When AI gets your brand wrong
Ask ChatGPT about your business. There is a reasonable chance it will confidently state something that is not true.
A wrong founding year. A product you do not sell. A competitor comparison that does not reflect reality. A description that conflates your brand with a similarly-named entity. A citation to a source that, when you check it, does not contain the information attributed to it.
This is not a rare edge case. In Entidex’s testing across hundreds of entities, cross-model disagreement on basic factual attributes is the norm, not the exception. AI engines regularly present fabricated details with the same confidence as verified facts — and most businesses have no idea it is happening.
How AI fabrication works in practice
AI engines do not intentionally lie. They predict plausible text based on patterns in their training data and retrieval sources. When the available information about your entity is sparse, inconsistent, or fragmented across sources, the model fills gaps with statistically plausible guesses.
Entity conflation
If your brand shares a name — or even part of a name — with another entity, AI models may blend attributes from both. Entidex documented a case where a podcast entity was consistently confused with its parent media company, with multiple AI engines attributing the parent company’s launch year to the podcast.
Citation fabrication
AI engines sometimes generate responses that include references to sources that appear authoritative but do not actually support the claims being made. In some cases, the cited page does not contain the referenced information at all. In extreme cases, the cited page does not exist.
Citation laundering
Google’s AI Overview consumed proprietary data from an Entidex entity page, stripped the provenance chain, and presented the information with fabricated attributions to unrelated sources. The data was accurate; the AI’s re-attribution made it untraceable.
Temporal drift
A model’s description of your business might have been accurate when the training data was current, but your business has changed since then. New products, new positioning, a rebrand — if the model has not ingested these updates, it is serving stale information.
Why this is hard to detect manually
You can ask ChatGPT about your business right now and check the answer. But that gives you one data point from one engine at one moment in time. The real risk is structural:
Different AI engines may each be wrong about different things. Gemini might have your location right but your services wrong. Perplexity might cite accurate sources but draw incorrect conclusions. ChatGPT might describe you perfectly today and differently next week after a model update. Checking manually does not scale, does not persist, and does not catch cross-model patterns.
How Entidex detects and tracks fabrication
Ground-truth comparison
Entidex maintains verified canonical records for tracked entities, built from authoritative sources (Companies House, Wikidata, official websites). When an AI engine’s output contradicts the canonical record, the discrepancy is flagged automatically.
Cross-model disagreement mapping
Entidex probes ChatGPT, Claude, Gemini, Perplexity, and Grok against the same entity and maps where they agree and where they diverge. Disagreement between engines on a factual attribute is a strong signal that at least one is fabricating.
Drift detection over time
Every observation is timestamped and stored. Entidex tracks how each engine’s description of your entity changes across model updates and data refreshes. When a previously accurate description degrades, you see when it happened.
Source attribution analysis
Entidex maps which upstream sources are feeding each AI engine’s representation of your entity. When a source goes stale or introduces an error, the downstream AI impact becomes visible.
What to do when you find errors
Wrong facts
Trace the error to its source. Often it is an inconsistency between your own properties. Align your sources, and AI representations typically correct on the next training update or retrieval cycle.
Entity conflation
This requires disambiguation signals. A Wikidata entry with a distinct QID, Schema.org sameAs markup, and consistent naming across platforms all help AI models distinguish your entity from similarly-named ones.
Citation fabrication
The best mitigation is building signal breadth — more authoritative, consistent, independently verifiable sources about your entity reduce the model’s need to guess.
Stale information
Ensure your most current information is on crawlable, frequently-updated pages. AI engines and their retrieval mechanisms weight recency.
Go deeper with Entidex
The full entity intelligence observatory — beyond Explore Entidex
- Continuous multi-source entity observation
- Alerts when sources diverge or drift
- Cross-surface consensus + visibility over time
- Evidence-anchored intelligence reports
Frequently asked questions
How common is AI fabrication about businesses?
More common than most people expect. In Entidex's entity-level testing, factual disagreement between at least two major AI engines occurs on basic attributes (founding date, headquarters, primary product category) for the majority of entities tested. The confidence of the delivery masks the uncertainty of the underlying data.
Can I get AI to remove wrong information about my business?
There is no "edit" button for AI engine outputs. The approach is to strengthen the upstream signals — your web presence, structured data, knowledge graph entries, and third-party sources — so that the model has better data to work with on its next update cycle. Entidex helps you identify which signals are weak or inconsistent.
What is citation laundering?
It is when an AI system consumes information from a source, strips the original attribution, and re-presents it with fabricated or misleading citations. The content might be accurate, but its provenance chain is broken — making it impossible for the reader to verify. Entidex has documented this happening with its own published data.
How quickly do AI engines correct errors?
It varies by platform and error type. Engines with real-time retrieval (like Perplexity) may reflect corrections within days. Engines relying primarily on training data (like ChatGPT without browsing) may take weeks or months, depending on model update cycles. Ongoing monitoring with Entidex lets you track correction timelines for your specific entity.