Keyword Clustering

Keyword clustering is the process of grouping a list of keywords into topic-based sets, so each cluster maps to one page or section. It turns a flat keyword list into a content plan — one article per cluster, rather than one thin page per keyword.

SERP-overlap tools group keywords that share ranking URLs on Google. This tool clusters by meaning: it embeds each keyword as a vector and groups by semantic similarity, so it catches phrasings that belong together even when Google's results don't yet overlap — closer to how AI engines read intent.

Up to 1,000 keywords, one per line. Larger lists cluster in seconds, and you control the granularity — looser for fewer, broader groups, tighter for more specific ones.

Each keyword is embedded as a high-dimensional vector, pairwise cosine similarity is computed, and hierarchical clustering groups them at an adaptive threshold. Each cluster is then given a representative label drawn from its most central members.

Each cluster becomes a content target — typically one pillar or article per cluster, with the individual keywords as the sections to cover. It's the fastest way to turn a keyword export into a pillar-and-spoke plan. The tool is free; sign-up is required so your runs can be saved.