How AI Recommends NYC Divorce Attorneys

Executive summary

Buyers facing a major life decision — divorce, fertility treatment, plastic surgery, a custody dispute — increasingly consult ChatGPT, Claude, Gemini, or Perplexity before they ever type a search query into Google. For attorneys in high-stakes consumer-services categories, the AI assistant has become the new shortlist generator. Whether you appear on that list is no longer downstream of your Google ranking; it's a separate, parallel, increasingly important visibility problem.

This report measures, with reproducible methodology and honest confidence intervals, who AI assistants recommend when buyers ask for NYC divorce and family law attorneys. We sent 240 buyer-style questions across four AI assistants, sampled across 13 conversation-state buckets, and split the corpus into a 544-response ranking layer (questions that ask AI to evaluate or recommend firms) and a 1,421-response editorial layer (questions that capture how AI talks to buyers in crisis, in early research, under financial pressure, or when AI is asked to explain a topic without naming firms). Every named firm is parsed against a pool of 770 NYC legal entities. Our findings:

• Four firms form the consensus tier — confidence-interval lower bounds above the noise floor. Aronson Mayefsky & Sloan leads at 21.0% share [17.8–24.6], with Berkman Bottger Newman & Schein in a statistical near-tie at 17.8% [14.8–21.3]. Blank Rome (14.7%) and Chemtob Moss & Forman (13.4%) round out the tier.
• AI assistants disagree meaningfully. In the ranking layer, Claude averages 2.5 firm citations per response. ChatGPT and Perplexity each average 1.7. Gemini averages 0.6 — it declines to name firms more often than not.
• Conversation state — not keyword intent — is the right axis. A buyer in crisis ("my wife filed yesterday") gets a different response shape than a buyer asking for three firms to interview, even when the underlying need is identical. Splitting the scan along conversation state surfaces firm-citation behavior that keyword-style GEO tools cannot see.
• Process- and crisis-stage queries route through directories. When the prompt is "I want to file for divorce, where do I start?", AI assistants cite Avvo, Martindale-Hubbell, and the NYC Bar Lawyer Referral Service more often than any individual firm. These appear in our editorial layer and are explicitly excluded from the ranking; counting them would deflate every firm's share.

The methodology underlying this report — versioned prompt sets, two-layer ranking/editorial split, honest confidence intervals, immutable snapshots, full verbatim source traceability — is published in full at viclaro.app/leaderboards/methodology. Every citation in the rankings below traces back to a specific AI response, viewable on the public leaderboard.

1. Why measure this at all

The classic SEO measurement playbook — Google rankings, organic search traffic, keyword positions — describes a world in which buyers type queries into a search box and click a blue link. That world is collapsing under a new layer: buyers asking a conversational AI for a recommendation before they ever open a search engine.

The conversational layer behaves differently in three important ways. First, it doesn't return ten links; it returns three to five firm names, often with a recommendation tone ("Berkman Bottger is a leading NYC family-law firm known for..."). Second, the buyer doesn't click — they read the recommendation, internalize it, and then search Google for the firm by name. Third, the AI's choice of who to recommend depends on signals that overlap with but are not identical to Google's ranking signals: schema markup, FAQ structure, quotable headline claims, named partners, and content that mirrors the buyer's actual phrasing.

For attorneys in NYC family law — a category where one new client can be worth $25,000–$200,000 in legal fees, and where almost every buyer is doing weeks of research before they engage — being on the AI's shortlist is now a material distribution channel. This report is the first reproducible measurement of who currently occupies that channel.

2. Methodology

We constructed a versioned prompt set ("divorce-attorneys v3.1") of 240 buyer-style questions grouped into thirteen conversation-state buckets — categories that describe where the buyer is in the recommendation conversation, not what keyword they would type. Four buckets feed the ranking; nine feed the editorial layer:

• Situation, Decision, Validation, Follow-up — the ranking layer: questions where the buyer is asking AI to evaluate or recommend firms.
• Crisis, Advice, Learning, Constraints — emotional / financial pressure states where AI typically does not list named firms.
• Process, Specific need, Conversational, Style/approach, Broad authority — topical questions where the brand may appear as a quoted source rather than as a recommendation.

The two-layer split. Not every question is designed to produce a firm recommendation. When a buyer in crisis asks "be honest, do I have a case," the appropriate AI response is usually advice — not a list of names. Counting those responses toward share would artificially deflate every firm. We therefore split the corpus into two layers:

• Ranking layer — the four ranking buckets above. Only citations from these scans count toward share % and rank. 544 responses in this run, from 97 distinct buyer questions.
• Editorial layer — the nine non-ranking buckets above. Verbatim responses appear on the leaderboard as qualitative evidence, but they never feed the ranking. 1,421 responses in this run, from 143 distinct buyer questions.

Each question was run against four AI assistants — Claude, ChatGPT, Gemini, and Perplexity — with multiple samples per (question × assistant) to capture how consistently each assistant gives the same answer. Each assistant received each question independently: no system instructions, no memory, no conversation history carried in (except for the Follow-up bucket, which explicitly stages a multi-turn exchange).

Every AI response was parsed into a JSON array of named businesses. Names were normalized (suffix-stripped, sub-group prefixes like "Matrimonial Law Group at X" collapsed onto the parent firm) and matched against a pre-harvested pool of 770 NYC legal entities. When an assistant mentioned a firm we hadn't indexed yet, we looked it up and added it to the directory so future scans would catch it.

We compute three statistics per firm in the ranking layer:

• Recommendation share — citation count / ranking-layer responses. A firm cited in every ranking-layer response would score 100%.
• Honest 95% confidence interval on the share. The interval widens as the sample shrinks; a firm with the same share at a smaller N has a wider interval.
• Cross-assistant coverage — how many of the four assistants (out of 4) cited the firm at least once. A firm cited by all four is a structurally different signal from one cited only by Perplexity.

Tier classification: Consensus if the CI lower bound is clearly above the noise floor; Mid-tier if cited by ≥ 2 assistants with share > 3% but the CI still spans the floor; Long tail otherwise. Long-tail firms are still ranked and shown, but the position should be read as ordering, not a statistical claim.

Snapshots are immutable. Every ranking is reproducible against the same prompt-set version and the same assistants, within measured sampling variance. Every citation in the rankings below has a verbatim-AI-response permalink, available to research and commercial partners.

Bucket structure and per-version question counts are published at viclaro.app/leaderboards/methodology. Verbatim question text isn't published — the wording is part of what makes the Atlas measure conversation-state behavior rather than search-engine behavior.

3. The consensus tier — four firms AI assistants recommend across the board

The strongest finding from the scan is the consensus tier: four firms with confidence-interval lower bounds clearly above the noise floor at this sample size. Three are cited by all four assistants; the fourth (Chemtob Moss & Forman) is cited by two but at a share high enough to clear the bar regardless.

Three observations about this tier:

The top two are a near-tie. Aronson Mayefsky & Sloan leads at 21.0% [17.8–24.6]; Berkman Bottger Newman & Schein follows at 17.8% [14.8–21.3]. The confidence intervals overlap on the inner edges — Aronson is meaningfully ahead, but not so far ahead that snapshot-to-snapshot rank swaps would be surprising. Blank Rome (14.7%) and Chemtob Moss & Forman (13.4%) are the clear #3 and #4.

Cross-assistant coverage is the cleaner authority signal than raw share. Three of the four consensus-tier firms are cited by all four assistants. A firm cited by 4/4 shows up regardless of which AI a buyer happens to be using, which is the structural definition of category authority. Chemtob (2/4 coverage at 13.4% share) is a different kind of signal: very strong on the assistants that cite it, invisible on the others.

The drop-off to mid-tier is sharp. Rank #5 (Cohen Clair Lans Greifer Thorpe & Rottenstreich) scores 7.9% — roughly half the bottom of the consensus tier. There is real separation between "AI recommends you across conversation states" and "AI recommends you only when the prompt is shaped just right."

4. The mid-tier — real signal, partial visibility

Below the consensus tier sit three firms in the mid-tier: cited by at least two assistants with share above 3% but with confidence intervals that still span the noise threshold. These are real recommendation winners on some buyer questions and invisible on others. Cohen Clair Lans Greifer Thorpe & Rottenstreich (7.9%, 4/4 coverage) is the clearest case — cited everywhere, but not often enough on any single assistant to clear the consensus bar. Kasowitz Benson Torres (7.5%, 3/4) and Warshaw Burstein (4.0%, 3/4) round out the tier.

Two patterns recur as we look further down the long tail:

• Single-assistant dominance. Many firms appear repeatedly on Perplexity and almost nowhere else. Perplexity is web-search-grounded and surfaces firms with strong site presence even when their training-data footprint is thin. If a meaningful share of your buyers use Perplexity, the competitive set looks different from the cross-assistant consensus tier.
• Sub-specialty positioning. Firms that publish content explicitly about a niche — collaborative divorce, business-owner divorce, LGBTQ-affirming practice — surface on the questions that match that lane and almost nowhere else. The cost is breadth; the benefit is a defensible territory that the consensus-tier generalists don't compete in.

5. The editorial layer — where AI sends buyers in crisis

The reason the editorial layer is separated from the ranking is that it does something the ranking layer can't show. When a buyer is in crisis ("my wife filed yesterday"), early research ("walk me through how NY divorce works"), or working under hard financial constraints ("I have $10K total"), AI assistants typically don't list named firms at all. They route the buyer toward directories, referral services, and process information — Avvo, Martindale-Hubbell, the NYC Bar Lawyer Referral Service, the New York State court system's self-help portal.

This matters because these are exactly the high-intent moments that historically arrived at firm websites via informational SEO content — "what to expect in a NYC divorce," "how does NY divorce work," "how to find a divorce lawyer." On Google, the buyer lands on a firm-authored explainer page. On ChatGPT, Claude, or Gemini, that same buyer is routed to a directory listing, because AI assistants find structured directory content more quotable than firm prose for these queries.

For firms, there are two responses. The first is to publish process- and crisis-stage content explicitly designed to be quoted: short, structured answer blocks under question-shaped H2s, with FAQPage JSON-LD schema. The second is to accept that directory placement is now its own distribution channel — getting listed prominently on Avvo and Martindale is no longer vanity SEO; it's how AI now routes early-funnel buyers.

The editorial layer is what makes this visible. A ranking-only methodology would either drop these queries (losing the signal entirely) or count them and dilute every firm's share with the directory mentions. Splitting the two layers lets us measure both honestly: the ranking layer reflects who AI recommends when asked, and the editorial layer reflects what AI does instead when the buyer hasn't yet asked for names.

6. Per-assistant divergence — why "AI visibility" isn't one thing

The four AI assistants tested do not behave the same way. Across the ranking-layer responses each one produced for this category:

The implication for firms is that "are you AI-visible?" is not a single binary question. A firm strong on Claude and ChatGPT but absent from Gemini is invisible to roughly the Gemini-using fraction of the market. A firm cited only on Perplexity reaches the search-augmented user but not the casual ChatGPT buyer.

Gemini's conservatism is particularly notable. It hedges heavily on legal recommendations — its 0.6 citations per response figure suggests Google's product team has deliberately calibrated against naming attorneys directly. A firm targeting Gemini-using buyers cannot rely on standard GEO tactics; the answer is to optimize for being mentioned in Google's own indexed sources (review aggregators, business directories) that Gemini's grounding system surfaces.

7. What separates the consensus tier from the long tail

The 770 firms in the harvested pool divide into three groups in this snapshot: 4 consensus-tier firms, 3 mid-tier firms, and 104 long-tail firms ranked. The rest were cited too rarely to enter the ranking at this sample size. Examining the websites of the consensus tier alongside the long tail reveals several structural differences:

• Question-shaped page titles. The consensus-tier firms have pages titled around the buyer questions — "What is a high-net-worth divorce?", "How does contested custody work in New York?" — not generic service pages ("Family Law", "Divorce Services"). AI assistants match content to query phrasing; question-shaped titles win.
• Named partners on landing pages. Pages that name specific attorneys by full name in H2s and structured author markup get cited far more than anonymous "our team" pages. AI assistants are pattern-matching on "person + practice area + location," and explicit named attribution is what they latch onto.
• Quotable thesis statements. The cited firms have a position — "we are the only firm in NYC that specializes exclusively in matrimonial law," "we represent business owners and partnership stakeholders," "our practice is built around collaborative outcomes." AI assistants prefer to quote claims, not adjectives.
• FAQPage JSON-LD on relevant pages. Schema markup is not the only signal but is a consistent differentiator. Firms with valid FAQPage schema on their highest-traffic pages are over-represented in the consensus tier.
• Press footprint. Firms cited in the Wall Street Journal, New York Times, or specialty trade press accumulate citation footprints that survive into AI training data. The consensus-tier firms each have multiple recent press mentions; most long-tail firms have none.

None of these signals is sufficient alone. But the consensus-tier firms all exhibit four or more of them; the long-tail firms typically exhibit zero or one.

8. Methodological honesty — what this report does not measure

A report of this kind must be explicit about its limits.

• We measure citation share, not quality. A firm cited often by AI is not necessarily a better attorney than a firm cited rarely. We measure visibility in a specific layer; legal outcomes are a different question.
• We measure four AI assistants, not all AI surfaces. Microsoft Copilot, ChatGPT in voice mode, Claude in mobile apps, and various agent-based wrappers may behave differently than the API-accessed assistants we test. The big four cover the dominant share of buyer behavior today, but the surface is fragmenting.
• Mid-tier rank movement is uncertain. The 95% confidence intervals on mid-tier firms span 5–7 percentage points. Reading rank changes from #15 to #12 between snapshots as a "rise" is not statistically supported at this sample size. Top-7 ranks are more stable; mid-tier requires larger N before movement is meaningful.
• We do not measure click-through or conversion. Being cited by ChatGPT does not necessarily produce a phone call. The recommendation channel exists, but the conversion economics of that channel are still being measured.

9. Conclusions

Three things are true at once:

AI recommendation visibility is now a measurable, ranked, reproducible distribution channel. The methodology underlying this report — versioned questions, multi-sample scans, honest confidence intervals, immutable snapshots — produces ranks that survive re-running and can be defended in writing to a sophisticated reader.

The channel is structurally winnable. The gap between consensus-tier firms and the long tail is large but not random. Firms that publish question-shaped, named-partner, schema-marked content occupy the consensus tier. The pattern is reproducible, and the cost of publishing the right content is bounded.

The conventional "AI optimization" pitch is partially wrong. Most tools in this space measure a single assistant, a single prompt format, or a single "AI visibility score." This report's data demonstrates that those simplifications hide more than they reveal. Per-assistant divergence is real. Conversational questions surface different firms than keyword queries. Process-stage questions route through directories. A firm that optimizes for an averaged score risks optimizing for nothing in particular.

The full live index for this category — updated as we re-scan, with verbatim source traceability per citation — is at viclaro.app/leaderboards/nyc/legal/divorce. Methodology disclosure is at viclaro.app/leaderboards/methodology.