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Optimising for an answer is not optimising for a ranking.

Generative Engine Optimization: the work that makes your content usable as an answer.

A ranking rewards a page. An answer rewards a sentence. Generative engines assemble responses out of fragments they can lift without losing the meaning, and they prefer fragments that are specific, attributable and internally consistent.

GEO is the discipline of writing and structuring content for that behaviour - and of removing the contradictions across your site that make a model skip you rather than risk being wrong.

What it covers

  • Answer-first structure and extractable paragraphs
  • Fact consistency across the whole site
  • Structured data that supports the claim in the text
  • Comparison, alternative and 'best for' content formats
  • Measurement of whether the change actually changed the answer

How it works

01

Find the sentence the model would lift

For each question that matters, we identify the one paragraph that answers it completely and check whether it survives being read alone. If understanding it requires the paragraph above, a model will not use it.

02

Remove the contradictions first

Two different numbers for the same thing in two places on your site is enough for a model to skip you rather than risk being wrong. This is unglamorous work and it is usually the fastest available gain.

03

Write for being cited and for being recommended - separately

We measured that between 35% and 48% of brand mentions in answers come from the link list rather than from sentences. Being used as a source and being named as the answer are two outcomes, and content that earns the first does not automatically earn the second.

04

Verify against the answer, not against the page

After publishing we re-run the prompts. The only proof that GEO worked is a changed answer - rankings, traffic and time-on-page say nothing about it.

What you get

  • Answer-first rewrite of the pages that matter most
  • Fact and figure reconciliation across the whole site
  • Structured data that backs what the text claims
  • Comparison and alternative pages in the formats models reach for
  • Before-and-after measurement on the same prompt set