Family Law Firms AI Visibility Audit: ChatGPT, Gemini and Perplexity

An AI visibility audit is a measurement of how AI assistants represent your family law firm when someone researches a sensitive, personal decision. When a person types “how does custody work in a divorce” or “best family law attorney near me” into ChatGPT, Gemini, or Perplexity, those tools return a written answer and often name specific firms. The audit counts whether you are named, how often, in what context, and how you compare to the firms that appear instead of you. It turns a quiet worry, “are we even in these answers,” into a set of numbers you can act on.

This is a diagnostic, not the fix. The audit tells you where you stand and where the gaps are; it does not, by itself, change what the models say. That distinction matters, so we will keep it clear throughout this page: an audit is measurement, benchmarking, and source analysis. The work of actually improving how AI tools cite and recommend you is a separate program, and we will point you to it at the end.

The audit sits inside the broader family law marketing program we run, alongside our wider law firm marketing work. It is usually the first step, because family law is deeply local and reputation-driven, the matters are private, and prospective clients research carefully before they ever pick up the phone. Knowing how these tools already treat your firm is the fastest way to decide what is worth doing next, without guessing.

Family law is one of the most trust-driven legal categories, and the research behind it has moved. Prospective clients no longer run a single search and call. They ask AI assistants to explain how the divorce process works in their state, to describe how custody and child support are decided, to compare mediation with litigation, and, more and more, to name firms worth a consultation. Every one of those exchanges is a chance for your firm to be mentioned or overlooked, and none of it shows up in a traditional rankings report.

Because these matters are emotional and private, people lean on AI tools precisely because they can ask hard questions without talking to anyone yet. A single early mention in an AI answer can shape a client’s shortlist days before they call, and the firm that reads as capable and understanding in that quiet research is often the one they contact. That is exactly why measurement has to come first. Assuming you look the same in Perplexity as you do in ChatGPT, or guessing which prompts you appear in, wastes effort. A family law audit measures the questions your actual prospective clients ask, across matters from divorce and custody to adoption, guardianship, and protective orders, in the tools they actually use, so the picture reflects your market and not a generic keyword list.

The audit follows a fixed methodology so the results are repeatable and comparable over time. Each step produces a specific, documented output rather than an impression.

We start by building a prompt set from real client questions, not invented ones. We draw on the questions people ask during consultations, the searches that already drive traffic in your market, and the phrasing people naturally use with a chatbot, which tends to be longer and more conversational than a Google search. The set spans the full journey: early prompts from someone just beginning to consider a divorce or worried about custody, mid-consideration prompts comparing firms and asking how support and property division work, and decision-ready prompts that ask for a recommendation in your city. A representative prompt set is the foundation of the whole audit, because you can only measure visibility against the questions you test.

We run the prompt set across the assistants your prospective clients actually use to research a matter: ChatGPT, Google’s Gemini and AI Overviews, Claude, and Perplexity. Each engine draws on different sources and phrases its answers differently, so a firm can be well represented in one and invisible in another. Measuring them separately is the only way to see those gaps. We record the full answer each engine returns for each prompt, including which firms are named and which sources are cited, so nothing rests on a single snapshot.

This is where the audit becomes numbers. Mention rate is the share of prompts in which your firm is named at all, engine by engine. Citation share goes further: of all the firms named across the prompt set, what proportion of those mentions are yours. If ten competitors are named across a hundred prompts and you appear in eight of them, your citation share is small even if your name technically comes up. We also grade the sentiment and context of each mention, because being listed as a trusted, capable option is very different from being mentioned in passing. These metrics give you a baseline you can re-measure later to see whether visibility is moving.

Your numbers only mean something in context, so we benchmark them against the firms that keep appearing in your market’s answers. The benchmark shows who the AI tools currently treat as the default family law authorities, how far ahead of you they sit, and which prompts they win that you do not. For a practice where a prospective client quietly builds a shortlist from these answers before reaching out, knowing who is already on that shortlist, and by how much, is often the single most useful part of the audit.

Finally, we look at where the engines are pulling their answers from. AI assistants lean on a recurring set of sources: legal directories, review platforms, editorial articles, bar association listings, and the firms’ own sites. The source-gap analysis maps which of those sources feed the answers in your market and where your firm is absent from them. It is diagnostic, not prescriptive here: the audit identifies the gaps and quantifies them, and the program that closes them is a separate engagement. The point of this step is to turn “we are not showing up” into a specific, prioritized list of where the visibility is being decided.

The deliverable is a clear report, not a data dump. For each engine you receive the raw metrics, the benchmark, and a plain-English read of what they mean. Here is the kind of information each section gives you and the decision it informs.

An audit is only worth running if it leads to a decision. The report ends with a prioritized read of where the visibility is being lost and which gaps are worth closing first, but closing them is a distinct body of work, and we keep it separate on purpose. Improving how AI assistants answer and cite is the job of answer engine optimization for family law firms, and earning durable presence and recommendation inside generative tools is the job of generative engine optimization. The audit hands those programs a measured starting point and a benchmark to improve against, rather than a guess. If you want the full picture of the search and AI stack those programs sit within, our AI SEO and GEO services lay it out.

Running the audit first also protects your budget. Because you know your baseline mention rate, citation share, and benchmark before any optimization begins, you can re-measure later and see whether the work actually moved the numbers. Measurement before and after is what separates a visibility program you can hold accountable from one you simply hope is working. If you want to see how that spend maps to the rest of a program, our family law SEO pricing page breaks down what moves the number.

The audit is only as good as the research behind it, and that is where we are built differently. Fuel Online runs the only in-house AI SEO research department in the space, a team dedicated to studying how AI assistants select, cite, and recommend the businesses they name. That research feeds our proprietary Fuel AI Index, which measures the information-gain advantage of content against the answers these engines already give. For your audit, that means the prompt set, the citation-share math, and the source-gap analysis are grounded in continuous measurement of how these models behave, not a one-off spreadsheet. You get a diagnostic built by people whose full-time job is understanding this exact problem.

It also means the audit is honest about its limits. AI answers shift as models update, so we treat the audit as a baseline you re-measure, not a permanent scorecard. We report what we can measure and we do not dress up a snapshot as a guarantee. We never promise a ranking, an AI citation, or a case outcome, and every deliverable is written to respect the state bar advertising rules your firm answers to and the discretion these matters demand. That is the only responsible way to advise a family law firm making a serious investment in how prospective clients find it.

We have grown businesses through search since 1998, earned more than 100 industry awards, and been named a top agency by Forbes, Yahoo, and Clutch. We have generated 4.25 million marketing leads for clients, driven hundreds of millions in client revenue, and delivered 143% average traffic growth for enterprise brands. Our team is 100% U.S.-based with no outsourcing, and our AI work is backed by the only in-house AI SEO research department in the industry and the proprietary Fuel AI Index. For a family law firm, that combination means an audit run by a partner that understands both the bar rules the work has to respect and the sensitivity these matters require. You can review our client case studies to see how we measure and report growth.