How SEO, GEO and AEO Actually Differ | Adviora

The New Search Stack: How SEO, GEO and AEO Actually Differ

For twenty years, search optimisation had one unit of success: a ranked blue link. A page earned a position, and a share of the people who saw it clicked. Everything a marketer measured hung off that single event.

That model still works, but it is no longer the only one. The same page can now be summarised inside an AI Overview, lifted as a featured snippet, or quoted by ChatGPT to someone who never opens a results page.

Below are the definitions, the operational distinctions, and an honest account of what Google has published about AI answers. Adviora's GEO & AEO Visibility module scores these layers separately, because a site can be strong on one and invisible on another.
Why the SEO vs GEO vs AEO Distinction Matters in 2026

The distinction is not academic. It changes what you brief, what you build and what counts as a win.

• Ownership. SEO fixes land with engineering; AEO fixes are editorial; GEO adds crawler policy and off-site corroboration.

• Failure mode. SEO fails as “we are not ranking.” AEO fails as “the answer above us satisfied the query.” GEO fails as “the model named a competitor.”

• Feedback. Rank is reported first-party within days; AI citations are volatile and unreported.

Adviora's GEO & AEO Visibility module reports an AEO score, a GEO score and an AI Visibility Index separately, because one blended number hides the broken layer.

SEO: Optimising to Be Ranked

Search engine optimisation is the practice of making a page the best available result for a query so a search engine ranks it, and making that listing worth clicking. The target is a position; the payoff is a visit.

• Crawlability and indexation — robots.txt, canonicals, sitemaps and internal links that make a page eligible at all.

• Page experience — Core Web Vitals at the 75th percentile: LCP under 2.5 s, INP under 200 ms, CLS under 0.1.

• Presentation — title, meta description and structured data decide whether a ranked listing earns the click.

SEO's advantage is an honest first-party scoreboard: Search Console reports clicks, impressions, CTR and average position. Adviora's Technical SEO Governance module covers the technical half, page by page.

AEO: Optimising to Be Extracted

Answer engine optimisation is the practice of structuring a page so a search engine can lift a short, complete, correct answer out of it and show that answer in place. Asked how to influence featured snippet selection, Google answers in two words: “You can't.” AEO is a lever on the conditions, not the outcome.

• Placement — the answer sits immediately under the heading that poses the question.

• Shape — a definition sentence for “what is”, an ordered list for “how to”, a comparison for “X vs Y”.

• Self-containment — the passage must survive being lifted with zero context, so name the subject rather than writing “it”.

• Snippet controls — nosnippet removes you entirely, data-nosnippet excludes one element, max-snippet caps length and guarantees nothing.

One myth worth retiring: Google publishes no minimum text length for snippet eligibility, so the familiar word-count “sweet spot” is a convention, not a requirement.

GEO: Optimising to Be Cited

Generative engine optimisation is the practice of making your content the passage a generative engine chooses to ground its answer in, and to cite. There is no list to climb: the engine retrieves a few candidates, synthesises a response and attributes some.

• Retrieval access — ChatGPT's search runs on OAI-SearchBot, and OpenAI states that blocking it means the site will not appear in ChatGPT search answers. Perplexity uses PerplexityBot; Anthropic runs Claude-SearchBot.

• Training versus visibility — GPTBot and ClaudeBot crawl for training, a separate decision. Google states Google-Extended “does not impact a site's inclusion in Google Search nor is it used as a ranking signal.”

• Passage quality — a claim stated once with a number or named entity attached is easier to lift. “Leading provider” cannot be cited; “76 checks across four categories” can.

The common own goal is a robots.txt that blocks every AI user-agent in one sweep — training and search crawlers together. Adviora's AI crawler access audit separates those decisions agent by agent, as our AI Crawler Access Audit guide explains.

LLMO: The Layer Underneath All Three

Large language model optimisation is the practice of making your brand and claims unambiguous to a language model, so that when your content is retrieved the model attributes it to the right entity.

• One canonical brand name, cased identically in navigation, bylines, alt text, schema and social profiles.

• Explicit definitions. “Adviora is an AI-powered marketing intelligence platform” is retrievable; “we help brands grow” is not.

• Quotable facts — thresholds, counts and named methods survive summarisation; adjectives do not.

LLMO is the cheapest layer to fix: editorial standards, not engineering.

SEO vs GEO vs AEO: The Five-Axis Comparison

Comparisons that list tactics make the three look like one job. Compare on these five axes instead.

• What it optimises for — SEO: a ranked position. AEO: an answer block that resolves the query. GEO: attribution inside a generated answer.

• Unit of success — SEO: a ranked link. AEO: an extracted answer. GEO: a cited passage.

• Who consumes it — SEO: a human scanning options. AEO: a human who wants one answer now. GEO: a model, relaying a synthesis to someone who never sees your page.

• How it is measured — SEO: clicks, impressions, CTR, average position. AEO: snippet presence and impressions holding while clicks fall. GEO: citation coverage, share of model, AI Visibility Index.

• Who controls selection — SEO: you influence it heavily. AEO: Google selects. GEO: the engine selects at inference time.

SEO metrics are first-party, AEO must be inferred, and GEO must be sampled. Every term is defined in our Glossary of Modern Search Metrics.

What Google Actually Says About Optimising for AI Overviews

Google's documentation on AI features is unusually direct: “There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary.” To be eligible, a page must be indexed, be eligible to appear in regular Search with a snippet, and meet Search's technical requirements. Google explicitly rules out new machine-readable files, AI-specific markup and special schema.org structured data.

What Google does recommend is ordinary discipline: allow crawling, make content findable through internal linking, deliver a good page experience, and present important content as readable text.

So a vendor selling secret files or proprietary markup for AI Overviews is selling something Google says it does not use. But eligibility is not selection. Google guarantees you can be considered, not that you will be the passage lifted — and that gap is uncharted once you leave Google.

Best Practices

1. Write one definitional sentence per key concept, directly under the heading that asks about it.

2. Answer in the shape the query implies — definition, ordered procedure, or like-for-like comparison.

3. Audit robots.txt line by line and decide training access and search access separately for each AI vendor.

4. Keep structured data consistent with the visible page; mismatches routinely cost rich results.

5. Report the three layers separately; a blended score hides which layer is failing.

The Future of the Search Stack

The likely direction is not that one acronym wins. Surfaces will multiply while the underlying requirement — clear, verifiable content from a source worth trusting — stays stable. Proposals such as llms.txt will keep appearing without any major engine committing to honour them. Teams that stay visible treat clarity as infrastructure, not a campaign.