Key takeaways
- GEO (Generative Engine Optimization) optimises for being cited inside an AI's answer, not for appearing in a list of ten links. It is a different objective from classic SEO, though it shares the same foundations.
- ChatGPT exceeds 800 million weekly users, and a growing share of B2B purchase decisions now starts with a question to an AI rather than a Google search.
- The most actionable data point: 81% of pages cited by ChatGPT, AI Overviews and Perplexity contained schema markup, and pages with schema plus FAQ blocks get roughly 44% more citations.
- GEO does not replace SEO: it extends it. If your site is not crawlable, fast and technically sound, no amount of GEO helps.
GEO (Generative Engine Optimization) is the discipline of structuring your content and brand presence so that models like ChatGPT, Perplexity, Claude or Google AI Overviews cite you inside their answers. It differs from SEO in the unit of success: SEO seeks a position in a results list; GEO seeks being the source the AI uses and names when synthesising the answer.
GEO, AEO and SEO: three things that are not synonyms
The three terms circulate interchangeably, so it is worth pinning them down:
- SEO (Search Engine Optimization). Optimising to appear in a results list. The user sees your link, clicks and lands on your site. The metric is position and click.
- AEO (Answer Engine Optimization). Optimising to occupy the direct answer: featured snippets, answer boxes, People Also Ask. The user sees the answer and may not click at all.
- GEO (Generative Engine Optimization). Optimising to be cited as a source inside an answer generated by an LLM. There are no ten positions: there is one answer with two or three named sources. Either you are in it or you do not exist.
| Criterion | SEO | AEO | GEO |
|---|---|---|---|
| Goal | Position in the list | Own the direct answer | Be cited inside the answer |
| Where it shows | Linked results | Featured snippet | Generated answer with 2-3 sources |
| Success metric | Position and click | Snippet impression | Citation and brand mention |
| Format rewarded | Full page | Answer paragraph | Self-contained passage + schema |
| User click | Required | Optional | Often never happens |
What GEO is NOT: it is not a trick to fool the model, nor a replacement for SEO. An LLM cannot cite a page it cannot crawl, that takes eight seconds to load, or that is blocked by default in robots.txt. The technical base is the same; what changes is how content is structured on top of it.
The analogy we use: SEO is getting your book onto the library shelf. GEO is getting the librarian to cite you from memory, by name, when someone asks.
Why this is no longer a discussion about the future
ChatGPT exceeds 800 million weekly users according to OpenAI data from April 2026, and a growing share of pre-purchase B2B research ("which company implements AI agents in Spain?", "n8n or Make for my case?") now starts there rather than in a traditional search engine.
The underlying shift is that users no longer receive ten links to compare: they receive one answer with two or three sources. The long tail of mid-range positions disappears. Ranking seventh on Google still delivered some traffic; being the fourth source an AI does not mention delivers nothing.
For a B2B company with a consultative sales cycle, the practical consequence is direct: AI has become the first vendor filter. If you are not in that answer, you do not make the shortlist.
Key market data
- ChatGPT exceeds 800 million weekly users (OpenAI, April 2026), and many B2B purchase decisions start with a question to an AI rather than a search (InboundCycle, 2026).
- AccuraCast analysed more than 2,000 prompts across ChatGPT, AI Overviews and Perplexity: 81% of cited pages contained schema markup. Pages with schema and FAQ blocks get around 44% more citations in AI answers.
- LLMs do not evaluate your content alone: they evaluate whether the entity behind it (brand, company, person) is recognisable, consistent and corroborated across the rest of the web.
Our reading of that 81%: schema is not magic. Schema is the most reliable marker of a structured, explicit, disambiguated page, which is exactly what a model needs to extract a passage and cite it without risk. Schema is the signal, not the cause.
The five fronts of GEO
1. Technical access. If the model cannot read you, nothing else matters.
- Check you are not blocking AI crawlers in
robots.txtwithout having decided that consciously. Distinguishing a training crawler from one that feeds cited answers is a commercial decision, not a technical one. - Critical content must be in the served HTML, not dependent on JavaScript to render.
- Consider publishing an
llms.txtfile at the root: a markdown index that tells models what your site contains and where the relevant parts are.
2. Entity recognition. The model has to know who you are and be confident you are one single thing.
- Name, description, address and contact details identical across your site, business listings, LinkedIn and directories. Any variation dilutes the entity.
- Complete, consistent
Organizationschema withsameAspointing to your official profiles. - Explicit authorship on content: who writes it and why they have standing.
3. Citable evidence. This is where most of the content work sits.
- Self-contained paragraphs of 40-60 words that answer one concrete question and make sense out of context. A paragraph starting with "as we mentioned earlier" is uncitable.
- Concrete data with linked sources instead of vague claims. "Improves efficiency" does not get cited; "reduces response time by 60-80%" with a source link does.
- Explicit definitions of the concept in the same section where it appears.
- Tables and numbered lists: generative engines extract structured content far more readily than continuous prose.
- FAQ blocks with real questions and self-contained answers.
4. External corroboration. An LLM cross-checks what you say against what others say about you.
- Mentions in industry media, reputable directories, forums where your actual audience asks questions.
- Consistency between what you claim on your site and what exists outside it. A contradiction reduces trust in the whole entity.
5. Measurement. The step almost nobody builds, and without which everything above is faith.
- Periodically run a fixed set of prompts across ChatGPT, Perplexity and AI Overviews, and record whether you appear and how you are described.
- Segment referral traffic from AI assistants in your analytics. It is small in volume but usually converts better, because it arrives pre-qualified.
Common use cases
Case 1: Restructuring content for extraction.
- Problem: long, well-written articles that were never cited, with chained paragraphs and anaphoric references.
- Solution: rewriting into self-contained blocks, explicit definitions, comparison tables and an FAQ block per article, without changing the argument.
- Stack: extractability audit + rewrite + Article and FAQPage schema.
- Result: the content becomes splittable into citable passages without losing meaning.
Case 2: Entity consolidation.
- Problem: the company appears under three legal names and two different descriptions across site, LinkedIn and directories.
- Solution: normalising name and description across all properties,
Organizationschema withsameAs, and correcting stale listings. - Stack: mention inventory + schema + profile cleanup.
- Result: the model stops treating the brand as ambiguous entities and starts associating it with a single profile.
Case 3: Citation tracking panel.
- Problem: no way to know whether the GEO work is paying off.
- Solution: a fixed set of 30-50 prompts representative of the funnel, run periodically across several engines, logging appearance, position within the answer and the description attributed to the brand.
- Stack: prompt battery + historical log + AI-assistant traffic segmentation.
- Result: the conversation moves from "we think we're showing up" to a time series.
How to implement it step by step
- First check you are accessible.
robots.txt, critical content rendered in HTML, speed. If you fail here, the rest is decorative. - Define your prompt battery before touching anything. 30-50 questions your real customer would ask an AI. Run them and save the output: that is your baseline, and without it you cannot demonstrate improvement.
- Consolidate the entity. Identical name, description and details everywhere,
Organizationschema withsameAs. It is quick and it underpins everything else. - Add schema to the pages that matter:
ArticleandFAQPageon content,ProductorOrganizationas applicable. It is the best effort-to-result work available in GEO today. - Rewrite for extraction, not for length. Self-contained paragraphs, explicit definitions, sourced data, tables. A well-structured 1,200-word article gets cited more than a 3,000-word one in continuous prose.
- Publish an
llms.txtindexing your model-relevant content. - Measure monthly with the same prompt battery and adjust. GEO is a cycle, not a project with an end date.
Common mistakes (and how to avoid them)
Mistake: treating GEO as a replacement for SEO. → Reality: the foundations are shared. A slow, uncrawlable site, or one with JavaScript-dependent content, does not get cited. GEO builds on technical SEO, it does not stand in for it.
Mistake: blocking all AI crawlers by default. → Reality: there is a difference between a crawler gathering training data and one feeding answers with citation and link. Blanket blocking means forfeiting the channel. Decide by user agent.
Mistake: writing longer, assuming it signals authority. → Reality: models extract passages. A long text padded with filler dilutes the citable passages. Information density beats length, always.
Mistake: no authorship or date markup. → Reality: source trust is part of the calculation. Content with no identifiable author or update date competes at a disadvantage against equivalent content that has both.
Mistake: different brand descriptions on each platform. → Reality: the model evaluates the entity, not just the page. Three different descriptions create ambiguity, and ambiguity reduces the odds of being cited.
Mistake: not measuring and deciding on intuition. → Reality: without a fixed prompt battery run periodically, you do not know whether you are improving. And because model answers vary between runs, it has to be measured repeatedly, not once.
Realistic timelines and effort
- Accessibility audit and baseline prompt battery: 1-2 weeks. It comes first and it sets the success criterion.
- Entity consolidation and
Organizationschema: 1-2 weeks. High impact, low effort. ArticleandFAQPageschema across existing content: 2-4 weeks depending on page volume.- Rewriting for extraction: ongoing. Prioritise by commercial intent, not by current traffic.
- First observable change in citations: 4 to 12 weeks, with wide variation by engine. Perplexity and AI Overviews react sooner than models relying on trained knowledge.
- Where the hidden effort sits: in entity consistency outside your own site. Stale listings and abandoned profiles with old details are the most tedious work and the biggest source of ambiguity.
Metrics to track from day 1: share of your prompt battery where you appear cited, how the model describes you (literally, saving the text), schema coverage by page type, and referral traffic from AI assistants with its conversion rate. That last one usually surprises: low volume, high quality, because the user arrives with the problem already framed.
Frequently asked questions
What is GEO and how does it differ from SEO?
GEO (Generative Engine Optimization) optimises for an AI model citing your content inside the answer it generates. SEO optimises for appearing in a results list where the user chooses and clicks. The difference is the unit of success: position and click versus mention inside the answer.
Does GEO replace SEO?
No, it extends it. A model cannot cite a page it cannot crawl, that loads slowly, or whose content depends on JavaScript. Technical SEO remains the base; GEO adds a layer of structure and entity consistency on top.
What helps most in getting an AI to cite your page?
The available data points to structured markup: 81% of pages cited by ChatGPT, AI Overviews and Perplexity contained schema markup, and those including schema and FAQ blocks get around 44% more citations. Schema acts as a marker of a structured, disambiguated page.
Should I block AI crawlers in robots.txt?
Distinguish by user agent. A crawler gathering training data and one feeding answers that cite and link your site are different things. Blocking everything closes an acquisition channel; allowing everything without criteria is not the answer either.
What is an llms.txt file?
It is a markdown file at the domain root that indexes and describes the site's relevant content for language models, analogous to what robots.txt does for crawlers. It helps a model locate what matters without crawling the entire site.
How do you measure whether GEO is working?
With a fixed battery of 30-50 representative prompts run periodically across several engines, recording whether you appear cited and how you are described. Complement it with segmenting AI-assistant referral traffic in your analytics.
How long does it take to show results?
Between 4 and 12 weeks for the first observable changes, with wide variation by engine. Systems that retrieve information in real time, like Perplexity or AI Overviews, reflect changes sooner than models relying on trained knowledge.
Is GEO worth it for a small B2B company?
Especially so. In niche B2B queries the competition to be the cited source is far lower than on generic terms, and users arriving from an AI usually come with the problem already framed, which shortens the sales cycle.
Does ChatGPT cite you when someone asks about your sector?
At Naxia we treat GEO as a structure and data problem, not a bag of tricks: model-accessibility audit, entity consolidation, schema on the pages that matter, rewriting for extraction, and a prompt battery to measure whether you are actually being cited.
If you want to know what an AI answers today when asked about your sector, and whether your name comes up, talk to us. No commitment, no 40-slide decks.
Or explore our implementation process first.