Proof/AI visibility analysis

What answer engines say about you, and why.

A full analysis for Northlane, a fictional B2B SaaS company, across a frozen 40-prompt set.

The short answer

An AI visibility analysis measures how often a brand is cited across a fixed prompt set on each major answer engine, diagnoses why the gaps exist, and returns a prioritized fix list tied to the prompt classes each fix should move.

  • The prompt set is frozen at baseline so progress cannot be manufactured by swapping prompts
  • Results are reported per engine and per prompt class, not as one vanity score
  • Every gap names the mechanism, not just the symptom
  • Fixes are sequenced and mapped to the prompt class they should move

Example data. Northlane is a fictional B2B SaaS company we use to show the exact format of our deliverables. Nothing on this page is a client result.

Step one

Freeze the prompt set.

Forty prompts across five intent classes, written down and locked before any measurement happens.

Prompt set

Example account: Northlane

Intent classExample promptCount
Problemhow do compliance teams reduce manual review time8 prompts
Solutionwhat is compliance workflow automation8 prompts
Categorybest compliance workflow automation tools8 prompts
Comparisonalternatives to the incumbent case management tool8 prompts
Qualificationcompliance automation that keeps an audit trail for regulated firms8 prompts

Step two

Measure citations, engine by engine.

Citation results, baseline

Prompt classEngine AEngine BEngine CEngine D
Category prompts2 of 81 of 80 of 83 of 8
Comparison prompts1 of 80 of 80 of 81 of 8
Solution prompts4 of 83 of 82 of 85 of 8
Problem prompts1 of 81 of 80 of 82 of 8
Qualification prompts3 of 82 of 81 of 84 of 8

Cited means the brand is named in the answer. Being linked in a source list without a mention is tracked separately, because it converts at a far lower rate.

Step three

Diagnose the gaps.

Gap diagnosis

GapMechanismSeverity
No comparison or alternatives pages existComparison prompts are answered almost entirely from third-party review sites, so the brand is absent from the shortlist stage.Critical
Category description is inconsistent across sourcesThe site, the review profiles, and the press coverage each describe the product differently, so engines have no stable entity to cite.Critical
No answer-first definitions on money pagesPages open with context rather than a quotable definition, which is what engines lift.High
Thin structured dataNo FAQPage, no Product schema, no llms.txt, so machine readers work harder than they need to.High
Regulated-firm qualification is buriedThe strongest differentiator appears only in a PDF, not on an indexable page.Medium
No original dataNothing on the domain is worth citing on its own, which caps ceiling regardless of on-page work.Medium

Step four

The prioritized fix list.

Fix list

RankFixTimingPrompt class it should move
1Publish four alternatives and comparison pagesWeeks 1 to 4Comparison prompt citations
2Normalize the category description everywhere the brand appearsWeeks 1 to 3Entity consistency and category prompts
3Rewrite the top six pages answer-first with definitionsWeeks 2 to 6Solution prompt citations
4Add FAQPage, Product schema, and llms.txtWeek 2Machine readability across all prompt classes
5Move the regulated-firm qualification onto an indexable pageWeek 5Qualification prompt citations
6Ship one original data assetWeeks 6 to 12Third-party citations and ceiling

The benchmark data behind the severity ratings comes from our own study of 160 software websites, published in full on the AI Discovery Technical Hygiene Benchmark. The channel detail sits on our AEO service page and GEO service page.

Keep reading the operating system

Every artifact lives in the proof library. The engagement itself is documented in our methodology, the commercial terms are on pricing, and the fastest paid entry point is the Growth System Diagnostic.

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