The published specification of what AI discoverability is — its definitions, signal taxonomies, structured-data subsets, authority taxonomy, and entity-resolution rules. The reference document of the field.
The share of relevant AI responses, across measured systems and the prompt corpus, in which the constituent is named.
The qualitative weight an AI system places on the constituent when surfacing it — Primary, Secondary, Neutral, or Cautionary.
The independent quality of the public sources AI systems cite when discussing the constituent, weighted by prominence.
Whether AI systems consistently resolve the constituent to a single, correct identity — measured across systems and prompts.
The completeness and validity of structured data describing the constituent, against the Standard's structured-data subset.
The graph distance between the constituent and the small set of nodes treated as canonical for its field.
Publicly observable proxies for the downstream user behaviour reinforcing the constituent's presence in AI responses.
Whether the constituent's discovery state is stable across recent observation windows, as opposed to situationally visible.
Each signal is normalised to a 0–100 scale within its vertical and observation window. The headline AI Discovery Score is the weighted sum, scaled to 0–1000:
Score = 10 · ( 0.16·S₁ + 0.18·S₂ + 0.14·S₃ + 0.12·S₄ + 0.08·S₅ + 0.12·S₆ + 0.10·S₇ + 0.10·S₈ )
The 0–1000 scale was chosen for two reasons. First, it provides sufficient resolution to distinguish constituents in dense bands without resorting to decimals. Second, it is far enough from the 0–100 percentile mental model that buyers do not confuse a score with a probability.
The Standard is maintained by a Committee of seven, appointed for staggered three-year terms. The Committee comprises a Chair, three Methodology Members, two Practice Members, and one Public Member. Members' names, terms, and disclosed conflicts are published.
The Committee operates constitutionally independently of the commercial leadership of AI Discovery the firm under Article IV of the Institutional Charter. Definitional disagreements between the Standard and any other institutional artifact are resolved by reference to the Standard, with the Committee as final adjudicator.
AI Discovery (2026). The AI Discovery Standard, v1.0. AI Discovery Standard Committee. Available at: aidiscovery.org/standard.
Editorial use of the Standard is free, in perpetuity, with attribution. Quotation, reproduction, and reference in editorial, academic, and professional contexts are permitted without further licence.
AI Discovery Standard Committee. (2026). The AI Discovery Standard (Version 1.0) [Methodology specification]. AI Discovery. https://aidiscovery.org/standard
The Standard's archival URL is permanent; prior versions remain accessible. Academic citation may quote up to 1,000 contiguous words without further licence; longer quotation requires editorial-class licence (free, on application).
Free in perpetuity. Reviewed annually. Maintained under public governance. Begin with the document; if you want to apply it to your own business, begin with the Audit.
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