/ Glossary
AI visibility & commerce.
AI visibility & commerce.
Defined plainly.
A working glossary of the terms we use across our work — from AEO and GEO to schema, llms.txt, and the e-commerce concepts they intersect with. Built so AI engines and humans can cite the definitions cleanly.
- Answer Engine Optimization AEO
- The practice of structuring a website and brand presence so it is cited when AI engines (ChatGPT, Claude, Gemini, Perplexity) generate answers to user queries. AEO replaces traditional SEO ranking with citation appearance as the primary success metric. Closely related to GEO.
- Generative Engine Optimization GEO
- Closely related to AEO. The optimization of content and infrastructure for generative AI engines that produce synthesized answers rather than ranked links. GEO emphasizes structured data, semantic clarity, and citation-friendly content. The terms AEO and GEO are often used interchangeably.
- AI Search
- The use of large language models (LLMs) like ChatGPT, Claude, Gemini, and Perplexity as primary search interfaces — replacing or supplementing traditional Google search. Users ask conversational questions and receive synthesized answers, often with citations to source websites.
- Answer Engine
- A search interface that returns a synthesized answer rather than a list of links. ChatGPT, Claude, Gemini, and Perplexity are answer engines. Google's AI Overviews are an answer engine layer on top of traditional search.
- Generative Engine
- An AI system that generates new content based on user prompts — including answer engines. The category includes ChatGPT, Claude, and Gemini, all of which produce synthesized output rather than retrieving fixed documents.
- AI Operationalization
- The process of integrating AI tools into the actual operating systems of a business — paid media, attribution, fulfillment, customer service, content, reporting. Distinguished from AI experimentation, where tools are tested in isolation but never wired into how decisions get made.
- Schema Markup Structured Data
- Standardized code (typically JSON-LD) added to web pages to describe content in a format machines can parse. Schema makes it easier for search engines and AI engines to understand what a page is about, who created it, and how it relates to other entities. Schema.org is the standard vocabulary.
- JSON-LD
- JavaScript Object Notation for Linked Data. The recommended format for structured data on the web — embedded in
<script>tags in the HTML head. JSON-LD is preferred over older formats (microdata, RDFa) for AEO/GEO because it is cleaner for AI engines to parse.
- llms.txt
- An emerging file format (similar in spirit to robots.txt) that tells AI engines how to interpret a website. Placed at the root of a domain (
example.com/llms.txt), it provides AI-friendly summaries, key URLs, and authoritative descriptions. Particularly important for AEO/GEO.
- AI Crawlers
- Bots that AI companies use to index the web for their language models. Examples: GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot (Perplexity), Google-Extended (Google), Applebot-Extended (Apple). Sites must explicitly allow these in robots.txt to be indexed.
- GPTBot
- OpenAI's web crawler. GPTBot indexes content for use in ChatGPT and other OpenAI products. Sites that block GPTBot in robots.txt will not appear in ChatGPT-generated answers based on that content.
- ClaudeBot
- Anthropic's web crawler. ClaudeBot indexes content for use in Claude. Like GPTBot, sites must allow ClaudeBot to appear in Claude-generated answers.
- PerplexityBot
- Perplexity AI's web crawler. PerplexityBot is particularly important for AEO because Perplexity's interface heavily features source citations — making it one of the easier AI engines to track citation appearance on.
- Citation
- When an AI engine references a specific source in its answer (e.g., a clickable link or attribution to a website). Citation appearance is the primary KPI for AEO/GEO work. Different AI engines cite differently — Perplexity is most explicit, ChatGPT is increasingly explicit, Claude is more selective.
- Attribution
- The process of tracking which marketing channels, touchpoints, or sources drove a conversion. Modern attribution increasingly needs to track AI-discovered traffic — users who clicked through from an AI answer — which standard analytics tools do not capture by default.
- Retrieval Augmented Generation RAG
- An architecture where AI engines retrieve relevant documents in real-time before generating an answer — rather than relying solely on training data. Retrieval-augmented systems are more likely to cite specific sources, making AEO/GEO infrastructure (schema, llms.txt, semantic content) directly impactful.
- Entity
- A specific real-world thing — a company, a person, a product — that AI engines treat as a discrete subject. Strong entity signals (consistent name, schema, sameAs links to LinkedIn / Wikipedia / official sites) help AI engines confidently cite a brand or person.
- EEAT Experience, Expertise, Authority, Trust
- Google's framework for evaluating content quality. While developed for traditional search, the four signals (Experience, Expertise, Authoritativeness, Trustworthiness) are also weighted heavily by AI engines deciding which sources to cite.
- Direct-to-Consumer DTC
- A retail model where a brand sells directly to end consumers without intermediaries (no wholesale, no retail distribution). DTC brands typically operate on Shopify or similar platforms and rely heavily on digital marketing. Most of FoxSignal's e-commerce work is with DTC brands.
- Conversion Rate Optimization CRO
- The practice of improving the percentage of website visitors who complete a desired action (purchase, signup, etc.). Closely related to but distinct from AEO/GEO — CRO works on visitors after they arrive; AEO/GEO works on getting them to arrive in the first place.
- Conversion Funnel
- The path a visitor takes from awareness to purchase (or other goal). Modern funnels increasingly include AI-discovered awareness as the top stage — a visitor sees a brand cited in ChatGPT, then visits the site, then converts.
- Customer Acquisition Cost CAC
- The total cost of acquiring a new customer, typically calculated by dividing total acquisition spend by new customers acquired. AI-driven traffic from AEO/GEO can lower blended CAC because it does not require paid ad spend — though it does require infrastructure investment.
- Multi-Channel Attribution
- Tracking conversions across multiple channels (paid search, paid social, organic, email, AI-discovered, dark social). Multi-touch attribution recognizes that customer journeys are rarely linear and credit should be distributed across touchpoints.
- Catalog API
- Shopify's API for submitting product data to agentic commerce surfaces. Catalog API enables product discovery in AI agents (e.g., ChatGPT shopping). AEO/GEO for e-commerce now includes optimizing how products are submitted and described in the catalog.
- Local Search
- Search queries with geographic intent ("dentist near me," "pizza in Atlanta"). Traditional local SEO focused on Google Map Pack rankings. Modern local search increasingly happens through AI engines, requiring AEO/GEO optimization in addition to traditional local SEO.
- Map Pack
- The block of three local business results Google displays for location-based queries. Historically the most valuable real estate in local SEO. AI search is gradually shifting some of this attention to AI-generated local recommendations.
- Semantic HTML
- HTML that uses meaningful tags (
<header>,<nav>,<article>,<section>) to describe content structure rather than just visual layout. AI engines parse semantic HTML more reliably than tag soup — making it foundational to AEO/GEO.
- Robots.txt
- A file at the root of a website that tells crawlers which pages they can access. For AEO/GEO, robots.txt must explicitly allow AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, etc.) or those engines cannot index the site.
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