GEO explained: what generative engines look for

When a generative AI system describes a business, it typically synthesises information from multiple sources — the website, directory listings, reviews, and other pages that mention it. The result is only as accurate as the underlying material allows.

Consistency is the foundation

One of the more overlooked GEO signals is simple factual consistency: does the phone number on the contact page match the one in the footer and the one in a press release from two years ago? Do service descriptions agree across the homepage, the services page, and any linked brochure? Small inconsistencies that a human reader would skim past can produce a materially wrong summary when synthesised by a model that has no way to know which version is current.

Structure helps, but doesn't replace substance

Structured entity data — organisation schema, service schema, consistent naming — gives generative systems a clearer signal to work from. But structure alone can't compensate for thin or vague content. A page that structures an empty claim is still an empty claim.

What can reasonably be measured

• Whether key facts are stated consistently across a site's own pages.
• Whether structured data on the page matches its visible content.
• Whether content is specific enough to summarise accurately, rather than relying on vague marketing language.

What can't reasonably be measured or promised is a guarantee of how any specific AI platform will phrase a summary, since that depends on model behaviour outside any website owner's control. The realistic goal is giving generative systems accurate, unambiguous material to work with — and checking, periodically, that nothing has drifted out of sync.