AI Search Visibility (GEO): Get Found and Cited by ChatGPT, Gemini, Claude and Perplexity

More and more people now ask an AI assistant instead of typing a few words into a search box. They ask ChatGPT which accounting software suits a small shop, ask Gemini for a dentist near them, ask Perplexity to compare two hosting plans, or read an AI summary at the top of a Google results page. AI search visibility (often called GEO, generative engine optimization) is the work of making sure that, when these systems answer, they can find your website, understand who you are, and have good reasons to mention or cite you.

This guide explains, in plain language, how AI assistants and AI search features find and cite websites, what is publicly documented and what is still uncertain, and which levers a site owner actually controls. It also describes exactly what Rutba does to help: an AI crawler manager for robots.txt, an llms.txt generator and editor, an "AI search readiness" category in the SEO audit, structured data for your brand entity, an E-E-A-T signals check, and AI visibility tracking that asks real questions to AI engines and records whether your brand is mentioned and which sources are cited.

One honest statement before we start: nobody can guarantee that an AI assistant will mention your brand. The engines decide on their own, their behaviour changes often, and much of how they choose sources is not published. What you can do is remove the obstacles, make your site easy to understand, publish content worth citing, and measure the trend over time. That is what this page, and the Rutba features it describes, are about.

What AI search is and how assistants find websites

"AI search" is not one technology. Several different mechanisms are mixed together under the same name, and it helps to separate them, because each one is influenced by different things.

1. Knowledge learned during training

Large language models are trained on very large collections of text, which include public web pages. When an assistant answers without looking anything up, it answers from patterns it learned during that training. If your brand was written about often and consistently on the public web before the training cut-off, the model may "know" it. If your brand is new, small, or described inconsistently, it may not know it at all, or may confuse it with something else. You cannot edit what a model learned; you can only influence what future training runs see.

Training data is collected by crawlers such as GPTBot (OpenAI), ClaudeBot (Anthropic), CCBot (Common Crawl, an open dataset used by many model builders), and others. Google uses a separate robots.txt token, Google-Extended, to let site owners decide whether their content may be used for training Gemini models; Google states that this token does not affect inclusion in Google Search.

2. Retrieval: searching the web at answer time

Many assistants now search the web when they answer, then summarise what they found and show links to the sources. ChatGPT search, Perplexity, Claude with web search and similar tools work this way. Here, being cited depends on whether the assistant's search system can fetch your pages, whether your page appears among the candidates it retrieves for that question, and whether your page contains a clear, relevant answer it can quote. Vendors run dedicated crawlers for this purpose, such as OAI-SearchBot (OpenAI) and PerplexityBot (Perplexity), and Anthropic documents Claude-SearchBot.

3. User-triggered fetching

When a person pastes a link into an assistant, or asks it to read a specific page, the assistant fetches that page on the person's behalf. These fetchers have their own user-agent names, for example ChatGPT-User, Claude-User and Perplexity-User. Some vendors note that user-initiated fetches may not be treated exactly like automated crawling with respect to robots.txt, so it is worth reading each vendor's current documentation.

4. AI features inside classic search engines

Google's AI Overviews and AI Mode are built on Google Search itself. Google's own guidance for site owners says there are no special extra requirements to appear in these features: a page must be indexed and eligible to be shown with a snippet in Google Search, and the usual SEO best practices apply. Google also says that its standard snippet controls (such as nosnippet and max-snippet) apply to these features. In other words, for Google's AI features, good classic SEO is the foundation, and Googlebot, not a separate AI bot, does the crawling. Bing's Copilot answers similarly draw on the Bing index.

Understanding these four paths prevents a lot of confusion. Blocking a training crawler does not remove you from Google Search. Blocking a search crawler such as OAI-SearchBot does remove your pages from the sources that assistant can cite. And no file you publish can make a model "remember" you overnight.

What is known and what is still uncertain

The field is young and full of confident claims. A trustworthy approach separates what is documented from what is guessed.

Documented and reasonably certain:

• AI vendors publish the names of their crawlers and say they respect robots.txt for automated crawling. You can allow or block them per user-agent.
• Google states that AI Overviews and AI Mode rely on the Google Search index and the same quality and spam policies, and that no special markup or file is required.
• Assistants that search the web cite pages they can fetch. A page that is blocked, very slow, or hidden behind a login cannot be fetched and therefore cannot be quoted.
• Answers vary. The same question asked twice, or by two people, or in two languages, can produce different brands and different sources. Consumer apps may add personalization, location and conversation history.

Uncertain or unproven:

• The exact ranking or selection logic of AI search systems is not published. Lists of "the 37 GEO ranking factors" are opinions.
• llms.txt is a proposal, not an adopted standard. No major AI search engine has confirmed that it uses the file to choose sources.
• There is no reliable public dataset showing how often each assistant mentions each brand. Any tool that measures AI visibility, Rutba included, samples answers and should be read as a trend, not an exact count.
• Whether structured data directly influences generative answers is not confirmed by the vendors. It clearly helps classic search engines understand entities, and those engines feed some AI features, but a direct effect on, say, ChatGPT answers has not been documented.

Keeping this distinction in mind protects your budget. Spend effort first on the things that are known to matter (crawl access, indexable pages, clear content, a consistent brand identity, genuine reputation), and treat the rest as low-cost experiments.

Classic SEO and GEO: the real differences, without hype

Some marketers present GEO as a replacement for SEO. It is not. Most of what makes a site visible in AI answers is the same work that makes it visible in search results. But the goals and the measurements do differ in a few useful ways.

The practical conclusion is simple: do classic SEO well, then add a thin GEO layer on top. That layer is about access (do not block the bots you want), clarity (state facts plainly so they can be quoted), identity (make it obvious which name belongs to which site), and reputation (be present where the engines look). Anyone who tells you to abandon SEO for GEO, or who promises "guaranteed placement in ChatGPT", is selling something that does not exist.

The technical gate: AI crawlers and robots.txt

If an AI search crawler cannot fetch your pages, nothing else on this page matters for that assistant. The robots.txt file at the root of your domain is where you tell crawlers what they may fetch.

Robots.txt works by user-agent groups. A group such as:

User-agent: OAI-SearchBot
Allow: /

tells that crawler it may fetch everything, while Disallow: / would ask it to fetch nothing. If no group names a crawler, it follows the general User-agent: * group. Two facts to remember: robots.txt controls crawling, not indexing; and it is a request, not an enforcement. Reputable crawlers follow it; badly behaved scrapers may not.

Three purposes, three different decisions

It is useful to classify AI crawlers by purpose:

• Search bots fetch pages so the assistant can find and cite them in answers. Blocking them removes your pages from that assistant's sources.
• User bots fetch a page because a person asked the assistant about it right now.
• Training bots collect content to train models. Blocking them has no effect on Google Search and does not remove you from AI search answers that are based on live retrieval; it only limits use of your content in future training.

Many businesses choose to allow search and user bots (because they want to be cited) and block training bots (because they do not want their content used for training without a direct benefit). Others allow everything, and some publishers block everything. Each choice can be right; it is a business decision, not a technical one.

Common robots.txt mistakes that hurt AI visibility

• A security plugin or hosting firewall adds a blanket block for "AI bots" that also catches search bots.
• A rule copied from a forum blocks User-agent: * with Disallow: / on a live site.
• The file blocks the CSS and JavaScript the page needs, so crawlers see a broken page.
• Someone blocks GPTBot thinking it stops ChatGPT search; in fact ChatGPT search uses OAI-SearchBot.
• The file is edited by hand and a typo silently changes its meaning.

llms.txt: what it is and what it is not

llms.txt is a proposal published at llmstxt.org. The idea is a plain Markdown file at /llms.txt that gives AI tools a short, human-readable map of a site: its name, a one-sentence summary, and lists of the most important pages with short descriptions. It is meant to help a language model find the useful parts of a site without parsing menus, scripts and ads.

Here is the honest status, which Rutba also shows in the product:

• llms.txt is not a web standard and is not adopted by all AI systems.
• Google Search does not use it. Google has said its AI features rely on normal crawling and indexing.
• No major AI search engine has confirmed that it uses llms.txt to decide which sources to cite.
• Some AI tools, developer assistants and documentation tools do read it.

So should you publish one? It costs little and does no harm, as long as you treat it as optional and low impact. A good llms.txt is short, accurate and points to pages that are already crawlable and indexable. A bad one lists outdated or redirected pages, or tries to stuff keywords. Never let work on llms.txt delay the things that matter far more: allowing the right crawlers, making pages fast and indexable, and publishing clear answers.

Entities and structured data: making your identity unambiguous

Search engines and AI systems think in entities: things with a stable identity, such as an organization, a person, a product or a place. If your brand name is a common word, if you write it in Arabic and Latin letters, or if another company has a similar name, systems may struggle to connect the name with your website.

Structured data in JSON-LD format, using the Schema.org vocabulary, is the documented way to describe that identity to search engines. For a business site, the key pieces are:

• Organization (or LocalBusiness for a place customers visit, or Person for a personal brand) on the home page, with the official name, URL and logo.
• sameAs links to your real, official profiles: social accounts, business directory listings, a Wikipedia or Wikidata entry if one genuinely exists.
• A WebSite node linked to the organization as its publisher.
• Article markup with a named author on articles, and Product markup with accurate details on product pages.

Google uses Organization markup to understand which logo and details belong to a business, and it is part of how Google connects a brand name with a domain. Whether AI assistants outside Google read this markup directly is not documented, so present it honestly: it is a solid foundation for entity understanding in search, and a cheap, low-risk step that may help other systems too. The markup must match what is visible on the page; marking up content that visitors cannot see is against Google's guidelines.

Consistency matters as much as markup. Use the same brand name, address and phone number on your site, your profiles and directory listings. If you are known by several spellings, mention them naturally on your About page.

E-E-A-T and trust: why "who is behind this" matters

E-E-A-T stands for Experience, Expertise, Authoritativeness and Trust. It comes from Google's Search Quality Rater Guidelines, the document used by human raters to evaluate search quality. It is important to be precise here: E-E-A-T is a framework that describes what high-quality, trustworthy content looks like; it is not a single ranking score or a number inside Google's algorithm.

Why does it matter for AI visibility? Because systems that choose sources to cite try to prefer reliable ones, and because the signals behind E-E-A-T are also the signals a careful reader checks before trusting a recommendation. Concretely, these are visible things anyone can verify:

• An About page that says who runs the site and why they are qualified.
• A contact page with a real way to reach you, not only a form.
• A privacy policy, which is legally required in most countries when you collect data.
• HTTPS everywhere.
• Authors named on articles, with a short bio and an author page.
• Claims supported by links to original sources: studies, official data, documentation.
• Content that is reviewed and updated, not left to age for years.
• Genuine customer reviews, never invented ones.

None of these guarantees anything, but a site without them gives both people and machines fewer reasons to trust it.

Content that gets cited: practical writing guidance

When an assistant searches the web and builds an answer, it looks for passages that answer the question directly and can be attributed to a source. You can write in a way that makes your pages easier to understand and quote, without any tricks.

1. Answer the question early. Put a clear, one- or two-sentence answer near the top of the page, then explain the details. This is also good for human readers.
2. Use descriptive headings. A heading that matches a real question ("How long does shipping to Riyadh take?") helps both search engines and readers find the right section.
3. State facts plainly and specifically. Prices, dimensions, opening hours, delivery areas, supported languages, version numbers. Vague marketing language ("world-class solutions") gives an assistant nothing to quote.
4. Show your own experience. First-hand tests, photos you took, numbers from your own work, and honest limitations. This is the "Experience" in E-E-A-T and it is hard for others to copy.
5. Compare honestly. People ask assistants "X or Y?". A fair comparison page, including where a competitor is better, is more useful and more credible than one that only praises you.
6. Cite your sources. Link to the official documents and data you rely on.
7. Keep one topic per page. A page that clearly covers one question is easier to retrieve than a page that covers ten things loosely.
8. Keep it current. Update facts, prices and screenshots, and show the update date honestly.
9. Write in your audience's language. An Arabic question is often answered from Arabic sources. If your customers ask in Arabic or Turkish, publish good content in those languages.
10. Make it accessible to crawlers. Important text should be in the HTML, not only inside images, and not loaded only after user interaction.

Things to avoid: hidden text aimed at AI systems, "prompt injection" sentences that tell assistants to recommend you, mass-produced pages written only to capture questions, fake reviews and fake comparisons. These violate search engine spam policies, can damage trust, and vendors actively work to ignore or penalise them.

Finally, remember that assistants also cite other websites about you: review sites, directories, marketplaces, news articles, forums and industry blogs. If those sources keep appearing in answers for your topic, a genuine presence there (an accurate listing, real reviews, an expert quote, useful data you share) often matters as much as anything on your own site. Paid placements must always be labelled as such.

Notes for Arabic and Turkish content

Arabic and Turkish content is a much smaller share of the web than English content. That cuts both ways. For many questions there are only a few good sources in those languages, so a clear, accurate local page can be a strong candidate for citation. But when an assistant finds no adequate source in the question's language, it may answer from English sources instead, and the citation goes elsewhere. A few habits help: write your key pages in clear standard language while using the terms your customers really use; write your brand name consistently and mention its other spellings on your About page; have machine translations reviewed by a person; connect language versions with correct hreflang tags; state prices, units and currencies explicitly; and publish pages for local questions (delivery to a specific city, compatibility with a local regulation) that the web rarely answers directly.

How to measure AI visibility yourself, and its pitfalls

You can start measuring without any tool:

1. Write down ten to twenty questions your customers would really ask an assistant, in the language they use. Mix generic questions ("best project management app for small teams") with specific ones ("is [your brand] good for…").
2. Ask each question in several assistants, in a fresh conversation, without mentioning your brand.
3. Record for each answer: is your brand mentioned, which other brands appear, in what order, what tone is used, and which websites are cited.
4. Repeat at regular intervals, for example every week, and compare.

The pitfalls are real. Answers vary between runs, so a single test means little. Your own account history and location may bias results. Mentioning your brand in the question makes the test worthless. Manual tracking takes time and is easy to abandon after a few weeks. And counting mentions by eye is error-prone, especially in Arabic, where a name can be written with or without hamza, with an attached prefix, or in Latin letters.

This is exactly the part Rutba automates, as described below.

How Rutba helps you get ready for AI search

Rutba brings the AI-related work together in a few places in your site's workspace: the Crawl files screen (robots.txt, AI crawlers and llms.txt), the SEO audit (with its "AI search readiness" category), Structured data, Content insights (with the E-E-A-T signals tab) and AI visibility. Here is what each one does, in the order you would normally use them.

AI crawler manager for robots.txt

In Crawl files, the AI crawlers tab lists sixteen known AI crawlers, each with its company, its product and its purpose, and shows whether your current robots.txt allows or blocks it:

You can switch each crawler on or off, or apply one of three presets: Allow all, Allow AI search, block training, and Block all AI crawlers. The screen reminds you that blocking search crawlers removes your pages from those assistants' answers, that blocking training crawlers does not affect Google Search, and that robots.txt is a request reputable crawlers follow.

Rutba writes these rules inside a clearly marked block of your robots.txt, between # BEGIN Rutba AI crawlers and # END Rutba AI crawlers. The rest of your file is never touched. To keep the file short, it only adds a rule for a crawler when your wish differs from what the rest of the file already says. The same screen includes a full robots.txt editor with a validator that flags problems, a URL tester that tells you whether a given crawler may fetch a given URL, and starting templates (WordPress standard, online store, allow everything).

llms.txt generator and editor

The llms.txt tab drafts a file from your latest audit. The draft follows the llmstxt.org proposal: a heading with your site name, a short summary from your home page description, then a "Main pages" section (pages linked from most of your site, such as menu and footer pages) and a "Content" section, with up to forty indexable pages ordered by how many internal links point to them, each with its description. When the WordPress connector is installed, Rutba uses clean post titles from your site, so you get "About us" rather than "About us - Site name".

You can edit the draft freely, see whether a file is already published, and reload a fresh draft after a new audit. The screen states plainly that llms.txt is an emerging proposal, that Google Search does not use it and that only some AI tools do. Rutba does not present it as a ranking factor, because it is not one.

The "AI search readiness" audit category

Every SEO audit includes an "AI search readiness" category with three site-level rules:

• AI search crawlers are blocked in robots.txt (warning). It checks OAI-SearchBot, ChatGPT-User, PerplexityBot, Perplexity-User, Claude-SearchBot and Claude-User, and lists any that are blocked from the whole site. Training crawlers are deliberately not checked here, because blocking them is a legitimate choice.
• robots.txt has no policy for AI crawlers (notice). It is informational: no rule mentions any known AI crawler, so they all follow your general rules. It asks you to make your intent explicit, without telling you which choice to make.
• No llms.txt file (notice). It explains that the file is optional, non-standard and has no proven ranking or visibility benefit.

This category carries a small weight in the overall audit score, which reflects reality: crawlability, on-page quality, content and performance matter far more. Each finding comes with an explanation of why it matters and how to fix it, written in the same honest tone.

Structured data for your brand entity

In Structured data, you describe your site's main entity as an Organization, a LocalBusiness (with subtypes such as Store, Restaurant, Dentist or Hotel) or a Person, with name, URL, logo and sameAs links to your official profiles. Rutba builds a connected graph in which a WebSite node points to your entity as its publisher, shows a live JSON-LD preview, and validates it before you can apply it. If Rank Math is active on the site, Rutba uses the same identifiers so search engines can merge the two descriptions, and it reminds you to keep the name and logo identical in both. Page-level types such as Article (with author), Product and FAQPage are available for individual pages.

E-E-A-T signals check

In Content insights, the E-E-A-T signals tab scores ten visible, verifiable signals from your latest audit, grouped by pillar: an About page, a contact page, a privacy policy, HTTPS, who is behind the site described in structured data, official profiles linked with sameAs, authors shown on articles, long pages citing outside sources, content updated in the last two years, and real customer reviews shown on the page. Each check says whether it passes, gives details where useful (for example how many of your articles name an author) and explains how to improve it. The tab states clearly that E-E-A-T is a framework from the rater guidelines, not a ranking score.

AI visibility tracking

The AI visibility screen is where you measure. You add the questions your audience asks AI assistants, one per line, in the language they use. Rutba then asks each active question to each active engine and reads every answer.

Engines. Up to four engines can be offered: ChatGPT, Claude, Gemini and Perplexity. An engine appears only when the platform has an AI provider configured for it and your plan allows the model; you can choose which of the available engines to use for each site. Rutba records which model actually answered each question.

Honest method. Questions are sent the way an ordinary person would ask, with an instruction to answer normally, name brands where useful and cite sources, and with no hint about which brand is being tracked. The screen explains that answers come from the vendors' APIs and can differ from the consumer apps, which may use other models, personalization or live web search. Engines whose model searches the web by itself are marked "web search"; the others answer from what they learned. Read the score as a trend, not an exact measurement.

What is recorded for each answer:

• whether your brand is mentioned, matched against your site name, your domain and any other names you add (for example the Arabic and Latin spellings). Matching ignores case and accents and, for Arabic, handles hamza and alef variants, diacritics and attached prefixes such as "و" "ب" "ل" and the article "ال";
• your position among the brands named (1 means you were the first brand mentioned);
• the tone of the mention (positive, neutral or negative), classified by a short second AI call only when you are mentioned;
• the competitors and other domains that appear, with your tracked competitors flagged;
• the URLs cited in the answer.

What the dashboard shows: a visibility score (the share of completed answers that mention you), the number of answers checked, the average position when mentioned, the tone of the mentions, a trend over recent runs, results by engine, results by question with the full text of each answer, the other brands in the answers by share of answers, and the sources the engines cite most often, with your own site flagged. Failed answers, for example when a provider is down, are excluded from every ratio, so an outage never looks like "not mentioned".

Schedule and cost. Runs can be scheduled automatically, daily or weekly depending on your plan, and you can also run them manually once a day. Every answer uses AI credits from your team's balance. Before you switch on the schedule, Rutba shows an upper estimate of the credits per answer, per run and per month, and your remaining credits. If credits run out, the run stops cleanly and resumes at the next run. AI visibility tracking and the number of active questions you can track depend on your plan; pausing a question frees its place without deleting its history.

Recommendations you can act on

Below the results, a "How to appear in AI answers" panel turns what Rutba found into concrete, honest actions, with the statement that no one can guarantee a mention. Depending on your data, it may suggest: allowing AI search bots that your robots.txt blocks (with a link to Crawl files); getting a genuine presence on the domains the engines cite most for your questions; publishing a page that answers each question where no engine mentioned you, with a Create content button that starts a content document from that question; describing your brand with Organization or LocalBusiness markup if you have not yet; adding the other names of your brand so every mention is counted; and a reminder that llms.txt is optional and low impact.

Safety, control and privacy

Changing robots.txt is one of the riskiest edits you can make to a website: one wrong line can hide an entire site from search engines. That is why Rutba never writes these files directly.

• Every save is a proposal. Changes to robots.txt, AI crawler access and llms.txt become a change set that you review on a diff screen before anything reaches your site.
• Risk levels. robots.txt changes are classified as high risk and llms.txt changes as moderate risk in Rutba's safety engine. As with every write through the WordPress connector, a restore point is created before the change and a health check runs after it, with automatic rollback if something goes wrong. Read more on the safe changes page.
• Without the plugin. If your site is not connected through the WordPress connector, Rutba gives you the text to copy and upload to your site's root yourself.
• Honest measurement. The visibility engines are never told which brand you track, so the results are not biased in your favour.
• Data stays scoped. Your questions, answers and settings belong to your team and site. AI calls go through Rutba's central AI hub and are metered in your credits ledger, and you see the estimate before scheduling.
• Your choice, not ours. The audit never tells you that blocking training crawlers is wrong. It only flags blocks on search crawlers, because those directly remove you from AI answers.

Who this is for

• Business and service sites that want to appear when people ask an assistant for a recommendation in their field or city: clinics, law firms, agencies, schools, consultants.
• Online stores whose customers ask "which product is best for…" and compare options in AI tools before buying.
• Bloggers and publishers who want to decide deliberately which AI crawlers may use their work, and want to be cited rather than silently summarised.
• Agencies that manage several client sites and need a consistent, honest way to explain AI visibility to clients, with numbers that are clearly labelled as samples.
• Arabic and Turkish sites in particular, where brand names are written in several ways and most generic tools miss mentions. Rutba's matching was built with Arabic spelling variants in mind.

AI search readiness checklist

1. Your robots.txt does not block the search and user crawlers of the assistants you care about.
2. Your decision on training crawlers is written explicitly in robots.txt.
3. Your important pages are indexable, fast, and return a 200 status.
4. Important text is in the HTML, not only in images or behind interactions.
5. Your home page has Organization, LocalBusiness or Person structured data with logo and sameAs links.
6. Your brand name, address and phone number are consistent everywhere.
7. You have About, contact and privacy pages, and HTTPS everywhere.
8. Articles show a named author with a bio.
9. Key pages answer the main question in the first lines, with specific facts.
10. You have honest comparison and FAQ-style content for the questions customers ask.
11. Claims link to original sources; content has been reviewed recently.
12. You know which external sites the engines cite for your topic, and you have a genuine presence there.
13. You track a fixed set of real questions over time and read the trend, not single answers.
14. Optional: a short, accurate llms.txt that lists pages which are already crawlable.

Mistakes and myths to avoid

• Myth: "llms.txt will get you into ChatGPT." No major AI search engine has confirmed it uses the file. It is optional.
• Myth: "Blocking GPTBot removes you from Google." Google Search uses Googlebot; Google-Extended controls Gemini training and does not affect Search. GPTBot is OpenAI's training crawler.
• Myth: "GEO replaces SEO." Google's AI features are built on its search index; classic SEO remains the foundation.
• Myth: "There is a GEO score that engines use." Scores in tools, including Rutba's visibility score, are measurements made by the tool, not values the engines use.
• Mistake: blocking everything by accident. Security plugins and firewalls sometimes block AI search bots without telling you. Check robots.txt and server rules.
• Mistake: testing with your brand in the question. It makes every result look good and tells you nothing.
• Mistake: reading one answer as the truth. Answers vary; look at many answers over several runs.
• Mistake: hidden instructions for AI. Text that tells assistants to recommend you is manipulative, can break search spam policies, and vendors work to ignore it.
• Mistake: fake reviews or paid mentions without labels. They damage trust and can break the law in many countries.
• Mistake: ignoring your audience's language. If customers ask in Arabic, English-only content may never be retrieved for those questions.

How AI visibility connects with Rutba's other services

AI visibility is not a separate world. It sits on top of the rest of your SEO work:

• The SEO audit finds the crawl, indexability, speed and content problems that keep both search engines and AI crawlers away, and contains the AI search readiness category.
• SEO fixes resolve many audit findings, including titles, descriptions and structured data, often in one click.
• Safe changes is the engine behind every robots.txt and llms.txt edit: review, restore point, health check and rollback.
• Google search data shows your real impressions and clicks from Google Search Console, which remain the most reliable signal of search visibility, including the traffic that Google's AI features send.
• Rutba AI helps you write the clear, specific content that answers your customers' questions, starting directly from a question where no engine mentioned you, always with your review before publishing.

Getting started in six steps

1. Create your account and add your site. Install the WordPress connector for one-click, reversible changes, or verify your site for read-only analysis.
2. Run an SEO audit. Look at the AI search readiness category and fix any blocked search crawlers first. Fix the major crawlability and content issues too.
3. Set your AI crawler policy. Open Crawl files, choose a preset or set each crawler, review the diff and apply it.
4. Describe your brand. Add Organization, LocalBusiness or Person structured data with your logo and sameAs links, then check the E-E-A-T signals tab and fill the gaps you can.
5. Optionally publish llms.txt. Review the draft, remove anything outdated, and publish it, knowing it is low impact.
6. Track real questions. In AI visibility, add the questions your customers ask, add the other spellings of your brand, choose engines, check the credit estimate and switch on the schedule. Then act on the recommendations and watch the trend.

AI search is changing quickly, and nobody can promise you a place in an assistant's answer. What you can do is make sure the door is open, your identity is clear, your content is worth quoting, and your progress is measured honestly. Run the free SEO check to see where your site stands today, or start your free trial and set up AI visibility tracking for your site. See pricing for what each plan includes.