The Content Optimizer evaluates competing hypotheses by modelling each claim's win rate as a Beta(α, β) distribution, where α represents historical rank improvements and β represents failed attempts. Before drawing a sample θ to select the best hypothesis, the engine applies rank-factor-class diversity weighting, exponentially penalising over-tested categories so the system balances testing unproven classes with exploiting high-performing hypotheses.

Once a candidate is selected, the system generates an edit and measures the rank shift against a baseline; if the observed rank improves, a binary Bernoulli reward increments α, whereas a non-improving round increments β to immediately update the posterior mean for subsequent rounds. While Thompson sampling serves as the default Bayesian acquisition policy, the architecture can also swap in alternative policies such as UCB or epsilon-greedy to navigate the exploration-exploitation trade-off.