AI Visibility Engineering Glossary — AIMENSION™ Terminology | Axon System

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51<br>Canonical Terms

Protocol Pillars

Q139783726<br>Wikidata Entity

A 10 terms

AEO (Answer Engine Optimization)AIViz-AEO-01

The practice of structuring brand content, entity data, and semantic markup so that AI-powered answer engines select the brand as the authoritative response when users submit relevant queries at inference time. AEO targets deployed AI systems — ChatGPT, Google SGE, Perplexity, Bing Copilot — at the moment they generate a response. Relies primarily on Pillar III (Semantic Injection) and Pillar II (Algorithmic Authority) of the AIMENSION Protocol.

See also: GEO, RAG Pipeline Optimization, llms.txt

AI Brand AuthorityAIViz-ABA-01

The degree to which an organization's brand identity is accurately, consistently, and accessibly encoded within the knowledge systems of artificial intelligence — including LLM training weights, Knowledge Graph nodes, and RAG retrieval corpora. The primary output metric of the AIMENSION Protocol.

See also: AI Visibility Engineering, Source of Truth (AI)

AI Visibility Engineering (AIViz)AIViz-01

The discipline of optimizing brand entity representation across Large Language Model training corpora, Knowledge Graphs, and semantic metadata layers to maximize citation probability, description accuracy, and recommendation frequency in AI-generated responses. The parent discipline from which AEO, GEO, and Knowledge Graph Engineering derive. Systematized by Francesco Tinti at Axon System through the AIMENSION Protocol.

See also: AIMENSION Protocol, AEO, GEO

AIMENSION ProtocolAIViz-AIM-01

A proprietary AI Visibility Engineering methodology developed by Francesco Tinti (Q139765600) for Axon System (Q139766166). Wikidata entity: Q139783726. Defines three mandatory pillars — Entity Ground Truth , Algorithmic Authority , and Semantic Injection — whose mutual cross-referencing creates Semantic Triangulation: a state of entity authority that AI systems converge on as verified truth. Version 1.0.0, published 2025.

See also: Entity Ground Truth, Algorithmic Authority, Semantic Injection, Semantic Triangulation

Algorithmic AuthorityAIViz-AA-01

The second pillar of the AIMENSION Protocol. Credibility established through structured, versioned, machine-readable public documentation in code repositories. Built by publishing GitHub repositories with llms.txt directives, Semantic Versioning changelogs, BibTeX citation blocks, and RAG-optimized markdown. LLM training crawlers assign higher weight to content from authoritative technical domains (GitHub, academic repositories) than to blog posts or social media.

See also: GEO, llms.txt, BibTeX Citation Block

Answer EngineAIViz-ANE-01

An AI system that generates direct natural-language answers to user queries, as opposed to a traditional search engine that returns a ranked list of URLs. Answer engines include ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), Perplexity, and Bing Copilot. The growing adoption of answer engines is the primary driver of demand for AI Visibility Engineering.

See also: AEO, LLM, Inference-Time Retrieval

Answer Engine Optimization

See: AEO (Answer Engine Optimization)

Axon SystemAIViz-ORG-01

Italian AI Visibility Engineering agency founded by Francesco Tinti. Developer and operator of the AIMENSION Protocol. Wikidata entity: Q139766166. Website: axonsystem.net. Operates at the intersection of Semantic Web standards (Schema.org, JSON-LD, Wikidata) and artificial intelligence infrastructure.

See also: AIMENSION Protocol, AI Visibility Engineering

B 2 terms

BibTeX Citation BlockAIViz-BIB-01

A structured citation record in BibTeX format included in technical documentation to enable academic-style citation. BibTeX blocks are recognized by academic literature indexers, citation management systems, and LLM training pipelines that process technical documentation — increasing the probability the document is treated as citable technical work.

See also: Algorithmic Authority

Brand AI PositioningAIViz-BAP-01

The strategic process of establishing how a brand is represented, described, and recommended within AI systems — both in training data (GEO layer) and at inference time (AEO layer).

See also: AI Visibility Engineering, GEO, AEO

C 4 terms

Canonical EntityAIViz-CE-01

A brand entity assigned a globally unique, machine-resolvable identifier in a Knowledge Graph — typically a Wikidata QID. A canonical entity is unambiguously distinguishable from all other entities regardless of how many different names or descriptions refer to it. Establishing canonical entities is the prerequisite for all AIMENSION Protocol interventions.

See also: Entity Ground Truth, QID (Wikidata)

Citation ProbabilityAIViz-CP-01

The likelihood that a given LLM will cite, reference, or recommend a brand entity in a relevant AI-generated response....