The Content Optimizer uses an iterative Bayesian optimization loop that models hypothesis win rates via Beta distributions, awarding binary rewards whenever an edit improves median rank against competitor snippets in multi-sample re-ranking passes. An acquisition policy such as Thompson sampling selects hypotheses across a six-tier Rank Factor Class taxonomy, prompting an Ideator to craft isolated snippet variations or patch-level page edits that progressively maximize visibility.