Dattva Services
AI Hallucination Detection — What Is AI Saying About Your Company to Your Buyers?
AI hallucination, in a business context, is when ChatGPT, Perplexity, Gemini, or Claude describes a company with information that is wrong: an outdated service list, a former executive's name, the wrong founding year. This happens without warning and without notification. Most B2B companies have no idea what AI is currently saying about them to the buyers evaluating them right now.
AI models generate descriptions of a company from training data, a snapshot of the web that can be months or years old. When a company pivots, rebrands, changes leadership, or grows into new markets, the model often does not know and keeps describing the company as it used to be. The fix has to happen at the source data the model trusts, not by asking the model to correct itself.
What AI Gets Wrong About B2B Companies, and Why
AI models generate descriptions of a company from patterns in training data, a snapshot of the web that can be months or years out of date. When a company has pivoted, rebranded, launched new services, changed leadership, or expanded into new markets, the model frequently does not know. It keeps describing the company as it used to be.
The errors that show up most often follow a small number of recognisable patterns:
- Wrong founding year — common in companies that have rebranded or restructured
- Wrong services listed — describing offerings a company no longer provides or never provided
- Wrong company size — extrapolated from outdated data
- Wrong geographic focus — describing markets a company does not actually serve
- Wrong leadership — surfacing former executives or incorrect titles
- Competitor conflation — where two similarly named companies get merged into one description
- Category misclassification — where a specialist firm gets described as a generic player
The underlying mechanism is the same across all of these. A model does not look up a company's website fresh every time a buyer asks about it. It generates an answer from patterns learned during training. Prompting the model to correct itself does not fix this, because the model isn't wrong about what it learned — it is working from outdated or conflated sources. The fix has to happen at the source level, in the data the model treats as authoritative.
A hallucination costs a company deals it never sees. A buyer comparing vendors on Perplexity or ChatGPT can be shown an inaccurate, outdated description of one option and simply move to the next, without the affected company ever knowing the comparison happened. The loss never appears in a company's own analytics because the conversation happened entirely inside someone else's AI session.
Why Hallucination Harms B2B Revenue, Without You Ever Knowing
These two scenarios are illustrative, not case studies. No real company or client is involved in either.
A procurement head uses Perplexity to compare two IT services companies for a cloud engagement. One is described accurately. The other is described using a service list from two years earlier, before the company pivoted into a different specialisation. The second company does not make the shortlist. Nobody at that company ever learns this comparison happened.
A startup founder asks ChatGPT to name GEO agencies in India. ChatGPT describes one candidate as a traditional SEO agency that is expanding into GEO, sourced from an old blog post written before that company's pivot was complete. The founder reads the description and moves on to the next name.
The pattern in both scenarios is the same. The loss is invisible from the inside. There is no dropped call, no unanswered email, no rejection to notice. The buyer's shortlist simply forms somewhere the company was never in the room to correct.
That invisibility is exactly why most companies never think to check what AI is currently saying about them — not until an inaccuracy surfaces on its own, in a candidate interview, an investor call, or a client conversation. The gap between what a model says and what is actually true is not permanent or unfixable. It is fully diagnosable: every major AI platform can be queried directly, its answers compared line by line against reality, and every discrepancy mapped before a single correction is made.
The Detection and Correction Approach
The approach has two parts.
- Detection. We identify what each major AI platform — ChatGPT, Perplexity, Gemini, and Claude — is currently saying about a company, compare it against current ground truth, and document every discrepancy found.
- Correction. We fix the issue at the source level, through structured entity data, authoritative content signals, and third-party directory updates. This does not involve prompting AI models to correct themselves, since that approach does not work reliably. It involves correcting the underlying sources the models already treat as trustworthy.
The timeline: detection completes in the first week. Corrections are deployed across a 30-day period. A verification audit at 6 to 8 weeks confirms whether the corrections have been picked up by each platform.
The initial correction engagement is a focused 4 to 8 week process, not an ongoing subscription by default. But because AI models update continuously, new errors can surface as training data refreshes, which is why companies that are growing, pivoting, or operating in fast-moving markets tend to build a quarterly check into their routine.
Related visibility work that compounds with accurate entity data is covered in Generative Engine Optimisation.
Is Hallucination Detection a One-Time Fix or Ongoing?
The initial correction engagement is focused, typically 4 to 8 weeks. It fixes the errors that exist in AI platforms today.
The open question afterward is whether new errors emerge as a model's view of the web changes. AI models update continuously, and a company that is growing, pivoting, or operating in a fast-moving market is more likely to generate a fresh set of discrepancies over time than one that is largely static. For that reason, a quarterly check is worth building into the routine rather than treating the first engagement as a one-time fix.
The first step is finding out what AI is currently saying. Most companies are surprised by what they find.
Frequently Asked Questions
What is AI hallucination for businesses?
The descriptions generated by AI depend on the data used in its training, and in case the data is obsolete or inaccurate, the AI describes the wrong version as though it is entirely sure of its accuracy. Some of the problems generated include listing of the wrong services offered, the year of establishment, leadership that left two years ago, or business model no longer used by the firm. AI is not aware of any of these inaccuracies since it treats all data inputted to it as being true.
How common is this for B2B companies?
Very common, particularly for companies that have pivoted, rebranded, or grown significantly in the last two years. Most have simply never checked what AI says about them, because until recently there was no reason to. The companies most exposed tend to be the ones whose reality has changed faster than the internet's record of them has.
How do I find out what AI is saying about my company?
Open ChatGPT and Perplexity in an incognito window and run a few direct queries: "[company name] what do they do," "[company name] founded when," "[company name] services list." Read every sentence back against current reality, line by line. Incognito matters here — it avoids any personalization or memory skewing the result toward what you already know.
Can I ask ChatGPT to correct what it says about me?
The process of asking a model to correct its perception of your business will change its reaction only for the current interaction; all other people who ask the question will still receive the wrong answer based on previous knowledge. To correct a record, you need to do it where it is needed: use markup, entities, and authoritative information, which the model uses in its responses. There is no such thing as a legitimate request to "update your records" from any AI company.
What sources does AI pull company information from?
The training dataset is composed of content from your website, Wikipedia, Wikidata, listings from third parties, press releases, news articles, and social profiles. An outdated source could include an old press release or an outdated entry in some directory. The problem here is that if there is any such an outdated piece of content out there, it can cause problems on various AI platforms.
How long before corrections show up in AI responses?
Usually, the period required for structured correction to appear is between 2-6 weeks, although it can be different from one platform to another, and from one type of incorrect data embedded into the training process to another. Some corrections appear much faster if retrieved via live data retrieval, rather than via training data; however, there is never any guarantee when it will happen.
Is this a one-time service or ongoing?
The first interaction deals with the present situation but not with a final resolution because of possible changes in AI descriptions in the future when new pieces of information become available. In case of organizations and markets experiencing fast growth and constant change of AI descriptions, the period for such interactions should be quarterly.
Find out what AI is saying about your company
Get a free hallucination check across ChatGPT, Perplexity, Gemini, and Claude — every discrepancy mapped against ground truth before any correction begins.