EntityMap
A machine-readable map of an organization's entities, such as its terms, products, people and their relations, published as /entitymap.json so AI systems can retrieve verified definitions and relationships instead of inferring them from prose.
This term is part of the 3LA EntityMap → /entitymap.html
Related terms
A2B Economy
coreThe emerging economy in which AI agents, not humans, conduct research, evaluate options, and make purchase decisions on behalf of users. In A2B (Agent-to-Business) interactions, the agent retrieves from structured, verifiable sources. Organisations without a clean, connected entity graph are not in the agent's consideration set. EntityMap is the infrastructure required for A2B visibility.
Retrieval-Augmented Generation
coreA technique used to improve the quality, accuracy, and freshness of AI responses by grounding them in retrieved source documents. RAG systems retrieve relevant content from indexed sources before generating a response. EntityMap improves RAG output quality by providing structured, pre-verified entity relationships and evidence chunks rather than requiring the model to infer relationships from unstructured prose.
Bot Detection
coreServer-side detection and classification of AI crawlers visiting a website. Part of the Signals layer in 3LA. Logs which AI systems crawl which pages, at what frequency, and whether they access machine-readable files like /entitymap.json and /llms.txt. Enables measurement of actual EntityMap consumption by AI systems.
llms.txt
L3A proposed standard: a Markdown file at /llms.txt that gives large language models a curated, plain overview of a site's most important content. It does not control access; that is what robots.txt does.
