DEJAN's approach treats AI search and chat platforms as an optimization surface divided into parametric memory (what models recall unprompted) and retrieval-grounded search (what models cite from the live web). Using the ARC engine, they audit brand associations, test relevance judgments, and run Bayesian snippet experiments to systematically shift model ranking preferences.