AI visibility checker
An AI visibility check asks the AI engines the questions your clients ask, and shows you the answer: which firms are named, which are not, and where each engine got its information. This page explains what the check measures, what you get back, and how to read it. The check itself is at the bottom of the page.
What an AI visibility check does
Before a general counsel calls the firm a colleague recommended, she checks it with AI in private. Before a CFO approves next year's outside-counsel budget, he asks AI whether the current firm is still the right choice. Neither search reaches your website. Neither shows up in your reports. The answer forms, the decision follows, and the only trace is a matter that went somewhere else.
Your next client asks AI who to hire. Your current clients ask AI whether to keep you.
An AI visibility check makes that hidden conversation visible. It takes the questions a client of yours would ask, puts each one to the engines that client uses, and records the answers: which firms are named, in what order, and which sources each engine used. Then it shows you where your firm appeared, where it did not, and who was named in your place.
That is all it is. It is a record of what the engines say when asked, and that record is the only thing your client will ever see.
The firm, each practice, each lawyer. On five engines.
To an AI engine, a law firm is three things. The firm as a whole: what you are known for and how you are described everywhere the engine looks. Each practice on its own: whether your employment group is named for employment questions, whatever the firm as a whole is doing. And each lawyer by name: when a client asks who handles cross-border restructuring, the answer is a person, and the work follows that name.
A check that gives you one number for the whole firm has blended your strongest practice and your invisible one into the same result. For a firm of 200 lawyers that number means almost nothing. The check has to look at all three separately, or it cannot tell you where the problem is.
It also has to look at each engine separately. Your clients use ChatGPT, Perplexity, Gemini, Claude, and Google's AI Overviews, and each one builds its answer from different sources. You can be recommended on one and missing from the next. A blended score averages those two facts and hides the engine where you are losing.
Who AI names instead, where it got its answer, and what to fix first
A score is the least useful line on a real check. What you get is the shortlist itself: the firms each engine named for each question, so you can see exactly who is being recommended in your place. Behind each answer, the sources the engine used, because the source is the thing you can change. And the findings in order, so the first thing you fix is the thing that moves the most.
One example, from one of our audits. A regional firm with a strong commercial litigation practice ran the check. On one engine it was named for that practice most of the time. On a second engine it was named once in five. On the third it was missing entirely, and the firm named instead was a two-lawyer boutique that had published deeper content on exactly the questions clients ask. The source behind that answer was a bar association article the larger firm had never seen.
Three things the result means, and one it does not
Missing is different from ranking low. In Google, position ten is still on the page. In an AI answer, a firm that is not named does not exist for that client. There is named, and there is not.
The source is the thing you can change. When an engine recommends someone else, it did so for a reason it can show you: a directory profile, a news mention, a practice page, a forum thread. Each of those is a place your firm appears well, appears badly, or does not appear. The check points at the source. Fixing it is the work.
One run is a snapshot. In a 1,600-answer Perplexity study, about half the firms named showed up in only one of three identical runs (InterCore, 2026). What tells you where you stand is measuring again: how often you are named, on which engines, and whether that is rising. A check run once tells you where you were. A check run every month tells you where you are.
What it does not mean: that a low result says anything about the quality of your work. The engines grade the evidence they can find. A firm with a deep practice and a thin footprint reads as a thin practice, and that gap is what the check is built to find.
Generic checkers score one brand name. A law firm has hundreds.
Most AI visibility checkers were built for consumer and software brands, and they do that job well. A brand has one name to look for and a huge number of questions asked about it. The tools built for brands ask thousands of generic questions and report how often the brand shows up.
Legal buying turns on a small number of questions where one answer is worth millions: the matter that walks in, the renewal that does not come. A thousand generic questions can say your firm is fine while the one question your best client asks returns three rivals. A check built for law has to start from your firm, your practices and your named partners, and ask the questions your clients ask. If you are comparing platforms, the seven questions to put to each one are in how to choose an AI visibility platform, and how an engine builds its shortlist is in AEO explained.
The actual questions your clients are asking
A generic checker asks AI "best law firm for X" a thousand times. Your clients ask seven kinds of questions, and most of them are about checking a firm they already have rather than finding a new one. The check asks all seven, about your firm, your practices and your lawyers.
The shortlist question
"Best firms for cross-border insolvency in New York."
The classic, and the most contested.
The situation question
The buyer describes a problem, not a category, and AI names firms on the way to the answer. A buyer described a letters-of-credit dispute and never typed "law firm." One bar association article put a New York firm first.
The vetting question
The referral already produced your name. The general counsel checks it in private, four questions deep. Your firm is not competing to be found. It is competing not to be quietly killed.
The person question
"Who is the leading FDA lawyer for biosimilars?"
The lawyer level, where new work starts and where firms know the least.
The evidence question
"Is this a reputable firm?"
A CFO or procurement lead asks for citations and comparisons before approving outside counsel. The GC picks your firm. Then the CFO types that question into Claude before the engagement letter gets signed. Nobody at your firm ever sees it, and neither does the GC.
The referral question
"Do I need a lawyer for this, and who should I call?"
Sometimes the buyer asks it outright. More often they ask about their problem and the AI volunteers the referral, sometimes with a name. AI has joined the referral channel.
The reputation question
"Any red flags with this firm?"
Where wrong facts, retired partners, and practices your firm never had do their damage.
Three of these decide new business. Four decide whether your firm keeps the business it has.
Frequently asked questions
What is an AI visibility checker?
An AI visibility checker asks the AI engines the questions a client would ask, records which firms each engine names and which sources it uses, and shows you where your firm appears and where it does not. For a law firm it should look at the firm, each practice and each lawyer, on each of the five engines clients use: ChatGPT, Perplexity, Gemini, Claude, and Google's AI Overviews.
What does an AI visibility check measure?
Whether your firm, your practices and your named lawyers are recommended when a client asks the engines a real question about hiring a lawyer, which firms are named in your place, and which sources each engine used to get there. A good check shows each engine separately rather than blending them into one score.
Is a free AI visibility check accurate?
A single run is a snapshot. In a 1,600-answer Perplexity study, about half the firms named showed up in only one of three identical runs (InterCore, 2026). The free check gives you your live result and the sources behind it, which is accurate for that moment. Where you stand over time comes from measuring again every month against where you started.
Why do law firms need a different AI visibility check?
Because generic checkers score one brand name across thousands of questions, and a law firm has a firm, many practices and many lawyers, and wins work on a small number of high-value questions. A check built for law starts from your practices and partners and asks the questions your clients ask, so it can find the practice or the partner that is missing while the firm's average looks fine.
See what AI says about your firm.
Enter your website and your work email. The check runs live on questions tailored to your firm and practice, and you see your result right away: your standing on each engine, the firms on the shortlist in your place, and the sources behind each answer.
- Built only for law firms, on buyer questions tailored to your firm and practice.
- Free, with the sources shown, so a partner can check the result for themselves.
We don't ask you to believe us. We show you your own result.
We detect your firm name automatically. You will instantly see your live AI visibility score, who AI names in your place, and why the gap exists.
Free. Your data stays private.
See what AI says about your firm.
It costs nothing. You will see your score right away, who AI names instead, and exactly why.
Free. Built for law firms.