DEJAN systematically optimizes for retrieval-grounded search by monitoring real-time citations and unselected browsed sources across search-enabled models such as Gemini, ChatGPT, and Claude. Through the Bayesian Content Optimizer, the platform tests structured hypotheses categorized across a locked rank factor taxonomy—such as substance, architecture, and alignment. An iterative loop proposes line-level page edits or extractive snippets, evaluates the candidate against competing search results using multi-sample LLM rankers, and updates Beta-distributed posterior probabilities. This process identifies exactly which phrasing, proof points, and structural patterns compel an AI assistant to select and cite the target page rather than competitor domains.