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AI search visibility

Be the answer when they ask AI.

Your customer no longer types a keyword and picks from ten links. They ask a question and get one answer, with three or four sources named inside it. This is the work of being one of those sources.

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EngagementMonthly retainer, 6 month minimum
ReportingShare of answer, by engine and prompt
Engines trackedChatGPT, AI Overviews, AI Mode, Perplexity, Gemini, Claude, Copilot
First movementTypically 6 to 10 weeks
The problem

Three names, one discipline

  • ChatGPT
  • Google AI Overviews
  • AI Mode
  • Perplexity
  • Gemini
  • Claude
  • Copilot
  • Entity architecture
  • Structured data
  • llms.txt
  • Citation tracking
  • Share of answer

GEO, AEO and LLMO get sold as three products. They are three names for the same program, written from three points of view, and their tactics overlap by roughly 80%. Anyone selling you all three as separate retainers is selling you the same work three times.

The distinctions that do matter are these. AEO is the broadest: appearing in direct answers anywhere, including voice assistants and featured snippets, which predate generative AI. GEO is the generative subset: being cited inside a model-written response. LLMO is the same discipline named from the technology side, focused on how a model retrieves and attributes a source. In 2026 GEO is the label most teams use, and the program underneath is single.

What does need separating is the engines. Only about 11% of domains cited by ChatGPT are also cited by Perplexity. They source differently, weight differently, and reward different work. A single "AI SEO" tactic list applied to all of them is why most programs stall.

The distinction

What the three names cover

ScopeSurfaceWhat moves it
SEORanked linksGoogle, Bing organic resultsCrawlability, relevance, links, page experience
AEODirect answersFeatured snippets, voice assistants, AI answersQuestion-shaped headings, concise factual answers, schema
GEOGenerative responsesChatGPT, AI Overviews, Perplexity, Gemini, ClaudeEntity clarity, quotable claims, third-party corroboration
LLMOModel retrieval and attributionThe retrieval layer inside all of the aboveChunkable structure, stable entities, machine-readable facts
Per engine

Every engine sources differently

Only about 11% of the domains cited by ChatGPT are also cited by Perplexity. A single tactic list applied to all of them is why most AI visibility programs stall in month three.

  • ChatGPT

    Leans on external validation. Wikipedia presence and the volume of third-party mentions carry the most structural weight, with Reddit, Quora, G2 and comparable communities acting as social proof. Blocking OAI-SearchBot guarantees you are excluded from real-time recommendations, so the crawler policy is checked first.

  • Perplexity

    Behaves like a research assistant. It favors fresh, tightly structured content and weights the last twelve months heavily. Visible publication and update dates, short parseable answers, and a presence in the communities it reads are what get you quoted.

  • Google AI Overviews and AI Mode

    Draws on the Google index, filtered through experience, expertise, authoritativeness and trust. Pages need a direct answer at the top of each section, figures with named sources, and consistent Schema.org markup. Pages cited in AI Overviews have been measured earning around 35% more organic clicks than uncited competitors on the same results page.

  • Gemini, Claude and Copilot

    Smaller volumes, but the same underlying levers, with different index dependencies. We track them so a shift in any one of them shows up in your reporting before it shows up in your pipeline.

Deliverables

What the retainer covers

The program runs on a monthly cycle. Month one is heavy on architecture, and every month after it is content, corroboration and measurement.

  • Baseline answer audit. We run 100 to 300 real buyer prompts across every tracked engine and record what is said about you, your competitors and your category today.
  • A prompt set that stays. The audit becomes your permanent measurement frame, re-run every month against the same prompts so movement is real rather than anecdotal.
  • One description of the business. Who you are, what you sell and who you serve, worded identically across your site, your labelled data, your profiles and your press. Models match up businesses, not pages, so a description that changes between places gives them nothing to match.
  • Everything on the page, labelled. Your company, products, services, answers, guides, articles and people, each one labelled and linked to the others, so a machine reads one connected description rather than scattered facts.
  • Quotable claim library. Cited sentences in AI answers run short: an average of 9.27 words, a median of 10, with the 6 to 10 word band accounting for 45.2% of citations. We write your facts as short, standalone, attributable declaratives, and put them where they can be lifted.
  • Answer-first rewrites. Complete answers in the first two sentences, question-shaped headings matched to how people phrase prompts, explicit entities, consistent terminology, visible last-updated dates on a 30, 90 and 180 day refresh cadence.
  • llms.txt and crawler policy. Shipped, with an honest note on what it does and does not do today.
  • Third-party presence plan. Wikipedia and Wikidata readiness, the review and comparison sites in your category, the communities each engine reads, and the digital PR that earns a mention worth citing.
  • Citation tracking. Which engine, which prompt, which page, which competitor took the slot you did not.
  • Monthly share of answer reporting. One number per engine, the prompts behind it, and what we are changing next month.
Method

The monthly cycle

The same sequence every time, so you always know what is happening this week and what you will see next.

  1. Audit

    Prompt set built and run across every engine. You get the raw transcripts, not a summary.

    Weeks 1 to 2

  2. Architect

    The single description of your business, the labelled data, the rule for AI readers, and the content gaps that decide whether you are quotable at all.

    Weeks 2 to 4

  3. Publish

    Answer-first rewrites of what exists, then new pieces against the prompts nobody currently answers well.

    Ongoing

  4. Corroborate

    Third-party mentions, community presence, comparison listings and digital PR. This is what ChatGPT weighs and what most programs skip.

    Ongoing

  5. Measure

    Same prompts, same engines, same day each month. Share of answer, movement, and the reason for it.

    Monthly
Evidence

One we ran like this.

Client names and identifying details are changed. The figures are not.

Quanta Journal article layout with featured story and sidebar
Publisher · GEO + technical SEO

Quanta Journal

A publisher moved onto a custom WordPress build, then twelve months of technical SEO, one consistent description of the publication everywhere it appears, and rewrites that put the answer in the first two sentences. The library became quotable as well as rankable. Every article now names its author, shows when it was last updated, and carries labelled data tying it back to the publication.

+68%Organic sessions
2.9kRanking keywords
12 moEngagement
See all work →
Questions

Asked before every engagement.

What is the difference between GEO, AEO and LLMO?

They describe closely related practice from different angles. AEO, Answer Engine Optimization, is the broadest: appearing in direct answers on any answer-capable surface, including voice assistants and featured snippets. GEO, Generative Engine Optimization, is the generative subset: being cited inside an AI-written response from ChatGPT, Gemini, Perplexity or Google AI Overviews. LLMO, Large Language Model Optimization, is the same discipline named from the technology side, focused on how a model retrieves and attributes content. Their tactics overlap by roughly 80%, and serious teams run them as one program.

How long before GEO shows results?

We usually see the first movement in share of answer between six and ten weeks, and a meaningful shift by month four to six. Entity and schema changes propagate quickly. Third-party corroboration, which is what ChatGPT weighs most heavily, is slower and is the reason we ask for a six month minimum.

Is GEO traffic worth anything, or is it just visibility?

It converts unusually well, because the visitor arrives pre-qualified by the model. Visitors arriving from Perplexity have been measured converting at roughly eleven times the rate of traditional organic search traffic, and pages cited in Google AI Overviews earn around 35% more organic clicks than uncited competitors on the same page. The volume is smaller than classic organic. The intent is much higher.

Do I still need SEO if I am doing GEO?

Yes, and they are not in tension. Google AI Overviews draws from the Google index, so a page that cannot be crawled and indexed cannot be cited. Traditional SEO earns the index position; GEO earns the citation. We run both, and the technical foundation is shared.

Can you guarantee ChatGPT will recommend us?

No, and no honest agency can. Model outputs vary by prompt, by session, by region and by model version, and no provider offers a placement product. What we can do is move share of answer against a fixed prompt set, show you the transcripts each month, and tell you which competitor is taking the slot when we do not.

Does llms.txt matter?

Barely, today. No major AI provider has publicly committed to reading llms.txt in production as of early 2026, and Google has stated it does not support it. We publish one because a handful of smaller providers do fetch it and it costs an hour. Anyone selling llms.txt as the centerpiece of an AI visibility program is selling you the cheapest part of it.

Find out where AI sends your customers today.

We run your brand through the answer engines your customers use, then send you the transcript of what they say about you. Free, and yours to keep whether or not we work together.

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Usually bought alongside this.

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