Generative engine optimisation

GEO

Whether generative engines will cite this store when composing a recommendation.

What it costs to fail

A disallowed agent is a hard exclusion, not a ranking penalty — the store is absent from the answer rather than low in it, and no amount of catalogue quality compensates. Where there is no vendor and no date, a model has nothing to attribute a claim to and prefers a source that does.

What is scored

llms.txt presence and quality, per-agent policy for GPTBot / ClaudeBot / PerplexityBot / Google-Extended, quotable claim density, named entities, dates, vendor and provenance attribution, freshness (updated_at).

Each of these is a published criterion with a published weight — the full rubric.

What fixes it

Publishing llms.txt states your terms to generative crawlers instead of leaving them to infer. Naming a vendor and keeping updated_at current give a model something to attribute the claim to and a reason to prefer the record over a stale one. Unblocking an agent in robots.txt is the one change that removes a hard exclusion rather than improving a ranking.

The other three

Every scan runs all four, in every tier. A catalogue that clears this pillar can still be invisible to the systems the others cover.

SEO

Whether conventional search engines can crawl, parse, and rank the catalogue.

Engage

AEO

Whether the catalogue can be lifted as a direct answer to a shopper's question.

Engage

AIO

Whether an AI system can correctly parse and act on the record without guessing. Includes transactability as a named sub-score.

Engage
◎ Scan Free