How AI represents the firms clients hire.
When a general counsel asks an AI engine which firm to call for a specific matter, does your name surface — and in what context?
The current state of AI visibility in legal.
General counsel and in-house teams use AI engines for triage on practice-area expertise and matter-specific firm recommendations. Engines lean heavily on bar directories, court filings, ranking publications, and firm-published thought leadership. Boutiques with deep practice-area authority frequently outperform full-service firms in narrow-question recommendations.
General counsel decisions moved into AI answers.
GCs increasingly draft initial shortlists from AI triage. Absence at the practice-area surface costs RFP position before procurement formally begins.
AI restatements of firm capabilities can implicate state-bar advertising rules. Firms remain responsible for misrepresentations that propagate from their owned content.
Engines lean on court filings and Chambers/Legal 500 references. Firms with thin court-citation footprint are systematically under-recommended.
Practice-group authority signals predict lateral attraction. AI-surfaced strength is now part of the lateral due-diligence process.
The categories that carry the most decision weight here.
All ten AIVS measurement categories apply to every sector — that is what makes scores comparable across industries. But in legal, six categories carry disproportionate decision weight. We emphasize these in your dashboard, your reporting, and your competitive analysis.
The contribution model is proprietary. Sector emphasis is editorial — we tell you which categories matter most for legal decision-makers and why. We do not publish the numeric weighting.
How often AI surfaces your firm in practice-area triage. Practice-area absence costs RFP shortlist position.
See your AI Presence evidence →When you surface, whether you are recommended or merely listed. Recommendation language drives engagement.
See your Recommendation Strength evidence →Which sources AI cites alongside your firm — court filings, Chambers, Law360 outweigh self-published content.
See your Citation Authority evidence →How accurately AI resolves your firm — practice areas, lead partners, geographic footprint, alumni. Entity errors propagate.
See your Entity Authority evidence →Your win rate against named peers when GCs ask AI 'who would you call for X'.
See your Competitive Position evidence →Your standing in Chambers, Legal 500, Law360, AmLaw rankings, and bar-association references.
See your Industry Authority evidence →Why these categories matter more here.
Legal recommendations from AI answer engines are increasingly the first input to a general counsel's firm-selection process. Entity authority matters more here than in most sectors because firms have layered structures — partners, practice groups, offices, alumni — that AI engines must resolve correctly to recommend confidently. Practice-area authority also dominates: AI engines lean heavily on Chambers, Legal 500, Law360, and AmLaw rankings, plus court-citation footprint, when answering practice-specific questions.
Recommendation strength is the dimension most often misread by firms. The difference between 'one of the firms handling this kind of matter' and 'a leader in this practice area' is decisive in GC triage. Our reporting tracks the language of recommendation, not just the presence of a mention.
Patterns we observe at sector scale.
Across the six engines and millions of legal prompts in our universe, recommendation behavior follows distinctive patterns. These are the patterns most consequential to how your institution surfaces.
Engines defer to Chambers, Legal 500, and Law360 in practice-specific questions; full-service firms with thin practice-area coverage are under-recommended.
Evidence available to customers →Engines surface name-partner authority differently from firm authority; firms with weak partner-level digital footprint are recommended less confidently.
Evidence available to customers →Recommendation patterns shift sharply by court, regulator, or jurisdiction; firms strong nationally can be invisible in specific venues.
Evidence available to customers →Practice-area moves take 6–9 months to reflect in AI-recommendation patterns; lateral hiring teams often surface for prior firms longer than expected.
Evidence available to customers →Engines surface notable alumni in firm-recommendation answers; alumni network density is a stronger predictor than headcount.
Evidence available to customers →What AI engines actually read about legal.
Across our prompt universe, AI engines cite a recurring set of sources when answering legal questions. We track these continuously and weight your visibility analysis to the sources that move the needle.
We do not publish the relative weights we assign to these sources — that is part of the proprietary engine. We do publish the sources, so you can audit which inputs we read.
| Chambers and Partners | Practice-area band rankings and editor commentary. | High |
| Legal 500 | Practice-area firm and individual rankings. | High |
| Law360 | Firm-attributed news and practice-area coverage. | High |
| AmLaw 200 | Financial and headcount league tables. | Medium |
| Court filings (PACER) | Counsel-of-record evidence across federal courts. | Medium |
| State bar directories | Admission and disciplinary records. | Medium |
| American Lawyer features | Long-form editorial profiles. | Recurring |
| Self-published practice content | Firm thought leadership and matter announcements. | Recurring |
Six ways legal institutions go invisible in AI.
These are the patterns we encounter most often in legal client engagements. Each maps to a specific category in your AIVS readout.
Full-service firms with limited Chambers and Legal 500 coverage in a practice area are systematically under-recommended in matter-specific GC queries.
Engines surface partner-level authority detached from firm — firms get credit only when both signals align.
Strong national firms can be invisible in specific courts or regulatory venues without local court-citation footprint.
AI engines attribute incoming laterals to their former firms for 6–9 months; the receiving firm loses credit during the lag.
Practice-deep boutiques outperform full-service firms in narrow questions; full-service firms with shallow specialist content lose recommendations.
Firms that are mentioned but not recommended — 'firms handling this' rather than 'leaders' — lose the GC triage downstream.
Who AI is recommending in legal, right now.
Sector ranking, refreshed hourly. Methodology, confidence, and movement transparent on every list.
| # | Organization | Score | Δ 7d | Strength |
|---|---|---|---|---|
| 01 | Legal brand A | 921 | -5 | |
| 02 | Legal brand B | 911 | -2 | |
| 03 | Legal brand C | 901 | +0.1 | |
| 04 | Legal brand D | 891 | +1.2 | |
| 05 | Legal brand E | 881 | +2.1 | |
| 06 | Legal brand F | 871 | +3.8 | |
| 07 | Legal brand G | 867 | -4.3 | |
| 08 | Legal brand H | 857 | -1.2 | |
| 09 | Legal brand I | 847 | +1.2 | |
| 10 | Legal brand J | 837 | +2.5 |
Where legal competitors win — and how to close the gap.
Every loss to a competitor in an AI answer maps to one of six gap categories. We surface the category, the evidence, and the prioritized remediation.
Peer surfaced in a practice-area or matter-type query; you did not.
Both surfaced — peer was a 'leader'; you were 'one of the firms'.
Engines cited the peer's court filings or Chambers entries — not yours.
Engines describe the peer with stronger practice authority language.
Chambers, Legal 500, and Law360 favor the peer in the practice band.
Engines attribute recent lateral hires to your peer.
Three legal playbooks our research team runs.
Build the practice-area citation depth required for high-stakes GC recommendations.
Read playbook brief →Translate name-partner authority into firm-level AI surface area.
Read playbook brief →Build venue-specific recommendation strength in markets where you are systemically under-recommended.
Read playbook brief →What our legal research team published this quarter.
Practice depth beats brand breadth in matter-specific AI triage.
Engines continue attributing lateral partners to prior firms for nearly a year.
PACER density correlates more closely with GC-prompt recommendation than headcount.
Quarterly intelligence built for legal leadership.
Practice-band benchmark across AmLaw 200 and elite boutiques, with the GC-triage recommendation patterns mapped to authority sources.
Request the report →Catalog of partner-affiliation and practice-area misrepresentations.
Request →How the AIVS methodology applies in legal.
The AI Visibility Score is calculated identically across every sector — same ten categories, same composite scale, same governance. What differs in legal is which categories carry more decision weight, which evidence sources we monitor most heavily, and which patterns we surface as priority remediation. This page is our editorial mapping. The underlying methodology is published at our Methodology page.
Sector emphasis is editorial — we do not publish numeric weighting per sector. The contribution model is part of the proprietary engine.
Legal questions we answer most often.
The legal research roadmap.
Sector intelligence improves continuously. These are the rankings, reports, and analyses scheduled for release.
Litigation, M&A, tax, IP tracked separately.
First named-entity ranking of practice-area boutiques.
Recommendation patterns by federal and state venue.