The AI platform Legal Services use to get recommended on
One matter can be worth a year of marketing, and the shortlist now forms inside a chat window. LBOX measures what the engines say about your firm, shows which firms and directories they name instead, and builds the evidence that changes it.
Measured across
Perplexity
Gemini
Google AI
Claude
Copilot
Meta
DeepSeekGrok... and moreEvery one of them is competing for the same answer. One name comes back when a buyer asks.
What makes Legal Services different
Not general marketing advice. These are the constraints your category actually operates under.
The model answers the legal question, so the call never happens.
Can I break my lease. Do I have a case. How long do I have to file. Those questions used to start a consultation. Now they get a confident paragraph, and the person only reaches out if a firm was named in it.
Lead sellers are quoted in your place, then sell you back the lead.
The engines lean on Avvo, FindLaw, Justia and Martindale because those sites are structured and dense. Several of them monetise by selling your name back to you, so the source answering for you is also your vendor.
Practice area and jurisdiction get flattened.
A model that recommends your firm for a matter you do not take, or in a state where nobody at the firm is admitted, creates a problem a plumber does not have. Wrong is not just a wasted call here, it is an intake and advertising exposure.
Reputation is your whole business and it is being summarised by strangers.
Referrals and standing decide who gets the call. When a model summarises your firm it is reading review counts and directory bios, not the outcomes you would actually put in front of a client.
What you get back
The console, measuring a company in Legal Services against the questions its buyers ask. Every brand named, every domain cited, every engine separately.
A worked example on an illustrative company, shown to display the console. Your own numbers come from measuring your questions through the engines’ own APIs.
See which firms the engines name
Visibility and position on the unbranded who-should-I-hire questions your prospective clients ask, measured per engine.
- Ranked against the firms the engines actually named
- See where a model reads your pages without naming you
You against the field, engine by engine
unbranded questions| Brand | Gemini | AI Overviews | OpenAI | Perplexity | Claude | ChatGPT |
|---|---|---|---|---|---|---|
| Baldwin Reyes | 30% | 28% | 26% | 22% | 14% | 29% |
| Callow Dunn | 25% | 22% | 21% | 18% | 11% | 24% |
| Harrow & Finchyou | 20% | 11% | 17% | 14% | 8% | 4% |
| Pike Warrender | 12% | 9% | 8% | 10% | 5% | 9% |
Turn what the answers get wrong into work that ships
Findings arrive as a worklist sorted by who can act: what LBOX writes, what needs your decision, what needs a partner to review.
- Scored against your practice areas and jurisdictions, not a generic SEO checklist
- Nothing publishes without your sign off
The worklist
Prove the change, quarter after quarter
Every measurement is stored, so the line lengthens each cycle and the movement is on the record rather than asserted.
- The same questions, asked again and checked
- Sources broken out per model, per page
Visibility over time
every measured runKnow when the models come to read your practice pages
Crawler activity captured at the edge, so you can see which models fetch your practice area and attorney pages.
- Every model, every page, logged over time
- Robots and content signals kept current
Crawler activity
hits by model, last 30 daysWhere you disappear as the client gets closer to calling
Presence falls away as intent rises. The who-should-I-hire questions are the ones that become a matter, and the ones directories win.
- Visibility split by where the question sits in the journey
- Names the stage that costs you the intake
Visibility by buying stage
where you show up in the journeyThe sources the engines trust about law firms
Legal directories decide which firms get named. Several of them also sell leads, so the site answering for you may also be billing you.
- Owned versus earned, so you know what to build and what to earn
- Every citation traced to the answer it supported
Which domains the engines cite
904 citationsThe program for Legal Services
Everything below is included. Measured through the engines’ own APIs, so the same questions can be asked again and the result checked.
- Measure what every major AI engine says about your firm today, through the engines own APIs
- The questions your prospective clients actually ask, mapped by practice area and jurisdiction
- Where a directory or lead seller is answering in your place
- Whether the engines state your practice areas, jurisdictions and bar admissions correctly
- Which sources the engines cite for your practice areas, and which of those you control
- Practice area and attorney pages built so the engines can read and quote them accurately
- Re-measurement on the same questions, so the change is provable rather than asserted
What this does not show: what any individual user saw. LBOX measures what the engines answer through their own APIs, which is repeatable and checkable. It is not a record of any one person’s session, and it does not predict revenue.