How AI forms its opinion of your brand
AI engines don’t hold opinions. They hold associations. Understand how the association forms — and you can change what every model says about you.
When someone asks ChatGPT, Claude, Gemini, Perplexity and Grokabout your company, the answer feels like a judgement. It isn’t. The model has resolved your name to an entity and placed that entity in a vast space defined by everything it has read. Your position in that space — and therefore what the model says — is pulled by whatever authoritative sources repeatedly associate you with. There is no opinion being weighed. There are only associations being averaged.
That is good news. An opinion is hard to argue with. Associations are observable, measurable, and — with the right record in front of the model — correctable.
AI runs on associations, not opinions
Models learn which entities matter, and why, from high-authority earned media — news outlets, trade press, analyst notes, reference data and respected industry sites. When credible sources discuss your brand in a consistent context, that context becomes part of how the model represents you. When the sources are sparse, fragmented or contradictory, the model fills the gap with whatever is statistically plausible. That is where confident, fluent, wrong answers come from.
So the question is never “what does the AI think of us?” It is “which associations is the AI working from, and are they the right ones?” Entidex answers that by resolving every mention back to one canonical entity and measuring the associations directly: AI Visibility (how often and how clearly you surface), Sentiment (the tone of the consensus), Share of Voice (your slice of the conversation versus peers), and the surfaces feeding each.
Why two engines disagree about you
Each engine reads a different slice of the web and retrieves differently at answer time, so your entity lands in a slightly different position for each. ChatGPT may describe you favourably off a strong reference-data presence; Claude may hedge based on a critical discussion thread; Perplexity may surface a single recent review and weight it heavily. Entidex maps this as a Consensus / Divergence Score across ChatGPT, Claude, Gemini, Perplexity, Grok plus reviews, social, news and search — and tracks whether the engines are converging on the truth or drifting apart. High divergence is the clearest early signal that an association is weak, stale, or wrong.
The resolution layer: fixing what AI gets wrong
Measuring the problem is half of it. The reason Entidex exists is the other half — the resolution layer. You can’t edit a model, but you can change the record it reads. The loop is deliberately simple and runs continuously:
Truth Gap
Entidex compares every engine’s claims against a verified canonical record built from authoritative sources. Where an engine contradicts the truth — a wrong founding year, a conflated competitor, a fabricated citation — the gap is flagged with evidence and provenance, per engine.
Entity Knowledge Statement
A machine-readable, citable statement of the verified facts — plus explicit corrections to the claims AI commonly gets wrong — published at a crawlable URL that GPTBot, Claude-Web and Google-Extended can ingest. It is the source of truth you put in front of the models.
Correction channel (MCP)
Models and agents read the verified record — and submit corrections back — through one open standard, the Model Context Protocol. Entidex is the canonical channel where AI both reads what is true about an entity and flags what it got wrong, for review.
Drift & correction timeline
Every observation is timestamped. As engines re-ingest the corrected record, Entidex tracks the lag — showing when each engine updates and confirming the association has actually moved, not just that you published a fix.
Observe, then resolve — in one place
Most tools stop at observation: they tell you what AI said. Entidex is built around the resolution layer — detect the gap, publish the verified record, expose it to the models over an open protocol, and prove the correction landed. That is the difference between knowing AI is wrong about you and doing something about it.
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 does AI decide what to say about my brand?
AI engines do not form opinions the way people do. They turn your brand into an entity — a position in a high-dimensional space — and pull it toward whatever authoritative sources repeatedly associate it with. News coverage, reference data, reviews, owned content and social discussion all shape that position. The model then describes you based on where you sit, not on a considered view. Change the associations and you change the description.
Why do ChatGPT, Claude and Gemini say different things about the same brand?
Each engine is trained on a different mix of sources and retrieves differently at answer time, so the same entity lands in a slightly different position for each. ChatGPT, Claude, Gemini, Perplexity and Grok can disagree on founding date, category, even sentiment. Entidex measures that disagreement directly as a Consensus / Divergence Score — high divergence is a reliable signal that at least one engine is working from weak or stale associations.
Can I correct what AI believes about my brand?
There is no edit button on a model. But you can change the inputs it reads. Entidex closes this with a resolution layer: a verified Entity Knowledge Statement (a machine-readable, citable record of the facts) that AI crawlers and retrieval systems can ingest, plus a correction channel over the Model Context Protocol so models can read the verified record — and submit corrections back — through one open standard. You fix the associations at the source; the engines catch up on their next cycle.
What is the difference between this and prompt monitoring?
Prompt monitoring tells you what one engine answered to one prompt on one day. Entity intelligence resolves all of those answers to one canonical entity and tracks the underlying associations — visibility, sentiment, share of voice, cross-surface consensus and drift — over time. The opinion is the symptom; the associations are the cause. We measure the cause.
How long does it take to change what AI says?
It depends on the engine. Engines with live retrieval can reflect a corrected, well-structured record within days; engines leaning on training data move on their update cycle. Publishing a verified Knowledge Statement and strengthening consistent, authoritative sources shortens that lag. Entidex tracks the correction timeline per engine so you can see when each one updates.