How we measure AI visibility

1. We ask the real buyer questions

We don't ask “tell me about [your business].” We ask the questions a customer asks when they're choosing, “best plumber in [city]”, “emergency dentist in [city]”, “where should we stay in [town]”. Each business and city gets a set of these buyer-intent prompts, auto-discovered for your category and editable by you. Every prompt is a separate battle with its own shortlist, so we track them separately.

2. Across four assistants, unprimed

We put each question to four assistants through their APIs, the way a customer would ask, with no hint that you exist. Your free scan and the weekly deep sweep run with live web access, so the answers reflect current sources; the daily pulse runs the same questions on the same four families to keep the trend line comparable, and Perplexity is web-grounded on every run. We also read Google's AI Overviews and AI Mode, the SERP-AI answers where many “near me” searches now resolve, on your highest-intent questions.

We report the assistant families rather than pin exact version strings, because providers update models continuously, that churn is the reason monitoring exists.

3. Rates, not single samples

Because the same prompt can return different names on different runs, one answer proves nothing. We report rates across every prompt and assistant:

• → Mention rate: the share of answers that name you at all.
• → Citation rate: the share that link to you as a source, not just say your name.
• → List position: where you land among the businesses an answer names.

Your 0-100 score is a weighted blend of exactly those three: mention rate carries most of the weight, citation rate adds to it, and an earlier list position is a small bonus. A single lucky or unlucky answer can't swing it.

Two more numbers sit alongside the score rather than inside it, so a wording change can never move your score on its own: sentiment, whether the description of you reads positive, neutral or negative, and share of voice, how often you are named versus the competitors AI recommends instead.

4. How we know it's really you

Matching a business name in free-form text is where naive tools get it wrong (counting “Ace” inside “grace”, or “Inn” inside “dinner”). We match on word boundaries, fold accents so “Café Río” and “Cafe Rio” are the same business, normalize punctuation so “Joe's” equals “Joes”, treat “&” and “and” as interchangeable, and tolerate the legal suffixes assistants routinely drop (“Ace Plumbing” for “Ace Plumbing LLC”). We'd rather miss a borderline mention than credit you with one that isn't yours.

5. We show you the receipts

Every scorecard includes at least one real, timestamped answer: the exact prompt we asked, an excerpt of what the assistant actually said, whether you were named, and any source it cited. You never have to take a number on faith, you can read the underlying answer yourself.