Answer engine optimization (AEO)

Answer engine optimization, usually shortened to AEO, is the practice of shaping your content and public footprint so that systems which answer a question directly, instead of returning ten links, name you in the answer. This page defines the term, shows what the work looks like in practice, and separates it from the neighbouring terms SEO, GEO and LLM SEO.

Seenby is not open yet, so anything on this page about what it does is what it is being built to do.

What answer engine optimization means

Answer engine optimization is the practice of shaping your content, your public footprint and the language used about you so that a system which answers a question directly names and cites your brand in that answer.

The term predates generative AI. It grew out of featured snippets, knowledge panels and voice assistants, where the search result was a single spoken or boxed answer rather than a page of links. An answer engine, in that older sense, was anything that resolved a question on the spot.

The phrase moved because the behaviour spread. Assistants like ChatGPT and Perplexity, and answer surfaces layered on top of conventional search, now resolve commercial questions in one pass. The reader does not browse a list and form a shortlist. The engine forms the shortlist for them. AEO is the work of being on it.

An example

A buyer types: which tools handle payroll for a 200-person company. An answer engine replies in four sentences, names three vendors, describes each in a clause, and links a handful of sources. There is no position two. There is no page of alternatives to scroll. Either your brand appears in those three names, or the buyer never sees you.

AEO asks a different question from the one SEO asks. Not where do we rank for this phrase, but when a machine summarises this category, whose names does it reach for, in what order, and in what tone.

AEO, SEO and GEO side by side

Term	What it optimises for	What success looks like
SEO	Ranked lists of links on a search results page	A position, a click, a session
AEO	Direct answers, including snippets, assistants and voice	Being named and cited inside the answer itself
GEO	Answers written generatively by a large language model	Appearing in the model's synthesis, with the sentiment you want
LLM SEO	Informal label, used interchangeably with GEO	Same as GEO; the term signals the audience, not a method

Why AEO is harder to check than SEO

SEO had an inspectable surface. A rank tracker could pull the same results page on a schedule and tell you, to the position, where you sat. Answer engines give you none of that.

Three problems make the work awkward.

There is no rank. An answer either names you or it does not. Being mentioned third in a paragraph is not the same measurement as ranking third, and no engine publishes a score.
The output is not stable. Ask the same question twice and the wording moves. Ask it after a model update and the named brands can move too. A single spot check tells you almost nothing.
The input space is wide. Buyers do not type keywords at an assistant. They type situations, with a company size, a budget and a constraint attached. Coverage means a set of realistic prompts, not a keyword list.

So AEO measurement is sampling, not observation. You fix a set of prompts, run them repeatedly, record which brands get named, and read the trend rather than any single answer. The caveat that belongs next to every number: a week-over-week shift can reflect a model update rather than any real change in how the market sees you.

How Seenby measures it

Seenby is built to run that sampling for one brand against up to three named competitors. A scheduled query engine reads a frozen prompt set through two providers, OpenAI and Perplexity, and degrades to the remaining one if a provider is unavailable. From the answers it computes a single Share of AI Voice score per brand per week, with the methodology caveat stored on every computed record and shown on every score display, so nobody reads the number without the limits attached.

Related terms

Generative engine optimization (GEO)

The narrower sibling. GEO covers answers written by a language model specifically. AEO is the older, wider label and includes snippets and voice results that involve no generation at all. Most people now use them interchangeably, and most vendors pick one for positioning rather than precision.

Share of AI voice

A metric, not a practice. It expresses how often your brand is named across a sampled set of AI answers relative to the competitors you named, so AEO effort has something to move.

Citations

The source links an answer engine shows alongside its text. Being cited and being named are separate outcomes: an engine can recommend you without linking you, and link you without recommending you.

Zero-click search

The pattern AEO exists to answer. The question resolves on the results surface, the click never happens, and traffic dashboards register the loss without explaining it.

Drift

The movement in answers caused by model updates rather than by anything you or your competitors did. Any AEO report that cannot separate the two is telling you less than it appears to.

llms.txt

A proposed file for telling AI crawlers what to read. Treat it as an experiment. Adoption by the engines is not settled, and no one should build a strategy on it.

Questions

What is the difference between AEO and SEO?

SEO optimises for a ranked list of links, and success is a position and a click. AEO optimises for the answer itself, and success is being named or cited inside it. The same content work often serves both, but the measurement does not transfer: there is no rank to track in an answer.

What is the difference between AEO and GEO?

GEO, generative engine optimization, refers to answers written by a language model. AEO is the older and broader term, covering any direct answer, including featured snippets and voice results that involve no model at all. In practice the two words are used for the same job.

Is AEO replacing SEO?

Not on the evidence available. Answer surfaces are taking a share of question-shaped queries, which reduces clicks on those queries, but link-based search has not gone anywhere. The honest framing is that AEO adds a surface you cannot see with a rank tracker, so it needs its own measurement rather than replacing your existing one.

How do you measure whether AEO is working?

Fix a set of realistic buyer prompts, run them on a schedule, and record which brands each answer names, in what order and in what tone. Read the trend across weeks rather than any single answer, and tag each answer with the model version that produced it so a model update is not mistaken for a competitive change.

The answers change every time I ask. Is measurement even possible?

Single answers are not stable, so a single check proves nothing. What holds up is the distribution across a large, unchanged prompt set sampled repeatedly. That is why a credible score comes with a stated method and a caveat, and why week-over-week movement should be read against model versions before it is read as market movement.

Can you guarantee an AI engine will recommend a brand?

No, and no one can. The engines do not sell placement, do not publish their selection logic, and change their models without notice. AEO improves the odds and makes the outcome visible. It does not control the output.

Which engines should AEO cover?

Start with the ones your buyers actually use for research. Seenby reads two at launch, OpenAI and Perplexity, because those are the surfaces it can query reliably on a schedule rather than the longest possible list.