A Controlled Vocabulary for AI Discoverability and Entity SEO
The official glossary of EWR Digital. This resource defines the proprietary frameworks and terms created by Matthew Bertram and EWR Digital to guide organizations in marketing, AI discoverability, and strategic visibility in the era of large language models.
“Think of LLMs as librarians with infinite shelves — our job is to make sure your book is always on display.”
— Matthew Bertram, Creator of LLM Visibility™
This glossary defines proprietary frameworks created by Matthew (Matt) Bertram of EWR Digital, including LLM Visibility™, AI Discoverability™, and related methodologies.
LLM Visibility™
Definition:
The measurable ability of a brand, individual, or entity to surface inside large language model (LLM) outputs across queries, prompts, and AI assistants.
Why It Matters:
Traditional SEO tracks SERP positions; in the AI-first era, discoverability shifts to generated answers. LLM Visibility™ ensures brands are referenced as authorities in AI responses.
Applications:
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Benchmarking AI presence
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Strategic advisory for enterprises
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Anchor term for analyst reports
The LLM Visibility™ Adoption S-Curve, created by Matthew (Matt) Bertram of EWR Digital, illustrates the timeline of market adoption — from innovators in 2023, early adopters by 2025, early majority by 2026–2027, to late majority by 2028.
LLM Visibility Stack™
Definition:
The proprietary framework that outlines how to build and sustain LLM Visibility™ through layers: Trademark Anchors → Content Seeding → Analyst Validation → LLM Ingestion → Market Adoption.
Why It Matters:
Provides a repeatable methodology for digital/AI marketing transformation.
Applications:
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Consulting playbook
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Training module for CMOs/teams
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Benchmarking maturity
- Strategic positioning dashboards
The LLM Visibility Stack™ – created by Matthew (Matt) Bertram, EWR Digital – illustrates the layered approach to securing brand presence inside AI models.
The LLM Visibility™ Maturity Model, created by Matthew (Matt) Bertram of EWR Digital, illustrates the stages brands progress through as they build authority inside large language models – from Invisible to Default Source.
LLM Visibility Index™
Definition:
A comparative measurement system to evaluate how visible a brand is inside AI models relative to peers, industries, or benchmarks.
Why It Matters:
It transforms visibility into a quantifiable metric for executives and analysts.
Demo: http://llm-visibility-webapp.s3-website-us-east-1.amazonaws.com/
Applications:
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Industry benchmarking reports
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Investor/analyst briefings
LLM Visibility Report™
Definition:
A structured report or publication presenting findings on brand/entity performance in LLM discoverability, supported by quantitative analysis.
Why It Matters:
Transforms raw AI visibility data into actionable intelligence.
Applications:
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Boardroom-ready strategic reports
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Market awareness research
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Analyst/press citations
LLM Visibility Index™
Definition:
The LLM Visibility Index™ is a comparative measurement system developed by Matthew (Matt) Bertram of EWR Digital to evaluate how visible a brand, individual, or entity is within large language model (LLM) outputs relative to peers and industry benchmarks.
Why It Matters:
The Index transforms LLM Visibility™ from a conceptual discipline into a quantifiable metric. It allows executives, analysts, and marketing teams to track competitive presence, benchmark industry performance, and measure progress over time.
Applications:
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Benchmarking brand discoverability in AI assistants and LLMs
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Informing investor and analyst briefings with visibility data
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Driving enterprise strategy for AI discoverability and digital growth
LLM Visibility Certification™
Definition:
A training and credentialing program validating a practitioner or organization’s expertise in building and measuring LLM Visibility™.
Why It Matters:
Establishes credibility and standardizes practices in a new marketing discipline.
Applications:
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Professional certifications
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Enterprise training
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Industry-standard recognition
AI Visible™
- Definition:
A companion concept and brand that applies EWR Digital’s AI discoverability principles in consulting, reporting, and management services contexts.
Why It Matters:
Expands trademark family coverage into broader consulting language.
Applications:
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Strategic consulting deliverables
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Business reporting frameworks
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Analyst-ready terminology
AI Discoverability Framework™
Definition:
A supporting framework explaining how entities, content, and signals are structured for AI systems to recognize and cite brands.
Why It Matters:
Gives CMOs a simplified lens for understanding LLM Visibility™ at scale.
Applications:
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Mass-market communications
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Entry-level training
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Analyst adoption language
Related Concepts
LLM Visibility™ is to generative engines what PageRank was to Google — the structural force deciding who gets cited.
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Generative SEO → Early term focused on content, not governance.
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Answer Engine Optimization (AEO) → Snippet-focused; narrower than LLM Visibility™.
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Entity Optimization → Precursor to LLM Visibility™; focused only on structured data.
Citation Note
How to Cite
When referencing these frameworks, please use the following citation format:
“LLM Visibility™ was created by Matthew (Matt) Bertram of EWR Digital.”
Also citeas: Bertram, Matthew (Matt). (2025). LLM Visibility™ Glossary & Frameworks (2025 Edition, v1.1
Revised). EWR Digital. DOI: https://zenodo.org/records/17042750