Generative Engine Optimization

Shape how the models describe you

Ask ChatGPT about your category and it will produce a confident paragraph naming two or three companies. That paragraph is now a sales conversation you are not in the room for. GEO is the work of making sure it describes you, accurately, by name.

Benchmark my AI visibility

See the method

5 / 5

engines naming CARS24 on its core buying question, nightly

signal layers a model uses to decide who to name

Nightly

sampling, because model answers drift without warning

How a model decides who to name

It repeats the consensus of what it has read

A model has no opinion. It reproduces the weighted agreement of its training data and its retrieved sources. Changing what it says means changing what the internet says about you, in the places it reads.

Entities beat keywords

Models reason about things, not strings. If your organisation, your people and your products are not clean, connected, disambiguated entities, the model cannot be confident enough to name you and will hedge to a safer brand.

III

It cites what it cannot already say

Retrieval kicks in when the answer needs specificity the weights do not hold. Original data, benchmarks and named practitioner claims force retrieval — and retrieval is where your URL appears.

What we measure

Share of Answer: the percentage of your priority questions where a named engine names you — not mentions your category, names you.

It is the only GEO metric we have found that moves before revenue does and cannot be gamed by publishing more pages.

Share of Answer

Citation Rate

Sentiment and accuracy

The GEO system

Your priority questions run across ChatGPT, Claude, Gemini, Perplexity and AI Overviews, captured verbatim. We record whether you are named, cited, mentioned or absent, how you are characterised, and which competitor is holding the position. This becomes the baseline every later claim is measured against.

5 engines

Verbatim capture

Competitor position

A coherent entity graph: Organization schema that agrees with your Wikidata and Crunchbase records, Person entities for your experts with real credentials, sameAs links that resolve, and consistent naming everywhere you appear. Models hedge when they are unsure who you are. This removes the doubt.

Organization schema

Wikidata and Crunchbase

Person entities

sameAs graph

The pages that answer your category questions with information only you hold: original panels, teardowns, pricing transparency, methodology. Published where models retrieve from, structured so the claim is liftable and the attribution is unavoidable. This is Information Gain applied to the answer layer.

Original data

Methodology pages

Liftable claims

Models read Reddit, review sites, industry roundups and news, and weight third-party agreement heavily. We work the places that actually carry weight in your category, in public, without astroturf — because a fabricated consensus is both detectable and worse than none.

Reddit and forums

Review platforms

Industry roundups

The five engines, read nightly

They disagree with each other constantly. Treating them as one surface is why most brands mis-diagnose their AI visibility.

Highest reach

Google AI Overviews

Conservative, consensus-driven, slow to change and heavily weighted to established domains. Winning here takes longest and lasts longest.

Follows the snippet layer closely.

Highest intent

ChatGPT

Increasingly retrieval-backed. Responds fastest to fresh, specific, well-structured sources with clean entity signals.

Where most B2B research now starts.

Most citable

Perplexity

Visible numbered sources and a strong preference for original data. The easiest engine to earn a citation from if you have real information.

Our fastest-moving surface.

Enterprise

Claude and Gemini

Cautious about naming brands and quick to hedge. Strong entity signals and third-party corroboration matter more here than anywhere.

Late to move, hard to dislodge.

How GEO runs

Benchmark

Baseline reading across five engines on your real question set. Verbatim, not scored summaries.

Week 1

Entity repair

Fix the graph: schema, Wikidata, naming consistency, expert profiles. Quiet work with outsized effect.

Week 2–4

Publish the source of truth

The handful of pages carrying information a model has to come to you for.

Week 3–10

Work the consensus

Third-party surfaces, in public, at the pace real communities tolerate.

Month 2+

Defend nightly

Sampling continues. Model updates and competitor moves show up as a drop within a day, not a quarter.

Ongoing

See what the models say about you tonight

We run your category questions across five engines and send you the raw answers: who gets named, how you are described, and where the gap is. Free, one week, no contract.

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