The Content Optimizer evaluates ranking factors by generating testable optimization claims from content analysis and mapping them into a hierarchical Rank Factor Class taxonomy covering dimensions like alignment, substance, style, and proof. During iterative rounds, an acquisition algorithm selects hypotheses to implement as targeted snippet or line-numbered page modifications, which an LLM re-ranker then scores against competitor search results across multiple evaluation passes. Observed rank improvements update a Beta distribution representing the win probability of each claim, enabling the system to calculate precise Bayesian posterior shifts and determine which structural or rhetorical factors consistently influence AI ranking behavior.