Knowledge Graph

When someone asks an AI assistant who does technical SEO for a Shopify store, the model is not matching that sentence against a page. It is resolving entities — a company, a discipline, a platform — and deciding which ones it knows well enough to name. A brand it holds as a vague string gets left out of the answer. A brand it holds as a defined, connected entity gets cited.

That resolution runs on structure: an unambiguous definition, a declared type, and enough links to neighbouring concepts that the meaning is not in doubt. It is the same structure a classical search engine uses to decide what a page is genuinely about, which is why the work pays twice.

So we built one on our own domain rather than describing the idea in a deck. Every entity here is a real page with a plain definition, a declared schema.org type, its own FAQs, and links to the entities and services it actually relates to — 423 authored sections and 287 questions across 76 entities, wired together by 216 internal links.

The honest limit: a graph on one domain does not make a model cite you on its own. External signals — consistent citations, authoritative mentions, alignment with the graphs the engines already read — do the rest. This is the half we can show you, and the half most agencies sell without having built.