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Generative Engine Optimization

Generative Engine Optimization

Generative engine optimization services

AI models cite what they are confident about. We build the entity definition, structured data and independent corroboration that turn your brand from a vague mention into a named recommendation.

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Average growth in AI citations for clients

50+

SaaS and enterprise brands served globally

500+

Long-form articles published and ranking

6+ yrs

In B2B tech content and search

What is generative engine optimization?

Generative engine optimization (GEO) is the practice of shaping how AI models understand, describe and recommend a brand across generated answers. It works on the brand entity rather than a single page: consistent naming and descriptions, Organization and Service schema, knowledge panel alignment, and corroborating mentions on review sites, industry publications and communities the models draw on. Where AEO makes a page quotable, GEO makes the brand credible enough to quote.

Ask an engine to describe a mid-market B2B company and you get one of three outcomes: an accurate summary, a vague and generic one, or a confident description of the wrong company. The second and third are the expensive ones, and neither is fixed by publishing another blog post.

Who this is for

Brands that are structurally sound on-page but under-represented off it. Common signs: engines confuse you with a similarly named company, describe a product you sunset two years ago, or list competitors when asked for vendors in your category while never naming you.

What is included

What GEO work actually involves

Most of the leverage sits outside your own domain.

Entity audit. How each engine currently describes you, what it gets wrong, and which source is feeding the error.
Organization and Service schema. A complete, validated entity graph with sameAs links tying your profiles together.
Knowledge graph alignment. Consistent name, category, founding details and descriptions across every property you control.
Corroboration programme. Placements and profile work on the review sites, directories and publications that models actually draw on for your category.
Community and forum presence. Reddit, Stack Overflow and niche community sources carry disproportionate weight in several engines.
Statistic and quote assets. Original data and named expert quotes, which materially increase how often a passage gets cited.
Misinformation correction. Chasing down and fixing the specific sources producing wrong descriptions of your product.

Detail

Where each engine gets its confidence

Source pools overlap but are not identical, which is why one score across all engines hides the real picture.

Primary signal emphasis by generative engine.
Engine	Leans heaviest on	What tends to move it
ChatGPT search	Live web results plus model priors about the brand	Extractable pages, strong entity clarity, recent credible mentions
Google AI Overviews	Google's index and Knowledge Graph	Traditional ranking strength, schema, knowledge panel accuracy
Perplexity	Fresh crawled sources, cited inline	Recency, clean structure, direct answers, listicles and comparisons
Gemini	Google index and Knowledge Graph, tuned differently	Entity consistency and authoritative corroboration
Claude	Model priors plus retrieved web sources	Well structured reference content and consistent factual framing

Process

How a GEO engagement runs

Entity baseline

We record how all five engines describe you today, and trace inaccurate descriptions back to their source.

Structural foundation

Full Organization and Service schema, sameAs graph, and consistent descriptions across owned properties.

Corroboration build

A prioritised list of the third-party sources that matter in your category, worked through monthly.

Re-measure

The same engine queries re-run monthly, tracking description accuracy and recommendation frequency.

Proof

What this has produced

Average growth in AI citations

Across accounts where GEO ran alongside answer engine optimization.

Client average

Forbes

Technology Council contributor

The kind of third-party corroboration that models weight heavily when deciding whom to name.

Authority signal

FAQ

Questions we get asked

What is the difference between GEO and AEO?

AEO is on-site and page-level: making a passage easy to extract and attribute. GEO is off-site and brand-level: making the entity well defined and corroborated so a model is confident naming you at all. A page can be perfectly structured and still never get cited if the model has no confidence in the brand behind it. Most categories need both.

Can you control what an AI says about our company?

Not directly, and anyone claiming otherwise is overselling. What you can control is the evidence available to the model: schema, consistent descriptions across your properties, and the quality and accuracy of third-party sources. In practice, correcting the underlying sources is what changes the output.

How long does GEO take?

Slower than AEO. Schema and owned-property consistency land in weeks, but corroboration work compounds over one to three quarters because it depends on third parties publishing and engines re-crawling them.

Is this the same as digital PR?

It overlaps. Digital PR chases coverage and links for human and ranking value. GEO selects targets specifically for their weight in AI source pools, which means review platforms, structured directories and technical communities often outrank a glossy trade publication.

Do we need original data for this to work?

It is not mandatory but it is the highest-leverage asset available. Original statistics and named expert quotes are among the most reliably cited content types, because they give an engine something no competitor page can provide.

Find out how AI describes your brand

The free audit includes an entity check: exactly how each engine describes you today, and what it gets wrong.

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