Methodology
How Anovox Scores AI Visibility: The Open Model
Every dimension we measure, every formula we use, published openly — so you can check our math instead of trusting a black box.
6 min read
Why publish the scoring model at all
Most AI-visibility tools show you a score and ask you to trust it. We think that's backwards: a metric you can't audit is a metric you can't act on with confidence. So here is the complete Anovox scoring model — every dimension, every formula, every weighting. If any of it looks wrong for your situation, we'd rather you told us than silently distrusted the dashboard.
Share of Voice: citations divided by runs
The headline metric. For each workspace, engine, and day: SOV = (answers that name your brand ÷ total answers collected) × 100, rounded to one decimal. A prompt run across 4 engines produces 4 answers; if 2 name you, that's 50% for the day on those prompts. Scores aggregate upward from prompt to workspace, and trend lines compare the same prompts week over week — so a rising score means genuinely more citations, not a changing prompt mix.
Citation detection: names, aliases, domains, position
A citation counts when your brand name, any alias you registered, or your domain appears in the answer text. We also record the position of the first mention — named in the opening sentence versus buried in paragraph six are different outcomes, and the citation board shows you which. Competitors are detected with the same matcher against their names, aliases, and domains, so an overtake (competitor cited, you not) is a directly comparable event, not an inference.
Sentiment: how you're described, not just whether
Each brand citation is classified positive, neutral, or negative from the surrounding answer text. A prompt where you're named with caveats ('X is good but pricey') scores the citation but flags the sentiment — because being mentioned badly is not the same as being recommended. Sentiment feeds the gap ranking: a negatively-framed citation outranks a missing one in urgency.
Gap ranking: severity × priority × recency
Aim ranks every open gap by three explainable factors. Severity: how invisible are you (zero citations across all engines outranks cited-once). Priority: the prompt's business weight. Recency: fresh gaps outrank stale ones you've already decided to ignore. An optional fourth factor — relative demand from prompt-volume signals — can be toggled in. No hidden weights: each factor is shown on the worklist next to the score it produced.
AEO Content Score: structure, authority, citability
The site grader scores three axes out of 100. Structure: can a model extract a clean answer (direct-answer-first copy, headings, comparison tables, schema markup). Authority: do independent sources corroborate your claims, and are your pages crawlable by AI agents at all. Citability: would a synthesis step choose your page — clear claims, defined terms, quotable numbers. Each axis ends in concrete fixes that deep-link into the content generator.
What we deliberately don't score
We don't score 'brand awareness' or 'share of conversation' as vague composites, we don't blend engines into one opaque number without showing the per-engine breakdown underneath, and we don't present relative demand signals as search volume. Where a number is an estimate, it's labeled as one. The dashboard always lets you click from any aggregate down to the raw stored answer that produced it — the receipts are one click away, always.