The ECHO glossary

Google's AI-generated answer block, placed above the traditional results and synthesised from multiple retrieved sources.

Any system that responds to a question with a synthesised answer rather than a ranked list of documents: AI Overviews, ChatGPT, Perplexity, Google's AI Mode.

The digital property you own and control where every core fact about your business is stated clearly, structured for machine readability, and kept current.

A passage of content retrieved and used on its own, separated from the page it came from.

An explicit source attribution in a generated answer: the link or reference an answer engine shows for a claim.

The principle, drawn from information retrieval research, that a fact confirmed by multiple independent authoritative sources is treated as ground truth.

The second ECHO pillar. The independent confirmation of your entity's facts across sources the machine already trusts.

Establishing which of several possible referents a name points to.

The framework described in this book: Entity, Corroboration, Hooks, Output.

The first ECHO pillar, and the machine's unit of understanding.

The set of web pages and data points that reference a business, with the entity home at its centre.

The measurable degree of certainty a search or AI system assigns to the identity of your brand as a single, disambiguated, real-world entity, built from the consistency, structure and corroboration of the facts it can find about you across the sources it trusts.

The specific page from which search and AI systems learn your identity, attributes and values.

A fact you publish about yourself, on a property you control.

The listing that supplies a large share of a local business's entity data.

What a system treats as settled fact rather than as a competing claim.

Anchoring a generated answer in retrieved source material rather than in the model's training data alone.

A generated statement presented with confidence and unsupported by any source.

The third ECHO pillar. Structured, extractable, self-contained passages that an AI can lift out and attribute without losing meaning.

What a document adds beyond what already exists on a topic.

A structured store of entities and the relationships between them.

The public-facing surface of a knowledge graph entry.

Name, address, phone: the core identity data for a local business.

The fourth ECHO pillar. The measurement layer: knowing whether the other three are working, and connecting AI visibility to leads, traffic and revenue.

The decomposition of one user question into many simultaneous sub-queries, each retrieving different content, which the system then synthesises into a single answer.

Generating an answer from live retrieved documents rather than from training data alone.

A schema.org property that links an entity's canonical page to its other identity points across the web.

Structured data added to a page in a vocabulary machines already parse, stating facts explicitly rather than leaving them to be inferred from prose.

ECHO's primary output metric. Across the queries that matter to your business, the percentage of AI-generated responses in which your brand appears.

The older visibility metric: the percentage of search results for target keywords that feature your brand.

Facts published in machine-readable form, so a system can extract them without interpretation.

A fact about you published by someone else: a review platform, a directory, a publication, a licensing body.

A query resolved without the user visiting any result.