What is AI search engine optimization?
AI search engine optimization is optimising for search experiences that answer with generated text — an umbrella term covering GEO, AEO, and LLM SEO.
AI search engine optimization, sometimes shortened to AI search optimization, is the umbrella term for optimising your presence in search experiences that answer with generated text rather than a list of links. This article defines the term, sets out what falls inside it, and explains how to approach it without getting lost in acronyms.
The term is broader than the alternatives and correspondingly vaguer. Where GEO, AEO, and LLM SEO each pick out a particular mechanism, AI search optimization simply names the goal: be present, accurately described, and cited across the AI-mediated surfaces where people now search. In most conversations it is the safest term to use, because it does not commit you to a definitional argument.
What counts as an AI search surface
The set is wider than most teams assume, and each surface behaves differently.
- Conversational assistants such as ChatGPT, Claude, and Gemini, which answer from a mix of training data and live retrieval.
- Answer-first search engines such as Perplexity, which retrieve heavily and cite visibly.
- AI answers inside conventional search, including Google AI Overviews and AI Mode, which sit above the traditional results.
- Assistant layers in other products, including Microsoft Copilot and the AI features embedded in browsers and productivity tools.
- Vertical and in-product assistants, which increasingly retrieve from the open web for recommendations.
They differ enough that a single blended score hides more than it reveals. A brand can be well represented in Perplexity, which retrieves aggressively, and nearly absent from a model answering from memory.
The three levers
Whatever the surface, only three things determine whether you appear.
Retrievability. Can the engine fetch and parse your content at all. This is conventional technical SEO plus the specifics of AI crawlers — robots rules, server-side rendering, response codes, and bot protection that does not treat every non-browser agent as an attacker.
Extractability. Given that your page was retrieved, can a model lift a passage that answers the question and stands on its own. This is where structure, direct answers, and self-contained definitions matter.
Reputation. What does the wider web say about you, and how consistently. This shapes both which sources an engine trusts and what a model believes about your category position when it answers without searching.
How to approach it
- Choose the surfaces that match your buyers rather than trying to cover everything. For most B2B categories that is ChatGPT, Perplexity, and Google's AI answers.
- Build a question set that reflects real buying decisions, not keyword variants.
- Establish a baseline per surface, measured repeatedly enough to see through the variance in generated answers.
- Audit retrievability before content. A blocked crawler or a client-rendered page invalidates everything downstream.
- Rewrite priority pages so each section states a question and answers it immediately.
- Work on the off-site record — the roundups, comparisons, and reference pages that describe your category — because these feed both retrieval and training.
- Track mention rate and competitor gap per surface, monthly.
What to be sceptical about
The field is new enough that confident claims outrun the evidence. Be wary of anyone promising guaranteed placement in AI answers, since no engine sells or guarantees inclusion. Be equally wary of advice built entirely on schema markup, which helps engines understand a page but does not by itself win answers. And treat single-answer screenshots as anecdotes: generated answers vary run to run, so any claim about improvement needs repeated measurement behind it.
Common questions about AI search optimization
- Is AI search optimization the same as GEO? It is the broader umbrella. GEO, AEO, and LLM SEO each name a particular mechanism; AI search optimization names the goal, which is why it is the safest term to use in a mixed audience.
- Which surfaces should I actually cover? Those matching your buyers, not all of them. For most B2B categories that is ChatGPT, Perplexity, and Google's AI answers. Adding surfaces your market does not use dilutes attention and multiplies cost.
- What are the three levers? Retrievability, extractability, and reputation. Whatever the surface, only these three determine whether you appear, and they should be fixed in that order.
- Can I optimise once for every engine? Partly. Retrievability and extractability transfer well across engines. Reputation effects are model-specific and move on a much slower timescale.
- How do I avoid wasting money in a new field? Be sceptical of guaranteed placement, of schema-only strategies, and of single-answer screenshots presented as evidence. Insist on repeated measurement behind any claim of improvement.
- Where should a small team start? A frozen set of twenty to thirty real buying questions, a manual baseline across two engines, and a retrievability check. That costs almost nothing and tells you whether there is a problem worth funding.
Tip: pick one term and use it consistently inside your organisation. The acronym you choose matters far less than everyone meaning the same thing by it, and most of the confusion in this field is vocabulary rather than substance.
Still stuck? We typically reply within 1 business day.
Contact support