# Venoh: full site text > Venoh is an AI search visibility (GEO/AEO) diagnostic for B2B companies: it measures how often you are named or cited by ChatGPT, Perplexity, Gemini and Claude for your buyers' questions, who appears instead, and what to fix, plus hands-on implementation. Source: https://venoh.com. Content last updated September 25, 2026. This file contains the plain-Markdown text of every page. A shorter index is at https://venoh.com/llms.txt. --- # venoh.com: Make your company easy for AI to find, understand and cite. Venoh is an AI search visibility diagnostic for B2B companies. We put the questions your buyers ask to ChatGPT, Perplexity, Gemini and Claude, show you who gets cited instead of you and why, and give you a prioritized list of fixes with the content and markup ready to use. ## How Venoh works - **Measured, not guessed.** Every number in the report is counted from real AI answers to your buyers' questions, and every statement is checked against your own pages. - **No guarantees.** Nobody controls what an AI engine says. We show where you stand and fix verifiable gaps; we never promise rankings or citations. - **No calls needed.** Pay, and the report arrives by email as a PDF. No logins, no CMS access, no meetings. ## What GEO is When a buyer asks an AI assistant which suppliers to consider, the assistant does not show ten links to scan. It writes one answer, drawing on pages it was able to find and read. Whether your company is in that answer, and described correctly, depends on things you can actually inspect: can crawlers reach your pages, is the content in the HTML, are your facts clear and consistent, is there structured data that matches the page, and which other sites describe your category. That is what GEO (and its older sibling AEO, answer engine optimization) is about. It sits alongside SEO rather than replacing it. Read the full explanation in [What is GEO?](https://venoh.com/what-is-geo), or how it differs in [GEO vs SEO](https://venoh.com/geo-vs-seo). ## How the diagnostic works 1. **Ask the engines.** We write around 30 questions a buyer in your category would ask, without naming you, and put each to ChatGPT, Perplexity, Gemini and Claude with live web search, twice, because answers vary. 2. **Count who shows up.** How often you are named or cited on each engine, which competitors appear instead, and which third-party pages the engines rely on. All tallied by software, not estimated. 3. **Check your site and your facts.** Whether AI crawlers can read your site, what structured data and FAQ content it has, and whether the engines describe your company accurately, checked against your own pages. 4. **Hand you the fixes.** A prioritized fix list tied to what we measured, plus FAQ answers drafted from your own pages, ready-to-paste structured data and comparison page outlines, in a PDF by email. ## Offers - **GEO Diagnostic: $400 one time (₹18,000 in India).** Your buyers' questions on four AI engines, competitor share of voice, an accuracy check of what AI says about you, AI crawler access, and a prioritized fix list with ready-to-use content and markup. PDF by email. No obligation to continue. - **GEO Retainer: $1,200 per month.** Ongoing hands-on implementation of the diagnostic's findings: schema markup, FAQ and answer-first content, and page-level fixes. ## What Venoh does not do - Guarantee that any AI engine will cite you or rank you. Nobody can. - Track AI answers continuously. The engine test is a dated snapshot; a monitoring platform is the right tool for ongoing tracking. - Test engines without a public API, such as Meta AI or Microsoft Copilot. The report names exactly which engines were tested. - Analyse backlinks or domain authority. - Invent testimonials, case studies or statistics. You will not find any on this site. - Ask for logins, passwords or CMS access. ## Frequently asked questions ### What is Venoh? Venoh is an AI search visibility diagnostic for B2B companies. It measures how often your company is named or cited when buyers ask AI engines such as ChatGPT, Perplexity, Gemini and Claude about your category, who appears instead, and what to fix, and delivers the result as a PDF report by email. ### What is GEO/AEO? Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are about how your company shows up when someone asks an AI system such as ChatGPT, Perplexity, Gemini or Claude a question your buyers would actually ask. It is a different discipline from traditional search: these engines read your pages for structured, extractable, citable facts, not just keywords. ### What exactly do I get in the diagnostic? A PDF report with: around 30 of your buyers' questions asked to ChatGPT, Perplexity, Gemini and Claude with live web search (twice each); how often you were named or cited on each engine; which competitors appeared instead and their share of voice; which pages the engines cite in your category; what the engines say about your company, checked against your own site; whether AI crawlers can read your site; a prioritized fix list; and FAQ answers, structured data and comparison page outlines ready to use. ### How long does it take? The diagnostic is automated. After payment you get a confirmation straight away, and the report arrives at the same email address when it is ready, normally within a few business days at most. ### Do you guarantee my company will be cited by ChatGPT or other AI tools? No, and we will not tell you otherwise. No one can guarantee a specific AI model's output. What we do is show where you stand today and which concrete, verifiable gaps make it harder for AI engines to find, trust and cite you. ### Does the diagnostic test what ChatGPT or Perplexity say about us? Yes, as a dated snapshot. Around 30 buyer questions go to ChatGPT, Perplexity, Gemini and Claude with live web search, and we also ask them directly about your company and check what they say against your own website. The report names exactly which engines were tested and on what date. ### Is this a one-time thing or ongoing? Both are available. The diagnostic is a one-time report at $400 (₹18,000 for companies in India). For companies that want the findings implemented, there is a monthly retainer at $1,200 covering schema markup, FAQ content and page-level fixes. There is no obligation to continue past the diagnostic. ### Do I need to get on a call? No. Everything happens by email: you pay through the link on the pricing page, and the report arrives as a PDF. If you have questions, reply to any of our emails or use the contact form. --- # What is generative engine optimization (GEO)? URL: https://venoh.com/what-is-geo Last updated: September 25, 2026 Generative engine optimization (GEO) is the work of making a company's information easy for AI answer engines to find, understand and cite. This guide explains the terms, how these systems appear to pick their sources, what tends to matter, and, just as importantly, what is still uncertain. ## The short definition GEO is the practice of improving how accurately, and how often, your company appears in answers written by AI systems such as ChatGPT, Perplexity, Google's AI Overviews and AI Mode, Gemini, Claude and Microsoft Copilot. Instead of competing for a position in a list of ten blue links, you are competing to be the source an AI system draws on, quotes, or names when it writes a single synthesized answer. It helps to split the goal into two parts. The first is being **findable**: the systems that feed AI answers must be able to crawl, index and retrieve your pages. The second is being **usable**: once your page is retrieved, its content has to be clear, specific and structured enough that a model can lift an accurate statement from it and attribute it to you. Most practical GEO work is one of those two things. The phrase "generative engine optimization" was popularized by a 2023 academic research paper that studied how content changes affect visibility inside generative search responses (see the sources below). The paper is a useful starting point, but it was run in a controlled research setting; it does not tell you how ChatGPT, Perplexity or Google behave on your specific pages today. ## GEO, AEO and LLMO: which term should you use? You will see several overlapping labels. Nobody governs these terms, so usage varies between writers and vendors. This is how they are commonly used: | Term | Usually refers to | Notes | | --- | --- | --- | | GEO (generative engine optimization) | Being cited or mentioned in answers generated by AI systems. | The most common label for the whole discipline. | | AEO (answer engine optimization) | Being the direct answer to a question. | Older term, used since featured snippets and voice assistants; now used for AI answers too. | | LLMO (large language model optimization) | Influencing what language models say about you, including what they "know" from training data. | Broader and less precise; sometimes used for training-data influence rather than live citations. | In practice the page-level work overlaps almost completely, so Venoh uses GEO and AEO interchangeably. What matters is the underlying activity, not the acronym. See the [glossary](https://venoh.com/glossary) for short definitions of every term used on this site. ## How AI answer engines pick sources Each product works differently and changes often, and the vendors do not publish complete details. A simplified picture that fits most search-connected assistants looks like this: 1. **Interpret the question.** The system works out what is being asked. Some systems split one question into several searches to cover different angles. 2. **Retrieve candidates.** It pulls pages or passages from a search index or by fetching pages live. If your page is not in the index, or the crawler was blocked, it cannot be retrieved. 3. **Filter and rank.** Candidates are scored for relevance and, in ways the vendors do not fully disclose, for signals of quality and trust. 4. **Generate a grounded answer.** A language model writes the answer using the retrieved passages as its raw material. This step is called grounding. 5. **Attach citations.** Many products show which sources supported the answer. Not all do, and a source can influence an answer without being shown. There is a second route that involves no retrieval at all: a model can repeat something it absorbed during training. That route is slow to change and gives you no citation trail. Most of what a company can actively influence today sits on the retrieval route, which is why crawlability and clear on-page content come first. ## What tends to influence whether you get cited The following factors are widely discussed by practitioners and consistent with how retrieval-based systems work. Treat them as well-reasoned working assumptions rather than proven ranking factors; we say more about that below. ### 1. Crawlability and indexability If AI-related crawlers cannot fetch your pages, nothing else matters. Common blockers are a blanket `Disallow` in robots.txt, a firewall or CDN bot-protection rule that challenges automated visitors, and content that only appears after JavaScript runs. Our [how-to guide](https://venoh.com/how-to-get-cited-by-ai) lists the specific crawler names and how to test access. ### 2. Answer-first, extractable content Retrieval systems work on passages. A heading that states a question, followed immediately by a direct two-to-three sentence answer, is easy to lift. The same information buried inside a long marketing paragraph is not. Lists, short tables and definitions are all easy for both people and machines to extract. ### 3. Structured data and clear entities Structured data (schema.org markup, usually written as JSON-LD) states facts about a page in a machine-readable form: who the organization is, what a product costs, which questions a page answers. It helps parsers and search indexes understand a page unambiguously. Whether it directly changes what a language model writes is not established by public evidence, and Google states that its AI features need no special schema.org markup. What it does is remove guesswork at the retrieval and indexing stages, and it is inexpensive to do properly. ### 4. Specific, verifiable facts Concrete statements (what you do, who it is for, what it costs, what you do not do) give a model something safe to repeat. Vague superlatives give it nothing. If a claim needs a number or a source to be believable, provide it and show where it comes from. ### 5. Corroboration elsewhere on the web Systems that cross-check tend to trust facts that several independent sources agree on. That means consistent descriptions of your company across your own site and third-party pages such as directories, publications, partner pages and reviews from real customers. This is the slowest factor to improve and the hardest to fake, which is exactly why it is credible. ### 6. Freshness and consistency Out-of-date pricing, conflicting product names or two different descriptions of the same offer make it harder for a system to state anything confidently about you. Keeping key facts current and identical everywhere is unglamorous and effective. ## What is uncertain or unproven This field is young and the systems are black boxes. Anyone selling GEO should be able to say what they do not know. Here is our list: - **Ranking factors are not published.** Every claim about "what AI engines reward" is inference from experiments, observation or first principles. - **Answers vary.** The same prompt can produce different answers on different days, in different countries, or on repeated runs. A single test proves very little. - **Engines differ.** What helps in one product may do nothing in another, and each changes without notice. - **Schema's direct effect on language models is unproven.** It is good practice for machine readability, but we cannot show that adding it makes a specific model cite you. - **llms.txt is a proposal, not a standard.** Support by AI providers is not guaranteed; check its current status before relying on it. - **Vendor studies have limits.** Correlations found in a company's own dataset can reflect how that dataset was built. Read the method, not just the headline. > **No honest provider can guarantee a citation or a ranking.** What can be done is to remove concrete, verifiable obstacles and improve the quality of what is on the page. That is what Venoh's [diagnostic](https://venoh.com/methodology) is built around. ## A practical order of operations 1. Confirm the right crawlers can reach your pages, and that the content is in the HTML rather than produced only by JavaScript. 2. Make your basic entity facts (name, description, offerings, pricing, contact) identical and easy to find across your site. 3. Publish answer-first pages for the questions your buyers actually ask, in the wording they use. 4. Add structured data that matches what is visibly on each page, and nothing that is not. 5. Earn independent mentions through directories, publications, partnerships and genuine customer reviews. 6. Measure with a fixed set of buyer prompts, repeated over time, and record what you see. The [how to get cited by AI](https://venoh.com/how-to-get-cited-by-ai) checklist turns each step into concrete tasks with a way to verify it. If you would rather have the first pass done for you, read how the [Venoh diagnostic works](https://venoh.com/methodology) and what it [costs](https://venoh.com/pricing). ## Who should care about GEO GEO matters most for businesses where buyers research and shortlist suppliers before they ever speak to anyone, which describes many B2B purchases. If people can ask an AI assistant "what are the best options for X" or "how does Y compare with Z" and act on the answer, then whether the assistant can find, understand and correctly describe your company is a genuine commercial question. It matters less for businesses whose customers arrive almost entirely through other channels, though the underlying hygiene (clear pages, accurate facts) is useful regardless. ## Frequently asked questions ### Is GEO the same as SEO? No, but they overlap heavily. SEO aims to rank pages in a list of results; GEO aims to have your information used and cited inside a generated answer. Crawlability, quality content and authority help both. See the full comparison in our GEO vs SEO guide. ### Can anyone guarantee that ChatGPT will cite my company? No. AI systems are not controlled by the companies that optimize for them, answers vary between runs, and the ranking factors are not public. A provider can remove concrete obstacles and improve your content, but should not promise specific citations or rankings. ### What is the difference between GEO and AEO? The terms overlap and are not formally defined. AEO is the older label for being the direct answer to a question; GEO usually refers to being cited or mentioned in AI-generated answers. In practice the page-level work is the same. ## Sources - [Aggarwal et al., "GEO: Generative Engine Optimization" (arXiv:2311.09735)](https://arxiv.org/abs/2311.09735) - [Google Search Central: AI features and your website](https://developers.google.com/search/docs/appearance/ai-features) - [OpenAI: crawler and user-agent documentation](https://platform.openai.com/docs/bots) - [llms.txt proposal](https://llmstxt.org) - [schema.org](https://schema.org) --- # GEO vs SEO: what is different and what is not URL: https://venoh.com/geo-vs-seo Last updated: September 25, 2026 SEO and GEO are not rivals. They share a foundation, and most of what makes a site good for search also makes it easier for AI systems to use. The differences are in the goal, the unit of competition and how you measure success, and they change what you prioritize. ## The same foundation Before comparing them, it is worth being clear about what they share. Both depend on search systems being able to crawl your site, understand what each page is about, and trust that the page is a good answer. Fast, accessible pages, clear headings, honest content, sensible internal linking and a reputation built over time help in both worlds. This matters because some AI features are built on top of conventional search infrastructure. Google's own documentation on its AI features says that SEO best practices remain relevant and that there are no additional requirements, nor special schema.org markup or AI text files, needed to appear in AI Overviews or AI Mode (see the source below; check the current wording, as it is updated). If your site is hard for a search engine to crawl or understand, fixing that is the first GEO task too. ## Where they differ | Dimension | Traditional SEO | GEO | | --- | --- | --- | | Goal | Rank a page in a list of results so people click it. | Be the source an AI system uses, quotes or names in a generated answer. | | Unit of competition | The page (and its position for a query). | The passage, the fact and the entity (your company as a clearly understood thing). | | What success looks like | Rankings, impressions, clicks, conversions from visits. | Accurate mentions, citations, correct descriptions, and visits or enquiries that follow an answer. | | Where the user ends up | On your page. | Often in the answer itself, sometimes with no click at all. | | Measurement | Mature, deterministic tools and years of practice. | Young tools; outputs vary between runs, engines and locations, so you must repeat and sample. | | Time to see an effect | Well understood, though still variable. | Less well understood; retrieval-based changes may show faster than changes that depend on training data. | | Off-site signals | Links from other sites remain central. | Independent mentions and consistent descriptions across the web matter, whether or not they are hyperlinks. | ## What this changes in practice ### Write for extraction, not just for ranking A page can rank well and still be a poor source for an AI answer if the useful statement is buried. GEO pushes you toward headings phrased as questions, direct answers in the first sentences beneath them, definitions, short lists and tables. These formats are also better for human readers who skim, so they rarely hurt. ### Treat your company as an entity Search engines and AI systems both try to work out who and what you are. If your homepage says one thing, a directory listing says another, and your pricing page uses a third product name, a system has to guess. Consistent naming, descriptions and facts, backed by structured data, make you easier to describe correctly. Being described accurately is as much a GEO goal as being described often. ### Expect a multi-engine world SEO effort has traditionally concentrated on one dominant search engine. Buyers now use several assistants, and each uses different sources and behaves differently. You cannot optimize for one and assume the rest follow, which is why fundamentals (access, clarity, consistency) beat engine-specific tricks. ### Measure by sampling, not by a single lookup Because answers vary, one screenshot of an assistant naming you (or ignoring you) is an anecdote. Reasonable measurement uses a fixed list of buyer questions, run repeatedly across the engines you care about, recording whether you were mentioned, cited and described correctly. The [how-to guide](https://venoh.com/how-to-get-cited-by-ai) includes a simple template. ## Do not abandon SEO Nothing here is an argument to stop doing SEO. Organic search still drives a great deal of discovery, and the technical and content work underpinning it also supports GEO. The sensible framing is layering: keep the SEO fundamentals healthy, then add the GEO-specific work (answer-first content, entity clarity, structured data that matches the page, and third-party corroboration) on top. ## When GEO deserves more attention - Your buyers do their research before contacting anyone, and comparison or "best option for" questions are part of it. - You sell something that is hard to explain in one search result and benefits from a synthesized answer. - Your category is crowded, so being named in a short AI-written shortlist is valuable. - You have noticed AI assistants describing your company incorrectly, or not at all. ## Mistakes to avoid - **Chasing tricks.** Hidden text, prompt-injection snippets aimed at AI crawlers, and mass-produced thin pages are the modern equivalent of keyword stuffing. They are poor practice, can be treated as spam, and are the opposite of building trust. - **Fabricating proof.** Invented testimonials, reviews or statistics may be repeated by a system and can seriously damage a company's credibility when discovered. - **Blocking everything.** Some site owners disallow all AI crawlers by default and later wonder why they are absent from AI answers. Decide deliberately which agents to allow. - **Judging by one prompt.** Optimizing for a single query you tested once tells you little about how buyers actually ask. If you want to see how your own pages score on the fundamentals, the Venoh [diagnostic](https://venoh.com/methodology) checks them page by page and labels every finding by how it was determined. Pricing is on the [pricing page](https://venoh.com/pricing). ## Frequently asked questions ### Should I replace my SEO strategy with GEO? No. They share a foundation, and organic search still matters. Keep your SEO healthy and add GEO-specific work on top: answer-first content, consistent entity facts, structured data that matches the page, and independent mentions. ### Does good SEO automatically make me visible in AI answers? Not automatically. It helps because AI systems often rely on the same crawling and indexing, but a page can rank and still be a poor source for an AI answer if its useful statements are hard to extract or its facts are inconsistent. ### How do I measure GEO progress? Use a fixed list of real buyer questions, run them repeatedly across the AI engines you care about, and record mentions, citations and accuracy over time. A single test is an anecdote because answers vary. ## Sources - [Google Search Central: AI features and your website](https://developers.google.com/search/docs/appearance/ai-features) - [Aggarwal et al., "GEO: Generative Engine Optimization" (arXiv:2311.09735)](https://arxiv.org/abs/2311.09735) --- # How to get cited by AI answer engines: a checklist you can verify URL: https://venoh.com/how-to-get-cited-by-ai Last updated: September 25, 2026 There is no switch that makes an AI engine cite you, and no honest provider can promise one. There are, however, concrete things that make it structurally easier for these systems to find, parse and trust your pages. Below are ten of them. Every item says how to verify it, so you are never taking our word for it. > **How to use this list.** Work top to bottom. Items 1 and 2 decide whether anything else can work at all. Items 3 to 8 are on-site improvements. Items 9 and 10 are slower and off-site. Vendor behavior changes often, so check each vendor's current documentation before making decisions that are hard to reverse. ## 1. Let the right crawlers in AI products use different automated agents for different purposes: gathering training data, building a search index, or fetching a page because a user asked about it. Blocking the wrong ones can remove you from AI answers; allowing all of them is a business decision about training use. These are the names most site owners meet, as documented by their vendors at the time of writing: **Common AI-related crawler names (verify against each vendor's current documentation)** | Vendor | Agent name | What it is generally used for | | --- | --- | --- | | OpenAI | `GPTBot` | Crawling that may be used for model training. | | OpenAI | `OAI-SearchBot` | Indexing pages for ChatGPT search results. | | OpenAI | `ChatGPT-User` | Fetching a page when a user's request calls for it. | | Anthropic | `ClaudeBot` | Crawling that may be used for model training. | | Anthropic | `Claude-SearchBot` | Search-related crawling for Claude. | | Anthropic | `Claude-User` | Fetching pages on behalf of a user request. | | Perplexity | `PerplexityBot` | Indexing pages for Perplexity's search results. | | Perplexity | `Perplexity-User` | Fetching pages when a user's request calls for it. | | Google | `Googlebot` | Crawling for Google Search, which AI Overviews and AI Mode draw on. | | Google | `Google-Extended` | A robots.txt control token governing use of content for some Google generative AI products; it is not a separate crawler. | | Microsoft | `Bingbot` | Crawling for Bing, whose index is commonly used to ground Copilot answers. | A permissive robots.txt that names the agents you welcome keeps your intentions explicit: ``` User-agent: * Allow: / User-agent: OAI-SearchBot Allow: / User-agent: PerplexityBot Allow: / User-agent: Claude-SearchBot Allow: / Sitemap: https://example.com/sitemap.xml ``` **How to verify:** open `yourdomain.com/robots.txt` and confirm there is no blanket `Disallow: /`. Then test as an automated visitor: `curl -I -A "OAI-SearchBot" https://yourdomain.com/` should return a 200 status, not a 403 or a challenge page. Firewalls and CDN bot-protection rules are the most common hidden blockers. ## 2. Put the content in the HTML Many crawlers do not run JavaScript, or run it inconsistently. If your homepage text, headings or links only appear after a script executes, those crawlers may see an empty page. A particularly easy mistake is a homepage whose only job is a JavaScript redirect to a language version: a visitor's browser follows it, but a crawler sees nothing. **How to verify:** run `curl -s https://yourdomain.com/ | grep -i " **Be realistic about Google.** Google's documentation on its AI features states that you do not need special schema.org structured data, or new machine-readable or AI text files, to appear in AI Overviews or AI Mode. Structured data is still worthwhile as good hygiene: it removes ambiguity for search indexes and other systems, and for people who read your results. But do not expect it to be a switch for Google's AI features. ``` ``` A note on expectations: Google narrowed the search results that show FAQ-style rich snippets in 2023. That concerns how Google displays results in its classic listings, not whether the markup helps machines understand the page, and it does not change the value of writing genuine Q&A content. **How to verify:** paste the page URL into the schema.org validator or Google's Rich Results Test and confirm the types are detected without errors. Then compare the markup to the visible page; every marked-up statement should be there in plain text. ## 5. Write FAQs the way people ask them - Use questions taken from real conversations, in the buyer's own wording. - One question per heading; a self-contained answer of a few sentences that does not depend on the paragraph before it. - Include the honest "no" answers ("Do you guarantee X?"). Candid answers are more believable and more quotable. - Keep the visible text and any FAQPage markup identical. **How to verify:** copy any answer on its own into a blank document. If it still makes sense and is accurate without its surrounding page, it is extractable. ## 6. Keep your entity facts identical everywhere Decide the exact wording of your company name, one-sentence description, product and service names, pricing model and contact details, then use them consistently on your site and on every profile you control (LinkedIn, directories, partner pages, review sites). Conflicts force a system to guess, and guesses show up as errors in answers. **How to verify:** search your company name in two or three AI assistants and in a normal search engine. Note anything wrong or outdated, find where that information comes from, and correct the source. ## 7. Consider publishing an llms.txt file llms.txt is a proposal for a plain Markdown file at the root of your site that gives language models a concise, curated index of your most useful pages. It is cheap to create. It is also not an established standard, and you should not assume any particular AI provider reads it. Google's documentation for its own AI features says no such files are needed. Treat it as a low-cost, low-risk extra, not a substitute for the items above. Check llmstxt.org for the current state of the proposal. **How to verify:** `yourdomain.com/llms.txt` returns 200 with readable Markdown, and every link in it resolves. This site publishes one at [/llms.txt](https://venoh.com/llms.txt) if you want an example. ## 8. Publish an accurate sitemap A sitemap lists the pages you want indexed, with accurate last-modified dates. Reference it from robots.txt and submit it in Google Search Console and Bing Webmaster Tools. Because Bing's index commonly feeds Microsoft's assistants, Bing Webmaster Tools is easy to overlook and worth setting up. **How to verify:** `yourdomain.com/sitemap.xml` loads, lists every important page, and is reported as processed without errors in the webmaster tools. ## 9. Earn independent corroboration This is the slowest and most valuable item. Systems that cross-check information tend to be more comfortable repeating facts confirmed by several independent sources. Legitimate ways to earn that include being listed accurately in relevant industry directories, contributing genuinely useful material to trade publications, partner and integration pages that describe you, customer case studies published with permission, original data or research that other people cite, and reviews from real customers. > **Do not manufacture this.** Fake reviews, invented testimonials and purchased mentions are ethically wrong, may breach platform rules and the law, and are the fastest way to destroy the trust you are trying to build. **How to verify:** list the independent pages that describe your company. Do they agree with each other and with your site? Which important places are missing? ## 10. Measure with a fixed prompt set Write 20 to 50 questions your buyers would really ask, including comparison questions and "best option for" questions. Run them across the assistants you care about on a regular schedule, and log the results. Because answers vary from run to run, repeat each question several times before drawing conclusions. **A simple tracking template** | Date | Engine | Prompt | Mentioned? | Cited (link)? | Described accurately? | Notes | | --- | --- | --- | --- | --- | --- | --- | | YYYY-MM-DD | e.g. ChatGPT | "What are the best options for [your category]?" | Yes / No | Yes / No | Yes / Partly / No | What was wrong or missing | **How to verify:** the same prompt set, run monthly, gives you a trend line instead of anecdotes. Improvement means more accurate mentions over time, not one lucky answer. ## What to skip - Hidden text or instructions aimed at AI crawlers. - Hundreds of near-duplicate pages generated to match every phrasing. - Any promise of a guaranteed ranking or citation, from anyone. - Optimizing for a single engine and ignoring the fundamentals. If you would like these checks run against your own pages, with each finding labelled as observed, inferred or not checked, that is exactly what the Venoh [diagnostic](https://venoh.com/methodology) does. See [pricing](https://venoh.com/pricing) or read the background in [What is GEO?](https://venoh.com/what-is-geo). ## Frequently asked questions ### Should I block or allow GPTBot and other AI crawlers? It is a business decision. Training-oriented crawlers and search or user-request agents serve different purposes, and vendors document them separately. If you want to appear in AI answers, make sure the search-related and user-request agents are not blocked, and decide deliberately about training-oriented ones. Check each vendor's current documentation. ### Does adding schema markup make ChatGPT cite me? There is no public evidence that it directly does. Structured data removes ambiguity for parsers and search indexes and is good practice, but it is one factor among several and no guarantee of citation. ### Do I need an llms.txt file? No. It is a proposal rather than an established standard, and AI providers have not all committed to reading it. It is cheap and harmless to add, but do the fundamentals first. ## Sources - [OpenAI: crawler and user-agent documentation](https://platform.openai.com/docs/bots) - [Perplexity: bot documentation](https://docs.perplexity.ai/guides/bots) - [Google Search Central: AI features and your website](https://developers.google.com/search/docs/appearance/ai-features) - [Google Search Central: FAQPage structured data](https://developers.google.com/search/docs/appearance/structured-data/faqpage) - [schema.org](https://schema.org) - [llms.txt proposal](https://llmstxt.org) --- # How to get your company recommended by ChatGPT, Perplexity and Gemini URL: https://venoh.com/how-to-get-recommended-by-chatgpt Last updated: September 25, 2026 You cannot pay ChatGPT to recommend you, and there is nothing to submit. AI assistants recommend companies they can find, read, and see confirmed by other sources. That makes it slower than buying ads, but it is work you can actually do. Here is what matters, in the order we would do it. > **Where most B2B companies start** In our [September 2026 study of 103 B2B websites](https://venoh.com/ai-search-visibility-study-2026), the company's own site was cited in 17 of 311 AI answers to buyer questions about its category (5.5%), and 89 of 101 companies were never cited at all. Being absent is the norm, not the exception. ## 1. Make sure AI engines can read your site - Check robots.txt: crawlers such as OAI-SearchBot and ChatGPT-User (ChatGPT), PerplexityBot, Claude-SearchBot and Googlebot must be allowed. - Make sure your key content is in the HTML, not only rendered by JavaScript. - Get indexed by both Google and Bing. Submit your sitemap in Google Search Console and Bing Webmaster Tools. ## 2. Answer your buyers' questions on your own pages Write down the questions buyers ask before they choose a supplier in your category, then publish a clear answer to each: the question as a heading, a direct answer in the first two sentences, then specifics. AI engines lift statements they can quote. A page that says what you do, for whom, and with what results in plain words is easier to cite than one full of slogans. ## 3. Add structured data that matches the page Organization markup on your homepage (name, description, URL, profiles), and FAQPage markup on pages with real question-and-answer content. In our study, 51 of 103 sites declared no schema.org structured data at all on the pages we read, and only 19 declared FAQPage markup. ## 4. Be mentioned where the engines look When AI assistants answer "best X for Y" questions, they lean heavily on review sites, comparison articles and publisher lists. Find the pages the engines cite for your category's questions, then work on being included: complete review-site profiles, contribute genuine data or expertise to the writers of those lists, and publish comparison pages of your own that fairly explain where you differ. ## 5. Keep your facts consistent If your pricing, location or product names differ between your site, directories and profiles, engines fill the gaps on their own, sometimes wrongly. Ask the assistants what they say about your company and correct the sources behind any wrong answer. ## 6. Measure, then repeat Ask the same set of buyer questions on each engine before and after your changes, without naming your company in them. Treat each result as a dated snapshot. Changes to indexing and site structure can show up within weeks; being mentioned by other sources takes longer. If you would rather have this measured for you, Venoh's [AI visibility audit](https://venoh.com/ai-visibility-audit) runs around 30 of your buyers' questions on ChatGPT, Perplexity, Gemini and Claude, shows who is recommended instead and why, and gives you the content and markup to fix it. ## Frequently asked questions ### Can I pay to get my company recommended by ChatGPT? No. There is no advertising product or submission form that puts a company into ChatGPT's recommendations. Assistants recommend companies they can find, read and verify across the web. ### How long does it take to get recommended by AI assistants? Fixing crawl access, indexing and on-page answers can show up within weeks. Being mentioned by the review sites and articles that engines rely on usually takes months. ### How often are B2B companies cited by AI search today? In Venoh's September 2026 study of 103 B2B websites, companies' own sites were cited in 5.5% of 311 AI answers about their own category, and 89 of 101 companies were never cited. --- # How visible are B2B companies in AI search? Data from 103 websites URL: https://venoh.com/ai-search-visibility-study-2026 Last updated: September 25, 2026 In September 2026 we analysed the public websites of 103 B2B companies and asked an AI answer engine with live web search the questions their buyers ask. Most of these companies never appeared in the answers. This page publishes the aggregate numbers, how they were measured, and their limits. > **Key findings** Across 311 buyer questions put to Claude with web search, the company's own website was cited in **17 answers (5.5%)**. Only **12 of 101 companies (11.9%)** were cited even once. **36 of 101 sites (35.6%)** had an llms.txt file, **19 of 103 (18.4%)** declared FAQPage structured data on the pages we read, and **51 of 103 (49.5%)** declared no structured data at all on those pages. ## Finding 1: most B2B companies are absent from AI answers about their own category For each company we wrote a small set of questions a buyer in its category would ask, without naming the company, and put each to an AI engine with live web search. We then checked whether the company's own domain was among the sources the answer cited or drew on. **Citation test results, September 2026** | Measure | Result | | --- | --- | | Companies tested | 101 | | Buyer questions asked | 311 | | Answers that cited the company's own site | 17 (5.5%) | | Companies cited in at least one answer | 12 of 101 (11.9%) | | Companies never cited | 89 of 101 (88.1%) | | Answers that cited other websites | 160 (51.4%) | In 160 of the 311 answers (51.4%), the engine cited other websites instead. In most of the rest it answered without citing any source. Either way, the company whose category was being asked about was usually not part of the answer. ## Finding 2: the basic machine-readable signals are often missing **Site signals across the companies analysed, September 2026** | Signal | Sites with it | Share | | --- | --- | --- | | An llms.txt file at the site root | 36 of 101 | 35.6% | | An XML sitemap we could find | 79 of 94 | 84.0% | | FAQ-style content anywhere we could confirm | 42 of 101 | 41.6% | | Any schema.org structured data on the pages read | 52 of 103 | 50.5% | | Organization (or Corporation) structured data | 45 of 103 | 43.7% | | FAQPage structured data | 19 of 103 | 18.4% | Denominators differ because a check only counts when it gave a definite answer. For example, a site whose server timed out on the sitemap request is left out of the sitemap row rather than counted as having none. None of these signals guarantees a citation, and this data does not show that any of them causes one. They are the parts of AI visibility a company controls directly, which is why they are the first things the [how to get cited by AI](https://venoh.com/how-to-get-cited-by-ai) checklist covers. ## What this means for a B2B team - **Assume you are not in the answer until you have checked.** In this sample, nearly nine in ten companies were never cited for their own category's questions. - **Find out who is being cited instead.** When an AI answer cites sources, you can see them. They show which sites shape how your category is described. - **Fix what you control first.** Clear answer-first pages, FAQ content that matches real buyer questions, and structured data that matches the visible page are all within a site team's reach. See [what GEO is](https://venoh.com/what-is-geo) for the wider picture. ## How the data was collected - **Sample:** 103 B2B company websites that Venoh researched in September 2026: software and technology companies (32 of them from Y Combinator's company directory) and B2B companies in engineering, manufacturing and services, many based in India. It is not a random sample of all companies. - **Site checks:** for each company, the homepage plus up to four pages its own navigation links to, read as plain HTML, plus separate requests for /llms.txt, the sitemap and common FAQ addresses. The exact method is on the [methodology](https://venoh.com/methodology) page. - **Citation test:** usually three buyer questions per company, written without naming the company, each asked once to Claude with its web search tool on the date of the research. A company counted as cited when its own domain was among the sources of the answer. - **Counting:** every figure on this page was tallied by script from the stored research records. Nothing was estimated or extrapolated. ## Limits - One engine. The citation test used Claude with web search only. Other engines, such as ChatGPT, Gemini or Perplexity, may cite different sources; this data says nothing about them. - A small number of questions per company, each asked once on one day. AI answers change with wording and over time. - The sample is companies Venoh chose to research, so it is not representative of all businesses. - Site checks read a few pages per site, not whole sites, and do not run JavaScript. You may quote or reuse these figures with a link to this page. To see the same test run on your own company with more questions, see the [diagnostic](https://venoh.com/pricing). ## Frequently asked questions ### How often do AI search engines cite B2B companies' own websites? In Venoh's September 2026 data, across 311 buyer questions put to Claude with web search, the company's own website was cited in 17 answers (5.5%), and only 12 of 101 companies were cited at least once. ### How many B2B websites have an llms.txt file? In Venoh's September 2026 sample, 36 of 101 B2B company websites (35.6%) served an llms.txt file at the site root. The sample is not random, so the share across all businesses may differ. ### How many B2B websites use FAQPage structured data? 19 of 103 sites (18.4%) declared FAQPage structured data on the pages Venoh read, and 51 of 103 (49.5%) declared no schema.org structured data at all on those pages. ### Can I use this data? Yes. You may quote or reuse the figures with a link to this page, together with its limits: one AI engine, a few questions per company, and a non-random sample of 103 B2B websites researched in September 2026. --- # AI visibility audit: see how ChatGPT, Perplexity, Gemini and Claude answer your buyers URL: https://venoh.com/ai-visibility-audit Last updated: September 25, 2026 An AI visibility audit answers one question: when your buyers ask AI assistants about your category, are you in the answer? Venoh measures it on four engines, shows who is winning instead and why, and gives you the content and markup to fix it. ## What the audit measures | Measure | How it is measured | | --- | --- | | Visibility per engine | Around 30 buyer questions put to ChatGPT, Perplexity, Gemini and Claude with live web search, each asked twice. You count as visible when an answer names you or cites your site. | | Who appears instead | The companies named or cited in the same answers, their share of voice, and which questions each one wins. | | Where the engines get their answers | The most-cited pages in your category, marked as competitor, review or list site, or publisher. | | Accuracy | We ask the engines about your company directly and check each factual statement against your own website. | | Can AI read your site | robots.txt rules for 13 AI crawlers, llms.txt, sitemap, FAQ content, structured data and how much of your homepage is readable without JavaScript. | Category questions never name your company, so the test is not biased toward you. Every count is tallied by software from the raw answers, and the written findings are checked against the evidence by a separate model before the report is sent. The full method is on the [methodology](https://venoh.com/methodology) page. ## What you receive - A PDF report with your AI visibility score, per-engine results, competitor share of voice and a question-by-question grid. - A prioritized fix list, each fix tied to something measured (a question you lost, a page competitors rely on, a blocked crawler, an inaccurate statement). - FAQ answers drafted only from facts on your own pages, with the source page for each. - Organization and FAQPage structured data ready to paste into your site. - Outlines for comparison pages against the competitors that appear most. ## Price and how to start $400 one time, or ₹18,000 for companies in India. Start from the [pricing page](https://venoh.com/pricing): pay with your work email, get a confirmation straight away, and the report arrives at that address. No calls, no logins, no CMS access. ## What it does not do - Guarantee citations or rankings. Nobody controls what an AI engine says. - Track answers over time. It is a dated snapshot; see [monitoring tools vs a diagnostic](https://venoh.com/compare). - Test engines with no public API, such as Meta AI or Microsoft Copilot. ## Frequently asked questions ### What is an AI visibility audit? An AI visibility audit measures how often and how accurately your company appears when buyers ask AI assistants such as ChatGPT, Perplexity, Gemini and Claude about your category, who appears instead, and what is stopping you from being cited. ### How much does Venoh's AI visibility audit cost? $400 one time, or ₹18,000 for companies based in India. It is delivered as a PDF by email; no call is needed. ### Which AI engines does it test? ChatGPT, Perplexity, Gemini and Claude, each with live web search. The report names exactly which engines answered and on what date. --- # Methodology: exactly what the Venoh diagnostic checks (and what it does not) URL: https://venoh.com/methodology Last updated: September 25, 2026 A diagnostic is only worth paying for if you can tell how it reached its conclusions. This page describes precisely what Venoh's GEO/AEO diagnostic does, what evidence each finding rests on, and the things it deliberately does not do. > **In one sentence:** Venoh fetches your real public pages, extracts specific signals from the HTML, has each finding written up with an evidence label, and gives you a prioritized list of what to fix first. It also puts around 30 of your buyers' questions to AI answer engines with live web search and records who they cite. It is a preliminary diagnostic, not a full-site audit and not ongoing monitoring. ## Step 1: fetch real pages The diagnostic starts from your homepage. It performs a normal web request, follows ordinary redirects, and reads the HTML that comes back. If the homepage turns out to be an almost empty stub whose only job is a simple browser-side redirect (for example, a language-selection page that sends visitors to a language version), it reads the destination written in the page and analyses that page instead, on the same site only. It then looks at the homepage's own navigation (links in the header, navigation and footer areas) and selects up to four more pages that matter most for AI answers: typically FAQ, services or products, pricing, about, and case-study pages, whichever the site actually links to. It never follows links to other websites. Before each additional page it reads your robots.txt and skips any path disallowed for all crawlers (`User-agent: *`). Requests identify themselves honestly with the user-agent `venoh-geo-research-bot/0.1 (+https://venoh.com)`. The diagnostic uses public pages only: no logins, no CMS access and no code changes on your side. ## Step 2: extract concrete signals From each fetched page the tooling extracts the following without any interpretation, so each item can be pointed to in the page source: - The `` and meta description. - Headings (H1 to H3). - The visible text and its word count. - Structured data: schema.org types declared in JSON-LD (including nested items and `@graph`) and in microdata. - Whether FAQ-style content is present, from the headings and text or from FAQPage markup. - Same-site navigation links, used to choose the extra pages in step 1. ## Step 3: ask AI answer engines your buyers' questions For the paid diagnostic, we work out around 30 questions a buyer in your category would ask (they never mention your company, so the test is not biased toward you) and put each one fresh to AI answer engines with live web search switched on. For every answer we record which sites were cited, whether yours was among them, and which other companies' sites appeared instead. The counts in the report are tallied by code from those answers, not written by a model. The report names exactly which engines were tested and on what date. AI answers vary from day to day and from one wording to the next, so this is a dated sample, not a ranking or a promise about tomorrow's answers. ## Step 4: write up findings with an evidence label The extracted signals are analysed and written into findings. Every finding carries one of three labels so you can see how much weight it deserves: | Label | Meaning | Example | | --- | --- | --- | | OBSERVED | Seen directly in the fetched page content. | "The homepage declares Organization and WebSite schema but no FAQPage markup." | | INFERRED | A reasonable conclusion drawn from what was observed, but not directly seen. | "The audience appears to be mid-market finance teams, based on the wording of the services page." | | NOT_CHECKED | Outside what the diagnostic examined. Listed explicitly rather than guessed. | "Whether AI assistants currently describe the company accurately was not tested." | If a page cannot be fetched or read, the diagnostic says so and marks the affected areas NOT_CHECKED. It does not fill the gap with assumptions. A finding that rests on a page the tooling could not read is a defect in the diagnostic, not a fact about your company, and we treat it that way. ## Step 5: prioritize, then check the write-up Findings and recommendations are each given a priority (high, medium or low) so you can see what to fix first. The report is written to be implementation-ready: a developer or content lead should be able to act on each item without a follow-up meeting to decode it. Before the report goes out, a separate model reads every statement in it against the captured page evidence and the engine results. Any statement it cannot back up stops automatic delivery until a person has read the report. ## What the diagnostic does not do Being precise about limits is part of the method. The diagnostic is a **preliminary** pass. It does **not**: - Audit your whole site. It reads the homepage plus up to four relevant pages. - Analyse backlinks, domain authority or off-site mentions. - Track AI answers over time. The engine test is one dated snapshot, on the engines the report names; engines it does not name were not tested. - Run JavaScript. It reads the HTML a crawler receives; apart from following simple, literally written redirects, content that only appears after scripts execute is not seen (which is itself often a finding). - Measure page speed, Core Web Vitals or accessibility. - Review content you have not published, or anything behind a login. - Guarantee that any change will lead to a citation or a ranking. No one can. ## What you receive - A written report of evidence-labelled findings, each tied to the page it came from. - A prioritized list of recommendations, written to be actioned directly. - A clear list of what was not checked, so nothing is implied that was not examined. For the deliverable and pricing, see [pricing](https://venoh.com/pricing). To understand the concepts behind the checks, read [What is GEO?](https://venoh.com/what-is-geo) and the [how to get cited by AI](https://venoh.com/how-to-get-cited-by-ai) checklist. Terms are defined in the [glossary](https://venoh.com/glossary). ## Opting out or asking questions If you would rather the diagnostic did not fetch your site, or you have seen the Venoh user-agent in your logs and want to know why, write to us through the [contact form](https://venoh.com/contact) and we will respond. Besides paid diagnostics, we run a lighter version of these checks on the public pages of companies we may contact about AI search visibility. Either way it makes a small number of ordinary page requests per run, and if you ask us not to, we will not fetch your site again. ## Frequently asked questions ### Does the diagnostic test what AI engines actually say about my company? Yes, as a dated snapshot. The paid diagnostic puts around 30 of your buyers' questions to AI answer engines with live web search and reports whether your site was cited and which sites were cited instead. The report names exactly which engines were tested; any engine it does not name was not tested. ### Which pages does it read? Your homepage plus up to four relevant pages that your own navigation links to, such as FAQ, services, pricing, about or case studies. It respects robots.txt disallow rules for all crawlers and never follows links to other sites. ### What do OBSERVED, INFERRED and NOT_CHECKED mean? OBSERVED means seen directly in the fetched page. INFERRED means a reasonable conclusion from what was seen. NOT_CHECKED means the item was outside what the diagnostic examined and is listed rather than guessed. --- # Pricing URL: https://venoh.com/pricing Last updated: September 25, 2026 Two offers, priced openly. A one-time diagnostic tells you what is wrong on your key pages and what to fix first. A monthly retainer does the implementation work. Below you will find exactly what is and is not included, plus dated, sourced context on what monitoring platforms publish for their own pricing. ## GEO Diagnostic: $400 one time (usually $799) **Launch price.** The diagnostic is currently $400 instead of $799. For companies based in India it is ₹18,000 instead of ₹34,000. A preliminary, evidence-based review of how your company's real pages look to AI answer engines. It is designed as a low-commitment first step: a clear, prioritized picture of the concrete gaps on your key pages, with nothing to sign up for afterwards. ### Included - A review of your homepage plus up to four relevant pages your navigation links to (for example FAQ, services, pricing, about, case studies). - Around 30 questions your buyers ask, put to ChatGPT, Perplexity, Gemini and Claude with live web search, twice each: how often you are named or cited on each engine, and which competitors appear instead (share of voice). - What those engines say about your company when asked directly, checked statement by statement against your own site. - The pages the engines cite most in your category (review sites, lists, publishers, competitors): where you need to be mentioned. - Whether 13 AI crawlers (GPTBot, PerplexityBot, ClaudeBot and others) are allowed to read your site. - Ready to use: FAQ answers drafted only from facts on your own pages, Organization and FAQPage structured data to paste, and outlines for comparison pages. - Delivered as a PDF report by email. The report names exactly which engines were tested and the date. - Structured-data findings: which schema.org types are present, which are missing, and whether they match the visible page. - FAQ and answer-first content findings: whether your pages give clear, extractable answers to the questions buyers ask. - Findings on citable specifics: concrete facts an AI system could safely repeat, and vague areas that give it nothing to use. - Every finding labelled OBSERVED, INFERRED or NOT_CHECKED, so you can see how it was determined (see the [methodology](https://venoh.com/methodology)). - A prioritized, implementation-ready list of what to fix first. - A plain list of what was not examined. ### Not included - A full-site audit, backlink or domain-authority analysis. - Ongoing monitoring. The engine test is a dated snapshot, not a dashboard, and AI answers change from day to day. - Speed, Core Web Vitals or accessibility testing. - Making the changes on your site. (That is the retainer.) - A guarantee of citations or rankings. No one can honestly offer one. ## GEO Retainer: $1,200 per month For companies that want the findings implemented rather than left in a report. The retainer covers ongoing hands-on work based on what the diagnostic finds: schema markup, FAQ and answer-first content, and page-level fixes. There is no obligation to continue past the diagnostic, and the diagnostic is a sensible first step before deciding on a retainer. - Prioritized implementation of the diagnostic's recommendations. - Structured data written to match your visible pages. - FAQ and answer-first content drafted from real buyer questions and reviewed with you before it goes live. - Page-level fixes to make content easier for AI systems to parse. Scope is agreed with you in advance by email. Because we cannot control what any AI engine outputs, the retainer is priced for the work done, not for a result. ## The two offers side by side | | GEO Diagnostic | GEO Retainer | | --- | --- | --- | | Price | $400 one time (usually $799). India: ₹18,000 (usually ₹34,000). | $1,200 per month | | What it is | A preliminary report on your key pages. | Ongoing implementation of the findings. | | Pages examined | Homepage plus up to four relevant pages. | Scoped with you in advance. | | Output | Evidence-labelled findings, a dated AI engine test on your buyers' questions, and a prioritized fix list. | Changes made: schema, FAQ content, page-level fixes. | | Commitment | None beyond the one-off fee. | Monthly; no obligation to continue past the diagnostic. | | Guarantee of citations or rankings | None. Nobody can honestly give one. | None. Nobody can honestly give one. | **Start now:** pay [$400 (international)](https://rzp.io/rzp/zb7dBRG) or [₹18,000 (India)](https://rzp.io/rzp/XGEj2AS) through Razorpay, using your work email. You get a confirmation straight away and the report arrives at that address by email. Questions first? Use the [contact form](https://venoh.com/contact). ## Who this is for - B2B companies whose buyers research suppliers with AI assistants and who want to know whether their site is easy for those systems to use. - Teams that want a clear, small first step with a fixed price before committing to anything larger. - Companies that suspect their site is invisible or described wrongly by AI systems but do not yet know why. ## Who it is not for - Anyone looking for guaranteed rankings or guaranteed citations. - Companies that need continuous, engine-by-engine share-of-voice dashboards; a monitoring platform is the better tool for that (see the [comparison](https://venoh.com/compare)). ## Context: what monitoring platforms publish Many buyers ask how the diagnostic compares with AI-visibility monitoring tools. They do a different job (see the [comparison](https://venoh.com/compare)), but the published prices below give useful context. These figures are as reported by third-party comparison articles and were collected on 19 September 2026. Plans, limits and prices change often. **Verify on each vendor's own website before relying on them.** **Published monthly prices as reported by comparison articles, as of September 2026** | Tool | Reported published price range | Notes | | --- | --- | --- | | Otterly | $29 to $489 per month | Self-serve plans (Lite to Premium); an Enterprise tier is quoted separately. The $29 entry plan is the lowest published price reported. | | Peec | $95 to $495 per month | Starter to Advanced on monthly billing; annual billing is reported to lower the rate. | | Profound | From $99 per month | Starter $99 and Growth $399, reported as billed annually only; the Starter plan is reported as covering one engine. | | Semrush AI toolkit | About $99 per month per domain | Reported per domain. | | AthenaHQ | A free tier; Starter about $295 per month | Reported as credit-based; extra-credit prices are not published. | | Scrunch | $300 to $500 per month | Starter and Growth on month-to-month billing; lower with annual billing. Enterprise is quoted separately. | | GEO agencies (audit) | $1,500 to $5,000 one time | Typical one-time GEO audit price reported in 2026 agency pricing guides; implementation retainers are commonly $2,000 to $8,000 per month. | | Venoh Diagnostic | $400 one time (usually $799) | Not a monitoring tool: a review of your real pages with every finding labelled by how it was determined, a dated AI engine test, and a prioritized fix list. No subscription. | | Ahrefs Brand Radar | Reported as $398 to $699 per month | The reporting source notes that Ahrefs's own pages show different figures (one lists the product from $199 per month). Confirm on ahrefs.com. | Sources: [AI Visibility Tool Pricing Compared (2026), Ryze](https://www.get-ryze.ai/blog/ai-visibility-tools-pricing-compared-2026) and [Best Generative Engine Optimization Tools: 2026 Review, SE Ranking](https://visible.seranking.com/blog/best-generative-engine-optimization-tools-2026/). Venoh has no commercial relationship with any tool listed. ## Frequently asked questions ### How much does the Venoh GEO Diagnostic cost? $400, one time (₹18,000 for companies based in India). It covers around 30 buyer questions on ChatGPT, Perplexity, Gemini and Claude with live web search, competitor share of voice, an accuracy check of what the engines say about you, a review of your homepage plus up to four relevant pages, evidence-labelled findings and a prioritized fix list. ### How much is the GEO Retainer? $1,200 per month for ongoing implementation of the diagnostic's findings: schema markup, FAQ content and page-level fixes. There is no obligation to continue past the diagnostic. ### Do you guarantee citations or rankings? No. Nobody can guarantee what an AI engine outputs. Both offers are priced for the work done, not for a result. --- # How much does a GEO audit cost in 2026? URL: https://venoh.com/geo-audit-pricing Last updated: September 25, 2026 A GEO audit can cost nothing or several thousand dollars, and the price mostly reflects how many AI engines and buyer questions are tested, how deeply the results are analysed, and whether anyone writes the fixes for you. These are the published figures we could verify, with a link to each source. > **Short answer** Automated scores and reports run from **free to a few hundred dollars**. Agency GEO audits are published at roughly **$2,000 to $4,500** for a one-off audit, or **from €2,000 to €9,000** by tier. Ongoing GEO retainers are quoted at about **$1,500 to $8,500 a month**. The Venoh diagnostic is **$400** one time (₹18,000 for companies in India). ## Published prices, September 2026 **Prices as published on each provider's own page or in a dated pricing article, checked 26 September 2026** | Provider / source | What it is | Published price | | --- | --- | --- | | Jasper GEO Diagnostic | Automated scores for ChatGPT, Claude and Gemini with recommendations | Free, no account needed | | GeoAnalyzer | Free 0-100 score; paid 15-25 page PDF report | Free / $19 one time | | Venoh GEO Diagnostic | Around 30 buyer questions on ChatGPT, Perplexity, Gemini and Claude, competitor share of voice, accuracy check, crawler check, fix list and ready-to-use content, PDF by email | $400 one time (₹18,000 in India) | | Agency one-off GEO audit (Mentionable pricing guide) | Audit scoped by prompts, engines and competitive depth | $2,000 to $4,500 | | Vaimo GEO audit | Core / Standard / Full audit tiers | From €2,000 / €5,000 / €9,000 | | GEO retainer (Mentionable pricing guide) | Monitoring plus content execution | $1,500 to $8,500 per month | | Mentionable tracking tool | Do-it-yourself AI visibility tracking | €79 to €299 per month | ## What changes the price - **How many engines and questions are tested.** Mentionable's guide ties retainer tiers directly to this: 20-30 prompts on 3 LLMs at the low end, 80-200 prompts on 7 LLMs at the high end. - **Whether a person analyses the results.** Automated scores are cheap because nobody interprets them for your business; agency audits price in analyst time. - **Whether the fixes are written for you.** A list of problems is cheaper than drafted FAQ content, structured data and page outlines your team can use as-is. - **Competitive depth.** Measuring who appears instead of you, and on which pages the engines rely, takes far more queries than checking your own site. ## How to choose - Use a **free score** to find out whether you have a problem at all. - Use a **one-off diagnostic** when you need to know why you are missing from AI answers, who is showing up instead, and exactly what to fix, before committing to a retainer. - Use a **retainer or monitoring platform** once you are implementing fixes and need to track answers over time. Whatever you buy, ask which engines are tested, how many questions, whether the questions avoid naming your brand (otherwise the test flatters you), and whether every number in the report is counted from real answers. Be wary of anyone who guarantees citations or rankings: nobody controls what an AI engine says. ## Frequently asked questions ### How much does a GEO audit cost? Published prices in September 2026 range from free automated scores and $19 reports, through Venoh's $400 diagnostic, to agency audits of roughly $2,000 to $4,500 (Mentionable's pricing guide) or from €2,000 to €9,000 by tier (Vaimo). ### How much is a monthly GEO retainer? Mentionable's 2026 pricing guide puts monitoring-plus-content GEO retainers at about $1,500 to $8,500 per month, depending on how many prompts and engines are covered and whether content is produced. ### Is a free GEO diagnostic enough? It can tell you whether you have a visibility problem. It usually will not tell you which competitors and third-party pages are winning your buyers' questions, or give you content and markup ready to publish. See our comparison of free and paid diagnostics. ## Sources - [How Much Does a GEO Agency Cost in 2026? Mentionable (updated 24 September 2026)](https://mentionable.ai/en/blog/geo-agency-cost-pricing) - [GEO Audit Services, Vaimo](https://www.vaimo.com/services/experience-optimization/readiness-audit-services/geo-audit-service/) - [GeoAnalyzer pricing](https://geo-analyzer.com/pricing) - [Jasper GEO Diagnostic](https://www.jasper.ai/diagnostics/geo-diagnostic) --- # Free GEO diagnostic or paid audit: which do you need? URL: https://venoh.com/free-vs-paid-geo-diagnostic Last updated: September 25, 2026 Free AI visibility checks are genuinely useful, and for some companies they are enough. A paid diagnostic earns its price only when you need to know why you are missing, who is winning instead, and exactly what to publish. Here is how to tell which situation you are in. ## What the free options give you - **Jasper GEO Diagnostic** is free with no account. According to Jasper, it tests ChatGPT, Claude and Gemini and returns scores for brand presence, citation rate and sentiment, with per-model breakdowns, competitor analysis and page-level recommendations. - **GeoAnalyzer** gives a free 0-100 GEO/AEO score, and a 15-25 page PDF report for $19. If you only want to know whether AI engines mention you at all, start with one of these. It costs nothing and answers that question. ## What a paid diagnostic adds | Question you need answered | Typical free score | Venoh diagnostic ($400) | | --- | --- | --- | | Do AI engines mention us? | Yes | Yes: around 30 buyer questions, each engine asked twice | | Which engines were tested? | Usually a fixed set | ChatGPT, Perplexity, Gemini and Claude, named in the report with the date | | Were the questions unbiased? | Varies | Category questions never name you; brand questions are asked separately | | Who shows up instead of us, and how often? | Sometimes | Competitor share of voice, per engine and per question | | Which pages do the engines rely on? | Rarely | The most-cited URLs in your category, marked as competitor, review/list site or publisher | | Is what AI says about us correct? | Sentiment only, in some tools | Each factual statement checked against your own site; contradictions flagged with the quote | | Can AI crawlers read our site? | Rarely | robots.txt checked for 13 AI crawlers, plus llms.txt, sitemap, FAQ and structured data | | What exactly should we publish? | General recommendations | FAQ answers drafted from your own pages, ready-to-paste structured data, comparison page outlines | | Is every number real? | Depends on the tool | Counted by software from the raw answers; the written findings are checked by a separate model before delivery | ## When to pay, and when not to - **Don't pay** if a free score shows you are already cited often for your key questions and nothing looks wrong. - **Pay** if you are missing from answers to questions your buyers ask, you don't know who is winning them, or AI engines describe your company incorrectly. - **Pay** if you need something your team can act on this week: specific pages to write, content drafted from your own facts, and markup to paste. Either way, treat any AI visibility result as a dated snapshot. Answers change with wording and over time, and nobody can guarantee what an engine will say. ## Frequently asked questions ### Is there a free GEO diagnostic? Yes. Jasper offers a free GEO Diagnostic with no account required, testing ChatGPT, Claude and Gemini, and GeoAnalyzer offers a free 0-100 score. They are a good first check of whether AI engines mention you. ### Why pay for a GEO diagnostic if free ones exist? A paid diagnostic such as Venoh's goes further: around 30 unbiased buyer questions on four engines asked twice each, competitor share of voice, the pages engines rely on, an accuracy check of what they say about you, AI crawler access, and ready-to-use FAQ content and structured data. ## Sources - [Jasper GEO Diagnostic](https://www.jasper.ai/diagnostics/geo-diagnostic) - [GeoAnalyzer pricing](https://geo-analyzer.com/pricing) --- # Monitoring platforms vs a one-time diagnostic: which do you need? URL: https://venoh.com/compare Last updated: September 25, 2026 These two kinds of product answer different questions. Monitoring platforms tell you how you appear in AI answers over time. A diagnostic tells you what on your pages is making that harder or easier, and what to change. Many companies eventually want both. This page explains the trade-offs so you can pick the right starting point, even if that is not Venoh. > **Why this page exists.** We are a small diagnostic and implementation service, not a monitoring platform, and we would rather you buy the right thing than the wrong thing from us. We describe categories here, not vendors' feature lists, because those change often; check each vendor's own site for current details. ## The two jobs ### Job one: measure Monitoring platforms run a set of prompts you choose against one or more AI engines on a schedule and record what comes back: whether your brand is mentioned, whether a page of yours is cited, how you are described, and how you compare with competitors. Examples of tools in this category include [Otterly](https://otterly.ai), [Peec](https://peec.ai), [Profound](https://www.tryprofound.com), [AthenaHQ](https://www.athenahq.ai) and [OptimizeGEO](https://www.optimizegeo.ai), among others. Their value is continuous data and trend lines. ### Job two: diagnose and fix A diagnostic examines your own pages for the concrete things that make them easier or harder for AI systems to find, parse and cite: crawlability, structured data, answer-first content, consistent facts. The output is a list of specific changes. An implementation service then makes them. Venoh offers both: a one-time [diagnostic](https://venoh.com/methodology) and a monthly [retainer](https://venoh.com/pricing). ## Strengths and limits of each ### Monitoring platforms - **Strengths:** ongoing data across engines, competitor benchmarking, dashboards for stakeholders, and trend lines that show whether things are getting better or worse. - **Limits:** many focus on telling you where you stand rather than what to change on your pages, so you may still need someone to act on the data. Costs usually scale with the number of prompts, engines or regions tracked. Because AI answers vary, data quality depends on how many runs and prompts are sampled. ### A one-time diagnostic plus implementation - **Strengths:** concrete, page-level findings and a fix list; a fixed, low price; no software to learn; works from your public pages without access to your systems. - **Limits:** it is a snapshot, not continuous tracking. The Venoh diagnostic is deliberately preliminary (a homepage plus up to four pages), its AI engine test is one dated snapshot of around 30 buyer questions on ChatGPT, Perplexity, Gemini and Claude, and it does not analyse backlinks. It cannot track how often you are cited over time. ## Side by side | Question | Monitoring platform | Venoh diagnostic and retainer | | --- | --- | --- | | Main question answered | How do we appear in AI answers, and how is that changing? | What on our pages makes us harder or easier for AI to use, and what should we change? | | Typical output | Dashboards and reports on mentions, citations and sentiment. | An evidence-labelled findings report with a prioritized fix list; implementation with the retainer. | | Time dimension | Continuous, over time. | A snapshot; the retainer adds ongoing work. | | Tests live AI engines | Yes, that is the point (which engines vary by vendor and plan). | No. Listed in every report as not checked. | | Examines your page structure | Varies by vendor; some include page-level audit features. Check each one. | Yes, that is the point. | | Who does the fixing | You or your agency. | Venoh, under the retainer; or your team using the report. | | Pricing model | Recurring subscription; published prices commonly run from tens to several hundred dollars per month (see [pricing](https://venoh.com/pricing)). | $400 one time; $1,200 per month for the retainer. | | Promises rankings | Should not, and you should be wary of any that does. | No. | ## When monitoring is the better first purchase - You already have strong, well-structured content and mainly need to know how AI systems describe you compared with competitors. - You have an internal team or agency ready to act on data. - Stakeholders want a dashboard and a trend line before approving any spend. ## When a diagnostic is the better first purchase - You do not know whether your site is even crawlable and readable by AI systems. - You suspect basic gaps (no structured data, no FAQ content, vague claims) and want to know exactly what and in what order. - You want a fixed price and a concrete to-do list rather than a subscription. - You have small pages or a small team and cannot yet make use of a stream of data. ## Using both The two are complementary. A sensible sequence for many companies: fix the fundamentals (a diagnostic and the changes it identifies), then track results with a monitoring tool to see whether accurate mentions increase over time. If a monitoring tool shows you are missing from answers you care about, a diagnostic of the relevant pages is often the next step. ## What to ask any vendor, including us - What exactly will you do, and what will you not do? - How is each finding or data point determined, and how many times is it sampled? - What will you not be able to tell me? - Do you promise rankings or citations? (If yes, be careful.) - What will I own at the end if I stop paying? You can see our answers on the [methodology](https://venoh.com/methodology) and [pricing](https://venoh.com/pricing) pages. For background on the field itself, read [What is GEO?](https://venoh.com/what-is-geo) and [GEO vs SEO](https://venoh.com/geo-vs-seo). ## Frequently asked questions ### Is a monitoring tool or a diagnostic better? They do different jobs. Monitoring tools track how you appear in AI answers over time. A diagnostic examines your pages for what makes you harder or easier for AI to use, and lists what to change. Many companies eventually want both. ### Does Venoh test what ChatGPT or Perplexity say about my company? Only as a one-off snapshot. The diagnostic puts around 30 of your buyers' questions to AI answer engines with live web search on one date and reports who was cited, but it does not track answers over time. If you need ongoing measurement of AI answers, a monitoring platform is the right tool. ### Does Venoh have a relationship with the tools you mention? No. They are named as examples of the monitoring category. Venoh has no commercial relationship with them and does not describe their features beyond the general category. --- # GEO and AEO glossary URL: https://venoh.com/glossary Last updated: September 25, 2026 31 short, precise definitions of the terms that come up when talking about how AI systems find and cite information. Usage in this young field is not standardized, so where a term is used inconsistently we say so. ## Terms A to Z ### AEO (answer engine optimization) The practice of making your content the direct answer to a question. The term predates today's AI assistants (it was used for featured snippets and voice assistants) and is now often used interchangeably with GEO. ### AI Mode Google's conversational search experience, which answers questions with a generated response and links to sources. Google's documentation describes how its AI features relate to Search; check the current wording, as it is updated. ### AI Overview A generated summary that Google can show at the top of some search results, with links to supporting pages. Whether one appears depends on the query. ### Answer engine A product that responds to a question with a written answer instead of only a list of links. ChatGPT search, Perplexity and Google's AI features are examples. ### Canonical URL The preferred address for a page, declared with a `rel="canonical"` link. It tells crawlers which of several near-identical URLs to treat as the original. ### Citation A visible reference to a source that supported an AI-generated answer, usually a link. A source can influence an answer without being cited. ### Crawler (user-agent) An automated program that fetches web pages and identifies itself with a name called a user-agent, such as `GPTBot` or `Googlebot`. Site owners can allow or disallow specific crawlers in robots.txt. ### Entity A distinct, identifiable thing such as a company, person, product or place. Search and AI systems try to work out which entities a page is about and what is true of each. ### Evidence tag (OBSERVED, INFERRED, NOT_CHECKED) Venoh's labelling of each finding by how it was determined: seen directly in the fetched page, a reasonable conclusion from it, or outside what was examined. See the methodology page. ### FAQPage A schema.org type for a page that contains a list of questions with their answers. It should be used only when those questions and answers are visible on the page. ### Generative engine A search or answer product that uses a generative language model to write its responses. The term was popularized by the 2023 research paper that named GEO. ### GEO (generative engine optimization) The work of improving how accurately and how often a company appears in answers generated by AI systems, by making its information easy to find, understand and cite. ### Grounding Supplying a language model with retrieved source material so its answer is based on that material rather than only on what it learned in training. ### Hallucination A statement generated by a language model that sounds plausible but is false or unsupported by any source. Clear, specific, consistent facts on your own pages reduce the room for errors about your company. ### JSON-LD A format for writing structured data as JSON inside a script tag, commonly used to publish schema.org markup. It is the format most site owners use for structured data. ### Knowledge graph A structured database of entities and the relationships between them. Search engines maintain them to connect facts about people, companies and things. ### Large language model (LLM) A neural network trained on very large amounts of text to predict and generate language. It is the component that writes the wording of an AI answer. ### LLMO (large language model optimization) A broad label for influencing what language models say about a subject, sometimes including what they learn during training. Usage is inconsistent between writers. ### llms.txt A proposal for a Markdown file at the root of a site that gives language models a curated index of useful pages. It is not an established standard and support by AI providers is not guaranteed. ### Mention An appearance of your company's name in an AI-generated answer, with or without a link. Mentions and citations are tracked separately. ### Organization schema A schema.org type that describes a company: its name, website, description and contact details. It is one of the most useful basics for stating who you are. ### Passage A short section of a page, often a heading plus the text beneath it. Retrieval systems frequently rank and extract passages rather than whole pages. ### Prompt The question or instruction a user gives an AI system. In measurement, a fixed set of representative prompts is run repeatedly to track how a brand appears. ### Query fan-out A technique where a system splits one question into several related searches and combines the results. It means a page can be retrieved for a query the user never typed. ### RAG (retrieval-augmented generation) An approach in which a system first retrieves relevant documents and then has a language model write an answer using them. Most search-connected AI assistants work in a way that resembles RAG. ### robots.txt A plain text file at the root of a site that tells crawlers which paths they may or may not fetch. It is a request that well-behaved crawlers honor, not a security control. ### Schema.org A shared vocabulary of types and properties (Organization, Product, FAQPage and many more) used to describe the content of web pages in a machine-readable way. ### Share of voice (AI) A measure of how often a brand is mentioned or cited in AI answers to a set of prompts, compared with other brands. Because answers vary, it must be sampled repeatedly to mean anything. ### Sitemap An XML file listing the pages of a site you want crawled, with optional last-modified dates. It helps crawlers discover pages but does not guarantee indexing. ### Structured data Markup that states facts about a page in a form software can read directly, typically schema.org types written as JSON-LD. ### Zero-click search A search in which the user gets what they need from the results page or the generated answer and never visits a website. --- # About Venoh URL: https://venoh.com/about Last updated: September 25, 2026 Venoh helps B2B companies get found, understood and cited by AI answer engines. ## What Venoh does Venoh offers two things. The first is a [GEO/AEO diagnostic](https://venoh.com/methodology): a preliminary, evidence-labelled review of a company's real pages for the concrete signals that make them easier or harder for AI systems to find, parse and cite. The second is a monthly [retainer](https://venoh.com/pricing) that implements what the diagnostic finds: structured data, FAQ and answer-first content, and page-level fixes. ## How we work - **Evidence first.** Every finding is labelled as observed, inferred or not checked, so you can see how much weight it deserves. - **Honest about limits.** The diagnostic is preliminary. Its AI engine test is a dated snapshot on the engines it names, it does not analyse backlinks or audit a whole site, and we say so in every report. - **No guarantees.** No one controls what an AI engine outputs. We do not promise rankings or citations. - **Public pages only.** We work from what is publicly visible, with no logins and no CMS access needed for the diagnostic. - **Clear pricing.** The prices are on the [pricing page](https://venoh.com/pricing), including what is not included. ## Who you will work with A small, senior team. The same people who review your pages write the findings and do the implementation work, so nothing gets lost in a hand-off. ## What we deliberately do not claim This site does not list customer logos, testimonials, case studies or performance statistics, because we would rather publish nothing than publish something we cannot back up. If and when we have permission to share real results, they will appear here with their source. ## Get in touch Use the [contact form](https://venoh.com/contact). It explains what to include so the first reply can be useful. --- # Contact URL: https://venoh.com/contact Last updated: September 25, 2026 Tell us about your company and we will reply within one working day with next steps. No sales calls needed to get started. ## What to include - Your website address. - A sentence on what you sell and who typically buys it. - Two or three questions your buyers would realistically ask an AI assistant when choosing a supplier. - Whether you are interested in the [one-time diagnostic](https://venoh.com/pricing) or want to talk about the monthly retainer. ## What happens next You will get a reply confirming the scope (the diagnostic is described in the [methodology](https://venoh.com/methodology) and priced on the [pricing page](https://venoh.com/pricing)) and the next steps. The diagnostic works from your public pages, so you do not need to give access to any system.