Your Buyers May Be Evaluating a Company That No Longer Exists

What if your buyers are evaluating a version of your company that no longer exists?

Not necessarily because your product is weaker. But because the version of your company they are evaluating does not reflect what you have actually built.

Imagine this. Your team spent the last year expanding enterprise capabilities, adding integrations, improving security, and building meaningful differentiation. You have invested in positioning, paid acquisition, content, events, and sales.

A prospective buyer hears about your company and asks ChatGPT, Gemini, or another AI assistant to compare you with three alternatives.

Your competitor gets recognized for enterprise readiness, integrations, and capabilities you already offer. Your company gets described as better suited for smaller teams.

You have made it into the conversation, but for the wrong reasons. And the buyer will never tell you what they read.

In this post I want to work through why that happens, how to tell a model error from a real evidence problem, and what I would actually do about it, starting with the sources rather than arguing with the chatbot.

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Summaries now appear even on explicit brand searches

This is not just a hypothetical concern about ChatGPT. Ahrefs recently analyzed 92.5 million branded-keyword Google search-result crawls, part of a 232.8 million crawl study of US desktop results from July to September 2026, published October 2. On September 29, AI Overviews appeared in 82.91%, nearly 83%, of the US desktop branded results it tracked. That is a single-day reading; the monthly branded averages rose from 61.3% in July to 65.1% in August and 73.3% in September.

That does not tell us how buyers behave. But it reinforces something important: AI-generated summaries are becoming prominent even when someone is explicitly searching for a brand.

A separate, much smaller panel in the same study tracked bare brand-name searches for 100 companies from Interbrand's Best Global Brands 2025. Of the 57 AI Overviews in that panel that showed sources, Wikipedia was cited in 41 (71.9%), YouTube in 22 (38.6%) and LinkedIn in 13 (22.8%). For the brands Ahrefs tracked, the summary drew mostly on pages the company does not control.

The limits matter. This is US desktop Google only, the source panel covers the world's largest brands rather than mid-market B2B, and Ahrefs notes that Google changes how AI Overviews appear, so the pattern may not hold. For a B2B company with no Wikipedia article, I would expect the sources to be whatever else exists: directories, old press releases, review profiles. That is my inference, not something the study measured.

Source: Ahrefs, AI Overviews Currently Appear for 83% of Branded Searches (October 2, 2026)

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Not every unfavorable AI answer is a hallucination

Here is what makes this particularly challenging. Your brand narrative is no longer communicated only through your website, campaigns, and sales conversations.

AI systems can synthesize information from product documentation, third-party reviews, customer stories, partner ecosystems, editorial coverage, and other publicly accessible sources.

Some of that information may be outdated. Some may lack context. And some of your strongest differentiation may have very little publicly verifiable evidence. The enterprise tier exists on your pricing page, but every review, case study and article about you still describes ten-person teams.

Not every unfavorable AI answer is a hallucination. Sometimes it is exposing a genuine gap between what your company claims, what it delivers, and what the market can independently verify. When those three disagree, an assistant that weighs third-party evidence will side with the third parties, and filing correction requests will not fix it. The fix is evidence.

So the first diagnostic I run on any unfavorable answer is simple: do the cited sources actually say this? If they do, and they are current, it is an evidence gap. If they do not, or they are stale or about someone else, it is a model or source error. The two need different work.

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How an answer ends up describing the old company

When the cited sources do not support the answer, one of four mechanisms is usually responsible. Each needs a different fix.

1. The fact was rare, so the model guessed

In September 2025, researchers from OpenAI and Georgia Tech published "Why Language Models Hallucinate," arguing that hallucination is a predictable statistical outcome that persists because evaluations reward confident guessing over admitting uncertainty. The relevant idea is the singleton rate, the share of facts that appear exactly once in training data: "if 20% of birthday facts appear exactly once in the pretraining data, then one expects base models to hallucinate on at least 20% of birthday facts." Models "seldom err on frequently referenced facts."

Your new enterprise tier, launched this year and described in a handful of places, is closer to an obscure birthday than a famous one. When an assistant answers from memory, that is where it fills gaps with the older, more frequently repeated story.

2. The answer retrieved a stale or third-party page

When an assistant searches before answering, it inherits whatever the retrieved pages say, and it is only as current as the pages it finds. A comparison article written before your repositioning, an old pricing page still live, or a directory profile untouched since launch are all real-world information. A grounded answer can cite them faithfully and still describe the wrong company.

The largest study of assistant accuracy on news points the same way. Led by the BBC and coordinated by the European Broadcasting Union, published October 22, 2025, it had 22 public service media organizations in 18 countries review more than 3,000 answers from ChatGPT, Copilot, Gemini and Perplexity. 45% had at least one significant issue, sourcing was the largest category at 31%, and 20% had major accuracy issues "including hallucinated details and outdated information." That study measured news, not company facts, so its percentages should not be transferred to brand queries.

3. Two companies with one name became one company

Chiang and Lee's ACL 2024 Findings paper, "Merging Facts, Crafting Fallacies," found that widely used open-source models mix information about distinct entities that share a name into single non-factual paragraphs; under a scoring method that accounts for this, factual precision dropped by more than 10%. The WhoQA benchmark (EMNLP 2024 Findings) found that same-name conflicts "significantly degrade" performance in retrieval-augmented settings. In practice, your company gets a namesake's headcount, headquarters or customer base, presented as one coherent profile.

4. The right page was cited and still misstated

Liu, Zhang and Liang's audit of four generative search engines (EMNLP 2023 Findings) found only about 51.5% of generated sentences were fully supported by their citations. Those engines have changed, so this is not a current rate, but the lesson stands: a citation to your site does not prove the sentence beside it came from your site.

Sources: Kalai, Nachum, Vempala and Zhang, Why Language Models Hallucinate (September 4, 2025); EBU, News Integrity in AI Assistants press release (October 22, 2025); Chiang and Lee, Merging Facts, Crafting Fallacies (ACL 2024 Findings); Pham et al., Who's Who (EMNLP 2024 Findings); Liu, Zhang and Liang, Evaluating Verifiability in Generative Search Engines (EMNLP 2023 Findings)

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The three things I would want to understand

For a founder or CMO, I would want to understand three things:

1. Where are we being misrepresented or excluded during important buying decisions? Not every prompt. The comparison, shortlist and evaluation questions buyers ask when they are choosing, especially the ones about security, integrations and scale, where your recent work should show up.

2. Which differentiators and product capabilities are missing, outdated, or insufficiently substantiated across our public information? For each reason customers actually choose you, look for independent evidence: a review that mentions it, a case study that shows it, documentation that specifies it, coverage that reports it. A differentiator that lives only on your own sales pages is a claim, not evidence.

3. What needs to change across product marketing, content, technical SEO, customer evidence, and third-party coverage to close those gaps? An evidence gap is rarely one team's problem, which is why it tends to persist.

This is not about generating another hundred AI-optimized articles or chasing a higher visibility score. It is about making your actual differentiation discoverable, understandable, and credible wherever buyers research their options. The workflow below is how I would put that into practice.

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A correction workflow that starts with the sources

You cannot edit a model's answer. You can change what it retrieves, what it can unambiguously attach to your name, and how much independent evidence exists for what you say.

Step 1: Write the answer key

One page: legal and brand name, primary domain, category in buyers' words, current products and tiers, retired products with dates, the segments you serve today, and the differentiators you want recognized, each with the public evidence that supports it. Without this, "wrong" becomes a matter of taste.

Step 2: Ask buyer questions, several times, and record the sources

Turn the answer key into the comparison and fact questions a buyer would ask, and run each on the assistants your buyers use more than once. CITECHOICE, a preprint posted September 14, 2026, found that re-running generation changed 15% of binary cited-or-not decisions in one search agent. It covers one GPT-based agent on one search provider, so treat the figure as a sense of scale, not a constant. For every unfavorable answer, record the cited URLs. For more on why one-off checks mislead, see How to Read an AI Visibility Statistic.

Step 3: Classify each answer

• Cited sources are current and support the answer: an evidence gap. Go to Step 6.
• No source, or sources that do not contain the claim: a guess. Make the fact easy to find and consistent everywhere.
• A cited page that is outdated: a stale source. Fix or retire it.
• A cited page about a different company: entity confusion. Strengthen disambiguation.
• A correct page, misstated: make the fact explicit, in plain text, near the top of the page.

Step 4: Fix your own pages first

Redirect or update retired pricing pages. Give discontinued products a short dated page instead of a live launch page. State your category and the segments you serve in plain sentences on the homepage and about page, and publish your enterprise, integration and security capabilities as specific, crawlable documentation rather than a gated PDF. The technical AEO audit guide covers the checks that determine whether these pages can be read at all.

Step 5: Tell Google exactly which entity you are

Google's Organization structured data documentation, last updated September 8, 2026, says the markup can help Google "disambiguate your organization in search results." It describes url as helping Google "uniquely identify your organization," legalName as the registered name, and sameAs as URLs of other pages with information about the organization. Do not assume other assistants read it the same way.

Step 6: Close the evidence gap through third parties, on their rules

Update the profiles you own: LinkedIn, review-site listings, marketplace and partner pages. Publish customer stories that reflect the current product and segment. For press coverage with outdated facts, ask the publisher for an update note; earned coverage carries weight in AI answers, as I covered in AI Citations Point to Earned Media.

Wikipedia's conflict-of-interest guideline, as revised September 16, 2026, defines a financial conflict as including "being an owner, employee, contractor, investor or other stakeholder." COI editors "are strongly discouraged from editing affected articles directly, and can propose changes on article talk pages instead," and paid editors "must disclose who is paying you." For Wikidata, check notability first: as of the September 28, 2026 revision, an item needs a valid sitelink, a "clearly identifiable conceptual or material entity" describable with "serious and publicly available references," or a structural need.

Do not manufacture corroboration. Anthropic's updated Usage Policy, announced October 8, 2026 and effective November 12, 2026, prohibits using Claude to "manipulate the sources from which search engines or AI systems draw answers by seeding them with content that misrepresents its origin, authorship, or independence," naming "networks of sites posing as unaffiliated sources corroborating the same claims." The rule binds Claude users only, but it names the tactic exactly.

Step 7: Re-test on a schedule and track sources, not wording

Re-run the same questions with the same repetition. Track whether the wrong source stopped being cited and the right one started, because that is the part you changed. None of the sources here gives a timeline for how quickly assistants reflect corrected pages, so set a recurring check rather than expecting a date.

Sources: Selvam and Ghosh, CITECHOICE (September 14, 2026); Google Search Central, Organization structured data (last updated September 8, 2026); Wikipedia, Conflict of interest guideline (revision of September 16, 2026); Wikidata, Notability (revision of September 28, 2026); Anthropic, Usage Policy (effective November 12, 2026)

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How to correct wrong company information in AI answers

When ChatGPT, Gemini, Copilot or Perplexity states inaccurate company details, such as a retired product, old pricing, the wrong segment or a namesake's headquarters, the order of the fixes matters more than any single fix. The workflow above is the full diagnostic. This is the short, ordered version for the moment someone forwards you a wrong answer and asks what you are doing about it.

Feedback buttons come fifth on purpose. A report flags one answer. The sources are what every future answer is built from.

1. Fix the facts on your own site first, with dates

Correct the homepage, about page, pricing page and product pages so each states the current fact in plain text, not only in an image, a PDF or a script-rendered widget. Where a fact changed, say when: "Enterprise tier launched March 2026" is harder to misread than an undated claim, and a visible last-updated date tells a retrieving assistant which version is current. Give a retired product a short dated page that says what replaced it instead of leaving the old launch page live. An assistant that searches before answering can only repeat a correction that exists somewhere it can retrieve.

Then ask the search engines to re-crawl the changed pages. Google's URL Inspection tool lets property owners request a crawl of individual URLs, but Google says crawling "can take anywhere from a few days to a few weeks" and that a request does not guarantee inclusion. IndexNow, an open protocol Bing promotes, lets you notify the search engines that support it when a page is added, updated or removed.

2. Update the third-party profiles and directories AI cites

Use the cited URLs you recorded when you tested the answers as the to-do list: review-site listings, marketplace and partner pages, LinkedIn, industry directories and old press coverage. Fix the profiles that actually appear in answers before the ones that do not. Where you cannot edit a page yourself, ask the owner for an update, and follow each site's own rules (the Wikipedia talk-page route above is the clearest example).

3. Add Organization schema that matches the corrected facts

Mark up your homepage or about page with Organization structured data: url, legalName, logo and sameAs links to the profiles you just corrected. Google documents this markup as helping it disambiguate an organization in its own results. No other platform I checked documents using it, so treat it as a consistency aid outside Google, not a fix.

4. Use each platform's feedback tools, knowing what they do

Here is what each platform documents as of November 2026. None of them promises that a report will change a specific answer, or says how long a change would take.

• OpenAI (ChatGPT): OpenAI's model behavior feedback page tells users to click the thumbs-down icon below a response to give feedback, or select Report to flag content that may violate its Terms of Use or applicable laws. I found no OpenAI route for a company to submit corrected facts about itself. What OpenAI does document is access: its crawler overview says sites that block OAI-SearchBot will not appear in ChatGPT search answers, and that a robots.txt change can take about 24 hours to take effect for search.
• Google (AI Overviews and the knowledge panel): Google's AI Overviews help page says to click the Bad overview icon, then Report a problem, and that the feedback includes your most recent search and its results. For the knowledge panel, Google lets an organization's representative claim the panel and then suggest changes with the flag next to a specific fact, describing the correction and linking to pages that verify it. Google notes that not all knowledge panels are claimable yet, and that local businesses should use a Business Profile instead.
• Microsoft (Copilot and Bing): Microsoft documents thumbs-up and thumbs-down feedback for Copilot in Microsoft 365 apps; I did not find equivalent public documentation for the consumer Copilot app. The more useful Microsoft tool for this problem is the AI Performance report in Bing Webmaster Tools, in public preview since February 10, 2026. It shows how often your pages are cited across Microsoft Copilot, AI-generated summaries in Bing and select partner integrations, with the grounding queries used to retrieve them, so you can see whether a corrected page starts being cited. Microsoft notes the data is a sample and that citations are not clicks.
• Perplexity: Perplexity's help center says to use the flag icon below an answer, or open a support ticket, and to include the URL of the query plus a description of the error and the expected result. Misinformation and outdated information are among the issue types it lists.

5. Re-check over time, because answers vary

Re-run the same buyer questions on each assistant, several times each, on a fixed schedule, and log the cited sources alongside the wording. The CITECHOICE result above is why one before-and-after check proves little: in that study, re-running generation alone changed 15% of cited-or-not decisions. What you want to see is the stale or wrong source drop out and your corrected page or profile appear, held across repeated runs. If the answer is accurate but unflattering, it is not a correction problem; see what to do about negative ChatGPT mentions.

Sources: Google Search Central, Ask Google to recrawl your URLs; Bing, IndexNow; OpenAI, Model behavior feedback; OpenAI, Overview of OpenAI crawlers; Google Search Help, AI Overviews; Google Knowledge Panel Help, Get verified on Google; Microsoft Support, Providing feedback about Microsoft Copilot with Microsoft 365 apps; Bing Webmaster Blog, Introducing AI Performance in Bing Webmaster Tools Public Preview (February 10, 2026); Perplexity Help Center, How can I report incorrect or inaccurate answers?

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Limits, stated plainly

I found no published study that measures how often AI assistants misdescribe B2B companies in a way that would survive scrutiny. The EBU and BBC study is about news. The academic papers use benchmarks built from Wikipedia entities and older models. The Ahrefs data covers US desktop Google and very large brands. Treat every percentage here as evidence about a mechanism, not as your error rate.

Nobody outside the labs can see which sources an assistant weighted, only which it displayed, so classifying answers by citation is a working method, not proof of cause. The opening scenario is illustrative, not a measured case.

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The takeaway

When an assistant describes the company you used to be, ask what it read before deciding it was wrong. If the sources were stale, misattributed or about someone else, fix them. If the sources were accurate and simply never caught up with what you built, the problem is evidence, and it belongs to product marketing, content, technical SEO, customer marketing and PR together.

Because for a growth-stage company, being known is not enough. You need to be understood for the reasons that make customers choose you. If you want a second pair of eyes on how AI answers describe your company, see how Genmark AI audits it.

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Sources

• Ahrefs, AI Overviews Currently Appear for 83% of Branded Searches, by Ryan Law with Xibeijia Guan (October 2, 2026)
• Kalai, Nachum, Vempala and Zhang (OpenAI, Georgia Tech), Why Language Models Hallucinate, arXiv:2509.04664 (September 4, 2025)
• European Broadcasting Union, Largest study of its kind shows AI assistants misrepresent news content 45% of the time (October 22, 2025)
• Chiang and Lee, Merging Facts, Crafting Fallacies, ACL 2024 Findings, arXiv:2402.05629 (v4, June 7, 2024)
• Pham, Ngo, Luu and Nguyen, Who's Who: Large Language Models Meet Knowledge Conflicts in Practice, EMNLP 2024 Findings, arXiv:2410.15737 (October 21, 2024)
• Liu, Zhang and Liang, Evaluating Verifiability in Generative Search Engines, EMNLP 2023 Findings, arXiv:2304.09848 (v2, October 23, 2023)
• Selvam and Ghosh, CITECHOICE: A Causal Audit of How Document Presentation Redistributes Citation Credit in Agentic Search, arXiv:2609.15164 (September 14, 2026)
• Google Search Central, Organization structured data (last updated September 8, 2026)
• Wikipedia, Wikipedia:Conflict of interest (revision of September 16, 2026)
• Wikidata, Wikidata:Notability (revision of September 28, 2026)
• Anthropic, 2026 Usage Policy Update (October 8, 2026)
• Anthropic, Usage Policy (effective November 12, 2026)
• Google Search Central, Ask Google to recrawl your URLs
• Bing, IndexNow
• OpenAI, Model behavior feedback
• OpenAI, Overview of OpenAI crawlers
• Google Search Help, Find information in faster and easier ways with AI Overviews in Google Search
• Google Knowledge Panel Help, Get verified on Google
• Microsoft Support, Providing feedback about Microsoft Copilot with Microsoft 365 apps
• Bing Webmaster Blog, Introducing AI Performance in Bing Webmaster Tools Public Preview (February 10, 2026)
• Perplexity Help Center, How can I report incorrect or inaccurate answers?