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AI Search

Generative Engine Optimization (GEO)

Get named in the answer that gets written. GEO makes your pages the sources ChatGPT, Perplexity, Gemini, and Google AI Overviews draw on and cite.

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What is GEO?

Generative Engine Optimization is the work of being cited when a model writes the answer. These engines do not hand back a ranked list. They read across many sources, compose a response in their own words, and name the handful of places they trusted. GEO is how you get into that handful.

A model cannot lift your sentence the way a snippet does, so structure alone is not enough. Three things decide whether you get named. Retrievability: can the engine find the page at all. Comprehensibility: can it restate your claim without mangling it. Corroboration: when sources disagree, does anything else back you up. Content that hedges, splits a fact across three pages, or only implies the answer fails all three quietly.

Measurement works differently too, because the output changes between runs. We track AI Share of Voice, meaning how often you appear across a fixed set of prompts sampled over time, reported apart from rank. A single answer proves nothing. A citation rate across dozens of runs is something you can act on.

GEO is the sibling of AEO, which covers engines that quote one source word for word. Related work on a different surface. Most clients run both.

What you get

  • Prompt Coverage Baseline

    A fixed set of buying-intent prompts run across each engine, recording who gets cited and how often.

  • Retrievable Content

    Pages written so a model can paraphrase your claims without garbling them or hedging them away.

  • Corroboration & Citations

    The third-party references a model leans on when it decides which source is worth naming.

  • AI Share of Voice

    Monthly citation rate across ChatGPT, Perplexity, Gemini, and AI Overviews, tracked against competitors.

The way in

Where to start

Citation rates shift as models are retrained, so this is tracked over time rather than shipped once. Take the baseline first, then decide which gaps are worth closing. Pricing follows that baseline, because closing a gap costs what it costs depending on how far behind you start.

AI Search Readiness Audit

one-time

The baseline. We run your buying-intent prompts through each engine and record who gets named, how often, and what they were cited for.

  • Citation baseline across four engines
  • Competitor citation comparison
  • Ranked list of gaps worth closing

Plans

  • Most Popular

    Generative Engine Optimization

    setup + retainer

    Continuous work to become a source models reach for, with reporting that keeps AI citations apart from search rank.

    • Retrievable content and entity schema
    • Corroboration and reference building
    • Monthly AI Share of Voice report

  • AEO + GEO Bundle

    bundle

    Add answer engines to the same programme. Much of the prompt and question research carries over, so the pair costs less than running them apart.

    • Model citations plus snippet and PAA coverage
    • A single monthly report instead of two
    • Priced below the two separately

Not sure which prompts matter in your market? We will run a few live on a quick call. Book a call

See Answer Engine Optimization (AEO)See Search Engine Optimization (SEO)

Frequently Asked Questions

How is GEO different from traditional SEO?
Ranking puts a link in a list that a person then chooses from. GEO puts your name inside an answer that has already been written for them, often with no click at all. Because these engines pick sources rather than order links, we measure citation rate instead of position, and report it separately from rank.
Can you actually influence what ChatGPT or Perplexity say?
You cannot control a model’s wording, and anyone promising a specific sentence is selling something they cannot deliver. What you can influence is whether the model can retrieve you, parse your claims correctly, and find corroboration elsewhere. Those are the inputs to being cited, and they move in aggregate even though any single answer varies.
How do you measure something that changes every time you ask?
With a fixed prompt set and repeat sampling. We run the same buying-intent prompts across each engine on a schedule and record how often you are named. One answer is noise; the rate across dozens of runs is a trend you can act on.
Does an llms.txt file help?
Not with Google, which ignores it. It costs almost nothing and a handful of crawlers read it, so we will ship one, but it is a footnote. Retrievable content, correct entity markup, and third-party corroboration are what actually move citation rates.
What if a model cites me but gets the facts wrong?
That is a retrievability problem, and it is common. Models paraphrase, so a claim that is hedged, split across three pages, or only implied gets reconstructed badly. The fix is stating the fact plainly in one place, marking up the entity so you are not confused with a similarly named company, and giving the model a corroborating source to check it against.

Find out if models are citing you

An AI Search Readiness Audit runs your buying-intent prompts through ChatGPT, Perplexity, Gemini, and AI Overviews, and shows who gets named instead of you.
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