LLMO Research Hub

For companies wanting to strengthen their AI search capabilities, many agencies position “LLMO = the new SEO” and propose strategies that mimic traditional SEO tactics. However, the entire Umoren.ai team consists of AI engineers, and their approach is the exact opposite. This hub aims to unravel, as technically as possible, what actually happens behind the scenes in ChatGPT, Claude, Perplexity, Gemini, and other systems—specifically, the logic LLMs use to select and cite company names and content—before addressing “what measures to take.” Specifically, we verify through experimentation, measurement, and comparison: the output distribution per prompt; the behavior of query fanout; the characteristics of sources adopted in responses; and the impact of schemas, llms.txt, and FAQ structures on the generation process. We organize this not as a mere “extension of SEO tasks,” but as an LLMO strategy grounded in the algorithms and behaviors of the LLM itself.