Fourteen terms that define how Salienci measures brand position inside AI answer engines.
This glossary defines the fourteen terms Salienci uses to describe how AI answer engines access, extract, and select brands, from foundational concepts like Recommendation Authority and AEO+ to diagnostic terms like the K-Matrix and Salienci Score. Each entry links to the related concepts it depends on.
The strength of a brand's ability to be understood, trusted, and selected by AI answer engines.
Recommendation Authority is the composite signal an AI system uses to decide whether to include a brand in an answer. It combines access, extraction quality, entity clarity, proof, and category authority.
Salienci's full-stack framework: Access, Extraction, Selection.
AEO+ connects the three layers most visibility tools measure in isolation: GEO for access, AEO for extraction, and EVS for selection. It is the full path from crawl to citation to recommendation.
Whether AI systems can crawl, retrieve, and process your content.
The infrastructure layer. Covers crawlability, robots directives, sitemaps, llms.txt, brand-facts, schema, canonicalization, and indexability by AI crawlers.
Whether AI systems can extract a clear, structured, citable answer from your content.
Covers answer-ready copy, semantic structure, entity references, FAQs, service definitions, and structured facts that let an AI produce a confident, sourced response.
When multiple brands qualify, whether the AI chooses yours.
Covers the authority signals that break ties in AI recommendations: citation quality, proof, category relevance, expertise indicators, and competitive positioning.
The compounding trust surface that lifts a brand into steady AI recommendation.
The layer above pure selection, where sustained citation velocity, entity consistency, and third-party proof create a defensible recommendation position over time.
Salienci's diagnostic framework for mapping where authority is gained or lost.
The K-Matrix visualizes signal strength across access, extraction, and selection. It identifies which specific gaps are limiting Recommendation Authority. Internal weights and formulas are proprietary.
A 0–100 benchmark of how likely AI systems are to understand, trust, and recommend your brand.
The Salienci Score is the outcome of the diagnostic, not the formula behind it. Scores below 60 indicate material recommendation risk. Scores above 80 indicate the compounding zone.
The rate at which a brand accumulates net-new AI-surface citations.
A leading indicator of selection strength. Sustained positive velocity signals that AI systems are converging on the brand as a reliable source in a category.
The likelihood that an AI engine chooses your brand when multiple candidates qualify.
A modeled estimate based on category, authority signals, and competitor position. Selection Probability is what determines whether a technically visible brand actually gets recommended.
The distance between your Recommendation Authority and the current AI default in your category.
AI answer engines converge on incumbents. Competitor Gap quantifies how far a challenger sits from the current AI default across access, extraction, and selection signals.
Content structured so an AI can extract a self-contained, factual answer without ambiguity.
Answer-ready content includes explicit questions, short extractable blocks, entity references, and structured facts. It is the practical form Extraction takes on a page.
How unambiguously an AI can identify your brand, offering, people, and places.
Consistent naming, schema, brand-facts, and cross-source alignment reduce entity ambiguity and improve every downstream selection signal.
A system that answers user questions directly instead of returning a list of links.
Includes ChatGPT, Perplexity, Claude, Gemini, Copilot, and integrated AI answers inside search products. These are the environments where Recommendation Authority is contested.
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