DEJAN's remediation loop begins by diagnosing factual discrepancies through the Fact & Gap Checker, which identifies specific topics where an AI model's native recall contradicts verified web sources. To fix these on-site, the Content Optimizer applies Bayesian rank-factor testing to target pages, generating line-level patch edits and structured snippet enhancements that improve page ranking across AI retrieval engines. For off-site reinforcement, the AI Outreach engine runs a red-and-blue agent loop where a writer agent generates natural articles adhering to strict co-link policies while a detector agent verifies that the placement cannot be flagged as sponsored. This synchronized push establishes a network of authoritative, crawlable citations that corrects real-time LLM grounding results and embeds accurate brand knowledge into future model training data.