DEJAN's Content Optimizer uses an acquisition policy—such as Thompson sampling or UCB—to select hypotheses from a structured Rank Factor taxonomy covering areas like alignment, substance, and proof. An ideator model then generates tailored snippet or page variants, which an LLM-based ranker scores across multiple samples against competitor results to update the Bayesian posterior distribution and converge on winning edits.