Vector topical maps

What it is

A topical map is a picture of the territory your site covers — and, more usefully, the territory it does not. It answers "what should we write next?" with evidence instead of instinct.

This goes beyond keyword lists. Pages and whole domains become vectors, and gaps are measured as distance in meaning space.

How it works

• Embedding clustering — group content by meaning rather than by shared words, so two pages about the same idea with different vocabulary end up together.
• Domain centroids — represent an entire site as a single vector, which makes sites comparable at a glance.
• Semantic gap analysis — find where competitors own topical space you do not, measured by cosine distance.
• 3D projection — project everything into a navigable semantic space you can look at, because a map you cannot see is not a map.

Embeddings default to a self-contained TF-IDF plus SVD pipeline, so there is no external dependency and no per-call cost. A real model can be plugged in when you want one.

What it does for your site

It gives you a content roadmap with a reason behind each item. Where are you strong and should defend? Where is a competitor's authority thin enough to take? Which clusters are half-covered, with a hub page but no spokes? Those are the questions a topical map answers, and each answer is a decision about what to write next quarter.

Where the results go

Maps are built from clusters and graph authority, and each identified gap becomes a forecast.