In DEJAN's methodology, parametric memory represents the internal knowledge frozen into an LLM's weights during pre-training, operating entirely without live web retrieval. The platform audits this raw recall across major AI providers (Google, OpenAI, Anthropic) using specialized probe runs and the Treewalker engine. Treewalker evaluates token-level log probabilities and alternative branching paths above set confidence thresholds to uncover latent brand associations and measure how reliably an entity is recognized.

These ungrounded outputs are parsed into ranked statements and processed by an LLM-driven curation layer, which consolidates raw lines across multiple runs into a unified ledger of canonical claims. The platform then pairs these claims with the Fact & Gap Checker, contrasting internal model beliefs against grounded research to classify facts as confirmed, contradicted, or unprompted gaps. Finally, a human-in-the-loop ruling system allows operators to review and lock authoritative verdicts on specific claims, ensuring optimization strategies address genuine perceptual deficits rather than transient hallucinations.