B2A Glossary

A — F

AI Agent

An autonomous or semi-autonomous software system powered by a large language model (LLM) that can search, summarise, recommend, and act on behalf of a human user. Examples: ChatGPT, Claude, Gemini, Perplexity, Copilot.

AI Branding

The practice of designing a brand to remain coherent and discoverable when content is summarised, quoted, or recommended by AI tools. Includes structured data, semantic markup, llms.txt files, and content architecture optimised for both human and machine extraction.

AI Brand Protection

Continuous monitoring of brand mentions across AI outputs, review platforms, social media, and domain registrations. Detects misuse, hallucinated claims, and unauthorised brand-adjacent registrations.

AI Digital Twin

A calibrated AI persona of a customer cohort, built from real data (CRM, support transcripts, prior research). Used as a complement to (not replacement for) traditional human research, with documented bias and accuracy controls.

AI Discoverability

How findable a brand is to AI-mediated discovery — including LLM citations, AI-search recommendations, and answer-engine surfacing.

AI Hallucination

An incorrect statement produced by an AI system as if it were factual. For brands, hallucinations may include wrong founding years, fabricated services, or invented quotes — and can damage reputation if not monitored.

AI Market Research

Competitive analysis, audience mapping, and trend detection delivered using AI tools. Faster than traditional research while complementing — not replacing — human-led methods.

AI Visibility Audit

An assessment of a website's readiness for AI-mediated discovery, scored across LLM Visibility, Structured Data, AI-Readability, Brand Signal Strength, and B2A Readiness.

Algorithmic Positioning

The discipline of positioning a brand to be recommended by algorithms (search, social, AI) — not just by humans.

Answer Engine

An AI-powered system that returns direct answers (not link lists). Examples: Perplexity, ChatGPT Search, Gemini, Microsoft Copilot.

B2A Marketing (Business-to-AI)

The discipline of optimising a brand's content so AI agents — which increasingly mediate buying decisions — surface, recommend, or cite the brand. Forecasts: 40% of search and purchase decisions will be AI-routed by 2027.

B2A Readiness

How prepared a brand is for AI-mediated discovery, measured by the coherence of its identity, the quality of its structured data, and its citation rate in AI outputs.

Brand Signal Strength

The clarity, consistency, and authority of a brand's machine-readable signals — name, services, location, ratings, citations — across the open web.

Citation-Friendly Content

Content written in a way that AI agents can quote verbatim or paraphrase with high confidence — short declarative statements, dated facts, and unambiguous claims.

CrUX Data

Chrome User Experience Report — Google's dataset of real-user performance metrics. Required for PageSpeed Insights field-data scoring.

FAQPage Schema

A schema.org type that marks up question-answer pairs in JSON-LD. Helps AI agents extract direct answers and cite the source.

G — L

Generative AI Branding

The use of generative AI to produce brand identity assets that adapt across contexts while remaining coherent — logos, visuals, copy, and tone variants generated from a unified brand system.

JSON-LD

JavaScript Object Notation for Linked Data — Google's preferred format for embedding structured data in web pages. The standard format for schema.org markup.

Knowledge Graph

A structured representation of entities and their relationships. AI agents traverse knowledge graphs to disambiguate brand mentions and link them to canonical identities.

LLM (Large Language Model)

A neural network trained on vast text corpora to generate, summarise, and reason about language. Examples: Claude (Anthropic), ChatGPT (OpenAI), Gemini (Google).

LLM Visibility

How discoverable a brand is to LLMs — measured by citation rate in AI outputs, presence in training data, and signal strength in real-time retrieval.

llms.txt

A plain-text file at the root of a website that declares brand identity, services, and crawl preferences for AI agents. Modeled after robots.txt but optimised for LLM discoverability.

llms-full.txt

An extended companion to llms.txt — long-form, citation-friendly content covering services, FAQ, methodology, and brand facts in markdown.

M — R

Machine-Readable Brand

A brand whose identity, services, and assets are exposed in formats AI agents can parse — JSON-LD, structured data files, well-known endpoints — not just human-readable web pages.

Microdata

HTML-attribute-based structured data syntax (older alternative to JSON-LD). Still readable by crawlers but less commonly used for new sites.

Organization Schema

A schema.org type that describes a company entity — name, URL, logo, founding date, services, ratings. The foundation of brand markup.

Prompt-Driven Content Audit

An audit that tests how well a brand surfaces when real prompts (e.g. "best AI branding agency in Bulgaria") are run against major LLMs.

Retrieval-Augmented Generation (RAG)

An AI architecture where the model retrieves relevant documents at query time and grounds its output in those documents. RAG-based AI tools cite sources actively — making structured data and llms.txt critical.

S — Z

Schema.org

A vocabulary of structured data types (Organization, Service, FAQPage, etc.) jointly developed by Google, Microsoft, Yahoo, and Yandex. The de facto standard for machine-readable web content.

Semantic HTML

HTML markup that conveys meaning, not just appearance — proper headings, articles, sections, lists. Helps AI agents extract content structure correctly.

Structured Data

Machine-readable metadata embedded in web pages, typically as JSON-LD. Includes Organization, Service, Product, FAQPage, BreadcrumbList, and more.

WebSite Schema

A schema.org type that describes a website entity, including its SearchAction (how AI agents can search the site programmatically).