Your brand is already being mentioned inside AI answers. The question is whether decision-makers are hearing the right version of it.
ChatGPT, Gemini, Perplexity and AI Overviews do not simply mention brands — they interpret, compare and characterise them. Tone, factual accuracy and framing are assembled in the moment an answer is generated, outside the channels most marketing teams monitor today.
Your analytics can show whether someone visited your site. They cannot show what an AI system told that person first — or why the visit never happened.
What can go wrong inside an AI answer
Traditional reputation monitoring was built around visible communication — news coverage, reviews, social posts. AI answers work differently: they synthesise brand descriptions from model knowledge, retrieved sources and the wording of a question, at inference time. The result can be technically plausible and still strategically wrong — too generic, wrongly framed, or compared against the wrong peer group. There is usually no comment field or publisher to correct it, and small distortions compound across models, prompts and time into something a single spot-check never catches.
AI Reputation Risk
The aggregate exposure a brand carries from how AI systems describe, compare and recommend it — the umbrella that the three failure modes below roll up into.
Read the definition →Hallucination & Brand Risk
A fluent, specific, entirely wrong statement about your pricing, features or fit — sounding authoritative enough that users rarely check it.
Read the definition →Negated Mentions
Being named only to be ruled out — "less appropriate for smaller companies," "often considered expensive." Mention counts record this as visibility; it's closer to rejection.
Read the definition →Phantom URLs
A plausible-looking citation to a page, report or pricing document that doesn't exist — a factual, reputational and UX failure in one.
Read the definition →Measuring more than positive or negative
Classic sentiment analysis compresses language onto one scale — positive, neutral, negative. That's not enough for AI answers, which are often cautious, conditional and multi-perspectival: a brand can be described positively while framed as unsuitable, or mentioned prominently while weakly connected to the category that matters. A useful model has to separate these dimensions rather than force them into a single polarity score.
AI Answer Sentiment
The immediate evaluative tone around a mention — trusted, established, expensive, risky — that sets the interpretive frame for everything that follows.
Read the definition →Multi-Axis Sentiment in GEO
Why a single polarity score hides what matters: tone, confidence and commercial framing can all point in different directions on the same mention.
Read the definition →Sentiment Drift
How the characterisation moves over time as models update — a category leader quietly reframed as a traditional or niche option, without one dramatic event.
Read the definition →Semantic Anchoring
Whether a brand is embedded in the model's own representation of a category, or only surfaces because one page was retrieved — durable presence vs. fragile exposure.
Read the definition →Brand Voice Match
Whether the AI-generated description still sounds like the brand, or reduces a differentiated proposition to generic category language.
Read the definition →From isolated tests to continuous control
A handful of manually tested prompts can surface examples — they can't establish whether those examples are representative. Reputation governance needs repeatable observation across models, buyer situations, prompt phrasing, competitors and time. Two instruments cover that: a one-time baseline, and continuous tracking.
AI Visibility Audit
Where a brand appears, how it's characterised, which sources support the answers, and where the material gaps and risks sit — a prioritised evidence base, not a snapshot of examples.
Read the definition →LLM Brand Tracking
Monitoring visibility, sentiment, accuracy and competitive framing as they change — so a hallucination, a drift or a lost semantic connection surfaces early enough to act on.
Read the definition →From the Insights series
Two essays go deeper on why the existing analytics stack can't see this layer, and what to track instead.
Why Your Analytics Stack Is Measuring the Wrong Layer
Rankings and clicks show where a brand appeared and whether a user arrived — not what the user was told first, or which alternatives were recommended instead.
Read the essay →How to Measure What You Can't See
GEO metrics for the B2B practitioner: what to track when buyers research through ChatGPT, Perplexity and Gemini before ever reaching your domain.
Read the essay →Visibility without interpretation is not enough
A brand can achieve high mention frequency and still be misunderstood — cited often but recommended rarely, prominent but associated with the wrong audience, positively worded but excluded from the final recommendation. The relevant unit of analysis isn't the mention. It's the complete representation: whether the brand appears, where, how it's framed, which claims are made, how confident the answer sounds, which competitors surround it, and what the user is implicitly encouraged to do next. That's the difference between AI visibility measurement and AI reputation intelligence.
Find out how AI systems currently describe your brand
An AI Visibility Audit establishes where your brand appears, what's being said, and which risks need attention — a structured baseline across tone, accuracy, recommendations, competitive framing and semantic positioning.
Request an AI Visibility Audit →