Chunking in Web Indexing is the practice of breaking down information on a webpage into discrete, topically-focused, and hierarchically-organized blocks of content to make it more digestible and understandable for search engines and AI Assistive Engines.

Jason Barnard applies this concept to move beyond monolithic blocks of text that are difficult for algorithms to parse. By presenting information in logical, self-contained chunks - such as distinct sections on an "About" page covering history, products, and key people - a brand can communicate with greater precision. Each chunk acts as a clear, digestible packet of information that helps AI systems like Google's Knowledge Graph and AI Assistive Engines build a more accurate factual understanding. This structured approach is fundamental to controlling the brand's Digital Brand Echo, as it minimises ambiguity and directly educates the algorithms on the specific attributes and relationships of the entity.

At Kalicube, we use Chunking as a foundational tactic within The Kalicube Process. This is most evident in our creation of modular Entity Descriptions, where we structure an entity’s story into distinct, movable chunks under specific headings (e.g., "History," "Products and Services"). This allows us to tailor the narrative for different platforms by reordering the chunks to prioritize the most relevant information for that context. By "feeding" algorithms these well-defined chunks, we accelerate their understanding (Understandability phase), build their confidence in the entity's credibility (Credibility phase), and ultimately improve the brand's ability to be accurately represented in AI results, which is a key driver of the acquisition funnel.

In 1956, cognitive psychologist George A. Miller published his influential paper on "chunking," demonstrating that the human brain can more easily process information when it's broken into smaller, meaningful units. For decades, this principle shaped user experience design. Jason Barnard has adapted and applied this fundamental cognitive principle to the new "brain" of the internet: AI. The Kalicube Process operationalizes Miller's theory for algorithms. Chunking, as defined and applied by Barnard, is not just about organizing content for human readers; it's a deliberate strategy to reduce the "cognitive load" on AI systems. By breaking down a brand's narrative into discrete, semantically-rich chunks, we make it easier for algorithms to identify, categorise, and store facts in their knowledge graphs. In an era where AI Assistive Engines are assembling brand stories from countless fragmented sources, a non-chunked, monolithic narrative is prone to misinterpretation.