LLMOcheki launches topic clusters, building the article structure AI is more likely to cite

Itera, Inc. (head office: Shinjuku-ku, Tokyo; Representative Director: Takayuki Muto) has begun offering a set of features in LLMOcheki, its tool for measuring and improving LLMO, that support topic clusters — the pillar page strategy — from design through building the internal links to verifying the effect.

Name a pillar article and internal links from the related articles are inserted automatically. Citations from AI and search traffic are tracked cluster by cluster, so design through to verification happens in one tool.

Background: AI search breaks a question apart and consults several sources

Generative AI breaks a single question into finer sub-questions, finds a source suited to each, and assembles its answer from them. For a question like “how to choose an LLMO tool”, information is gathered separately on the going rate, the differences in features, and the criteria for deciding on adoption.

A site with articles covering one theme systematically is therefore more likely to be referred to repeatedly across those separated questions.

Topic clusters are the established way to build that structure. But designing the pillar article, managing the internal links from related articles and verifying the effect all tend to be manual work, and few companies have been able to keep it up.

Feature 1: cluster map and keyword suggestions

The cluster structure for a theme is visualised as a diagram, so you can see at a glance where each article sits. Based on search demand data, the tool suggests the keyword for the pillar article and the long-tail keywords to place beneath it.

Articles you have already published can be taken into the structure, so there is no need to rebuild from scratch.

Feature 2: automatic internal linking

Name a pillar article and internal links suited to the context are inserted automatically into the related articles generated with AI. Missing links in existing articles can be repaired in bulk.

The inserted links can be checked and edited in the editing screen. Automation is there to make the work efficient; the final decision to publish rests with the editor by design.

Feature 3: verifying the effect cluster by cluster

Citations from AI and search traffic are measured for each cluster. Because tracking happens at the level of the theme rather than the individual article, you can see what changed as a result of building the structure.

Citation in AI search is also affected by external factors, so a change in the numbers is not necessarily down to the cluster alone. The metric assumes you read it alongside whatever else you have been doing.

Availability

Available on every LLMOcheki plan at no additional charge.

Comment from our Representative Director

“AI breaks a question apart and consults several sources, so however good a single article is, only so much of it gets picked up. That having a full set of articles per theme works has been said for a long time, but by hand it does not last. Connect design, production and verification on the same screen and it becomes work you can keep up.”

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