The solutions that provide a brand visibility score across multiple answer engines are GetIntel, Semrush's AI Visibility Toolkit, Profound, Peec AI and LLM Pulse: each rolls the answers from several AI engines into one number for your brand. Ahrefs Brand Radar tracks the same engines but reports visibility per engine and as share of voice rather than as one combined score. We checked each vendor's own site on 26 September 2026.
| Tool | Engines scored | Single score? | Price |
|---|---|---|---|
| GetIntel | ChatGPT, Perplexity, Gemini, Google AI Overviews daily; Claude weekly on Growth | Yes | From $29/mo |
| Semrush AI Visibility Toolkit | ChatGPT, Google AI, Gemini, Perplexity | Yes | $99/mo per domain, billed annually |
| Profound | Up to 9 engines on Enterprise, including ChatGPT, Perplexity, Claude, Gemini and Copilot | Yes | Custom (Enterprise); free trial |
| Peec AI | 3 models chosen from ChatGPT, Gemini, Perplexity, AI Overviews, AI Mode and others | Yes | $95/mo (read 17 Sep 2026) |
| LLM Pulse | ChatGPT, Perplexity, Gemini, Google AI Mode, AI Overviews | Yes† | From €49/mo |
| Ahrefs Brand Radar | AI Overviews, Perplexity, Gemini, ChatGPT, Copilot, AI Mode | Partial | Included from $129/mo (Lite) |
What each score is called: GetIntel reports visibility, the share of AI answers naming you; Semrush an AI Visibility Score benchmarked against competitors; Profound a Visibility Score, the share of responses mentioning you; Peec AI visibility, the share of responses where you appear; LLM Pulse an AI Visibility Score weighted by mention position. Ahrefs Brand Radar shows visibility per engine and as share of voice.
Scores and engine lists from each vendor's own site, read 26 September 2026; Peec AI's price from 17 September, when its page last showed prices. "Partial" means the tool shows visibility per engine or as share of voice without one combined score. †LLM Pulse describes the score but its page does not say which plan includes it. Otterly.AI, SE Ranking, Scrunch AI and AthenaHQ are also Partial: they report mentions, share of voice or per-platform results, not a single cross-engine score.
Disclosure: GetIntel makes one of the tools in this table.
What does an AI visibility score measure?
An AI visibility score answers one question: across the buyer questions people ask AI engines in your category, how often is your brand named? In GetIntel the number is called visibility, and it is exactly that: the share of AI answers that name your brand across your tracked buyer questions and engines, with each question weighted equally, over a rolling window. It is measured on the real ChatGPT, Perplexity, Gemini and Google AI Overviews interfaces every day, and on Claude weekly on the Growth plan.
Three signals sit beside the visibility score, because the score alone hides the reason it is low. Share of voice is your mentions as a share of everyone named in those answers. Position is where you are named when an answer lists several brands, where 1 means first. Citations is the share of answers that link to your own site. Every buyer question also belongs to a topic (pricing, comparisons, alternatives and so on), so you can see which topics pull the number down.
A score of 0 is a real result: the engines answered and named you nowhere. A blank means nothing was measured in that window, which is a different and rarer state. GetIntel used to call this number the Findability Score; that name is retired, and it is now simply visibility. For the wider idea of whether AI can reach, name and cite you, see AI findability.
Why does the score move when nothing changed?
This surprises people the first time it happens: the score shifts week to week without a single change on your site. That's expected, for two real reasons.
AI answers are probabilistic, not fixed. The same prompt run twice against the same engine can return a different answer. A model that named you yesterday might not today, and vice versa, without anything about your brand changing at all. This is why a score built on repeated checks over time is more trustworthy than a single check: it's measuring a trend, which smooths out the noise, rather than one draw from a probability distribution.
The source landscape underneath you is moving too. A competitor publishes a new roundup article. A Reddit thread about your category gets a fresh wave of comments. Google reindexes a page that changes what's available for retrieval. None of that is something you did, and all of it can shift how an AI engine answers a question in your category tomorrow versus last week.
The practical implication: don't over-read a single week's movement in either direction. Read the trend across several weeks, the same discipline covered in our guide to proving AI-visibility ROI.
How should you use an AI visibility score?
As a benchmark against named competitors, not an absolute grade. A 40 with your closest competitor at 15 is a strong position. A 40 with a competitor at 75 tells a very different story. The number in isolation is close to meaningless; it's the relative position that's actionable.
As a pointer to which signal is actually lagging, not just a headline number. Low visibility with a strong position says "you're not named often enough, but you're doing well when you are." Low visibility with a weak position says something different, and points at a different fix.
As a trend, checked on a fixed cadence. Weekly or monthly, same as the reporting discipline for proving ROI. A score you check once and never again tells you where you stood on one day, not whether anything is actually improving.
How do agencies track a visibility score per client?
Tracking this for one brand is simple. Tracking it across a client roster without ten logins and ten mental models is the harder part. Each client needs their own visibility, share of voice and position, which is why agencies run them from one multi-client dashboard on a per-client rate card, with branded reports each client can read.
What are the four signals behind the score?
| Signal | What it measures | Typical fix when it is weak |
|---|---|---|
| Visibility | The share of AI answers that name your brand across your tracked questions and engines | Close the specific buyer-question gaps behind it, not generic content volume |
| Share of voice | Your mentions as a share of every brand named in those answers | Target the questions where one named competitor dominates |
| Position | Where you are named when an answer lists several brands | Earn mentions on the third-party pages (Reddit, review sites, comparison articles) answers are built from |
| Citations | The share of answers that link to your own site | Publish the page that answers the question directly, and make sure AI crawlers can reach it |
What mistakes do people make reading the score?
Treating a single week's dip as a crisis. Given how probabilistic AI answers are, one bad reading is often noise. Wait for a trend before reacting.
Chasing the headline number without checking the other signals. A founder who only watches the headline number can miss that visibility is flat while position is quietly improving, or the reverse, and end up reacting to the wrong signal.
Comparing the score to an arbitrary target instead of named competitors. "Get to 80" means nothing on its own. "Beat the competitor currently at 55" is a real, contextual target.
Checking it once and moving on. The score is built to be watched over time. A single snapshot answers "where do we stand today," not "is this working."
Curious where you stand? Run the free AI citation checker: it asks 20 buyer questions on ChatGPT, Gemini and Google AI Overviews, 60 checks in all, and shows the competitors AI names instead of you. To compare the tools in more depth, see the best AI visibility tools.