Digital Readability
Digital readability shifts the question from mere visibility to the actual quality of understanding an organization achieves across its digital presence.
OpenGlossary
Glossary
Public, transitional and doctrinal lexical system to prevent terminological confusion and frame the market.
Machine-First
Machine-first means that the reading structure comes before the styling, not that humans come after machines.
OpenInterpretive Governance
Interpretive governance aims to make an organization less dependent on the randomness of reading across search engines, AI and human readers.
OpenBrand Disambiguation
Brand disambiguation reduces competing or blurred readings of a company, a person or a product.
OpenDigital Readability Audit
The digital readability audit helps locate where the public reading of an organization weakens before committing to a heavier intervention.
OpenMachine Reading
Machine reading allows us to discuss the various non-human readers without reducing the subject to a single engine or a single tool.
OpenCorpus
The corpus is not a stockpile of pages but the body of material that allows an organization to be publicly understood.
OpenInspectable Proof
Inspectable proof goes beyond assertion and provides a concrete anchor point for understanding what is actually done.
OpenAI perception drift
AI perception drift names a phenomenon to measure when AI systems no longer reconstruct an organisation correctly.
OpenInterpretive variability
Interpretive variability helps separate simple wording variation from meaningful category, proof or recommendability displacement.
OpenLLM perception drift
LLM perception drift names a phenomenon to measure when AI systems no longer reconstruct an organisation correctly.
OpenDelivery-layer fixation
Delivery-layer fixation explains why a repeated AI answer may reflect an application layer rather than native model stability.
OpenAI perception stability
AI perception stability names a phenomenon to measure when AI systems no longer reconstruct an organisation correctly.
OpenStochastic fixation
Stochastic fixation reminds us that a repeated answer may be a frozen probabilistic sample, not canonical truth.
OpenAI perception baseline
AI perception baseline names a phenomenon to measure when AI systems no longer reconstruct an organisation correctly.
OpenCategory drift
Category drift names a phenomenon to measure when AI systems no longer reconstruct an organisation correctly.
OpenRecommendability drift
Recommendability drift names a phenomenon to measure when AI systems no longer reconstruct an organisation correctly.
OpenCanonical Surface
A canonical surface helps concentrate useful reading and prevents a rich corpus from being summarized from the wrong page.
OpenAttested interpretive unit
An AIU states which exact unit was published, with which scope, and which transformations remain forbidden.
OpenAuthority scope
Authority scope prevents an official source from being treated as the general arbiter of everything said about itself.
OpenIntegrity attestation
Integrity attestation verifies a canonical unit. It proves neither truth, reputation, nor summary fidelity.
OpenInterpretive weighting
Interpretive weighting avoids two excesses: letting an external source distort official identity, or letting an official source erase qualified criticism.
OpenActivatable capability
Service, resource, or expertise that may be mobilized conditionally inside a SAL chain.
OpenApplicability condition
Positive criterion indicating that a capability may be considered in a specific situation.
OpenApplicability fidelity
Measured ability of an agent to preserve conditionality, evidence, and exclusions when mobilizing a capability.
OpenForbidden applicability inference
Inference an agent must not draw from a relationship among situation, need, and capability.
OpenNon-applicability condition
Discriminating scenario where a capability must not be mobilized despite apparent proximity.
OpenSituational applicability
Condition under which a capability becomes mobilizable in a given situation without becoming an automatic recommendation.
OpenEntity Graph
The entity graph helps make visible the relationships that structure an organization and that external readers cannot simply guess.
OpenContent Architecture
Content architecture distinguishes content that explains, qualifies, demonstrates and converts in order to build a readable corpus.
OpenGovernance Layer
The governance layer groups the discreet surfaces that help a site be discovered and understood more coherently.
OpenInterpretive Debt
Interpretive debt is the price you pay when approximations have taken root in the reading that machines make of your organization.
OpenInterpretive Capture
Interpretive capture occurs when a competitor dominates the understanding that AI systems have of your area of expertise.
OpenSemantic Compression
Semantic compression is the loss of critical nuances when AI systems summarize your offer in a few sentences.
OpenInterpretive Risk
Interpretive risk covers all the possible consequences when AI systems misunderstand who you are and what you do.
OpenInterpretive Hallucination
When AI systems lack reliable material about your organization, they fill the gaps by inventing, and what they invent becomes the official version.
OpenInterpretive Invisibilization
You exist online, but AI systems never mention you. This silence is often more damaging than an error. You simply do not exist for them.
OpenNeighbourhood Contamination
AI systems associate your brand with entities that frequently appear in the same context. If a competitor is more visible, their attributes contaminate your image.
OpenInterpretive Collision
When an AI merges two companies or two people in its responses, that is an interpretive collision, a boundary problem, not a content problem.
OpenInterpretive Inertia
You have evolved, but AI systems keep describing you with the words from three years ago. That is interpretive inertia, the slowness of machines to update their understanding.
OpenInterpretive Remanence
An old address, an old role, an old price: these details remain in AI responses long after they have disappeared from your site.
OpenInterpretive SEO
Interpretive SEO broadens search optimization to cover not only search engines but also LLMs and AI agents that reformulate your content.
OpenAI Disambiguation
AI disambiguation is the work that allows artificial intelligence systems to identify you without confusing you with another entity.
OpenInterpretive Smoothing
Interpretive smoothing is the tendency of AI systems to normalize your positioning by erasing what distinguishes you in favour of a generic description.
OpenInterpretive Trail
The interpretive trail is the persistence of old representations of your organization in AI responses, long after your changes.
OpenCanonical Fragility
Canonical fragility is the vulnerability that appears when AI systems cannot identify which version of your content is authoritative.
OpenStructural Visibility
Structural visibility is the ability to be correctly read by machines thanks to the technical architecture of your presence, not thanks to content volume.
OpenInterpretive Observability
Interpretive observability is the ability to measure what AI systems actually understand about your organization, not just how many visitors arrive.
OpenSemantic Calibration
Semantic calibration is the adjustment of digital signals so that the description produced by AI systems matches your reality.
OpenInterpretive Sustainability
Interpretive sustainability is the ability to maintain a digital presence correctly read by AI systems over the long term, not just at launch.
OpenEarly Machine Visibility
Early machine visibility is the strategic advantage gained by making your presence readable by AI before your competitors.
OpenExogenous Governance
Exogenous governance aims to reduce contradictions between third-party sources that talk about you and the reality you publish on your own surfaces.
OpenResponse Conditions
Response conditions define what an AI system can legitimately produce from your published content, and what should remain beyond its reach.
OpenAgentic
Agentic describes the ability of AI systems to compare, filter, recommend and act autonomously, without human intervention at every step.
OpenAgentic readiness
Agentic readiness measures whether a site can become a reliable action environment for agents, not only an indexable page.
OpenLighthouse Agentic Browsing
Lighthouse Agentic Browsing checks signals such as WebMCP, the accessibility tree, CLS and llms.txt without becoming an SEO score.
OpenWebMCP
WebMCP is a way to expose web capabilities as structured tools, with names, descriptions, parameters and limits.
OpenAgent-friendly form
An agent-friendly form exposes its role, fields, errors, CTAs and consequence explicitly.
Openllms.txt file
An llms.txt file can help systems find important resources, but it does not replace content, architecture or governance.
OpenState Decoupling
State decoupling occurs when your organization evolves but its representation in AI systems remains anchored in a previous version of your reality.
OpenAgentic navigability
Agentic navigability measures whether an AI agent can understand an interface, choose the right action and execute it without critical ambiguity.
OpenAccessibility tree
The accessibility tree acts as a functional map of the interface: it exposes the roles, names and states of important elements.
OpenAuthority Conflict
An authority conflict occurs when your own digital surfaces contradict each other, preventing AI systems from determining which version of your reality is correct.
OpenThe hub orients. The framing decides.
The hub serves to clarify the offer and orient the reading. When the problem becomes real, the diagnostic remains the right entry point to qualify the depth of the workstream.