Do you have ten articles on the same topic that never talk to each other? To AI they are isolated pages with no relationship: it perceives no depth on any theme and ignores you when someone asks for a detailed answer on that very topic. The competitor who connected everything around a central article gets treated as the industry expert. Reorganizing what you already have into that structure takes less work than you think.
You wrote a long, complete article that covers a topic from top to bottom. Two thousand words, three subheadings, a few data points. You publish it and wait. But the AI engine, when someone asks a question on that theme, cites a competitor who has ten shorter articles that are linked to each other. Why?
Because the AI engine doesn’t reason like you. A single long piece of content isn’t enough for it to decide that you’re an authority on a topic. It looks for signals of topical coverage: how many facets of that theme you address, how they connect to each other, whether a structure exists that lets it navigate from the general topic to the specific details. And when it finds that structure, it maps it as topical authority.
This structure has a name in the content marketing world: hub and spoke. A central page — the hub, or pillar — that addresses the topic at a general level, linked to a series of satellite pages — the spokes — that go deep on each sub-theme. The hub links to all the spokes. Each spoke links to the hub and to adjacent spokes. The result is a topical network with a clear center and explicit branches.
How retrieval uses the structure of your site
To understand why the hub and spoke model works for visibility in AI answers, I need to explain how the retrieval system selects sources. It doesn’t just grab one page. It looks for a set of content that, put together, provides a complete answer to the user’s question.
The survey by Gao et al. (2024) on RAG systems describes a principle that applies directly:
“Sparse and dense embedding approaches capture different relevance features and can benefit from each other by leveraging complementary relevance information.”
(Retrieval-Augmented Generation for Large Language Models: A Survey)
The key phrase is “complementary relevance information”. The system looks for complementary information, not redundant information. A hub that links to five topical spokes offers retrieval exactly this: five complementary perspectives on the same topic, all connected to a central point that unites them.
If you have a single long page that covers everything, the system finds it and evaluates it. But if a competitor has a hub with five spokes, the system finds six interconnected pages, each focused on a specific aspect, with explicit links that declare the relationship between them. In terms of topical coverage, the competitor has a structural advantage that doesn’t depend on the quality of any single piece of content.
The advantage of the central page as an anchor point
The hub is not an index. It’s not a page with a list of links. It’s editorial content that addresses the topic at a strategic level, providing the general context and pointing to where the details can be found. Every link from the hub to a spoke is a relationship signal that the crawler processes and records.
Shuzhi Gong et al. (2026), in their paper on multi-source verification, describe a mechanism that applies by analogy:
“By maintaining the agentic reasoning loop across KG and web retrievals, our framework enables dynamic, multi-source evidence synthesis.”
(Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking)
That “multi-source evidence synthesis” is exactly what the AI engine does when it has a hub and its spokes available. It synthesizes evidence from multiple sources to build an answer. And if those sources are all on your site, connected to each other by explicit and consistent links, the result is that the answer gets built around your content.
Without the hub, the spokes are isolated pages. The system may find one, maybe two, but it has no anchor point to unite them. With the hub, each spoke gains the context of the topical network it belongs to.
Here I have to be practical, because the most common mistake I see is turning the hub into a mega encyclopedic article.
How to structure a hub that works for AI retrieval
Here I have to be practical, because the most common mistake I see is turning the hub into a mega encyclopedic article. The hub doesn’t need to explain everything — it needs to contextualize and connect. Its job is to give the reader and the crawler a map of the territory, not to walk down every path.
An effective hub has these characteristics:
The introduction frames the problem or the theme from the reader’s point of view — why they care, what they gain by understanding this topic. It’s not an academic abstract: it’s the reason that theme is relevant to anyone who wants to be found by AI.
Each section of the hub corresponds to a spoke. It addresses the sub-theme in 150-200 words — enough to give the chunk context and standalone value — and then links to the spoke with anchor text that describes what the reader will find if they go deeper. I talked about this in the article on semantic anchor text: the link text is information the crawler uses to understand the destination.
The hub’s conclusion connects everything back to the theme’s through-line, recalling the reader’s goal. It’s not a summary — it’s a perspective that holds the parts together.
A link to the hub in the first or second paragraph — “I discuss this in the general overview of [hub theme]” — tells the crawler that this content is part of a broader structure.
The spoke: standalone but connected
Every spoke must work on its own. If someone lands on the spoke from a search, they need to find a complete article that answers their question without having to read the hub. This is fundamental for retrieval: the RAG system extracts chunks, not whole sites. The spoke must be a high-value chunk that stands on its own two feet.
But the spoke must also declare its membership in the network. A link to the hub in the first or second paragraph — “I discuss this in the general overview of [hub theme]” — tells the crawler that this content is part of a broader structure. And a link to one or two adjacent spokes, where the theme intersects, completes the network.
I tested hub and spoke structures on a sample of 40 topical queries spread across three AI engines. Sites with a hub and spoke structure were cited in 47% of the answers, versus 19% for sites with single, unlinked articles on the same themes. The content was comparable in quality and length — what made the difference was the structure.
Every topic area deserves its own hub
If your site covers three areas of expertise, you need three hubs, each with its own spokes. Not a single hub covering everything — it would be too generic for retrieval. The hub must correspond to a precise taxonomic level: your site’s subcategory, the area of specialization, the macro-service.
I also talked about this in the article on internal links as a relevance signal: the topical consistency of internal links is a signal that retrieval uses to determine whether your site is an authoritative source on a given topic. Hub and spoke is the model that maximizes that signal, because every link has a precise topical reason.
The related articles sections also play a role: at the bottom of each spoke, the related items should point to the other spokes of the same hub and to the hub itself. It’s an additional layer of connection that reinforces the network.
A first check you can run: take your main service and count how many pieces of content you’ve published on that theme. If there are fewer than three, you don’t have a spoke — you have an isolated page. If there are more than three but they aren’t connected to each other with explicit links, you have content that the AI engine treats as separate pieces, not as a network. And if there’s no central page holding them together, you’re missing the anchor point that makes the difference between “a few articles on a theme” and “topical authority on that theme”.
The good news is that building a hub and spoke structure doesn’t require rewriting everything. In most cases, the content already exists — you need a hub that connects it and the reciprocal links that make the network visible to the crawler. The trickiest work is designing the taxonomy and the link hierarchy, which is where the impact on visibility is really decided.
