A primer
Generative Engine Optimization (GEO) is the practice of making a brand legible, verifiable and citable to AI systems - ChatGPT, Gemini, Perplexity, Claude, Google's AI Overviews and AI Mode - so that when people ask them what to buy, use or trust, you are in the answer.
You may have heard it called AEO, LLM optimization or AI search optimization. Same problem, different names. This page explains the terms, makes the case with published numbers, and shows who in your organisation it touches.
The terms
Search engines returned ten links and let you choose. Generative engines return one answer and choose for you. Every term below describes the work of getting chosen.
Coined by researchers at Princeton, Georgia Tech, IIT Delhi and the Allen Institute in a 2023 paper, presented at KDD 2024.1 It names the thing precisely: optimising for engines that generate an answer from what they can read and retrieve about you, rather than ranking a list of pages.
AEO predates generative AI. It grew out of featured snippets and voice assistants, where a single answer already won. In practice it is now used interchangeably with GEO; if your team says AEO, nothing on this page changes.
Vendor and agency shorthand for the same discipline. "AI SEO" is the most misleading of the set, because the research shows the classic SEO levers do not carry over (more on that below).
Whether, and how, AI systems mention, describe and recommend you when someone asks about your category. GEO is the work; AI visibility is the result. GenSight.AI measures the result and diagnoses the work.
Why now
Not predictions. Published, dated, linked. If someone on your team is not yet convinced, send them this section.
OpenAI reported 900 million weekly active users in February 2026, up from 400 million a year earlier.2 Google says AI Overviews reach 2.5 billion people a month and AI Mode passed 1 billion monthly users within a year of launch.3 The question "what should I buy / use / hire" is being asked, at scale, to systems that answer in prose.
Pew Research tracked 68,879 real Google searches by 900 US adults. When an AI summary appeared, people clicked a result in 8% of visits, against 15% without one; they clicked a link inside the summary just 1% of the time, and were more likely to end the session outright (26% vs 16%).4 Ahrefs' study of 300,000 keywords found AI Overviews cut the click-through rate of the top organic result by 58%.5 The answer is the destination now. Being cited in it is the new position one.
Adobe Analytics, across more than a trillion visits to US retail sites, measured generative-AI-referred traffic up 693% year on year in the 2025 holiday season, with those visitors converting 54% better than non-AI traffic on Thanksgiving and spending 45% more time on site.6 Similarweb counts 770 million AI referral visits a month worldwide, up 117% in a year, with every industry it tracks at least doubling.7 A visitor who arrives from an AI recommendation has already been told you are the answer.
Similarweb's July 2026 analysis puts ChatGPT at about 53% of generative AI web traffic, Gemini above a quarter, and Claude as the fastest-growing platform.8 Add Google's AI Overviews and Perplexity and you have five systems, each retrieving and reasoning slightly differently, all reading the same underlying signals about your brand. That is why GenSight.AI audits across engines rather than reporting on one.
The Princeton study that named GEO tested nine tactics across 10,000 queries. Adding statistics, citing sources and including quotations raised a source's visibility in generated answers by up to 40%; keyword stuffing, the reflex of a decade of SEO, did nothing.1 Generative engines reward content a model can verify and attribute. That is a different job from ranking.
What this adds up to
The first touchpoint with your brand is increasingly a sentence written by a model. If that model cannot resolve who you are, cannot read your site, or has no independent sources to cite, it recommends someone it can. You never see the conversation, and there is no click to miss. GEO is the discipline of fixing that upstream: making sure the machine that writes the answer has what it needs to write you into it.
One pattern from our own audits: a growing number of sites built on modern site builders deliver their content only after JavaScript runs. A person sees a full page; a non-rendering AI crawler sees an empty shell. Everything downstream, from entity recognition to citation, fails silently.
Who it is for
Anyone whose customers ask an AI "which bank / retailer / software / provider should I use". Retail, finance, travel, SaaS, healthcare, professional services. The question is asked about the company and about individual products, which is why we audit both.
"Who should I follow / read / book on this topic" is a recommendation too. Consistent identity across platforms, an entity record the models can resolve, and a body of work they can quote decide whether you are the name in the answer.
The signals a model reads about your brand live in six or seven functions that rarely share a roadmap. Each row below is something one team already owns and probably does not know is a GEO lever.
Owns: the category you want to be recommended in, the competitor set, the budget. GEO levers: positioning the model can restate in one sentence; being present in the comparisons and roundups engines cite; measuring share of answer, not just share of search.
Owns: what gets published and how it is structured. GEO levers: direct answers under clear headings, statistics with sources, quotable claims, FAQ content with schema, an llms.txt file, topical depth in the niche you claim.
Owns: the site itself. GEO levers: server-rendered content (not a JavaScript shell), robots.txt that admits AI crawlers, canonical tags, Organization / Product / FAQ structured data, semantic HTML. Most "missing" signals in an audit are engineering tickets.
Owns: the name and how it is written. GEO levers: one consistent name and description everywhere, disambiguation from namesakes, a Wikidata / knowledge-graph record, sameAs links that tie your profiles together so the model knows they are all you.
Owns: what third parties say about you. GEO levers: coverage in the publications and listings engines actually cite, review-aggregator presence, Wikipedia-grade notability, original data that journalists (and models) quote back.
Owns: product pages and catalogue data. GEO levers: per-product entity clarity, specifications a model can compare, reviews and ratings exposed as structured data, machine-readable availability and pricing.
Owns: measurement. GEO levers: tracking AI referrals separately, watching which AI crawlers fetch which pages, connecting fixes shipped to visibility gained. Without this the work is invisible to the business.
Owns: several of the above on a client's behalf. GEO levers: a repeatable, deterministic audit you can run across a portfolio, with owner-tagged fixes you can hand to the right team and re-measure.
GenSight.AI groups these into four pillars - Foundational, Entity Resolution, Technical & RAG, Authority & Citation - and tags every recommendation with the function that should own it. See the methodology.
Where to start
Run a free audit. It checks 35 signals deterministically, asks the engines what they actually say about you, and scores rivals by the same method.
The roadmap ranks the gaps, estimates the impact and ships paste-ready artifacts - schema, llms.txt, page briefs - tagged with the team that should own each one.
Connect AI Traffic to see which AI crawlers read which pages, then re-audit to watch the score move when something real changes.
Short answers
GEO is the practice of making a brand legible, verifiable and citable to AI systems, so that when people ask them for recommendations the brand is in the answer.
The term comes from a 2023 study by researchers at Princeton and partner institutions, which showed content with statistics, source citations and quotations was up to 40% more visible in AI-generated answers.
Largely, yes.
AEO (Answer Engine Optimization) predates generative AI and originally meant optimising for featured snippets and voice assistants. GEO is the term the research community uses for generative engines specifically. LLMO, AI SEO and AI visibility describe the same problem from different angles.
Yes. SEO gets you found by a crawler; GEO gets you understood and cited by a model.
Much of the foundation overlaps - a readable, server-rendered site, structured data, authority - but the levers differ. Keyword density does nothing for AI citation; statistics, citations and clear entity data do.
More than one, and the mix keeps shifting.
In 2026 ChatGPT's share of generative AI web traffic fell to about 53% while Gemini passed a quarter and Claude grew fastest, and Google's AI Overviews reach 2.5 billion people a month. Optimise for the category of engines, not a single model.
Nobody owns all of it, which is why it stalls.
The signals AI reads are spread across web engineering, content and SEO, brand, PR and communications, product and analytics. GEO needs an owner who can coordinate those functions, and tooling that tells each of them what to change.
Fetch your homepage without JavaScript and look at what is left.
If the headline, navigation and body copy are missing from the raw HTML, most AI crawlers see an empty page. An audit checks this and 34 other signals deterministically and tells you what to change.
Sources
Figures are quoted as published by their sources on the dates shown. Where a source has since updated its numbers, the later figure is used.