Generative Engine Optimisation
A lot of AI answers aren't a single quote from a single source. They're a blended paragraph built from four or five sources at once. GEO is about how you show up inside that blend, not just whether you get the one clean citation.
What Generative Engine Optimisation covers
Ask an AI system something like "best accounting software for freelancers" and you'll rarely get a single-source answer. You'll get a short paragraph mentioning four or five products, usually with a one-line description of each. That's synthesis, and it behaves differently to a direct-answer extraction.
In a synthesised answer, the model is weighing several things at once: how consistently a brand is described across the sources it's pulling from, whether those sources corroborate each other, and how recent the information looks. A brand described one way on its own site and a completely different way on a review platform tends to get flattened into a vaguer, less useful mention, or dropped altogether.
GEO overlaps with AEO but isn't the same job. AEO is about winning the single quoted line. GEO is about being accurately and favourably represented in the shared paragraph, even when you're not the only brand mentioned in it.
Why the blended answer matters as much as the single quote
In categories with several credible competitors, most of the AI-generated answers your buyers see are comparison-style by default. Nobody asks "what is the only good option in this category", they ask for the best two or three, and the model tries to give a fair answer across the field.
That means it's not really about beating everyone else for the one citation. It's about whether you're in that shared paragraph at all, and whether the one line describing you is accurate, current and reasonably flattering. We've seen brands with a strong single-source AEO presence still get left out of these blended answers, because the sources a model uses for synthesis weren't the same ones it uses for direct extraction.
How we approach GEO work
Map the synthesis prompts
We identify the comparison and recommendation-style questions in your category, the ones that reliably pull several brands into one generated answer rather than a single quote.
Audit current representation
We check how you're currently described in those blended answers, if you appear at all, and how that description compares to your competitors' in the same paragraph.
Strengthen the source material
We work on both your own content and the third-party pages models are drawing from, so the description staying consistent across sources instead of contradicting itself.
Track sentiment, not just presence
Being mentioned isn't the whole picture. We track whether the mention is neutral, favourable, or comes with a caveat attached, since that's often the more useful signal.
Re-test on a schedule
Generated answers shift as models get updated or add new sources, so this isn't a one-off project. We rerun the same prompts on a regular cadence and flag what's changed.
What you actually get
- A full list of synthesis-style prompts relevant to your category
- A current-state audit of how you appear in blended, multi-source answers
- Recommendations for your own content and for third-party sources models cite
- A sentiment tracker covering favourable, neutral, negative and absent mentions
- Scheduled re-testing with plain-language change reports
Works well alongside
- Answer Engine Optimisation, for winning the single quoted line, not just the blended mention.
- AI Brand Visibility, to measure how that blended presence compares to named competitors.
Questions people ask about GEO
How is GEO different from AEO?+
Can GEO fix a negative or inaccurate mention?+
Do my competitors see the exact same generated answer I do?+
What counts as a source in a generative synthesis answer?+
Want to see how you're described in the blended answers?
A free audit shows exactly how ChatGPT, Gemini and Perplexity answer your category today, and where Generative Engine Optimisation could close the gap.
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