Do your articles jump from one topic to another with no logical thread? To AI, your site is a generalist with no specialization: it perceives no depth on any subject and leaves you out of the industry answers. Meanwhile, the competitor who connected everything with a vertical structure gets cited as an expert — even with less content than you. Reorganizing what you already have is faster than you think, and the results are measurable.
Picture your site as a library. You have a hundred books — but they’re all piled up on a single shelf, with no order, no sections. A customer walks in and asks: “Do you have anything comprehensive on healthcare marketing?” You know you have fifteen titles on the subject. But the customer can’t find them, because they’re mixed in with cooking, gardening, and fiction. To them, your library specializes in nothing.
AI engines do exactly the same thing. When a retrieval system scans your site, it doesn’t just read the individual pieces of content — it analyzes how they’re connected to each other. If your articles on a specific topic link to one another forming a coherent block, the system perceives vertical expertise. If those links jump from one topic to another with no logic, it perceives confusion. And in confusion, it chooses a competitor with a clearer structure.
This is the principle behind silo architecture, and it has direct implications for your visibility in AI answers.
What it means to organize content into silos
A silo is a grouping of content that revolves around a macro-topic, connected by internal links and separated — in the link structure — from the other groupings. At the center sits a pillar page that covers the topic broadly. Around it, the supporting articles that dive into each sub-topic. The internal links follow a precise rule: every spoke links to the pillar, the pillar links to all the spokes, the spokes link to each other. Links to different silos pass only through the pillars — never from the spokes.
This isn’t a gimmick dreamed up by SEO consultants. It’s a structure that communicates hierarchy and expertise mechanically, through the link graph. A retrieval system that travels through your site and encounters twenty interconnected articles on a single topic, with a central page that gathers them, doesn’t need to “understand” that you’re an expert — it deduces it from the topology.
AI engines don’t look for the best answer in absolute terms. They look for the best answer from a source that demonstrates authority on the topic. And authority, in the world of retrieval, is measured partly by structure.
Why link structure defines perceived expertise
There’s a concept in the literature that clarifies this mechanism sharply. In the work of Daniel Borin et al. (2025) on the importance of retrieval for the quality of AI answers, we read:
“Overall, existing approaches highlight that fact-checking balanced accuracy is ultimately bounded by evidence retrieval quality, motivating the need for domain-specific IR frameworks.”
(Daniel Borin et al. 2025)
The research world is documenting that the quality of an AI answer depends on the quality of evidence retrieval — and that domain-specific information retrieval frameworks are needed. A topical silo is exactly that: a domain-specific framework inside your site. Each silo tells the retrieval system: “on this topic you’ll find everything here, organized, connected, complete.”
When your site has this structure, retrieval works better because the system doesn’t have to reconstruct the relationship between scattered pieces of content. It finds it ready-made in the link map. And retrieval that works better produces better answers — which means the AI engine has an incentive to draw from your silo rather than from a site where the same content exists but isn’t connected.
If you have a silo on healthcare marketing and one on technical SEO, the articles in the first must not contain direct links to the second.
Each silo is a recognizable vertical authority
The second mechanism concerns the diversity of domains that AI engines have to handle. In the work of Michele Battisti et al. (2024), a finding emerges that illuminates the problem:
“AI Search services differ significantly from each other in their domain diversity.”
(Michele Battisti et al. 2024)
AI Search services differ in the diversity of the domains they cover. Every AI engine has its strengths and its thematic gaps. When one of these engines looks for sources on a specific subject, it needs to quickly identify who is authoritative in that domain. A site with a complete silo on that topic — ten, fifteen, twenty articles all connected — is infinitely more recognizable as a vertical authority than a site with the same content scattered without structure.
The silo does for perceived expertise what an editorial series does for a publishing house. Twenty books on the same topic make you a specialist. Twenty across twenty different topics make you a generalist. AI engines apply the same logic — except they measure it through the density of internal links, not through the catalog.
The anchor text of every link should contain the topic of the destination page — never “read here,” never “learn more.”
The principle holds for specialized models too
There’s a third element that reinforces the mechanism. Zhao et al. (2023), in their survey on large language models, document how domain specialization works:
“Med-PaLM is a domain-specific PaLM, designed to provide high-quality answers to medical questions.”
(Zhao et al. 2023)
Med-PaLM is a model specialized for the medical domain. The point isn’t the model itself — it’s the principle: domain specialization improves the quality of answers. The same holds for the sources that feed these systems. A well-built topical silo is, for retrieval, the equivalent of a specialized source. You’re not saying “I have content on this topic” — you’re saying “I have a complete and interconnected structure on this topic.” The difference is the same as between having twenty articles and having a topical encyclopedia.
How to build a silo that works
The principle is simple, the execution requires method. Every macro-topic on your site should become a self-contained silo with three levels.
The pillar page is the gravitational center. It covers the topic broadly — not superficially, broadly. It must answer the main questions and link to every spoke for those who want to dig deeper. It’s the page that receives the most internal links in the silo, and that gives it the greatest weight in the hierarchy perceived by retrieval. I talked about this in the article on internal links as a relevance signal — the page with the most received links is treated as the main resource for the topic.
The spokes are the in-depth articles. Each one covers a specific sub-topic and links to the pillar (upward) and to the other spokes in the same silo (laterally). The anchor text of every link must contain the topic of the destination page — never “read here,” never “learn more.” The semantic signal of the link is an integral part of the silo structure.
The boundaries are the most important rule and the most ignored. Spokes don’t link to spokes in other silos. If you have a silo on healthcare marketing and one on technical SEO, the articles in the first must not contain direct links to the second. Links between different silos pass through the pillars. This isn’t rigidity — it’s thematic coherence. A link that leaves the silo dilutes the vertical expertise signal you’re building.
Then there are the contextual bridges — those transition paragraphs before a link that explain why the linked content is relevant. Within a silo, every link should have its bridge. Connecting isn’t enough: the text around the link is part of the signal that retrieval processes.
The most common mistake: silos on paper, chaos in the links
I analyzed about thirty sites that claimed to use a silo structure. In more than half, the structure existed in the navigation menu — defined categories, assigned content — but the internal links told a different story. Spokes from silo A linking directly to spokes in silo B. Pillars with no links from their own spokes. Isolated spokes. The result: a structure that looked organized to the human eye, but appeared chaotic to a crawler.
A silo works only if the link graph mirrors the taxonomy. If your navigation says “this content is a group” but the internal links don’t confirm it, the signal is contradictory. And faced with contradictory signals, the retrieval system doesn’t choose the most generous one — it chooses the safest one, which often means drawing from a competitor with a clearer structure.
A check to get started
Take your most important macro-topic — the one you want to be perceived as an authority on. List all the content you’ve published on that topic. Now open each one and check: does it link to the pillar page? Does it link to the other content on the same topic? Does it contain direct links to content on completely different topics?
If you find spokes that don’t link to the pillar, pillars that don’t link to the spokes, or links that leave the silo without passing through the pillar, you’ve found the cracks. Every crack is a point where the vertical expertise signal leaks away.
This is a starting check, of course. To map the internal link graph and verify that the topology matches the taxonomy, you need professional tools and a full structural analysis. But that first manual check already gives you a sense of how closely your site’s real structure matches the one you had in mind. In most cases, the gap is wider than you expect.
