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Most people have never seen what an AI Visibility Audit contains. This is a structured preview of an independent audit: a seven-day, four-engine analysis of how AI describes a well-known global-hiring platform, anonymized here as Company X. It was run on a real company, using only public information, to demonstrate the methodology. It is not a client engagement.

Independent sample · Not a client case study

Not commissioned, reviewed, or endorsed by the company analyzed. No internal data used. Directional, point-in-time evidence. Google AI and Copilot captures were reviewed separately as supporting public-interface evidence and excluded from the automated score.

The headline read

AI Visibility Score

8.0/ 10

Weighted composite. A directional expert judgment, not a calculated metric.

4 ENGINES · 20 PROMPTS · 480 STABILIZED CELLS
Retrieval Visibility8.5
Trust & Authority7.0
Narrative Control7.0

Highly visible across all four engines, but “visible” and “recommended first” are not the same thing.

What the audit found

40%

Present almost everywhere, first only four times in ten

Named in 92% of discovery prompts and in the top three 81% of the time, but the first provider mentioned in only 40% of answers.

54%

A concentrated, testable discovery gap

When buyers ask a bread-and-butter question — how does a small business hire a developer abroad? — Company X appears in only 54% of AI answers, weakest in Perplexity and Claude. A specific, fixable gap in the conversations that drive deals.

Narrative persistence

A persistent legal/risk narrative at the decision stage

When buyers ask whether the company is safe to use, every engine blends old allegations, lawsuits, and complaints with settled facts, without saying which is which. The fix is making current, authoritative status easy for AI to find and cite — accuracy work, not spin.

Also observed: fewer than one in ten captured citations resolved to Company X's own site; the remainder included third-party and unresolved sources.

A sample of the roadmap

01Publish a canonical coverage matrix so engines stop conflating figures.
02Make existing trust assets more retrievable and citable, rather than build new ones.
03Maintain current, first-party content so decision-stage answers cite accurate status.
04Track unprompted first-mention and the discovery gap as the primary KPIs.

Independence disclosure

This is an independent sample analysis. It was not commissioned, reviewed, or endorsed by the company analyzed, and it is not a client case study. No internal company data was used. All results are directional, point-in-time evidence from a fixed public-prompt sample, not a measure of market share, customer preference, or business performance.

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