DEJAN's AI influence methodology treats language models as a measurable optimization surface by dividing visibility into internal parametric memory and live, retrieval-grounded search. Through the ARC framework, the system runs multi-directional association probes (E2B, B2E, Q2B) and direct relevance queries across platforms like Google, OpenAI, and Anthropic. To evaluate unprompted model memory, tools like Treewalker inspect token-level log probabilities to map alternative branch associations without search tools. Finally, DEJAN applies a Bayesian Content Optimizer to iteratively test and refine page snippets and text structures, determining which specific rank factors and content variations cause LLMs to favor a brand in generated answers.