Reliability for Measuring LLM Brand/Site Recommendations, analyzed 15,600 AI-generated samples across 52 categories and locales. Using a multi-sampling framework, the research quantified the stability and variance of model recommendations, allowing for statistically defensible conclusions rather than single-response observations.

This research establishes a measurable framework for AI brand visibility, said Jim Liu, CEO of SEO Vendor. As AI systems become a primary layer of digital discovery, brands need more than screenshots of chatbot responses. They need reliable, reproducible metrics.

The research applies a consensus-based statistical algorithm to evaluate how consistently models surface specific brands or entities. By sampling responses multiple times across categories and contexts, the study demonstrates how recommendation patterns fluctuate and where stability can be mathematically validated.

To ensure transparency, SEO Vendor made the full study publicly accessible. The preprint is available on Zenodo, with accompanying code hosted on GitHub and the dataset published on Hugging Face. By releasing the methodology, source code, and data, the company invites independent review and replication by researchers, agencies, and developers.

While the open-source RankLens Entities study is separate from SEO Vendor's RankLens AI Visibility platform, it serves as the scientific backbone of the application. The findings validate that RankLens uses statistically grounded sampling rather than single-run prompts and applies a mathematically consistent framework for measuring entity visibility across models.

For agencies and enterprise brands, this development introduces a defensible way to evaluate AI visibility as part of broader SEO and digital strategy. As conversational AI continues to shape how users discover products, services, and information, measurable visibility within these systems is expected to become an increasingly important performance indicator.

SEO Vendor stated that it will continue expanding research into AI-native discovery metrics and large-scale LLM visibility testing. The company positions this publication as a foundational step toward standardized measurement in the AI search era.

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Contact Information:
Media Contact:

Natalie Vaghi

Account Director

natalie@seovendor.co

ask@seovendor.co

1-800-398-5212

675 S Green Valley Parkway #1243

Henderson, NV 89052

seovendor.co



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