An established law firm can have decades of experience, strong referral relationships and excellent lawyers—and still be absent when someone asks ChatGPT to recommend a firm.
Newer firms may appear because an established firm’s reputation is stronger than the public evidence a search or AI system can retrieve, interpret and verify. Appearance alone says nothing about which firm is better.
The useful question is:
Can a prospective client—and the systems helping that client research—find clear, current and independently supported evidence of what this firm is qualified to do?
A 30-year reputation that ChatGPT could not see
In one r/LawFirm discussion, a managing partner described a revealing intake conversation. A prospective client had asked ChatGPT for lawyers in the firm’s practice area before ultimately coming through a colleague’s referral. The 30-year firm did not appear. Several newer firms did.
This is an anecdote, not a controlled study. We cannot know why those firms appeared from one set of prompts. But the story is useful because it shows how discovery now happens: a referral and an AI answer can form part of the same decision journey.
The referral created trust, and the online check tested it.
US research points in the same broad direction. Clio’s 2025 Legal Trends Report found that experience and reputation remain important to consumers, while referrals, online search, firm websites and reviews all contribute to finding and evaluating a lawyer. Among US consumers who had used AI for a legal question, 28% said it directed them to contact a lawyer. That does not mean AI recommended a named firm, and the finding should not be treated as Australian prevalence. It does show why principals should take AI-mediated research seriously without abandoning the channels already producing good work.
ChatGPT is not reading the room
Partners and peers may know that a lawyer has handled difficult matters for 25 years. A former client may remember careful advice at a critical moment. Counsel may know who can be trusted with a complex brief.
ChatGPT cannot inspect those private relationships. When ChatGPT Search is used, OpenAI says it can retrieve current web sources, use the context of the question and provide source links. OpenAI also says the product uses third-party search providers and partner content. It does not publish a stable formula for ranking law firms. See Introducing ChatGPT search.
This creates a simple gap:
- the firm knows what it has done;
- the market may know the firm by reputation; but
- the public web may contain only a generic biography, an outdated profile and a page saying “trusted legal solutions.”
The experience may be real while the supporting evidence remains difficult to retrieve.
Five reasons an experienced firm can disappear
1. The crawler is blocked
OpenAI identifies OAI-SearchBot as the crawler used to surface websites in ChatGPT Search. Its crawler documentation says sites that opt out will not be shown in ChatGPT Search answers, although a navigational link may still appear.
This setting is separate from GPTBot, which relates to possible model training. A firm can decline training access while allowing search access.
Check robots.txt, CDN rules, firewall settings and server logs before commissioning a large content rewrite. Allowing the crawler only makes a page eligible. It does not guarantee selection.
2. The website describes virtues instead of evidence
“Experienced.” “Commercial.” “Client-focused.” “Trusted advisers.”
These claims may be sincere, but they do not tell a reader—or a retrieval system—which matters the firm handles, where it can act, what the lawyers know, how the process works or what supports the claim.
Microsoft’s guidance on content inclusion in AI search answers recommends descriptive headings, self-contained answers, clear evidence and content that can be understood in smaller passages. This is Microsoft guidance, not an OpenAI ranking rule. The underlying editorial lesson is still sound: make each important page specific enough to be useful on its own.
Compare:
We provide strategic, client-focused family-law solutions.
With:
We advise separating business owners on property settlements involving company interests, trusts and jointly controlled assets in Victoria.
The second sentence does not manufacture authority. It makes a real capability easier to understand and test.
3. The firm’s identity is inconsistent
A firm may use its legal name in the footer, an abbreviated brand on its website, an old address in a directory and a different telephone number in structured data. A lawyer may be listed under two name variations with no clear connection between profiles.
Google says Organisation structured data can help it understand and disambiguate an organisation. Useful properties include the legal and alternate names, URL, address, telephone number, logo and profiles that identify the same entity. Google also makes clear that structured-data features are not guaranteed to appear.
For Australian practitioners, the relevant official register can add a layer of verification. The Australian Legal Profession Register covers practitioners in New South Wales, Victoria and Western Australia and points users to the appropriate regulator for more detail. It does not rank firms or prove expertise, and a practising-certificate jurisdiction should not be treated as the practitioner’s office location.
Make truthful facts consistent and verifiable wherever a prospective client would reasonably check them. Scattering the firm’s name across more websites will not fix contradictory information.
4. The best proof is trapped offline
Imagine a principal who has presented at 20 professional seminars and handled a specialised category of dispute for two decades. The website contains a two-paragraph biography. The useful seminar papers sit in PDFs. Matter experience lives in internal pitch documents. Media commentary has never been linked from the lawyer’s profile.
To a colleague, this person is an obvious expert. To a retrieval system, the evidence is fragmented.
Turn approved knowledge into accessible proof without publishing confidential matter details:
- clear lawyer biographies connected to specific capabilities;
- practical answers to recurring client questions;
- de-identified matter patterns where professional obligations allow;
- articles carrying a named author and review date;
- links to genuine professional recognition and commentary;
- current practice, jurisdiction and service-area facts; and
- HTML summaries for useful material that would otherwise live only in a PDF, image or video.
5. The firm treats one prompt as a league table
Ask the same question with a different location, matter description or constraint and the answer may change. Ask again after sources change and it may change again.
OpenAI says follow-up questions can use conversational context. That means “Who is the best lawyer?” and “Which firm advises Australian manufacturers on shareholder disputes?” are not equivalent tests.
Microsoft’s AI Performance reporting makes another useful distinction: citations do not reveal ranking, authority, placement or the role a page played in an answer. The reporting covers Microsoft surfaces and selected partners, not ChatGPT specifically, but the measurement warning travels well.
Measure each stage separately: citation, recommendation, visit, enquiry and suitable retained matter.
An illustrative comparison: two credible firms
Consider two fictional commercial firms in Melbourne.
Firm A has operated for 28 years. Its partners are respected by peers. Its website lists “commercial litigation” among twelve services, but provides no detail about matter types, industries, jurisdiction, authors or recent insights. Several directory profiles use an old office address.
Firm B has operated for six years. Its lawyers publish specific, reviewed explanations of shareholder disputes and urgent injunctions. Biographies connect the authors to those pages. The firm’s name, address and contact information are consistent. Relevant articles cite legislation and authoritative sources. External profiles corroborate the lawyers’ roles.
Nothing in this example proves Firm B is better. Firm B has simply made more relevant evidence available for a specific question.
Firm A can publish the deeper proof it already possesses instead of imitating the newer firm’s volume.
A cautious success story
In another r/LawFirm discussion, a practitioner said ChatGPT began mentioning them for a narrow field without a separate AI-optimisation campaign. They attributed the visibility to writing and speaking on specific subjects and being cited by others. They also stressed that the field was small and the result should not be generalised to a highly competitive market.
Again, this is self-reported and does not establish causation. But it suggests a healthier strategy than chasing a loophole: become genuinely useful on a defined subject, publish that knowledge clearly and make external evidence easy to verify.
Research presented at KDD 2024 found that source presentation can affect measured visibility in experimental generative-search settings, but the effective approach varied by domain. The paper’s “up to 40%” result is a benchmark outcome—not a promise of traffic, enquiries or revenue for a law firm. See GEO: Generative Engine Optimization.
A six-part evidence audit
1. Crawl eligibility
- Is
OAI-SearchBotallowed? - Are important pages accessible without a login?
- Does the server return the correct status code?
- Are useful answers available in HTML rather than only in images or PDFs?
2. Entity clarity
- Is the firm’s legal and trading identity clear?
- Are office, service-area and contact facts current?
- Do lawyer biographies, regulator records and professional profiles refer to the same people consistently?
3. Specific expertise
- Does each priority practice page name the matters, clients, situations and jurisdictions it genuinely covers?
- Can a reader tell when the firm is—and is not—a suitable fit?
- Are important claims supported rather than repeated as adjectives?
4. First-party proof
- Are lawyer insights, matter patterns, methods and approved examples published?
- Are articles attributed to a qualified author and reviewed when the law or market changes?
- Can useful evidence be understood without downloading a brochure?
5. Third-party corroboration
- Are regulator and professional profiles accurate?
- Are real awards linked to the awarding body rather than displayed as unexplained badges?
- Can media commentary, speaking engagements and independent citations be verified?
- Are reviews genuine, current and handled within professional obligations?
6. Commercial measurement
- Does intake ask how the prospect first heard about the firm and what they used to evaluate it?
- Are AI referrals recorded separately from ordinary organic search where possible?
- Can the firm connect the source to suitability, retained status and an agreed value measure?
The final step matters. A firm can become more visible and still attract the wrong work. Paretoid’s law-firm marketing ROI model separates channel activity from suitable enquiries and retained matters.
What not to buy
Be cautious when a provider offers:
- a guaranteed position in ChatGPT;
- hundreds of generic AI-written pages with no lawyer involvement;
- fake reviews, invented case studies or purchased “best lawyer” badges;
- hidden prompt-injection text intended to manipulate an AI system;
- citation screenshots presented as stable rankings; or
llms.txt, schema or any single technical file as a complete strategy.
No responsible provider can guarantee a ChatGPT recommendation. OpenAI does not publish the comparison formula, and recommendation answers can change with the question, context and available sources.
A practical 90-day plan
Days 1–15: establish the baseline. Choose a small set of real client questions tied to priority matters and locations. Record the answers, cited sources and dates. Check crawl access, indexability and entity facts.
Days 16–45: repair the evidence gaps. Improve one priority practice page and its connected lawyer biographies. Publish two or three genuinely useful answers based on approved expertise. Correct inconsistent profiles and link independent proof.
Days 46–75: strengthen corroboration. Turn a seminar, presentation or recurring matter question into a useful public resource. Seek legitimate opportunities for lawyer commentary. Do not manufacture mentions.
Days 76–90: reassess carefully. Repeat the same prompt set, examine cited pages, review search visibility and check intake records. Treat any change as a signal to investigate, not proof that one edit caused the result.
Make reputation legible before chasing visibility
An experienced firm does not need to become noisier than every newer competitor. It needs to make the right parts of its experience accessible, specific and verifiable.
Start with one matter opportunity. Identify the proof a suitable client would need. Publish what can responsibly be shown. Correct the technical and identity gaps. Then measure whether discovery contributes to suitable enquiries and retained work.
Paretoid’s law firm SEO approach and paid Opportunity Map use that sequence: opportunity, evidence, delivery and measurement before more activity.