Glossary

The terms, defined once and consistently.

Plain definitions for the vocabulary of buying decisions shaped by AI, each linking to the fuller reference.

AI Recommendations: AI recommendations are the vendor suggestions an AI gives when it answers a buying question. They are the result of a behind-the-scenes decision you usually never see, and they change as the buyer adds requirements.
AI Search Monitoring (AI citation tracking): AI search monitoring tracks how your brand appears in ChatGPT and Google AI over time: mentions, citations, and share of voice. It shows movement. It does not explain why the recommendation changed or whether your work caused it.
AI Search Optimization: AI search optimization is making a company easy to find and accurately described across AI-powered search. It is about being found across the different tools, not about surviving a buyer's requirements.
AI SEO: AI SEO adapts search optimization for AI-generated answers. It works on getting found. Commercial evaluation works on whether you are chosen once you are found.
AI Visibility: AI visibility is whether AI systems can find, mention, and describe your company. It is necessary but not sufficient: being visible is not the same as being recommended.
AI Visibility Tools (AEO and GEO tools): AI visibility tools track whether your brand appears in ChatGPT and Google AI answers. They report presence. They do not tell you why AI recommends a competitor over you, or which requirement removed you.
Answer Engine Optimization (AEO): Answer Engine Optimization (AEO) structures content so answer engines quote it directly. It improves whether you get quoted, which you need to be considered but is not the same as surviving a buyer's requirements.
Are We Showing Up in AI?: Buyers now ask ChatGPT and Google AI for recommendations. 'Are we showing up?' is the right first question and the wrong last one. Here is how to check, and what showing up does and does not get you.
ChatGPT Recommendations: ChatGPT recommendations are the companies ChatGPT names when asked what to consider. They are the result of a behind-the-scenes decision, and they change as the buyer's requirements change.
Client Zero: We ran the method on ourselves first. This website is the first thing we
Commercial Buying AI: Commercial buying AI refers to AI systems that help buyers size up and choose companies. It is how more of the buyer's decision now happens before they ever contact you.
Commercial evaluation: This is the commercial evaluation concept as it appears in the research graph.
Commercial Evaluation Intelligence: We're trying to name and build a field: the study of how companies and AI
Competitor displacement: Competitor displacement studies substitution inside the recommendation set:
Evidence Strategy: Evidence strategy is figuring out what has to be true for an AI to believe you meet a requirement, and which specific proof would make that believable. It connects where you got cut to a ranked list of evidence to fix it.
Evidence tier: Every real claim on this site comes with a confidence rating, on a
Frontrunner movement: Frontrunner movement studies the vendor at the front of the recommendation.
Generative Engine Optimization (GEO): GEO (Generative Engine Optimization) is the practice of shaping content so AI systems quote and recommend it. It aims at getting found and quoted, not at being chosen once real requirements come in.
Google AI Mode: Google AI Mode is a conversational AI search where you keep asking follow-up questions. Because it takes those follow-ups, it is where you can actually watch a buyer's decision unfold.
Google AI Overviews: Google AI Overviews are AI-generated summaries at the top of search results. Being cited in an overview means you got found; surviving a buyer's requirements is a separate question.
How AI decides who to recommend: It is how a buyer decides which company to consider, check out, and finally choose. More and more, that decision happens through AI, before you even know the buyer exists.
How AI Recommends Vendors: When a buyer asks ChatGPT or Google AI for the best option in a category, the system does not rank a fixed list. It builds a recommendation from the buyer's requirements, and it changes as the requirements change.
LLM Optimization: LLM optimization tries to shape how large language models describe and recommend a company. It works on how you are described. Commercial evaluation works on the decision that description feeds into.
Machine representation: Your company has two versions. One is written for people: the website, the
Recommendation Intelligence: Recommendation intelligence is the practice of measuring and explaining how AI systems recommend, cut, and choose vendors, so a company can see why it wins or loses when buyers use AI.
Recommendation set formation: Recommendation set formation studies the opening move: the set of vendors an
Recommendation stability: Recommendation stability studies whether the same question and the same
Recommendation survivability: Recommendation survivability is whether a company remains in the recommendation
Requirement Based Evaluation: Requirement-based evaluation is how a buyer narrows a field: by applying specific requirements that vendors must meet. It is the mechanism that decides who survives and who gets cut.
Requirement interpretation: Requirement interpretation studies the step before elimination: how an
Requirements: Why do some vendors keep making the shortlist while others never get a
Validation and evidence: Validation and evidence studies what an evaluator appears to lean on when it
Vendor elimination: Vendor elimination studies the moment a company leaves the recommendation set:
Vendor Selection: Vendor selection is the point where a buyer, or a system acting for them, chooses one vendor from a shortlist. When buyers use AI, much of the narrowing that decides the winner happens before you ever engage.

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