When buyers ask AI about you, what does it say?
A growing share of your category's first impressions are now formed by a model, not a person. We read what it says, and fold it into the broader picture of how your organization is actually perceived and run.
Buyers increasingly form a first opinion by asking ChatGPT, Claude, Gemini or Perplexity, not by visiting your website. That answer arrives before any salesperson, retailer or press release is involved.
AI engines compile history. An outdated controversy, well answered years ago in the press, can persist in AI answers because the model weighs the accumulated record, not your latest statement.
Most brands know exactly where they rank on Google. Almost none know what an AI assistant currently tells a buyer who asks about them, their category, or their competitors.
GEO is one of the signals we fold into our public-data and operating-intelligence missions. We don't sell it as a standalone service.
A brand's external perception rarely tells the whole story on its own. We read it alongside internal systems, communications and interviews, because the most useful finding is usually where the two disagree: where the market's read on you has drifted from what leadership believes, or from what changed operationally months ago.
How we measure and take back control of your presence in AI answers.
The read comes from our AI monitoring platform, run against your category and your named competitors, not from a single spot-check.
Map
We run a structured set of buyer-relevant prompts across the major AI answer engines, and record what each one says: the position taken, the sentiment, and the sources it cites.
Compare
We situate the brand against its named and unnamed competitors inside those same answers, so you see not just what AI says about you, but why it recommends someone else instead.
Act, inside the mission
Findings feed directly into the same operating picture we build from systems and interviews. External perception is one signal among several, not a separate deliverable to manage on its own.
The scenarios below are examples of the kind of gap this work tends to surface, not verified client results, and no figures are implied.
Example engagement · Finance
Private banking & asset management
AI answers about fees and eligibility often lean on outdated public sources, well after the actual offering has changed.
Example engagement · Insurance
P&C and health
A handful of old reviews or unresolved disputes can dominate how AI describes a carrier's claims and service experience.
Example engagement · Luxury
Maison, after-sales service
Real operational improvement in after-sales care often takes far longer to show up in AI narratives than in the metrics themselves.