About LLMBP
LLMBP is a research-driven publication dedicated to understanding how brands, entities, and information appear inside answers generated by Large Language Models.
As AI assistants increasingly mediate how people discover products, companies, and knowledge, a new visibility layer is emerging — one where decisions are influenced before a traditional search even happens.
LLMBP studies this new ecosystem and explores the signals, structures, and strategies that shape visibility inside AI-generated answers.
The Rise of AI Discovery
Search is evolving from links to answers.
Instead of presenting ten blue links, AI systems synthesize knowledge from across the web and generate responses that directly influence user decisions.
This shift is creating a new discipline at the intersection of SEO, AI systems, and information architecture: Generative Engine Optimization (GEO).
Understanding how AI models choose which brands, sources, and narratives appear in their responses is becoming critical for companies operating in the modern digital ecosystem.
What We Study
LLMBP focuses on the mechanics behind AI-generated visibility and recommendation patterns across major language models.
Our research explores questions such as:
- Why certain brands are consistently mentioned in AI-generated answers
- How entities and narratives become reinforced across models
- What signals influence probabilistic inclusion in LLM responses
- How content architecture affects AI interpretation
- How companies can build authority within generative ecosystems
By analyzing these patterns, we aim to provide practical insights for marketers, SEO professionals, and organizations navigating the next phase of digital discovery.
Our Research Approach
The LLMBP platform is maintained by a small research team that conducts ongoing experiments to better understand how LLM systems generate answers and determine which brands and sources appear within them.
Our work includes prompt testing, competitive analysis, entity mapping, narrative analysis, and structured experimentation across multiple AI systems.
These experiments help reveal emerging patterns in how large language models interpret the web and construct answers for users.
About the Editor
LLMBP is managed by Eyal Fadlon, Chief Growth Officer (CGO) at 42A, a platform focused on AI brand visibility and generative optimization.
Eyal has more than two decades of experience in digital marketing, growth strategy, and search engine optimization. Throughout his career he has held multiple leadership roles across SEO, digital growth, and performance marketing.
He was among the early professionals working in SEO during the formative years of modern search marketing and has since led large-scale organic growth initiatives and digital visibility strategies across global markets.
In recent years his work has focused on the intersection of AI systems, LLM discovery, and brand visibility inside generative search environments.
Our Mission
Our mission is to make GEO research, experimentation, and practical frameworks accessible to the global community of SEO professionals, marketers, and digital strategists.
We believe the next generation of the internet will be shaped by AI systems that summarize and interpret the web on behalf of users. Ensuring that these systems rely on trustworthy, structured, and high-quality information is critical.
By publishing research, experiments, and analysis, LLMBP aims to contribute to a more transparent and reliable information ecosystem.
Join the Research
LLMBP welcomes collaboration with AI leaders, SEO professionals, GEO specialists, and digital strategists interested in exploring the future of AI-driven discovery.
If you are working in AI visibility, generative search, or large-scale SEO experimentation and would like to contribute insights, data, or research ideas, we invite you to get in touch.
You can reach us through the Contact page.
