The Content Optimizer is an iterative Bayesian testing engine that leverages multi-armed bandit strategies like Thompson sampling to formulate hypotheses, test snippet or line-level page rewrites, and rank variants against competitive search results. Across each optimization cycle, an LLM-based ranker measures performance shifts to update posterior beliefs across distinct rank factor classes, while integrated blast radius analysis monitors whether modifications unintentionally compromise rankings for other queries mapped to the same target URL.