How Kaivox measures AI visibility

We measure the app on their phone

Buyers don’t ask the benchmark champion. They ask the assistant they already have open, in its default mode. So Kaivox probes the consumer-default tier of each assistant, not the heavyweight models that score well in labs and answer almost no real shopping questions.

The lineup is reviewed monthly as each platform’s default routing shifts, every scan records exactly which models answered it, and lineup changes are published, so a moving trend line can always be traced to either the market or the instrument.

Two layers: what AI remembers, what AI finds

Every assistant is measured two ways, because an AI answer has two sources and they move at different speeds.

A gap between your memory score and your live score is a finding, not noise: strong memory with a weak live score means your fresh content is invisible to retrieval; strong live with weak memory means AI finds you but doesn’t yet know you. The two diagnoses have different fixes, which is the point of measuring both.

The questions are real buying questions

Each scan asks the assistants what a real buyer in your category would actually type, and the questions are shaped to your business. A national brand is measured on discovery and comparison questions like “best [category] for [need]” or “[rival A] vs [rival B]”. A local business is measured the way people really search for one: “who should I hire for [service] near me,” not a national best-of list. Across both, the set spans discovery, head-to-head comparisons against the rivals that truly compete with you, alternatives, problem-first questions, and trust checks.

We don’t invent these questions in a vacuum. They are grounded in real demand in your category: the keywords people actually search and the “People Also Ask” questions Google surfaces, so the panel reflects how buyers really phrase things rather than how we imagine they do. Two hard rules hold throughout: the questions never name your brand, because the entire point is whether the AI brings you up on its own, and they are grounded in today’s date, so a search-backed assistant is never steered into last year’s articles.

You see the full question set, the exact wording of every question, and approve it before any scan runs, and every scan asks that same approved set. That is what makes your score comparable scan over scan: when the number moves, it moves because the assistants’ answers changed, not because we asked different questions.

Asked five times, because once is a coin flip

The same question asked twice can return two different brand lists. A single-sample score cannot tell a real change from measurement jitter, so every question is asked five times per assistant, and your score is reported with a confidence band, for example 27.8 ± 2.0. That band is calibrated the hard way: we periodically re-scan a brand back to back with nothing changed and measure how far the number moves on its own, and the band you see is never allowed to claim more precision than those controlled repeats actually showed. In practice that means two scans of an unchanged brand can land a few points apart, so a genuine move is one that clears roughly five points or holds up across scans, not a wobble inside the band. Single-sample measurement dressed up as precision is the most common shortcut in this category; sampling every question, calibrating against real repeat runs, and showing you the band is how you can tell we’re not taking it.

What the score means

The Kaivox AI Visibility Score (0 to 100) blends four measured signals: how often you appear at all, how prominently you appear when you do (leading an answer counts for more than a passing mention), your share of voice against the rivals the assistants actually name, and how often your own site gets cited as a source. Each assistant is scored separately, then blended. No signal in the score is assumed, padded, or simulated; if we didn’t measure it, it isn’t in the number.

The blend is not a flat average. Each assistant’s influence on your score reflects how many real people actually use it, dampened so no single assistant dominates the number and smaller assistants still register. More than half of real-world AI questions go to ChatGPT, and your score weights it accordingly. When we recalibrate these weights against published usage data, your score carries a measurement-version stamp, and no trend line ever presents a recalibration as market movement.

The score is built only from questions where the assistant actually recommends a business. Pure how-to and cost questions, where the AI explains something and names no one, are measured separately as Answer Visibility, so they never drag your recommendation score down. And when an assistant declines to name anyone at all, that answer is set aside rather than counted as you being absent, because in some categories the assistant routinely refuses to name names. The number reflects the questions where being recommended was genuinely on the table.

Your corrections outrank our scan

Kaivox reads your brand from the public web, and the public web is sometimes wrong about you. Anything you correct, from your category to the names your brand goes by, becomes permanent ground truth: it overrides what we scanned, survives every rescan, and steers every future measurement. The system also audits its own data after every scan and shows you what looks wrong, so finding problems doesn’t depend on you stumbling into them.

What this measurement is not

• 01

  It is not every conversation. AI answers vary by user, location, and chat history. We measure a representative, repeatable slice under controlled conditions, which is what makes scan-over-scan comparison meaningful.

• 02

  It is not comparable across tools. Different products probe different models with different questions; a Kaivox score is built to be compared with your last Kaivox score and with competitors measured in the same scan, not with a number from another vendor.

• 03

  It is not static. The assistants change under everyone constantly. Our lineup discipline, date grounding, and confidence bands exist so that when your trend moves, you can trust the movement is yours.

• 04

  It is not a margin of error. The plus-or-minus band shows how repeatable the measurement is across the five samples, not a statistical confidence interval. In a small or niche market, once off-topic questions and refusals are set aside, a score can rest on a modest number of answers, so treat a small move as noise and a sustained trend as signal.

• 05

  It is not a cross-brand leaderboard. The 0-to-100 score is built to track ONE brand over time and against the rivals named in the same scan. Comparing the raw score of two unrelated brands, or two different categories, is not something the scale is built to support.

• 06

  It is not immune to the models changing under their own names. We hold the assistant lineup fixed and compare like-for-like scans, but when a provider updates a model without renaming it, a score can shift for reasons unrelated to you, which is why a sustained trend matters more than any single scan-to-scan move.

• 07

  It is not pinned to your customer’s exact location. When an assistant searches the web live, that search runs from our infrastructure, not your buyer’s town. Local questions name the place so the answers stay locally relevant, but the search origin is not your customer’s address.

• 08

  It is not a transcript of the consumer apps. We ask each assistant through its business interface, the same route every measurement tool in this category uses, with a neutral setup and no chat history. Published research shows the consumer apps can answer differently for different people, which is exactly why a controlled, repeatable instrument is the right way to measure change: your trend moves because the assistants moved, not because the measurement did.