How people find companies when the answer arrives before the list of links.
This is where most of my writing sits. What changed in how search surfaces answers, how assistants decide which sources to quote, and the measurement that shows whether any of it is happening to you. If your rankings are steady and your traffic is not, start here.
AI SEO for local business is being named when someone asks an assistant for a service near them. Assistants lean on the signals that feed the local pack plus independent corroboration: a complete Google Business Profile, matching contact details everywhere, and real reviews. The moves: lock entity consistency across directories, and publish extractable, location-specific answers.
AI SEO for professional services is being the firm an assistant recommends when a buyer describes a problem rather than a service. With no product to compare, assistants rely on how clearly you define your expertise and how independent sources describe you. The moves: a sharp specialism entity, demonstrable expertise on the page, and citable, problem-framed content.
AI SEO for B2B is staying in the consideration set when buying committees research solutions inside AI assistants. B2B is exposed because research-heavy journeys are full of category, comparison, and best-tool questions that assistants answer directly. The moves: own your category entity, earn analyst and review corroboration, and make solution and comparison pages extractable.
AI SEO for ecommerce is staying visible when shoppers ask an assistant what to buy rather than scan a list of results. Stores are exposed because product, category, and best-X questions are what assistants answer by naming a few options. The moves: clean product and collection entities, structured data on every listing, third-party review corroboration, and extractable comparison pages.
AI SEO for law firms is being the firm assistants name when someone asks for help with a legal problem. Law is a trust-sensitive category, so assistants weigh credentials, a consistent entity, and independent corroboration heavily. The moves: unambiguous practice-area and location signals, real attorney credentials on the page, and citable, jurisdiction-specific answers.
AI SEO for hotels is staying visible when travellers ask an assistant where to stay instead of scrolling a results page. Hotels are exposed because much discovery is now best-hotel-in-X questions that assistants answer by naming a few properties. The moves protect direct bookings: a consistent identity, strong structured data, authentic reviews, and content answering travellers' real questions.
AI SEO for SaaS is staying visible when buyers research software inside AI assistants instead of a list of links. SaaS is unusually exposed, because much of the buying journey is comparison and best-tool-for-X questions that assistants answer by naming a few products. The moves: own your category entity, earn third-party corroboration, and make your product and comparison pages extractable.
Schema markup helps AI systems understand what a page is and how its parts relate, which supports retrieval and entity clarity. It is not a ranking or citation lever, and adding it will not by itself get you into AI answers. The types worth your time clarify identity and content: Organization, Person, Product, Article, FAQPage, and BreadcrumbList.
ChatGPT and Perplexity both answer with cited sources, but they retrieve differently. Perplexity is search-first: it runs a web search on almost every query and cites heavily. ChatGPT blends its trained knowledge with web search when it judges a query needs current information, and cites more selectively. For visibility, the same short list of things gets you cited in both.
Google Gemini grounds its answers in Google Search, so showing up in Gemini is largely the same work as showing up in Google: be retrievable, rank for the query and its sub-questions, and write extractable answers. Because Gemini, AI Mode, and AI Overviews share the same model and index, most of what earns you one earns you the others. There is no separate Gemini placement to buy.
Microsoft Copilot grounds its answers in Bing's search index, so showing up in Copilot depends first on being indexed cleanly by Bing, then on being a citable source: retrievable, extractable, and clearly identified. The most overlooked step is Bing itself, which most marketing teams stopped checking years ago. Copilot cannot cite a page Bing has not indexed, no matter how well it ranks on Google.
AI Overviews are right often enough to be useful and wrong often enough that you cannot trust them blindly. They synthesise from web pages, so they inherit the errors and gaps of the sources they draw on, and occasionally state confident nonsense. For a business there is a second problem that matters more: an Overview can describe your own company inaccurately, and that one you can influence.
Does llms.txt work? Not as a ranking or citation lever, on current evidence. Google says it does not affect Search or AI Overviews, and studies find AI crawlers rarely fetch the file. Where it does work is documentation and agents, helping a tool find the right page to finish a task. It is cheap to add as a low-risk bet, but do not expect it to move your AI visibility today.
An llms.txt file is Markdown at your domain root: an H1 with your site name, an optional blockquote summary, then H2 sections of curated links, each page with a one-line description. A worked example is below. The rule that matters is curation: list the handful of pages you most want an AI to understand, not every URL, and generate the file so it never goes stale.
An AI Overview is generated, not looked up. When a query triggers one, Google breaks it into sub-questions, pulls passages from pages already ranking for those components, and uses a Gemini model to write a single answer with a few cited sources. It is a layer built on top of ordinary search ranking, not a separate index you submit to.
To track AI Overviews, watch three things on a fixed set of buyer queries: whether an Overview appears at all, whether it names or links you, and what it does to your clicks. Search Console will not break this out, so it is part manual observation, part rank tracker, and part a GA4 read of click loss on pages whose position held. The discipline is the same query set, re-run on a schedule.
You track AI search traffic by isolating the referrals assistants send you. In GA4, build a segment or channel matching the assistant domains, chatgpt.com, perplexity.ai, gemini.google.com and the rest, then watch which pages they land on. The catch: this captures clicks from assistants, not citations inside AI Overviews, which send no referral, so it is only half the picture.
An AI SEO strategy is a plan for staying findable as search splits into two outcomes: ranking in a list of links and being cited inside AI answers. It sets objectives for both, allocates effort to whichever layer is actually breaking, and measures rankings and citations separately. It is an extension of SEO strategy, not a replacement, and not the same as using AI tools to do SEO faster.
The generative engine optimization best practices that hold up are principles, not tricks: measure before you change anything, write answers that stand on their own, keep one consistent identity across the web, and earn corroboration you cannot fake. Most of the value is in avoiding the expensive mistakes, chiefly publishing volume and gaming the model, rather than in any single tactic.
Generative engine optimization services help your brand get retrieved, understood, and cited by AI assistants. They cover two jobs: diagnosis, which measures your AI visibility and finds what to fix first, and delivery, the technical and content work that closes the gaps. Start with diagnosis, keep it independent of delivery, and treat any guarantee of placement as a reason to walk away.
Generative engine optimization works by influencing each stage of how an AI engine builds an answer: it retrieves live sources, breaks them into passages, selects the most relevant and trustworthy ones, and synthesises a cited response. You cannot control the output, but you can improve your odds at every stage, from being retrievable to being the clearest passage to quote.
Answer engine optimization (AEO) is the practice of making your content the source an answer engine cites when it responds to a question directly, instead of returning a list of links. Answer engines include ChatGPT, Perplexity, and Google's AI Overviews. AEO is the same core work as SEO, aimed at being named in the answer rather than ranked beneath it.
Generative engine optimization (GEO) is the practice of making your content the source an AI system draws on and cites when it generates an answer, in tools like ChatGPT, Google AI Overviews, and Perplexity. It is the same core work as SEO aimed at a different outcome: being named in the answer, not ranked in a list of links a buyer no longer scrolls.
AI tools for SEO fall into a few categories: content optimisation, keyword and intent research, technical crawling, and the newer AI visibility trackers that show whether AI answers cite you. Each speeds up part of the work, but none replaces the judgement that decides what to fix first. Pick tools by the job you need done, not by the length of a features list.
The generative engine optimization strategies that work match how AI builds an answer: make your content retrievable by AI crawlers, make your identity consistent so a model knows who you are, structure key answers so they extract cleanly, and earn independent corroboration. The winning move is to fix your lowest broken layer first, not to spread effort thinly across tactics.
AI SEO is optimising your content to appear in AI-generated answers, not just ranked links. It means being retrievable by AI crawlers, being a clear entity a model can identify, and being cited when assistants like ChatGPT answer a buyer's question. It shares SEO's foundation but is measured on citation, not position. It is the same practice sold as GEO, AEO, and LLM SEO.
AI visibility is how often, and how accurately, your brand appears in the answers AI assistants like ChatGPT and Google AI Overviews give to buyer questions. You measure it by running a fixed set of prompts and recording whether you are cited, mentioned, or absent. It is the AI-era equivalent of a ranking, and most companies have never checked theirs.
Entity SEO is the practice of optimising content around recognised things, people, organisations, products, and concepts, rather than isolated keyword strings. Search engines and AI systems reason in entities connected through a knowledge graph, not in matching text. The goal is to make your brand an entity engines understand and trust, which decides your AI visibility.
AI search optimization makes your content easy for AI to find, extract, and cite, and builds your presence across the sources they read. It rests on four layers: access for AI crawlers, extractable answers, a clear entity, and originality worth citing. Most citations come from off your own site, so off-site corroboration matters too. It is SEO's foundation aimed at a new target.
To rank on Perplexity you have to be a source its answer is built from, not a page in a ranked list. Perplexity retrieves relevant pages, summarises across them, and cites the ones it leans on, so it rewards content that is clearly structured, gets to the answer fast, and comes from a recognised source. Be retrievable, extractable, current, and a clear entity, and you become citable.
Google AI Mode answers a query with a conversational fan-out: it breaks your question into many sub-questions, retrieves pages for each, and synthesises one answer. You do not rank in it directly. You show up by being a strong, retrievable source for the query and its sub-questions, with extractable answers and the entity authority a synthesised answer leans on. It is AI Overviews taken further.
LLM SEO is optimising your content so large language models find, understand, and cite it in their answers, rather than only ranking it in a list. It leans less on exact-match keywords and more on clarity, structure, entity precision, and corroboration. In practice it is the same work as GEO and AEO under a new name: strong foundations, extractable answers, a clear entity, and authority.
llms.txt is a plain-text Markdown file at your domain root that gives AI models a curated map of your key pages. It is cheap and low risk to add, but be honest about the payoff: Google has said it does not affect Search or AI Overviews, and there is little evidence yet that AI crawlers read it. Its clearest use today is documentation. Do not expect it to move rankings.
SEO is not dead, but the claim is not pure hype. Being found now has two layers: ranking in the classic results, and being cited in the AI answers above them. The fundamentals that made you rankable still matter, because AI answers are built on top of them. What dies is treating a ranking as the finish line; the work now is being the source the answer is built from.
In the age of AI, SEO's fundamentals did not stop mattering; they became the precondition for being retrievable. Crawlability, structure, internal linking, and authority are what let an AI find, read, and trust your pages before it can cite them. What changed is the goal: not only ranking a page but being the source an answer is built from, which rewards extractable answers.
Technical SEO for AI crawlers is mostly the technical SEO you know, aimed at new visitors: the bots that build AI search indexes and fetch pages for assistants. They need what classic crawlers need, reachable URLs, fast responses, and content in the server-rendered HTML rather than painted in by JavaScript. Get your content into the initial HTML and let these crawlers in, or you are invisible.
Answer engine optimization services help your brand get found, understood, and cited by AI assistants. They split into two offers: diagnosis, which measures your AI visibility and tells you what to fix and in what order, and delivery, which does the technical and content work. Most should start with diagnosis: paying for delivery before you know the cause spends budget on the wrong layer.
If AI describes your company vaguely, out of date, or as the wrong business, the problem is entity understanding: it has no clear picture of who you are. It comes from how consistently you are described across your site, structured data, and third-party sources. When those disagree, you become a fuzzy entity and a model prefers a clearer competitor. The fix is consistency, not more content.
A generative engine optimization checklist works best ordered by cause: the fixes are prerequisites for each other. Work in this order: make your content retrievable, make your identity consistent so a model knows who you are, make your key answers extractable, earn corroboration, and measure on a fixed schedule. Doing these out of order wastes effort on layers that cannot yet pay off.
GEO and SEO share most of their technical foundation, so you rarely need a separate agency or team for AI search. What you need is a provider who measures the right outcome and has reprioritised for it. Bring in a specialist only when your current provider cannot show a method for measuring AI visibility, or treats AI search as a product to upsell rather than a shift in the work they already do.
To check whether your brand appears in AI answers, run a fixed set of buyer questions through each assistant they use, in a clean session with personalisation off, and record whether you are cited, mentioned, or absent, and how you are described. Check each surface separately: ChatGPT, Gemini, Perplexity, and Google AI Overviews retrieve and answer differently, and one does not predict another.
You do not rank in an AI Overview directly. Google builds it from pages already ranking for the query, so the route in is to earn strong conventional visibility for the query and its sub-questions, then make your answer easy to extract: a direct response near the top, self-contained passages, and coverage of the sub-questions the Overview draws on. There is no separate Overview ranking to buy.
To show up in ChatGPT search, your pages must be reachable by its crawler, present in the index it queries, and clear enough to extract. Four things do it: let OpenAI's crawlers fetch your site, state your answers plainly near the top of each page, keep your name and category consistent everywhere, and earn mentions from sources ChatGPT trusts. There is no submission form and no paid placement.
An AI visibility audit measures how AI assistants find, describe, and cite your brand, and diagnoses why. A thorough one checks four things: whether your pages can be retrieved and rendered, whether your identity is consistent enough to understand, whether your content is extractable, and how you are described and cited across the assistants buyers use. It is a diagnosis, not a score.
AI answers cite the source that adds something others do not. When every page in your category repeats the same advice, a model has no reason to prefer yours, so it draws on whoever supplied the original material: first-hand data, a named method, the primary source. To be citable, stop restating the consensus, publish something only you could have written, and structure it to be extracted.
An assistant recommends the company it can most confidently connect to a category. A competitor gets named instead of you when the model has stronger evidence for them: they are retrieved more often, their pages answer the question directly, their identity is consistent, and other sources corroborate them. It is an evidence gap, not a quality gap, and each cause is diagnosable.
Publishing more content usually lowers AI visibility, because volume works against what these systems reward. A flood of thin, similar pages dilutes your entity, so a model is less sure what you are known for; it repeats the consensus, so none of it is worth citing; and it buries your genuinely strong pages. The fix is fewer, more original pages, and pruning the rest, not publishing faster.
Schema markup helps AI parse a page and connect it to a known entity, but it does not force a citation, override a confused identity, or lift a page with no clear answer in it. It is a comprehension aid, not a ranking lever. If schema is not moving your AI visibility, the problem is usually upstream: retrieval, entity consistency, or extractable content the markup describes but cannot replace.
SEO is optimising to appear in search engine results. AEO, or answer engine optimisation, is optimising to be the source an AI-generated answer draws on. GEO, or generative engine optimisation, means substantially the same thing as AEO and the two are used interchangeably. In practice these are not three separate disciplines but one discipline with a broader definition of where visibility happens.
Check your robots.txt for directives targeting GPTBot, ClaudeBot, PerplexityBot, Google-Extended and similar agents. Blocking them keeps your content out of AI answers that would otherwise cite you. Some agents control training use and others live retrieval: blocking training does not affect whether you are cited today, while blocking retrieval removes you entirely.
AI assistants that browse the web run a search, retrieve candidate pages, extract passages that address the question, and compose an answer from those they judge most relevant and reliable. Being citable therefore depends on three things in sequence: being retrieved at all, containing a passage that directly answers the question, and carrying signals that the source is trustworthy.
Build a fixed set of prompts covering how people describe your category, your brand, and the topics you claim expertise in. Run each in a clean session with memory and personalisation off, several times, across the assistants your buyers use. Score every result as cited, mentioned, or absent, and record which competitors appeared instead. Repeat on a fixed schedule.
If your positions are steady but your clicks are falling, the likely cause is that AI-generated answers are satisfying searches that used to send you a visit. The query still happens, your page still ranks, but the click never occurs. This is measurable in Search Console: find pages where impressions hold or rise while clicks fall, showing as a falling click-through rate at a stable position.
A migration protects search rankings through redirects, but redirects do not transfer citations in AI answers. Assistants cite URLs they have seen, and third-party pages still point at your old ones. Baseline your AI visibility before you migrate, keep every redirect a single hop to a page that answers the old page's question, and re-run the same prompts for 90 days after launch.