Tag: AI visibility audit

Google has added a paragraph to its “Creating helpful, reliable, people-first content” page warning site owners against fabricated author identities. It sits in the section on who created the content. Google’s page now shows a last-updated date of October 5, 2026 (UTC), and trade reports place the first appearance of the paragraph around October 1 to 2. Google announced no ranking update alongside it.

This piece sets out what the page says, what is new and what is not, and what the evidence does and does not show about bylines. It is written for teams responsible for search engine optimization and content marketing, and it ends with a short audit you can run on your own author pages.

What Google’s page says now

Read from Google’s documentation, the section on who created the content does three things in sequence. It encourages adding accurate authorship information, such as bylines, where readers might expect it. It tells site owners to avoid deceptive authorship information. And it says that fabricating creator profiles, for example with AI-generated headshots, made-up names or false credentials meant to make content look as if human experts wrote it, is a form of deception.

The consequence is stated plainly. Deception, Google says, “makes a page untrustworthy to both users and our automated quality systems.” The page adds that it is a signal of a low-quality page.

Two things about that wording matter. The target is fabrication: invented people, fake faces and false credentials. And the stated purpose is to make content appear human-expert written, which ties the problem to misleading readers about who is behind the content.

What is new, and what is not

The paragraph itself is new. Search Engine Journal’s Matt G. Southern reported on October 2 that an archived copy of the page from June 8 lacked it. The same revision added a definition of main content and four attributes that Google says its raters assess: effort, originality, talent or skill, and accuracy, with a higher bar for accuracy on topics that affect health, finances, safety or well-being.

The principle is not new. Google’s Search Quality Rater Guidelines already treat deception as grounds for the lowest rating. Section 4.5.3 of the September 11, 2025 edition covers deceptive information about a website or its content creators, and says pages using deception should be rated Lowest. Search Engine Journal adds that those guidelines already named fake creator profiles. Relevant Audience makes the useful point that what changed is where the statement appears: in public documentation aimed at site owners, not only in guidance for raters.

The date, checked at Google’s page

This is where care is needed. The date on Google’s page is the date of the latest revision, not necessarily the date the paragraph was added. Search Engine Journal described a version dated October 1 on October 2, and the page now says October 5, so it has been revised since. SEO consultant Marie Haynes said the change was made on October 2. Search Engine Journal also reported that Google’s documentation updates page did not list the edit, and we did not find an entry for it either.

The safest wording is that the paragraph appeared in early October 2026 and that Google has not published a dated announcement for it.

What it does not say

The guidance leaves several things open, and some readings going around go further than the page does.

It announces no ranking update, no system name and no penalty. It does not explain how Google would detect a fabricated profile. Google’s page also notes that its raters do not control how pages rank, so the rater attributes describe what quality evaluation looks for, not a published ranking formula.

It does not say bylines help you rank. Google’s Search Liaison said in January 2024 that “Just adding a byline doesn’t give a ranking boost.” The new paragraph does not contradict that. It says deception is a risk, not that honest authorship is a lever.

And it is not a ban on AI. The sentence concerns fabricated people presented as human experts. Our reading is that a clearly labeled editorial team, or AI assistance disclosed where readers would wonder, is a different situation from an invented doctor with a generated photo. Google’s page does suggest disclosing automation where readers would reasonably ask how content was made.

Bylines and AI citation: what the evidence shows

It is tempting to treat real authors as a way to win citations in AI answers. The evidence does not support that yet, so we would not frame it that way.

The most direct data we found comes from Discovered Labs, a vendor that sells answer engine services, so read it with that in mind. It tested Perplexity only, using 400 questions and 5,770 pages from Google’s first results page. Pages published by companies were cited more often than pages by individual authors: 39.5% against 27.1%. The study calls the gap an association, not proof of cause. It also classifies a staff byline on a company blog as company content, so it does not test bylines directly. It cites a separate study of 497 Google results across 30 queries that found no measurable effect from bylines and credentials on citation.

That is thin evidence in both directions. What it supports is modest: treat authorship as a trust and risk matter, not as a citation tactic. Real people behind your content protect you from the downside Google has now named, and they help readers judge whether to trust you. They are not a proven way to get cited.

What counts as a fabricated profile

Google’s sentence names three examples. In practice, a team should check for these:

An author who does not exist. A name invented to give content a face, with no real person who wrote, reviewed or answers for it.

A photo that is not the person. An AI-generated headshot or stock image presented as the author.

Credentials that cannot be verified. Titles, years of experience, degrees or licenses that the person does not hold.

A fourth case sits close to the line, and it is our reading, not Google’s. A real expert’s name placed on content they did not write or review. Google’s separate instruction to avoid deceptive authorship information is broader than the fabricated-profile sentence, so this is worth treating as a risk.

A byline audit you can run this week

Start with a list of every author name that appears on your site, including old posts and contributors who have left.

Check that each name belongs to a real person who can confirm they wrote or reviewed the content. If a byline is a pen name, replace it with a transparent label such as your editorial team, and say so.

Check the photo. Use a real photograph of that person, or no photo.

Check the credentials. Every claim should be something the person can document: a license, a publication, a role at a named employer.

Check the author page. Google’s page asks whether bylines lead to further information about the author and the topics they write about. Each byline should link to a page with a real bio, links to their professional profiles and a list of their work. A bio at the end of an article with no link to a profile page leaves that question half answered.

Check sensitive topics. Where content covers health, legal or financial decisions, the accuracy bar is higher. Name the expert reviewer and the review date. Teams in healthcare and legal fields should be the strictest.

Check AI disclosure. Where AI assisted the draft, decide what readers should be told, and make sure a named person is accountable for the final text.

Fix, do not delete by reflex. If an author profile cannot be verified, correct the byline, label it accurately, or have a qualified person review and sign off on the content. Removing posts without a plan can cost you pages that were working.

Why this matters for AI search visibility

AI assistants and answer engines draw on pages that readers and search systems already treat as credible. Our guide to how AI search works explains how a page can be cited without ranking at the top, and why clear answers, specific claims and self-contained sections matter more than decoration. For generative engine optimization, the practical lesson from Google’s change is to build content on accurate, attributable information so that no part of it depends on a fake face. Our full guide to generative engine optimization covers that groundwork.

The same logic applies to answer engine optimization. Answers drawn from your pages carry your name, so the people and claims behind them need to hold up. Our look at why third-party mentions matter in AI search makes a related point: independent credibility is earned, and it is hard to fake.

To see where your brand and its experts are actually mentioned, a free AI Visibility Audit checks real questions across platforms. Our complete guide to the AI Visibility Audit explains the method.

Where this leaves you

The confirmed facts are these. Google’s people-first content page now warns that fabricating creator profiles, such as with AI-generated headshots, made-up names or false credentials, is a form of deception that makes a page untrustworthy to users and automated quality systems. The page shows a last-updated date of October 5, 2026, and trade reports place the paragraph’s first appearance at October 1 to 2. Google announced no ranking update, penalty or detection method, and the page does not say bylines improve rankings.

The practical response is an audit, not a rewrite. Make sure every byline is a real person, every photo is that person, every credential can be verified and every author has a page that proves it. Whether the aim is generative engine optimization, answer engine optimization or plain search engine optimization, treat that as a baseline for trust, not as a trick.

Frequently Asked Questions

1. What did Google add to its people-first content guidance?
A paragraph in the section on who created the content saying that fabricating creator profiles, such as with AI-generated headshots, made-up names or false credentials to appear expert, is a form of deception.

2. When did Google add it?
Google’s page shows a last-updated date of October 5, 2026, which is the latest revision. Trade reports place the first appearance of the paragraph around October 1 to 2. Google has not published a dated announcement for it.

3. Is this a ranking update?
Nothing announced says so. Google named no update, system, penalty or detection method. The wording describes what quality evaluation looks for.

4. Do author bylines improve rankings?
Google’s Search Liaison said in January 2024 that adding a byline does not give a ranking boost. Google’s page encourages accurate bylines for readers, but does not call them a ranking factor.

5. Does this ban AI-generated content or AI headshots?
No. The sentence concerns fabricated profiles presented to make content seem expert-written. Google’s page does suggest disclosing automation where readers would wonder how content was made.

6. Will real authors help me get cited in AI answers?
There is no solid evidence that they do. One vendor study of Perplexity found company pages cited more often than individual-author pages, and a second study it cites reported no measurable byline effect. Treat authorship as trust, not a citation tactic.

7. What should I do first?
List every author on your site, confirm each is a real person with a real photo and verifiable credentials, and make sure each byline links to a genuine author page.

If you want to know how your brand and its experts appear across Google and AI answer engines today, that is exactly what we look at in a free AI Visibility Audit.

Every marketer working on AI visibility has the same suspicion: if AI assistants lean on third-party sources, the shortest route to a mention is to create those sources. A policy update from Anthropic, the company behind Claude, puts a line through part of that idea. Announced on October 8, 2026 and effective November 12, it prohibits using Anthropic’s products to seed the sources AI systems draw answers from with content that misrepresents who is behind it.

The wording is narrower than some headlines suggest, and the policy binds users of Anthropic’s products, not the web at large. This piece sets out what the text says, what it does not say, and the practical test it offers brands deciding between earned mentions and planted ones. It is written for teams planning generative engine optimization and digital PR programs.

What the policy says

The change sits in a new section of Anthropic’s usage policy on deceptive campaigns and artificial activity. Read from the policy page, it prohibits using Anthropic’s products to create fake personas, accounts, media outlets or organizations, or to impersonate real ones. It prohibits hiding the sponsorship of content meant to influence opinion or decision-makers, and spreading content through networks of fake or ostensibly independent sites that share a common source. It also covers building tools for deceptive campaigns and marketing any of these activities.

The clause that matters here prohibits manipulating the sources from which search engines or AI systems draw answers, by “seeding them with content that misrepresents its origin, authorship, or independence.” The policy’s own example is a network of sites posing as unaffiliated sources that corroborate the same claims.

Notice what triggers the rule. It is not seeding, volume or the use of AI. It is misrepresentation: content that hides its origin, its author or its lack of independence.

Who it binds, and who it does not

The policy applies to anyone who submits inputs to Anthropic’s products. That covers individuals using Claude.ai or Claude Code, businesses using the API, customers who reach Claude through cloud providers or resellers, and end users of products built on Claude. A marketing tool built on Claude puts its own users under the same rules, as Relevant Audience points out.

It does not bind search engines, other AI companies or the open web, and it does not govern how Claude ranks or cites content. Mike King of the GEO consultancy iPullRank makes the same point in his analysis: the policy is about how people use Claude.

Enforcement works against the account, not the website. Anthropic’s Safeguards Team monitors for violations, and if one is suspected Anthropic may warn, throttle, limit, suspend or terminate access to its products. That differs from Google’s spam policies, which act on sites.

A new rule, or a clearer one?

Search Engine Journal reads the update as a clarification, not a brand-new ban. The 2025 policy did not mention search engines or AI answers, but Anthropic says it already prohibited fake-account networks and fake news sites, spread across other sections. The new text states the principle directly and applies it to the sources AI draws on.

Search Engine Journal also describes the case Anthropic linked to the section. According to that report, Anthropic found a network of around 70 sites posing as independent local news outlets, tied to a France-based advertising agency. The network used Claude to produce articles designed to “boost their site’s authority rankings on search engines.” It published at least 8,913 articles in about 20 languages, though most got little engagement. Anthropic removed the account and banned the organization.

Two cautions apply. The coverage does not say whether those sites improved their rankings or appeared in AI answers, so the case shows the tactic being attempted, not working. And Search Engine Journal does not link the full text of Anthropic’s report, so its details rest on that outlet’s summary.

Where the line sits

Relevant Audience reads the clause as aimed at deception about who is behind content, not at producing content for AI visibility. In its reading, the policy does not ban using Claude to write content, structure pages for AI answers or publish at volume. That is the publication’s interpretation of the text, which Anthropic has not spelled out for edge cases. For example, Anthropic has not said how it would treat several openly branded sites run by one company, and the policy text points to misrepresented independence as the trigger.

Read together, the sources suggest a useful test, which Relevant Audience frames as its own and not Anthropic’s: would the tactic still work if every reader knew who wrote the content and who paid for it? King puts a similar idea in terms of durability, arguing that manufactured independence has “an expiration date.”

Earned versus planted mentions

That test separates two things that can look alike from outside.

An earned mention exists because someone independent chose to write it. A journalist cites your original research. An expert quotes you. A customer reviews your product. A publisher runs a contributed article under a real byline. A sponsored placement is labeled as sponsored.

A planted mention only looks independent. A network of sites that recommend the same brand while hiding common ownership. Reviews from fake personas. Undisclosed paid listicles. Forum answers posted by accounts that conceal their affiliation. iPullRank, which sells GEO services and so has an interest in the framing, lists these among the tactics to avoid. The policy text itself names fake personas, hidden sponsorship and networks of ostensibly independent sites.

This is the distinction our earlier piece on why third-party mentions matter for AI search turns on. The reason independent sources carry weight in AI answers is that they are independent. Faking independence takes away the thing that made the mention valuable, and it now carries account-level risk for anyone doing it with Claude.

What to do before November 12

None of this requires a strategy rewrite. It asks for a short audit.

Check your own content network. If you or an agency have used Claude to produce content for several sites that appear independent but share an owner or a sponsor, review it now. Search Engine Journal suggests this audit.

Ask vendors direct questions. Relevant Audience recommends asking agencies and tools how they build AI visibility and whether any sites in their plans are owned by them without saying so. A vague answer is information.

Label sponsorship. Where a placement is paid, say so on the page. Where a comparison page is brand-owned, make that obvious.

Use real bylines and real experts. Contributed content should carry the name of the person who wrote it and a verifiable role.

Fund the earned route. Original data, expert commentary and useful tools give journalists and publishers a reason to cite you. That is the work behind digital PR and ethical link building, and the slower route is the durable one.

Measure what you earn. Track whether your brand is mentioned and cited across AI platforms, and which sources drive the mentions. A free AI Visibility Audit does this across platforms and shows where earned coverage is missing.

What this does not tell you

The policy is not evidence that seeding works, or that it fails. The reporting does not show whether the network Anthropic banned gained rankings or appeared in AI answers.

It does not describe how detection works beyond general monitoring, and it does not cover other AI companies. ChatGPT, Gemini and Perplexity have their own terms, and Google has its own spam policies. Do not read this one document as a rule for all of GEO.

It also does not tell you that AI-written content is off limits. The text turns on misrepresentation, though edge cases remain untested. If you use AI tools to draft, the safe course is the same as it has always been: a real author, accurate claims and no pretense about who is speaking.

Why this matters for AI search visibility

AI assistants increasingly shape which brands get recommended, so the temptation to manufacture sources will not disappear. What this update does is move one major provider’s rules in the opposite direction, and mark provenance as the thing platforms are watching. For answer engine optimization, the safest assets are the ones that can survive scrutiny: clear answers, original information, real authors and accurate facts. Our full guide to optimizing for AI search covers that groundwork, and sound search engine optimization supports all of it.

Where this leaves you

The confirmed facts are these. Anthropic announced an updated usage policy on October 8, 2026, effective November 12. A new section prohibits using its products to seed the sources search engines and AI systems draw answers from with content that misrepresents its origin, authorship or independence. Enforcement is against accounts. The policy binds users of Anthropic’s products, not other companies or the open web.

The practical response is to build mentions that would still work if everyone knew who was behind them. That favors original research, real experts, labeled sponsorship and honest digital PR and link building, backed by measurement of what AI platforms actually say about you. Whether the aim is generative engine optimization, answer engine optimization or plain search engine optimization, the same provenance rules apply.

Frequently Asked Questions

1. What did Anthropic’s updated usage policy change?
It added a section on deceptive campaigns and artificial activity, which prohibits using Anthropic’s products to seed the sources search engines and AI systems draw answers from with content that misrepresents its origin, authorship or independence. It takes effect November 12, 2026.

2. Does it ban GEO?
No. The trigger is misrepresentation, not optimization. Writing clear content, structuring pages for AI answers and earning mentions are not what the clause describes, though Anthropic has not addressed every edge case.

3. Who does the policy apply to?
Anyone who submits inputs to Anthropic’s products, including individuals, API developers, customers using Claude through cloud providers and end users of products built on Claude. It does not bind search engines or other AI companies.

4. What happens if someone violates it?
Anthropic may warn, throttle, limit, suspend or terminate access to its products. In the case Search Engine Journal describes, Anthropic removed the account and banned the organization.

5. Is it a new rule?
Search Engine Journal reads it as a clarification. Anthropic says fake-account networks and fake news sites were already prohibited under other sections, and the update names the search and AI answer context directly.

6. How do I tell an earned mention from a planted one?
Ask whether the tactic would still work if readers knew who wrote and paid for the content. Independent coverage, real reviews, labeled sponsorships and bylined expert content pass. Fake personas, hidden-owner site networks and undisclosed paid listicles do not.

7. What should I do before November 12?
Audit any Claude-made content running across sites that appear independent, ask agencies and tools how they build AI visibility, label sponsored content and use real bylines. Then measure your actual mentions across AI platforms.

If you want to know where your brand is mentioned and cited across Google and AI answer engines today, and which sources are driving it, that is exactly what we look at in a free AI Visibility Audit

Picture a Monday morning report. Your rank tracker says a key page has fallen out of the top 10, and the pages that replaced it look unrelated. The usual reaction is to start rewriting. A new question belongs first: did the ranking change, or did the tool receive the wrong results page?

On October 10, 2026, the rank-tracking company Nozzle published research claiming that since September, Google has been returning complete, normal-looking results pages to its rank trackers with the real results swapped out. Nozzle sells rank tracking, and Google has not commented. This piece sets out what was reported, what is and is not established, and how to check before you react. It is written for teams responsible for search engine optimization reporting.

What Nozzle reported

Nozzle’s page analyzes 27.5 million search results pages from its customer datasets, collected between August 20 and October 9, 2026, across 49 countries, desktop and mobile.

From August 20 to September 9, about 0.17% of pages were poisoned, which is Nozzle’s baseline. On September 13, requests to Google began failing in large numbers. The first poisoned pages appeared on September 20. They peaked at roughly a quarter of all pages between September 27 and 29, at 24.9%, 26.7% and 27.0%. Since October 1, daily rates have run at about 4% to 11%. Across the period since September 13, Nozzle puts it at 8.0%.

The word poisoned matters. Google does not refuse these requests, according to Nozzle. It answers with a full page of wrong results. Nozzle puts it this way: “A poisoned request succeeds.” There is no error, no captcha and no change in the page template, so the only way to spot it is to compare a keyword’s results over time.

How it was measured, and a caution about the 8.0%

A keyword’s usual sites are those in the top 10 on at least 60% of its baseline pages. A page counts as poisoned when fewer than half of them appear on any page of the results, and they return later. Those thresholds are the study’s own choices.

Several limits apply. Detection is inferred from each keyword’s history, not from a signal Google has confirmed. The sample comes from Nozzle’s own customers, and the last few days are still filling in. And the headline 8.0% does not state its denominator: the daily table is labeled “All SERPs,” while the country and industry tables calculate against judged pages. Cite it as “Nozzle reports about 8%,” not as a measured rate of Google behavior.

What a poisoned page looks like

Nozzle reports these patterns on poisoned pages, compared with its baseline.

Organic results kept only 20.7% of the previous page’s sites, against 88.5% at baseline. A tracked project’s own site fell out of the top 10 on 58.6% of poisoned pages, against 2.7% at baseline. Reddit rose from 20.8% to 28.8% of top-10 appearances, and Facebook from 28.3% to 33.8%. An AI Overview appeared on 34.9% of poisoned pages, against 54.2%. Ads and shopping results were largely stable.

One example shows how complete the swap can be. For the desktop, US search “white label local seo,” all nine page-one sites on October 4 were the usual vendors. On October 7, all nine had been replaced by unrelated results, including a WordPress tool, a steakhouse and government sites.

The effect is usually brief. About 80% of poisoned spells lasted a single page, and 77.9% of poisoned pages had their usual sites back within three days. A real ranking change persists, which is how the two can be told apart.

Where it appears to hit hardest

Nozzle’s tables show wide variation. Since September 13, the poisoned share was 8.5% in the United States, 10.5% in Canada and 11.5% in Australia, with the United Kingdom lowest at 3.0%. Treat country figures as indications, since they depend on Nozzle’s sample sizes.

“Near me” searches were poisoned more often (11.4%) than other queries (7.8%), and navigational brand searches least often (5.3%). If you manage local SEO reporting, that is worth knowing.

By industry in the US, legal was highest at 15.9%, peaking at 51.3% on September 27. Pets was lowest at 4.3%. Firms in the legal sector should be the most careful about reacting to a single day.

The tools matter too. On the same days, one collection route returned 1% to 7% poisoned pages while another returned 9% to 30%, so how your vendor collects data affects what it sees.

A second vendor, and a reporter’s concern

Nozzle is not alone. DemandSphere, another rank-tracking company, posted on October 7 that Google returns spurious results when it suspects bot traffic, affecting about 11% of its daily results pages. PPC Land reported the figure on October 13. DemandSphere’s post does not describe a method or sample size. Both figures are vendor-reported and unaudited, and they agree on the mechanism and rough scale, little more.

Barry Schwartz of Search Engine Roundtable reported on October 12 that third-party rank tracking is returning incomplete or inaccurate data. He relayed the three tactics that Nozzle’s Derek Perkins described: wide IP blocking aimed at large residential proxy networks, a big increase in captchas, and injecting bad data into suspect pages. Schwartz singled out the third: “The third approach, injecting bad data, concerns me the most.” His reasoning is that tools may not notice it, so they can appear to work while their data is wrong. Several vendors, including Sistrix, SE Ranking and DataForSEO, reportedly posted notices about data problems.

What is not established

Google has not publicly commented on Nozzle’s or DemandSphere’s figures or on the September changes. Everything about motive comes from vendors and the trade press.

Nozzle reads the pattern as Google’s response to bulk collection of its results for AI grounding, which rank tracking resembles. DemandSphere’s founder makes a similar argument. Schwartz’s view, as reported, is that Google is aiming at AI bots and scraping services, not SEO tools. Roger Montti of Search Engine Journal adds that altered pages could also confuse spammers reverse-engineering rankings, a point Nozzle did not raise. None of these readings is confirmed.

There is also a confound. Google’s September 2026 spam update ran from September 24 to October 8, according to PPC Land and Looploop, so real ranking volatility and altered data overlapped in the same weeks. Our earlier piece on the September spam update covers the real-volatility side. A single dashboard showing a drop in that window could not tell the two apart.

Even first-party data can lag. Looploop reports that the Search Console Performance report ran more than 64 hours behind on October 11, so check the date range first.

How to tell a real drop from a poisoned page

Start with Search Console. If it still shows impressions and clicks for the same queries and dates, the site very likely did not drop. Confirm that its data is current first.

Compare against history. Look at the keyword’s usual top 10 over several weeks, not just yesterday, because yesterday may also have been poisoned.

Wait one to three days. Most altered pages clear quickly, while a real change persists.

Read the shape of the page. Nozzle says fewer AI Overviews, fewer “People also search for” modules, more image packs and social or forum sites, and shorter pagination are suggestive, but none is reliable alone.

Cross-check tools. A drop that only one tool reports is a question, not a conclusion.

Ask your vendor how it detects swapped pages and what its error rates are. Relevant Audience, citing PPC Land, reports that most do not yet publish comparable figures.

Hold off on changes. Do not rewrite pages from one chart. Our guide to reading your own traffic before you change anything sets out a three-step method that works here too.

What to change in your reporting

Make first-party data the reference. Search Console, analytics and conversions should anchor the monthly report, with the rank tracker used for trends. Our look at Google’s AI visibility reports in Search Console explains what that data does and does not include.

Set a rule for alerts. A drop seen on one day, in one tool, should trigger a check, not a client email. Require it to persist, or to appear in Search Console, before it is reported as a ranking loss.

Annotate your dashboards. Mark September 13 to October 9 as a period of suspected data problems, so later readers do not mistake a poisoned dip for a penalty.

Why this matters for AI search visibility

Nozzle’s data shows AI Overviews appearing less often on poisoned pages (34.9% against 54.2%). Any measurement that infers AI visibility from collected Google pages may pick up the same noise. That is our inference, not Nozzle’s finding, and tools that measure AI answers by other methods may behave differently.

The practical response is to avoid relying on one source. Generative engine optimization and answer engine optimization are about being cited and recommended in AI answers, and measuring that well means testing real buyer questions across several platforms, not reading a single scraped page. Keep the basics of generative engine optimization and answer engine optimization strong: clear answers, accurate information and consistent entities. Our complete guide to the AI Visibility Audit explains how that cross-platform measurement works. Strong search engine optimization remains the base for all of it.

Where this leaves you

The confirmed facts are these. Nozzle published research on October 10, 2026, covering 27.5 million results pages, and reports that since September 13 about 8.0% were poisoned, against 0.17% at baseline, with a peak near 27% in late September. DemandSphere reports about 11% on its own data. Google has not commented, the mechanism is Nozzle’s account, the motive is interpretation, and both figures come from vendors who sell rank tracking.

The lasting lesson is about method. A rank tracker is a sample of what Google returned to a tool, not a record of what your customers saw. Before reacting to a drop, check Search Console, compare with history, wait a few days and look at more than one source.

Frequently Asked Questions

1. What did Nozzle report about Google and rank trackers?
On October 10, 2026, Nozzle reported that since September, Google has returned complete, normal-looking results pages to rank trackers with the real results swapped out. It measured 8.0% of 27.5 million pages as poisoned since September 13, against 0.17% at baseline.

2. Has Google confirmed this?
No. Google has not publicly commented on Nozzle’s or DemandSphere’s figures or on the September changes. The explanations come from vendors and trade press.

3. Can I trust the 8.0% figure?
Treat it as Nozzle’s reported estimate. It is from a rank-tracking vendor and inferred from keyword history.

4. How do I know whether my ranking drop is real?
Check Search Console for the same queries and dates, compare with several weeks of history, and wait one to three days. Altered pages usually clear within days, while a real ranking change persists.

5. Does this affect Search Console data?
Nozzle’s study concerns data collected from Google results pages, not Search Console. Search Console can lag, though, so confirm the date range first.

6. Does this change how I should track AI visibility?
Tools that infer AI visibility from collected Google pages may pick up the same noise, which is our inference. Testing real buyer questions across several AI platforms does not depend on one scraped page.

7. Should I stop using my rank tracker?
No. Use it for trends, and confirm important changes in Search Console and analytics. Ask your vendor how it detects swapped pages and what its error rates are.

If you want to know how your brand appears across Google and AI answer engines, measured from real answers and not a single dashboard, that is exactly what we look at in a free AI Visibility Audit.

Several agencies and software vendors now offer a free AI visibility audit, and they are not the same product. Some are self-serve tools that return a preview in seconds. Some are manual reviews run by a strategist. Some are checklists built around a local business. Foreignerds offers one too, and we describe it below. Because the offers differ so much in scope, speed, and who they suit, the most useful thing we can do is explain what a good audit should measure and how to compare the ones you find.

An AI visibility audit tests whether AI answer engines such as ChatGPT, Claude, Perplexity, Google AI Overviews, and Microsoft Copilot mention, cite, and describe your business accurately when someone asks a question you would want to answer. It is different from a traditional search engine optimization audit, which checks how you rank in a list of links. A site can rank well on Google and still be absent from the answers people read in an AI assistant.

What a free AI visibility audit is, and what it is not

An audit is a snapshot. It records how AI systems treat your business for a defined set of questions at a defined moment. It is useful for finding gaps, prioritizing work, and setting a baseline. It is not a ranking guarantee, and it is not a substitute for ongoing measurement.

Free audits are also a way for a provider to start a conversation. That is a fair exchange, provided you know what you are receiving. Before you request one, it helps to know what the findings should contain and whether you keep them regardless of whether you hire the provider.

One caution applies to any audit, free or paid. AI answers can differ from one run to the next and from one platform to the next. A single screenshot of one prompt is weak evidence. A credible audit uses a set of prompts, tests each platform separately, and reports patterns.

What a useful audit should measure

Whatever the provider, a useful audit covers a handful of things.

Citations and mentions. A citation is a link to your site inside an AI answer. A mention is your business name appearing without a link. They are different signals, and a business can have one without the other.

Share of voice against competitors. Knowing that you appear is less useful than knowing who appears instead of you for the same question.

Accuracy. AI systems sometimes state wrong facts about a business, such as outdated services, incorrect locations, or invented details. An audit should check what is being said, not only whether you are named.

Content structure. Pages that answer questions clearly, with direct answers, headings, and lists, are easier for AI systems to extract from. An audit should review the pages that matter most to revenue.

Structured data. Schema markup helps machines understand who you are and what you offer, so gaps here are worth listing.

Third-party footprint. Reviews, directories, press coverage, and community discussion shape what AI systems say about you. This is often the most overlooked part, and the research below explains why it matters.

Why each platform has to be tested separately

If one provider tells you that you are visible in AI search, ask which platforms they tested. The evidence suggests platforms behave differently enough that a single result does not transfer.

Averi’s benchmark report, citing a Profound analysis of 680 million citations collected between August 2024 and June 2025, states that only 11% of domains are cited by both ChatGPT and Perplexity. That is a domain-level figure across the whole citation landscape, not a statement about your own brand, and different studies define overlap differently. The direction is consistent, though: the two platforms draw on different sources.

Ahrefs found a similar divergence from another angle. In an analysis of 15,000 long-tail queries published on August 11, 2025, only 12% of URLs cited by ChatGPT, Gemini, and Copilot also ranked in Google’s top 10 for the same prompt. Perplexity was the exception, with 28.6% overlap. Ranking on Google is not the same as being cited in an AI answer.

Slate HQ’s 90-day study of six B2B software brands shows the same spread at brand level. For one email security client, brand-owned citation share ranged from 4.5% on ChatGPT to 10.2% on Google AI Mode. That is a single client’s result and not an average, but the gap between platforms for the same brand is the point.

The practical consequence is simple. An audit that tests only one platform, or blends all platforms into a single score, hides the variation you most need to see.

Why third-party sources belong in the audit

A research paper by Chen, Wang, Chen, and Koudas, submitted in September 2025, classified the sources AI systems cite as earned media, brand-owned, or social. In its consumer electronics tests, about 93.6% of ChatGPT’s citations and 93.7% of Claude’s came from earned media, meaning third-party sources. Perplexity and Gemini drew a smaller share from earned media and more from brand-owned and social sources. The paper is a preprint and covers consumer product queries, so it does not guarantee the same mix for a services business.

Slate HQ’s study adds context: it found that 76.3% of citations across its tracked brands went to domains that were neither the brand nor its direct competitors. Together, these findings mean an audit that reviews only your own website misses a large part of the picture. What others publish about you matters, which is why generative engine optimization work reaches beyond your own site, and an audit should say where you appear, where you do not, and which third-party sources AI systems already trust in your category.

Three kinds of free audit you will find

Descriptions below come from each provider’s own website at the time of writing. Offers change, so confirm the details before you request one.

Self-serve tools. Ahrefs offers a free AI visibility checker that needs no signup. It queries ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews and returns total mentions, a platform breakdown, top associated topics, and top cited domains. The free version is a limited preview, with results capped at five items per section, and historical tracking and competitor benchmarking sit in a paid product. It is a quick way to see whether you register at all.

Manual agency audits. DerivateX runs a free audit by hand through a senior strategist, covering ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, and AI Mode. It says results arrive within 48 business hours of confirming fit, and that findings remain with you whether or not you hire the agency. It states the audit suits B2B software companies with roughly $5 million to $50 million in annual recurring revenue and is not suited to e-commerce, local, or consumer businesses.

Business-type checklists. LocalFi offers a free audit built around local search signals. It reviews website structure, service pages, location pages, Google Business Profile, content clarity, entity signals, reputation markers, and search presence across ChatGPT, Gemini, Perplexity, and Google AI Overviews. It describes timing as dependent on the website and scope.

These are different tools for different jobs. A quick self-serve check tells you whether to worry. A manual audit tells you what is being said and who is being recommended instead. A local checklist focuses on the signals local searchers rely on.

What the Foreignerds free AI Visibility Audit covers

Our free AI Visibility Audit tests five platforms individually: ChatGPT, Claude, Google AI Overviews and AI Mode, Perplexity, and Microsoft Copilot. It measures seven things: citation rate, brand mention rate, AI share of voice against named competitors, sentiment and accuracy of what AI says about you, content extractability, structured data coverage, and third-party brand footprint.

The deliverables are an overall visibility score benchmarked against three to five competitors, a custom test set of 20 to 30 buyer prompts, a platform-by-platform breakdown, a check for inaccurate statements, a schema and structured data gap report, a prioritized 90-day action plan ranked by revenue impact, and a live dashboard for tracking citation rate and share of voice over time.

It runs over four weeks. In week one we set the baseline and map the prompts. In week two we audit five to ten of your highest-priority revenue pages. In week three we review schema, entity consistency, and third-party footprint. In week four we deliver the report and a live walkthrough. It is free with no obligation.

The trade-off is time against depth. Four weeks is slower than a tool that answers in seconds or a review that returns in two days. It covers more dimensions than a quick preview, and it is built around your own buyer questions and competitors. Which fits depends on how much detail you need and how soon.

How to compare offers

Whichever provider you consider, these questions separate a useful audit from a sales form.

Who chooses the prompts? Strong prompts come from real buyer questions in your market, not generic ones.

How many platforms are tested, and are they reported separately? Blended scores hide the differences described above.

Are competitors benchmarked? Visibility is relative.

Does the audit check accuracy? Being mentioned incorrectly can be worse than not being mentioned.

Does it look beyond your website? Third-party footprint should be part of the review.

What do you keep? Confirm that you receive the findings without a commitment.

Who runs it, and how long does it take? Automated, manual, and hybrid audits differ in speed and judgment.

Is there a sample or a clear description of the report? If a provider cannot describe what you will receive, treat that as a warning.

Where this leaves you

A free AI visibility audit is a reasonable first step if you treat it as a baseline. Pick one that tests platforms separately, uses your own buyer questions, benchmarks competitors, checks accuracy, and looks past your own site. Then use the results to decide where to invest, whether that is content structure, third-party coverage, or wider generative engine optimization and answer engine optimization work. Repeat the check on a schedule, because the answers change.

Frequently Asked Questions

1. Which agencies offer a free AI visibility audit?
Answer: Several do, along with software vendors. They differ in scope: some are self-serve tools, some are manual reviews, and some are aimed at specific business types. Foreignerds offers a free AI Visibility Audit covering five platforms and seven measures over four weeks.

2. What does an AI visibility audit measure?
Answer: A useful audit measures citations, brand mentions, share of voice against competitors, accuracy of what AI says about you, content structure, structured data, and your third-party footprint.

3. How is it different from an SEO audit?
Answer: A traditional search engine optimization audit checks how you rank and perform in search results. An AI visibility audit checks whether AI systems mention, cite, and accurately describe you in their answers, which makes it a core input to answer engine optimization.

4. Why test each AI platform separately?
Answer: Platforms cite different sources. One study found only 11% of domains were cited by both ChatGPT and Perplexity, so a result on one platform does not predict another.

5. Is a free audit really free?
Answer: Offers vary, so confirm what you keep and whether there is any obligation. Foreignerds’ audit is free with no obligation.

6. How often should I repeat an audit?
Answer: AI answers change, so a periodic check against your original baseline is sensible. A quarterly review is a reasonable starting cadence, and a tracking dashboard can help between audits.

If you want to see how ChatGPT, Claude, Perplexity, and Google describe your business today, request your free AI Visibility Audit from Foreignerds.