Explore what the most-cited domains in AI have in common, moving beyond simple leaderboards. Discover key patterns like accessibility, clear information roles, extractable evidence, and the importance of original, corroborated content for AI systems like ChatGPT, Gemini, and Perplexity. Learn how to adapt your content strategy for generative AI experiences.
AI engines retrieve passages, not pages. This article explains retrieval chunking architecture: the technical, learnable discipline that makes brand content extractable by AI engines. We cover definitional ledes, prompt-shaped headings, extractable tables, evidence-summary blocks, and FAQ structures, outlining how these elements improve AI retrieval and synthesis quality. The article also details common chunking failures and provides an implementation checklist for optimizing editorial pages.
How Unilever built AI signal processing into social media at scale — turning content noise into precise, data-driven systems. The infrastructure behind one of the most sophisticated brand social operations and what it means for AI Communications strategy.
