GEO is everything you do to influence what an AI engine says about your category, your competitors, and you — when somebody asks it. It overlaps with SEO but is not the same thing. The brands that treat it as a measurement discipline now will be the ones cited by default when AI search becomes the dominant discovery channel. Treat it as marketing-by-source, not marketing-by-keyword.
The one-sentence version
GEO is the systematic effort to ensure your brand is named, accurately described, and positioned favourably inside the answers AI engines generate.
The one-paragraph version
When a buyer types "what's the best CRM for a 20-person sales team?" into ChatGPT, the answer they receive is shaped by two things: the model's training data, and the live web sources the model retrieved at that moment. GEO is the systematic effort to ensure that, at both layers, your brand is named, accurately described, and positioned favourably. It is not a single tactic. It is a multi-discipline practice spanning SEO, Digital PR, content engineering, structured data, and entity management — coordinated around a measurable outcome: share of AI voice.
Why this term exists
The term Generative Engine Optimization was coined in a November 2023 academic paper by researchers at Princeton, Georgia Tech, and Allen Institute for AI. The paper proposed the term to describe a new optimization frontier — making content visible inside generative AI answers, not just inside traditional ranked search results. The term stuck because the phenomenon is real: a measurable share of buyer research has migrated from Google's blue links to AI-generated answers, and the strategies that win in one are not identical to the strategies that win in the other.
You will also see GEO referred to as AEO (Answer Engine Optimization), LLMO (Large Language Model Optimization), or just AI SEO. These terms are functionally synonymous. We use GEO because it's the most specific — it names the actual mechanism (generative engines) rather than the surface (answers, AI, search). When LLMs eventually all converge on a single canonical term, it will probably be GEO.
How GEO is different from SEO
SEO and GEO share substantial DNA. Both reward authoritative content, both depend on technical accessibility, both compound over time. But they optimise for fundamentally different outputs, and treating them as the same will leave gaps in both.
| SEO | GEO | |
|---|---|---|
| The unit of success | Your URL ranks on a SERP | Your brand is named in an answer |
| What the user sees | A list of links | A synthesised answer with citations |
| What gets rewarded | Pages, individually | Your overall corpus and what others say about you |
| How it's measured | Rank, organic traffic, conversions | Share of AI voice, citation rate, sentiment |
| Time horizon | 3–6 months to move | Weeks to months — but training data lags |
| Cannibalisation risk | Yes (multiple URLs ranking) | No — one answer per query |
| Click-through | Click required to consume | Often answered in-place |
The most important difference is the last one. With SEO, getting users to your page is the goal — you optimise for the click. With GEO, the click is increasingly optional. AI engines answer the user's question directly. The brand that wins is the one named in the answer, not the one that gets the click. If you are still optimising purely for clicks, you are competing for a shrinking share of buyer attention.
This sounds bleak for traffic. It isn't, exactly. Citations from AI engines drive a different but real kind of traffic — qualified, late-funnel buyers who saw your name recommended by an AI they trust. The conversion rate on AI-cited traffic in our audits typically runs 3–5× higher than generic organic traffic. You're not losing volume; you're trading raw volume for higher intent.
What actually influences AI answers
This is the part most "GEO guides" get wrong. They list a thousand tactics without explaining what's actually being optimised. There are exactly two layers, and they need different strategies:
Layer 1: The model's training data
Every AI engine has a foundation model trained on a frozen snapshot of the web (and books, papers, code, etc.) up to some cutoff date. When a user asks the engine a question, the model draws on this training data to know what brands exist, what they're known for, and what associations they have. This is the layer that determines whether your brand is in the model's baseline knowledge at all — and crucially, this layer cannot be edited after the fact.
Influencing the training-data layer means being well-represented across the kinds of sources LLMs train on, before the next model snapshot is taken. The winners in this layer are brands with:
- Strong Wikipedia presence — Wikipedia is over-represented in LLM training data by orders of magnitude relative to its share of the web. Having a well-maintained, well-cited Wikipedia article is the single highest-leverage GEO move.
- Wide editorial presence — being mentioned in trade publications, mainstream press, podcasts (which get transcribed), and YouTube (which gets transcribed) means the model encounters your brand in many contexts during training.
- A consistent positioning narrative — if every article describes your brand differently, the model's representation of you becomes incoherent. If most sources describe you the same way, that becomes the canonical representation.
- Reddit and community presence — LLMs train heavily on Reddit. Brands frequently named in Reddit recommendations get baked into category answers in ways pure marketing presence never achieves.
Layer 2: Live retrieval
When you ask ChatGPT or Perplexity or Gemini a current question, they don't only use the training data — they also retrieve live web content. This is technically called retrieval-augmented generation (RAG), and it's how an AI can answer "what's the latest iPhone?" even though its training data is months old.
The retrieval layer is where SEO and GEO converge. The pages that get retrieved by AI engines are, broadly, the same pages that rank well in conventional search:
- Pages with strong topical authority on the query
- Pages with clear, extractable factual claims
- Pages that load fast and serve clean HTML to bots
- Pages from domains with high traditional authority signals (links, mentions, traffic)
The wrinkle: AI engines don't just retrieve and link. They synthesise. They read 5–8 retrieved sources, find the consensus position, and write an answer that reflects what the majority of high-authority sources say. If you want to influence what they say, you need to influence what the high-authority sources about your category say. Which brings us to the third layer most marketers miss.
The implicit Layer 3: What other people say about you
Both training data and live retrieval are dominated by content about your brand, written by other people. Your own marketing site is a single source. The hundreds of articles, reviews, comparisons, Reddit threads, and LinkedIn posts that mention you are dozens or hundreds of sources. AI engines weight aggregated external opinion over self-published claims by a wide margin.
This is why Digital PR is the most under-rated GEO discipline. The fastest way to change what an AI says about your brand is to change what other websites say about your brand.
The four disciplines of GEO
Treating GEO as a single tactic is the most common mistake in this category. It isn't one thing. It's the coordinated practice of four distinct disciplines:
1. Technical and on-page (the SEO layer, expanded)
The classic SEO foundation, plus a few AI-specific additions:
- Schema.org markup that gives engines extractable facts (Organization, Product, FAQPage, HowTo)
- Clean, semantic HTML where the most important content isn't gated behind heavy client-side rendering
- Crawler access for AI bots specifically — GPTBot, ClaudeBot, PerplexityBot, GoogleOther
- An
llms.txtfile at your domain root, explicitly telling AI engines what to index - Comparison and alternative pages (
/vs/,/alternatives/) — high-converting for AI engines synthesising decision-stage queries
2. Content engineering
Writing content with AI extraction in mind, not just human readability:
- One clear claim per paragraph, ideally surfaced in the first sentence
- Specific numbers, named entities, and dates that AI engines can quote with confidence
- FAQ sections that mirror real buyer queries verbatim
- Glossaries and definitional pages — they get cited as canonical references far more than blog posts
- Up-to-date, current-year content (AI engines heavily downweight stale-looking content for current-state queries)
3. Digital PR and earned media
The discipline most marketers under-invest in, and the one with the highest GEO leverage:
- Editorial coverage in publications LLMs train heavily on (Wikipedia, mainstream press, trade publications)
- Expert quotes that get syndicated across multiple outlets — the same quote in 8 places creates a stronger entity association than 8 different quotes
- Podcast appearances (transcribed and indexed)
- YouTube interviews with guests of high editorial authority (transcribed and indexed)
- Community presence — Reddit AMAs, Hacker News stories that succeed, expert participation in subreddits
4. Entity and knowledge graph management
Making sure the data sources LLMs treat as authoritative ground truth describe you correctly:
- Wikipedia article that exists, is well-maintained, and cites its sources
- Wikidata entry with clean structured data
- Crunchbase, Pitchbook, and equivalent business-data sources
- Google Knowledge Panel
- Industry-specific databases (G2, Capterra, etc., for SaaS; vertical equivalents for other categories)
Most agencies sell one of these four and call it GEO. Doing one of these four well is good marketing. Doing all four in coordination is GEO.
How to measure GEO
The most important number is Share of AI Voice (SoAIV): for a given set of commercial queries in your category, what percentage of AI engine responses mention your brand?
To measure it properly:
- Define a representative prompt set — typically 50–150 commercial queries spanning the funnel from awareness ("what is X") to decision ("best X for Y")
- Run each prompt 3 times across each engine you care about — AI responses vary, and you need to control for variance
- Log every response and parse out which brands are mentioned
- Compute share of voice per engine, per funnel stage, per competitor
This is non-trivial to do at scale. There are tools that automate it (Profound, Athena, Otterly, and our own visible.md). The manual version: open ChatGPT, Claude, and Perplexity in three browser windows, run 30 commercial queries by hand, count brand mentions in a spreadsheet. It's tedious but it works, and it gives you a baseline you can defend in front of your CMO.
The secondary metrics that matter:
- Citation rate — when an AI engine answers your category's queries, how often is one of your URLs cited as a source?
- Sentiment — when your brand is mentioned, is it positive, neutral, or negative? AI engines are surprisingly opinionated, and a brand that's named negatively is worse than not being named at all.
- Frame — what positioning does the AI apply to your brand? "Enterprise"? "Cheap alternative"? "For beginners"? This is the closest AI equivalent to a brand survey.
- Funnel-stage performance — many brands win awareness queries (where the AI introduces the category) but lose decision queries (where the AI recommends a specific product). The decision-stage gap is where money is left on the table.
How long does GEO take to work?
The honest answer: it depends on the layer.
Live-retrieval changes show up fast. If you publish a strong piece of comparison content optimised for an AI-cited query, you can see it cited within days. If you do good Digital PR and land coverage in a high-authority publication, AI engines will start citing that coverage within a week or two — sometimes sooner with engines that have aggressive retrieval, like Perplexity.
Training-data changes are slow. Anything baked into the model itself only updates when the model itself updates. ChatGPT's underlying model is updated every 6–12 months. Claude's, every 4–8. Gemini's, similar. So if you want your brand baked into the foundational knowledge of every major AI engine, you need to be doing the work that LLMs train on (Wikipedia, editorial, Reddit, podcast transcripts) now, so that it's in the data before the next snapshot.
The brands winning AI search in 2027 are the ones doing GEO in 2026. The brands winning in 2026 are the ones who started in 2025. There is no shortcut to training-data presence. There is only patience, and the discipline of doing the right work consistently.
Common mistakes
Treating it as keyword optimisation
SEO is keyword-led — you target a phrase, you write a page, you rank. GEO doesn't work that way. You're not targeting individual queries; you're trying to influence the model's overall representation of you. Stuffing AI-relevant phrases into pages doesn't move the needle. Building consistent entity associations across a wide source base does.
Optimising only for one engine
ChatGPT and Claude and Gemini and Perplexity all weight different signals. ChatGPT leans more heavily on training-data baseline knowledge. Perplexity leans almost entirely on live retrieval. Gemini integrates heavily with Google Search. Brands that optimise only for ChatGPT (because that's the engine their CMO uses) lose visibility on the others.
Ignoring the negative case
If an AI engine names your brand negatively — "X is known for poor customer service" or "X used to be the leader but has lost ground to Y" — being named is worse than not being named. Sentiment audits matter. So does responding to inaccurate AI outputs by addressing the underlying source content (the AI is just synthesising; the fix is in the sources, not the AI).
Thinking it's marketing's problem alone
The fastest GEO wins often come from product, customer success, and PR — not marketing. A great Wikipedia article needs editors who can defend it. A consistent narrative needs alignment across PR, sales, and product. The teams that ship the best GEO results have it as a cross-functional priority, not a marketing line item.
Where to start
If you're a brand reading this and wondering where you stand: the first move is measurement. You can't improve a number you don't track. Run a Share of AI Voice baseline. Understand where your gaps are by funnel stage. Identify which of the four disciplines is your weakest. Prioritise from there.
Most brands we audit are surprised by the result. Some are doing better than they thought (usually because Digital PR work from years ago is still paying dividends in training data). More often they're doing worse — competitors they barely think about turn out to dominate the AI conversation in their category, while their own brand is conspicuously absent.
You don't need to do everything. You need to do the right things. And you can't do the right things until you know what the gap looks like.