Large Language Model Optimization (LLMO)

LLMO is another name in the AEO/GEO family, focused specifically on how LLMs form and recall knowledge about a brand. It spans both parametric knowledge (what a model has learned during training) and retrieved knowledge (what it pulls in at query time via search or RAG). The aim is for the model to associate the right facts, category, and recommendation with your brand.

Because models absorb whatever is repeated consistently across the web, LLMO leans heavily on entity consistency, authoritative third-party mentions, and structured data. When the same accurate claim about your brand appears across many independent, credible sources, models treat it as settled fact and are more likely to reproduce it.

DeepLexa helps with LLMO by revealing what models currently say about your brand and where their knowledge is missing or wrong, then recommending the mentions and entity fixes that correct it.