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Barkhausen AI
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Conventions

Measurement conventions

Requirements-level specifications for measuring visibility in AI assistants — what a valid measurement must satisfy, not how any one system is built.

R. Henrik Nilsson · CC BY 4.0

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IDTitleVersionStatusEffective
BA-C-1The Barkhausen Ladder1.0Stable2026
BA-C-2Visibility metrics1.0Stable2026
BA-C-3Sampling-protocol requirements1.0.1Stable2026
BA-C-4Minimum disclosure requirements for AI-visibility claims1.0Stable2026
BA-C-5Reporting checklist for AI-visibility studies1.0Stable2026
BA-C-6A taxonomy of AI-related crawlers1.0Stable2026
BA-C-7Terminology for AI-visibility measurement1.0Stable2026
BA-C-8A query-intent taxonomy for visibility measurement1.0Stable2026

In detail

BA-C-1v1.0 · Stable12 defined terms

The Barkhausen Ladder

The Barkhausen Ladder is a nine-level maturity model (BL-0 through BL-8) describing an entity's readiness to be found, cited, and acted upon by AI assistants and answer engines. Each level is defined by three things: what the level means, the observable criteria that place an entity at it, and why it matters. The levels run from basic search hygiene (BL-0) through rendering and crawler access, structured data, content form, distribution, measurement, algorithmic optimization, and corpus presence to agent-readiness (BL-8). The Ladder is diagnostic, not prescriptive: it states conditions an assessment can verify, not techniques to apply. Levels are cumulative, and an entity is placed at the highest level whose criteria, and all lower levels', it satisfies. This convention uses normative keywords (MUST, SHOULD, MAY), cross-references the metric and sampling conventions (BA-C-2, BA-C-3) that define how visibility itself is measured, and closes with an assessment checklist for each level.

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BA-C-2v1.0 · Stable5 defined terms

Visibility metrics

This convention defines the metrics a credible measurement of entity visibility in AI assistants must report, and the form each report must take. It specifies three primary metrics: Visibility Probability (VP), the probability that an entity is mentioned in an answer to a query drawn from a distribution of real user phrasings, on a given engine, time window, and region; Share of Voice (SoV), the entity's share of all brand mentions in the same answers; and Discovery Depth (DD), the degree of query constraint at which an entity first enters the recommendation set. It adds supporting measures for prominence, citation support, rank-aware list overlap, and sentiment; requires every estimate to carry a confidence interval, sample size, engine version, window, region, and a stated detection method; mandates boundary-valid intervals near zero and one and composition-aware intervals for SoV; and states what claiming conformance requires. Sampling procedure is deferred to BA-C-3.

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BA-C-3v1.0.1 · Stable7 defined terms

Sampling-protocol requirements

AI assistants return different answers to the same question from one request to the next, so any measurement of how visible a brand is inside those answers is a statistical estimate, not a reading. This convention states the requirements a sampling design must meet for its estimates to be credible. It summarizes the public evidence that a single observation is uninformative and derives the sample size a stated confidence interval requires. It requires interval methods that stay valid near zero and one, representation of each query as a distribution over real phrasings, partial pooling, observations spread across the stated window, engine-version and measurement-channel disclosure, controlled region and personalization, change-point monitoring for silent engine updates, multiplicity disclosure, and refusals recorded as availability observations. It formalizes the Barkhausen Criterion — the conditions under which a claimed visibility change counts as real — and closes with a reporting checklist.

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BA-C-4v1.0 · Stable1 defined term

Minimum disclosure requirements for AI-visibility claims

Claims about visibility in AI assistants circulate widely — in dashboards, case studies, vendor material, and research summaries — and most cannot be evaluated, because they omit the facts a reader needs to judge them. This convention states the minimum any published AI-visibility claim must disclose: the entity and information need; the point estimate with a confidence interval and interval method; the sample size; the engine with interface and version; the measurement channel; the time window; the region; the phrasing representation; refusal handling; and the mention-detection method. A claim that omits an applicable item is not a measurement under this convention. Claims that visibility changed must additionally satisfy the Barkhausen Criterion defined in BA-C-3. The requirements are deliberately minimal: they demand no raw data, no query lists, and no tooling disclosure, so that any measurer — commercial or academic — can comply.

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BA-C-5v1.0 · Stable1 defined term

Reporting checklist for AI-visibility studies

AI-visibility findings are published as full study reports — a research paper, a public-data census, a vendor white paper — and a reader must be able to assess the whole study, not only its individual figures. This convention is a reporting checklist, in the tradition of the CONSORT and PRISMA guidelines, that an AI-visibility study report satisfies to claim methodological completeness. It organizes twenty-six items by report section: title and abstract, introduction, methods (queries, engines and channels, sampling, detection, analysis), results, limitations, and availability. Each item is a MUST or SHOULD requirement traced to the convention it derives from. The checklist composes rather than replaces the series — figure-level metric reporting (BA-C-2), sampling-design disclosure (BA-C-3), and single-claim disclosure (BA-C-4) — and requires no raw data, query lists, or tooling. Conformance is claimed per report, with partial conformance stated as named exceptions.

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BA-C-6v1.0 · Stable5 defined terms

A taxonomy of AI-related crawlers

"AI crawler" names a function, not a piece of software, and it conflates several. A single site is visited by crawlers that collect content for model training, that build retrieval indexes AI answers draw from, that fetch one page in real time to answer a specific prompt, that act autonomously on a user's behalf, and that maintain traditional search indexes AI features also consume. Each function has different consequences for a site, and a robots.txt rule aimed at one silently binds or misses the others. This convention defines five functional classes — training, retrieval, user-fetch, agent, and search — from vendor documentation and public observation, states how a published crawler registry or census must classify by function rather than operator, and sets out how robots.txt semantics interact with each class. It records where the scheme's boundaries are unstable, including the agent class, which user-agent strings systematically undercount.

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BA-C-7v1.0 · Stable11 defined terms

Terminology for AI-visibility measurement

Measurements of AI visibility are comparable only when the words that name them are stable: two studies that both report "visibility" compare nothing if one counts entity mentions and the other counts source links. This convention fixes the field-level vocabulary the rest of the series relies on and that no other convention owns — AI visibility, the phenomenon; answer engine and AI assistant, the systems; generative engine optimization, the practice; retrieval visibility versus parametric, closed-book visibility, two causal paths; mention and citation, two distinct observables; information need, query, and phrasing, three levels of a question; and recommendation set. It states why one canonical term per concept is a precondition for comparability, gives usage rules including first-use linking and a deprecated-synonym table, and defines conformance as using the canonical terms or explicitly mapping one's own. It claims authority over usage within this series only, not over the field's language.

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BA-C-8v1.0 · Stable2 defined terms

A query-intent taxonomy for visibility measurement

Answer engines respond differently to different kinds of question, and an entity's visibility on one kind does not carry to another. This convention classifies the query space a credible visibility study must cover into five intent classes — navigational, informational, comparative, constraint-based, and recommendation-seeking — defined by the goal a user's information need expresses, and states what a study must disclose about its coverage. Each class behaves differently under measurement: navigational visibility is near-degenerate, informational mentions are incidental, comparative needs are Share of Voice's natural home, constraint-based needs are where Discovery Depth applies, and recommendation-seeking needs yield the least stable and most commercially consequential recommendation sets. A study must disclose which classes it covers and in what proportion, must not generalize a class-specific result across classes, and should report per-class estimates where classes are mixed — all at the level of classes, never the concrete queries.

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