Generative Engine Optimization (GEO)

Generative Engine Optimization is the practice of influencing what AI systems say about you when they summarise your category. Search increasingly returns a synthesised paragraph rather than a list of options, and that paragraph is composed from sources the model considers reliable. If your brand is absent from those sources, you are absent from the answer — regardless of where you rank.

The narrative problem

Classical SEO asks whether you appear. GEO asks what is said. A model can cite you and still describe you inaccurately: attributing capabilities you dropped, positioning you in the wrong segment, or repeating a criticism from a years-old review that happens to be well-indexed.

That description is assembled from whatever the model has absorbed about you across the web — your own site, directories, forums, press coverage, comparison articles. Correcting it means correcting the underlying sources, not petitioning the model.

What actually moves generative output

Consistency across sources — models weight claims corroborated in several independent places, so contradictory descriptions of your own product dilute confidence
Clear, extractable positioning — a stated, unambiguous description of what you do and who for, in language a model can lift without interpretation
Third-party corroboration — independent coverage carries more weight than your own marketing copy, which models discount appropriately
Currency — outdated information persists in indexes long after you change, so stale sources need actively refreshing

Measuring an opaque system

Nobody has full visibility into these models. We work with repeatable sampling: a fixed prompt set run against the major systems on a schedule, logging whether you appear, how you are described, and who is recommended instead. That gives a defensible trend line rather than a guess.

What you get

Control Of Your Description

We correct the sources models draw on, so summaries describe your current positioning rather than an outdated or inaccurate version.

Presence In Category Answers

Being among the options a model names when asked for recommendations captures demand that never reaches a results page.

Competitive Visibility

Tracking who gets recommended instead of you turns an invisible surface into a measurable competitive picture.

Corroboration Where It Counts

Independent third-party coverage carries weight your own marketing copy cannot, and we build it deliberately.

How we run it

Step 1 — Generative Baseline

We run a fixed prompt set across the major AI systems and record exactly how your brand is described, cited or omitted.

Step 2 — Source Tracing

We identify which pages and third-party sources are shaping that narrative, including the outdated ones still driving it.

Step 3 — Narrative Correction

Owned content, structured data and third-party sources are updated so a consistent, current description is what models find.

Step 4 — Scheduled Re-Sampling

The prompt set is re-run on a cadence to track description accuracy and share of recommendation over time.

Frequently asked

They overlap heavily and we often run them together. AEO focuses on being cited as the source of a specific answer — getting your passage quoted. GEO focuses on the broader narrative: what models say about your brand and category, whether you are among the options named when someone asks for recommendations, and whether that description is accurate. AEO is about a question; GEO is about your reputation inside these systems.

Usually, though not by contacting the model provider. The description is assembled from sources the system absorbed — your site, directories, forums, old press coverage, comparison articles. Correction means finding which sources carry the wrong information and updating or outweighing them with consistent, current alternatives. It is slower than editing a page, because indexes and training data refresh on their own schedules, but the underlying cause is addressable.

Through repeatable sampling rather than claimed access. We maintain a fixed set of category-relevant prompts, run them against the major systems on a schedule, and log whether you appeared, how you were described and who was recommended instead. Because the prompt set and cadence stay constant, the results are comparable over time. It is a sample, not a census, and we present it as a trend rather than an exact share figure.

No, and treating it as a replacement would be a mistake. Generative systems draw heavily on the same web they crawl for search, so authority, quality content and technical health remain the foundation for both. GEO adds a narrative and consistency layer on top. Sites that abandon classical SEO to chase AI visibility usually lose both, since the signals that make you credible to a model largely overlap with those that make you rank.

The behaviour is already mainstream in many categories, particularly research-heavy B2B and considered consumer purchases where buyers now start with an assistant rather than a search box. The practical argument for acting early is that corroboration takes time to accumulate — you cannot compress a year of consistent third-party coverage into a launch week. Brands establishing consistency now are better positioned than those starting once the shift is undeniable.

Included

Google SGE Optimization
Brand Narrative Control
Generative Summary Tracking

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Related services

GEO Google SGE AI Search

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