DEJAN’s Content Optimizer evaluates snippets by first establishing baseline positions against competing SERP results and generating testable hypotheses via an automated seeder that extracts latent rank drivers. In each round, the acquisition engine balances exploration and exploitation by applying Thompson sampling across structured Rank Factor classes (such as alignment, substance, architecture, and proof) to pick a promising hypothesis, prompting an ideator model to rewrite snippets or produce targeted XML patch edits.

An LLM ranker subsequently scores multiple output samples against the competitive set to determine median positional changes and isolate true ranking signals from generative variance. The system records binary wins or losses to update the Beta distribution posterior for each tested claim, iterating until the candidate snippet achieves a top ranking, settles into proven/disproven states, or exhausts its configured round budget.