Entity SEO.

Entity SEO is the next step on from the classic keyword approach. Instead of tuning a page for search terms alone, the work is about making Google understand the thing behind the page — the entity. Who is this company, what does it stand for, which topics, places and people is it connected to? Google's Knowledge Graph is the underlying structure here: a database of things and their relationships rather than of strings and their frequency.

The background is that modern search and AI assistants work semantically. They assign content to a network of entities in order to capture context and meaning, rather than matching character strings. A company that is registered as a clearly delineated entity is more likely to be surfaced for relevant topics and named correctly in AI answers. A company that exists only as a set of keyword-optimised pages, with no consistent identity behind them, is much harder to place.

The practical levers are unglamorous but effective: consistent use of name, address and core topics across every channel; Organization and Person schema in JSON-LD with sameAs references to official profiles; topical depth around your actual areas of competence, which is what builds topical authority; and mentions on trustworthy third-party sources. Together these produce a coherent entity profile that says the same thing wherever a machine looks. The most common failure is not the absence of any one of them but disagreement between them — a schema block naming one legal entity while the imprint names another.

An example: a tax consultancy that uses the identical name, the identical specialisms and the identical named contacts on its website, in directories and in its schema markup is classified unambiguously by Google as the entity 'Tax consultancy X in Basel'. It then appears more reliably when someone asks an AI assistant for tax advisers in the region — not because it optimised for that question, but because there was no ambiguity to resolve.