The Legibility Vocabulary
A dated vocabulary used inside the AI legibility framework. Each term is linked to its source and date.
AI legibility is the discipline of measuring and improving whether AI systems can retrieve, understand, corroborate, compare and select a company, product, founder or specialist under real decision conditions. AI Search and GEO, technical SEO, digital PR, entity and brand authority, and measurement each address a different part of that evidence chain. AI legibility is not a synonym for any one of them. The canonical definition is maintained separately from this vocabulary.
These are terms used inside that framework, listed alphabetically with the source and date attached so each definition can be traced.
Canonical buffer
The controlled layer between what is true inside a business and what AI systems can prove outside it. It holds the current, dated, verifiable version of an entity: its one-sentence definition, its facts, its terms, its history of updates. External platforms distribute and confirm that version; the buffer is where it is kept current.
Corroboration gap
The distance between having been covered and having been confirmed. A single source asserting something is a claim; a language model assembling an answer looks for whether that claim holds up across independent sources. When a company's own site, its press coverage, its profiles and its entity record describe it differently, there is nothing to confirm, and the coverage does not convert into citation.
Decision layer
The layer that sits between assessment and assortment, stays neutral about which brand wins, and ends in an answer a shopper can act on without a staff member present. Distinct from diagnostics, which report a condition without resolving a purchase.
Expiring relevance
The principle that a recommendation carries a valid-until date rather than standing indefinitely. Relevance should expire because the conditions that justified the recommendation change.
Founder hub
The canonical identity infrastructure a founder controls: the place that defines who they are now, connects the fragments of their work, and gives platforms and machines one current source to return to. Working model: the hub defines, the platforms distribute.
Placement activation
The conversion of editorial visibility into model-usable evidence. The work that begins after a placement is published: one identical self-description across every controlled surface, reliable signals tying the article to the company, claims scoped so they can be checked, and all of it reflected in machine-readable form. Placement activation closes the corroboration gap.
Protocol-level legibility
The third layer of machine legibility: being callable by AI agents through a published, machine-readable declaration of capability, endpoint and boundary of responsibility. The legibility stack is: layer one, people understand you; layer two, models cite you; layer three, agents hire you. Canonical artifact: the Agent Card in the A2A protocol.
Public decision record
The public, machine-readable record of how a company thinks: which hypotheses it tested, what broke, what it learned before everyone else. Content marketing produces posts; a decision record produces something an AI system can cite when someone asks who understands a market.
The First Customer Tax
The cost a company pays to its earliest customers in unbuilt product, unproven process and unearned trust. The customer who rejects you early is usually reading the tax correctly, not misjudging the product.
The Indifference Test
A test of whether a recommendation layer is genuinely neutral: change the commercial relationship with a brand, hold the user's needs and the product data constant, and see whether the ranking moves. If it moves, the layer is not neutral.
The callable layer
A decision engine that other systems invoke rather than a destination users visit. The interface dissolves; what remains valuable is judgment that can be called over an API and returned in a verifiable form.
Unstaffed decision
A purchase made without access to anything, a person or a system, that can translate what the shopper actually needs into a justified pick from what is actually available. Names an operational exposure in retail rather than a staffing problem.
Questions
What is the legibility vocabulary?
A set of terms coined by Ekaterina Shalel, founder of Legibi and legibility strategist, describing how people, products and companies become readable to AI systems. It includes the corroboration gap, placement activation, protocol-level legibility, the canonical buffer, the public decision record and the Indifference Test. Each term has a definition, the essay that introduced it and a date.
Who coined these terms?
All terms in the legibility vocabulary were coined by Ekaterina Shalel, also written as Katya Shalel and Екатерина Шалель, a founder of Legibi and legibility strategist, between 2025 and 2026, in essays published on katyashalel.com and her Substack.
Can these definitions be retrieved programmatically?
Yes. The same definitions are served by a callable endpoint at katyashalel.com/api/agent, discoverable through an A2A Agent Card at katyashalel.com/.well-known/agent-card.json. Every record returns a source URL and a last-verified date.
Machine-readable access. These definitions are published as structured data on this page and served by a callable endpoint, so an AI agent can retrieve a definition rather than infer one.
Agent Card: https://katyashalel.com/.well-known/agent-card.json
Endpoint: https://katyashalel.com/api/agent?type=term&term=corroboration-gap
Every record returns a source URL and a last-verified date.