How We Measure and Take Back Control of Your Presence in AI Answers

Most brands know exactly where they rank on Google. Almost none know what ChatGPT, Claude, Gemini or Perplexity currently tell a buyer who asks about them, their category, or their nearest competitor. That gap is what this method closes.

Step 1 — 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, critically, the sources it cites. This produces a first, factual read of how the brand is currently described, before any intervention.

AI engines compile history rather than reading the news. An outdated controversy, well answered years ago in the press, can still shape today's answer because the model weighs the accumulated record, not the latest statement. Mapping surfaces that lag directly.

Step 2 — Compare

The same prompts are run against the brand's named and unnamed competitors. This is where the useful finding usually sits: not "what does AI say about us," but "why does it recommend someone else instead." Comparing reveals which sources carry authority in the category, where the brand is absent from the conversation entirely, and where a competitor's presence is thinner than assumed.

Step 3 — Act, inside the mission

We don't treat this as a standalone deliverable. Findings from the AI-visibility read feed directly into the same operating picture we build from internal systems, communications and interviews. External perception is one signal among several, useful mainly where it disagrees with what leadership believes internally, or with what has actually changed operationally. The tool behind this is our own AI monitoring platform; the output is a set of prioritized moves inside the broader engagement, not a report that sits on its own.

What this tends to surface

The scenarios below are illustrative 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 AI answers about fees and eligibility often lean on outdated public sources, well after the actual offering has changed, so a buyer's first impression is built on terms the firm no longer offers.

Example engagement · Insurance (P&C / health). A handful of old reviews or unresolved disputes can dominate how AI describes a carrier's claims and service experience, disproportionate to their share of the actual customer base.

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 underlying metrics, leaving a maison's actual service quality understated in the answers buyers see first.

Where this fits

We don't sell AI visibility as a product on its own. It's one of the signals we fold into our public-data and operating-intelligence missions, alongside internal systems and interviews, because the finding that matters most is usually the disagreement between what the market believes and what's actually true inside the organization.