The vocabulary of Answer Engine Optimization.

Plain-English definitions for the terms that will define how brands measure visibility in the AI-first discovery era. Bookmark this page. Every blog post links back here.

Answerprint

The unique trace a brand leaves in AI-generated answers. Every brand has one. How often it's mentioned, how it's described, which competitors appear instead. Auracle's term for the four-dimensional measurement of AI visibility (presence, sentiment, authority, position).

Example. Monday.com's Answerprint shows up in 28% of project-management queries, with neutral sentiment, no citations, and an average position of fourth-mentioned.

AI Visibility Index(AVI)

A 0–100 composite score combining mention rate, citation rate, sentiment, and entity clarity across major AI engines. The single number that summarizes your Answerprint.

Example. An AVI of 70+ is strong, 40–70 is moderate, below 40 needs attention.

Answer Engine Optimization(AEO)

The practice of optimizing content, structured data, and citations so that AI engines (ChatGPT, Claude, Perplexity, Gemini) include your brand in their answers. The successor discipline to SEO for the AI-first discovery era.

Example. AEO targets the question a buyer asks an AI engine, not the keyword they'd type into Google.

Mention rate

The percentage of relevant AI queries in which your brand name appears at least once. The most basic visibility metric: am I in the conversation at all?

Example. If 12 queries are run and your brand is named in 3 of them, mention rate is 25%.

Citation rate

The percentage of queries where AI engines cite your website (or a page on it) as a source. Citations are the premium outcome. They drive referral traffic and signal trust.

Example. A brand mentioned 10 times but never cited has low citation rate. A brand cited even once in 10 mentions has higher AEO authority.

Entity clarity

How well an AI engine can pin down what your brand is: its category, who it serves, what makes it distinct. AI engines build an internal entity model of your company from Wikipedia, Wikidata, Crunchbase, your website, and public mentions. Inconsistent or thin entity data drags down every other AEO metric.

Example. If LinkedIn says you're a 'marketing platform', your website says 'sales intelligence', and Crunchbase says 'CRM', your entity clarity is poor and AI will describe you vaguely.

Sentiment score

Whether AI engines describe your brand positively, neutrally, or negatively. Most brands sit firmly in the neutral zone, which is actually a problem, because neutral mentions don't advocate for you.

Example. Three positive mentions, six neutral, one negative. The sentiment score reflects this distribution.

Topical authority

The depth of expertise AI attributes to your brand within a specific subject. Topical authority is built by publishing content that comprehensively answers the questions buyers actually ask, not by keyword volume.

Example. A 12-post niche consultancy can outrank a 800-post SaaS platform on AVI if every post deeply answers a specific buyer question.

Knowledge graph

The internal database of entities and relationships AI engines use to reason about the world. Your brand exists as a node in this graph (or doesn't). Wikipedia, Wikidata, and authoritative third-party sources are the most reliable ways to get added.

Training data

The content AI engines learned from before they could answer questions. Brands well-represented in the training data of major engines have an inherited visibility advantage. Brands that emerged after a model's training cutoff rely entirely on real-time citations and retrieval.