Observed perception
The relevant answers, recurring patterns, disagreement between systems, and material blind spots.
AI Perception Audit
A focused diagnosis of whether AI chatbots find, understand, trust, recommend, or overlook a company — and why.
A compact point of entry
The audit tests a defined set of questions across multiple chatbots, compares their answers, checks the visible basis for those answers, and separates weak perception from weak reality.
What gets examined
What the company does, whom it serves, and which category it belongs to.
Which alternatives appear, what differentiates the company, and where it is left out.
The evidence, uncertainty, objections, and complaints that shape the answer.
Where answers rely on outdated, incomplete, contradictory, or ambiguous public information.
The deliverable
The relevant answers, recurring patterns, disagreement between systems, and material blind spots.
Documented examples, sources where available, and graphics where they make the findings easier to understand.
A reasoned sequence of possible actions, with uncertainty and open questions kept visible.
Two response levers
01
If the answers reveal credible product, service, or operating weaknesses, better communication is not the first fix. The underlying issue needs attention.
02
If the issue is outdated, missing, or ambiguous information, the public record can be strengthened with clear, authoritative, and verifiable material.
What comes after
Implementation can stay with the company. If the findings point to a larger strategic or operational problem, Consulting is the next route.
Explore ConsultingHey Lucy, scope an AI Perception Audit.
Company, website, market, and the decision this audit should support are enough to begin.Start with a question
Lucy can collect the essential context, clarify what is in scope, and route the request.
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