How to Fight AI Hallucinations About Your Brand [ready-to-use prompt inside]

As SEO specialist, I’ve watched the landscape shift. More and more users are making purchasing decisions based on answers provided by AI chatbots. This sounds like progress, but there is a hidden trap: language models often hallucinate information about companies, creating non-existent facts, opinions, comparisons, or even scandals.

In practice, this means a potential client might “learn” things about your company that never happened—and you have no direct control over it.

Understanding the “Liar” in the Machine

We need to remember that AI chats are just tools, and the answers we get depend heavily on how we ask. However, there is a fundamental difference between human and machine logic. For people, it is obvious that one should not lie; LLMs, on the other hand, often invent facts because a fabricated fact is statistically “better” for them than no answer at all.

We have little influence over how our potential clients question AI about our firms. This begs the question: Can we do anything to limit hallucinations about our company? Fortunately, yes. However, be warned – it is not easy and usually costs a significant amount of time and money. In many cases, it also demands close collaboration between multiple teams, including web developers, SEO specialists, HR, marketing, and PR professionals.

To fix the problem, we must understand why LLMs hallucinate:

• they are neither search engines and should not be treated as knowledge bases,
• they predict the next text fragments based on patterns,
• they do not reliably check facts but create answers based on probability,
• they lack an “I don’t know” mechanism as part of their default behavior; for a language model, admitting ignorance is statistically worse than generating coherent-sounding text,
• they optimize for the fluency of the answer, not the truth.

Identifying the risk zones

Our focus should be on the information our potential clients might ask for, as we want them well-informed about our services, products, and reputation. We must start by finding the places where LLMs simply do not have information about us.

Hallucinations appear particularly often when queries concern:

• small and medium-sized enterprises (SMEs),
• new brands,
• companies from local markets,
• companies operating in niche industries,
• brands without a strong presence in media and SEO,
• generally, the less data about a company online, the higher the risk of hallucination.

The Fact-Checking Audit

To diagnose the problem, I have prepared a comprehensive prompt that allows you to identify these “blind spots” and hallucinations.

Copy and paste the following prompt into an LLM (like ChatGPT or Claude), add your comapny name and website address to audit your own company.

Perform a factual audit of the company listed below. Your task is to audit facts, not to fill in missing information.
If you are unable to verify any piece of information with high confidence, YOU MUST: - mark it as “UNVERIFIED” - you are NOT allowed to guess or generalize - you are NOT allowed to rely on typical industry patterns or assumptions.
Every piece of information must be: - either supported by a source - or clearly marked as uncertain.

Company name: [INSERT COMPANY NAME HERE]
Website: [INSERT OFFICIAL WEBSITE URL HERE]

For each piece of information, provide: 
- source type (official website / external article / business database / no source) 
- confidence level (high / medium / low) 
- whether the information may be outdated

1.  Year of founding
2.  Full legal company name (exact wording):
3.  Legal form
4.  Does the company operate under a brand name different from its legal name?
5.  Registration number
6.  Owner / owners of the company
7.  Has the company been involved in any acquisition or merger?
8.  Location of the headquarters
9.  Number of physical offices
10. Number of employees (if officially stated)
11. Countries of operation
12. Business areas of the company (10):
- List ONLY those areas that are explicitly mentioned on the official company website.
- For each area, provide the page fragment or section from which the information is taken.
- If you cannot find 10 areas – DO NOT forcefully complete the list.
13. Main products or services (exactly as described by the company):
14. Examples of projects / case studies (only those publicly described)
15. Notable clients (if any are publicly listed):
- for each client, provide public proof of cooperation
16. Awards, certifications, or distinctions:
- only if listed on the website or in a reliable source
17. Has the company been involved in controversies, legal disputes, or
    rebranding?
- if no data is available, explicitly write “NO PUBLIC INFORMATION”
18. Rebranding or company name change
19. Change of business profile
20. Is it worth working at this company (culture, conditions, opinions)?
21. Information that could NOT be verified

Rules of response: 
1. If you do not have clear data – write “NO DATA”.
2. If the information comes from assumptions or patterns – DO NOT PROVIDE IT.
3. You are not allowed to: - assume company scale - guess clients - supplement business history 4. In case of conflicting information – describe the conflict instead of choosing one version.
Self-reflection: 
- Which of the above data points are most susceptible to hallucination?
- Which fields caused you the most difficulty and why?
- Which pieces of information about this company are most commonly misguessed by language models?

Moving from diagnosis to repair

Once you have analyzed the results from the prompt and identified where the model lacks information or provides inconsistent data, execute the following tasks to regain control over your brand narrative.

Phase 1: onsite corrections (your digital HQ)

Your website is the primary source of truth. If data here is missing or ambiguous, LLMs will rely on probability rather than fact.

• Audit your “About Us” page:
  • Verify core data: Ensure your “Year of founding” , “Full legal company name” , and “Registration number” are explicitly stated in text, not just in images.
  • Clarify ownership & leadership: Clearly list the “Owner / owners of the company” to prevent the model from guessing based on outdated associations.
  • Define location: Explicitly state the “Location of the headquarters” and the “Number of physical offices”. If you operate globally, list the specific “Countries of operation”.
• Structure your service offerings:
  • Standardize naming: Review your “Main products or services” and ensure they are described exactly as you want them to appear in AI answers.
  • Proof of work: Publish “Examples of projects / case studies” and “Notable clients” with public proof of cooperation. This reduces the chance of the AI “guessing clients”.
• Technical & metadata improvements:
  • Implement schema markup: Use metadata to tag your address, legal name, and founding date so crawlers understand the specific data points.
  • Address “No Data” zones: If the audit returned “NO PUBLIC INFORMATION” for controversies or rebranding, consider adding a clear history or timeline to your site to fill that void with positive, factual milestones.

Phase 2: offsite corrections (the ecosystem)

The less data available about a firm in the wider network, the higher the risk of hallucination. You must create a consistent footprint across the web.

Focus on creating content that fills the gaps where LLMs currently hallucinate, specifically regarding small and new brands, which are most susceptible to errors.

Standardize Business Directories:

Update all business directories ensuring your N.A.P. (Name, Address, Phone) and business description match your website exactly.

Resolve conflicts: If different sources list different founding years or employee counts, “describe the conflict” internally and fix the external sources to match the official version.

Align industry platforms & social media:

Review industry websites and social media profiles.

Ensure your bio and business profile history are consistent across LinkedIn, Crunchbase and niche industry portals.

If you have received awards, certifications, or distinctions, ensure they are listed on these external platforms as well as your site.

Content Strategy for “Unverified” Data:

If the audit marked valid information as “UNVERIFIED”, it means the signal is too weak. Publish press releases or blog posts specifically targeting those facts (e.g., “Celebrating 10 years of innovation”, to cement the founding date, “New cooperation with XYZ” to provide public proof of cooperation, “Opening our third regional office in Texas” to clarify the number of physical offices and exact headquarters location etc.).

Take Back Control of Your Brand Narrative

AI hallucinations are not a theoretical risk — they are already shaping how customers perceive companies. When a language model invents facts about your business, it doesn’t just create noise; it directly impacts trust, reputation, and revenue. Ignoring this problem means letting probabilistic systems define your brand story for you.

The good news is that hallucinations are not random. They appear most often where information is missing, ambiguous, inconsistent, or weakly signaled across the web. This gives companies a strategic opportunity: by systematically strengthening factual signals, clarifying official data, and expanding authoritative digital footprints, you can significantly reduce the risk of AI-generated misinformation.

Treat your website as the single source of truth. Standardize your data across all platforms. Actively fill informational gaps with structured, verifiable content. And most importantly, regularly audit how AI models describe your company, because if you don’t control the narrative, the machine will invent one for you.

Important limitation: Companies cannot fully control how AI systems describe them. What they can do is significantly reduce the probability and severity of hallucinations by strengthening, standardizing, and amplifying factual signals across authoritative sources.

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Krzysztof Starachowski

Written on: February 6, 2026

SEO specialist with over 15 years of experience.

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