DEJAN optimizes content for grounded AI retrieval using a multi-phase Bayesian optimization engine that formulates and tests discrete hypotheses against live or simulated search results.

The system maps competitors from search engine result pages (SERPs), generates baseline extractions, and uses specialized AI roles—such as seeders, ideators, and rankers—to iteratively test hypotheses. These edits are classified against an evolving Rank Factor taxonomy (spanning alignment, substance, architecture, style, framing, and proof) and evaluated across both snippet and full-page modes using re-ranking models. To prevent localized gains from harming broader search visibility, the framework incorporates blast-radius testing to measure how updates perform across related entity queries.