AI Search 101
What is answer engine optimisation?
Answer engine optimisation is the practice of making a company legible and quotable to systems that answer questions rather than rank pages. Where search engine optimisation competes for a position in a list, answer engine optimisation competes to be included in a composed answer, and to be described accurately when included.
The basics
| Abbreviation | AEO |
| Also called | Generative engine optimisation, GEO, AI search optimisation |
| Applies to | AI search, AI Overviews, assistants such as ChatGPT and Perplexity |
| Measured by | Mentions, citations, share of voice, accuracy of the description |
| Related to | Search engine optimisation, which remains a prerequisite rather than a replacement |
How is answer engine optimisation different from SEO?
Search engine optimisation competes for position. The unit of success is a ranking, the reward is a click, and the measurement is well established: impressions, positions, clicks, conversions.
Answer engine optimisation competes for inclusion and accuracy. There is no position to hold. A company is either part of the answer or absent from it, and when it is part of the answer the description may be right, partly right, or a description of a business it stopped being three years ago.
The second difference matters more than the first. Search engines send a visitor to a page where a company controls the words. Answer engines describe the company to the buyer directly, in words the company did not write, assembled from sources it does not own.
What does answer engine optimisation actually involve?
Four things, in roughly this order of importance.
Entity clarity. A model needs to know what the company is before it can recommend it. That means an unambiguous definition sentence, a consistent name, a resolvable legal identity, and structured data that says plainly what kind of organisation this is and what it does. Companies with generic names or vague positioning are systematically under-described, because the model has nothing firm to hold.
Extractable content. One question per page, the answer near the top, plain declarative sentences, and facts stated rather than implied. Retrieval systems lift passages. A passage that only makes sense after three paragraphs of build-up does not survive being lifted.
Consistency across sources. A model reconciles what it reads. When a company's own site, its LinkedIn page, a directory listing and a two-year-old press release describe four different businesses, the model hedges or averages. Aligning the description everywhere it appears is unglamorous and it is the highest-leverage work available.
Presence in the sources models read. Trade publications, comparison and alternatives pages, documentation, review sites and communities are quoted disproportionately, because they are independent of the company. Earning accurate mentions there does more than another page on the company blog.
What does not work
The instinct carried over from search is volume: publish more pages against more keywords. It fails here, and it can actively hurt.
A model does not count pages, it forms an impression. Twenty thin pages produce a vaguer impression than four precise ones, because there is more text to average and less of it says anything specific. Companies that spent 2024 and 2025 generating content at scale have often made themselves harder to describe, not easier to find.
Keyword density, exact-match repetition and the other mechanical habits of early SEO have no equivalent here. The system is not matching strings, it is building a representation.
How is answer engine optimisation measured?
Traffic is the wrong first metric, because most of the effect is unattributed. A buyer who reads a description in an assistant and later arrives through a branded search appears in analytics as branded search.
The measures that reflect the work are: whether the company is mentioned at all for the questions its buyers ask, how often relative to competitors, whether the description is accurate, and which sources the answer was built from. Monitoring tools now track these across models over time, and the first run is usually uncomfortable.
How answer engine optimisation is used in marketing
Production. Reference content written to be quoted: definitions, comparisons, and direct answers to the questions buyers ask before they are ready to talk to anyone.
Analysis. Baselining what assistants currently say about the company and its category, then tracking movement. Without a baseline there is no way to tell whether anything worked.
Distribution. Deliberate work on independent sources, because a mention in a publication a model trusts outweighs another page on the company's own domain.
How this impacts your business
A language model does not look a company up in a register. It composes a description from everything it read during training and whatever it retrieves at the moment of the question. Trade press, directories, forums, competitor comparison pages, and somewhere in that mix the company's own website too.
So the description a buyer sees is assembled, not published. No company controls it, but every company supplies a share of the material it is built from.
The traffic impact is still small, roughly one percent of sessions for most companies. The influence is not small, because the reader is often a buyer forming a first impression of what a company does and who it serves, before any page gets a click.
Checking takes ten seconds. Type the company name into ChatGPT and read the answer.
Frequently asked questions
Is answer engine optimisation replacing SEO?
No. Most AI systems retrieve from the open web, so pages that rank well are frequently the pages that get read and cited. Search engine optimisation is a prerequisite. Answer engine optimisation governs what happens after retrieval.
What is the difference between AEO and GEO?
Very little in practice. Generative engine optimisation is a competing name for the same discipline. Both describe optimising for systems that generate answers rather than rank links.
How long does answer engine optimisation take to show results?
Retrieval-based mentions can change within weeks of publishing, because the pages are fetched live. Anything that depends on training data moves at the pace of model releases, which is far slower.
Does a company need to block or allow AI crawlers?
Allow them, in almost every case. Blocking training crawlers removes a company's own material from the pool the model draws on, while leaving competitor and third-party descriptions intact.
Can answer engine optimisation fix an inaccurate description?
Often, but not instantly and not unilaterally. An inaccurate description usually comes from stale or conflicting sources, so the fix is correcting those sources rather than adding new pages.
Related definitions
- What is AI search?
- What is an AI citation?
- What are AI Overviews?
- What is a large language model?
- AI Search 101
Written by Lari Numminen, Generate More. Also available in Finnish. Updated 31 August 2026.