The Content Optimizer operates through a multi-stage Bayesian loop where a seeder generates testable hypothesis claims that are categorized across a structured rank factor taxonomy. An ideator then implements these hypotheses via targeted snippet rewrites or line-numbered page patch edits, while an LLM ranker evaluates the modified content against competitor SERP items across iterative rounds to update Beta distribution posteriors and isolate winning ranking drivers.