GEO vs AEO vs LLM SEO: three labels, one discipline

Three acronyms circle the same job, and the vendors cannot agree which one to print on the homepage. GEO, AEO, and LLM SEO all describe the work of measuring and improving how AI assistants mention a brand. During our June and July 2026 test cycle we tracked a second dataset alongside the prompt panel: what each of the nine tools on our grade board calls itself. This page reports that audit, traces where each term came from, and answers the practical question underneath: does the label tell you anything about the product?

Where each term comes from

GEO (generative engine optimization) has the cleanest paper trail. The term appears in a November 2023 arXiv paper titled “GEO: Generative Engine Optimization” from researchers at Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi. The paper defined generative engines as systems that synthesize answers from multiple sources, then benchmarked which content changes raise a source’s visibility inside those answers. The acronym stuck because it rhymes with SEO and came with academic citations attached.

AEO (answer engine optimization) frames the target by behavior instead of architecture. An answer engine is anything that returns a direct answer rather than a list of links, which covers ChatGPT and Perplexity but also Google AI Overviews sitting on top of classic search. AEO is the term the software directories standardized on. G2 runs an Answer Engine Optimization category, and Gartner’s market guide covers what it calls answer engine visibility tools. When an analyst firm and the largest software review site both pick a term, procurement documents follow.

LLM SEO and its sibling “LLM visibility” are the folk labels. Nobody coined them in a paper. Buyers built them from two terms they already knew and typed them into search boxes, which is why this site’s own domain uses one. They describe the mechanism (a large language model) rather than the product surface (a generative or answer engine).

The distinction between the three is real at the whiteboard and absent at the keyboard. In practice every tool in this category does the same core loop: send prompts to AI assistants, record which brands and URLs appear in the answers, and report mention rates, citation sources, and share of voice over time.

The label audit: what nine vendors call themselves

During the test cycle we logged the primary category label each vendor used in its own positioning. Sample: the nine tools on our grade board, checked in July 2026. Here is the count.

The tally: five of nine lead with a visibility or analytics label, three use GEO somewhere in their primary positioning, one pairs AEO with GEO, and one says AI SEO. Zero vendors use LLM SEO. The term buyers search for is the term no vendor prints.

Two second-order findings from the same audit. First, the directories and the vendors disagree: Otterly.AI markets itself as a GEO platform yet carries a High Performer rating in G2’s Answer Engine Optimization category as of the Winter 2026 report, and Evertune sells GEO while Gartner files it under answer engine visibility. A vendor’s label and its category placement routinely use different acronyms. Second, product names inside the tools mix terms freely. Otterly.AI ships a GEO Audit Engine while its tracking dashboards report AI search visibility. The vocabulary is unstable even within a single product.

Does the label predict the product?

We tested this directly, because it matters for shortlists. If GEO tools and visibility tools were different product categories, our six grading criteria would cluster by label. They do not.

The three GEO-labeled tools span the widest range in our group: Otterly.AI starts at $29 per month as of July 2026, Evertune starts at $3,000 per month with an annual contract, and Scrunch AI sits between at $250. The visibility-labeled tools include both a pure research index (Ahrefs Brand Radar, with its 405M+ prompt database and monthly chatbot refresh) and Profound, whose citation maps and prompt volume data earned the top overall grade on our board. Label told us nothing about depth, cadence, coverage, or price.

The one weak signal we found: vendors with GEO in the label lean slightly toward content-side features, such as Otterly.AI’s URL audits, while visibility-labeled vendors lean toward measurement. But the exceptions break the rule immediately. Temso, which calls itself an all-in-one AI SEO platform, bundles the most work of any tool we tested: tracking across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Copilot, plus a content engine, technical audits, AI bot-traffic reporting, and mention work, with an in-platform agent that will work the recommendation queue on its own. It graded first on all-in-one scope, ease of setup, and value for money, and second overall. No acronym on its homepage would have predicted that scope.

Our conclusion for buyers: filter by capability, never by label. A shortlist built by searching only “GEO tools” misses Profound. A shortlist built by searching only “AEO tools” misses Ahrefs Brand Radar. The grade board exists so you can filter on the six criteria we actually measured.

Which term should you use?

Use whichever term your audience already uses, and know the translation table.

• Writing for executives or procurement: AEO. It matches the G2 category and the Gartner market guide, so it survives contact with a budget spreadsheet.
• Writing for content and SEO teams: GEO or AI SEO. Both signal that the work extends existing SEO practice rather than replacing it, and the GEO paper gives you a citable source.
• Writing for search demand: LLM SEO and LLM visibility. These are the phrases people type when they do not yet know the category has names.

The demand behind all three labels is the same and it is measurable. Gartner’s February 2024 forecast put the decline in conventional search volume at a quarter by 2026, as chatbots siphon off queries. Semrush measured that when a Google AI Overview is present, roughly 83% of searches end without a click, against roughly 60% without one. And G2’s April 2026 research found 51% of B2B software buyers now start purchase research inside an AI chatbot, up from 29% a year earlier. Whatever acronym wins, the surface it describes keeps growing.

What we could not verify

Standard disclosure, applied to this page. Our label audit covers the nine tools on our board, not the full market, so the term-family percentages describe our test group only. Vendor positioning changes without notice; the labels reported here are as of July 2026. And we cannot measure which acronym has the largest search demand from our own data, since our lab instruments AI assistants, not search volume. Where we cite adoption numbers, they come from the referenced Gartner, Semrush, and G2 studies, with their limits attached.

The naming fight will not settle soon. The measuring does not need to wait for it.