DEJAN's Content Optimizer operates via an automated loop where an AI seeder proposes testable hypotheses—termed claims modeled as Beta distributions—that are categorized across structured rank factor categories. An acquisition policy such as Thompson sampling or UCB selects promising hypotheses, prompting an ideator to apply targeted edits before an AI judge model re-ranks the candidate against competitors across multiple samples, updating posterior win probabilities to isolate winning content patterns.