AI Visibility Audit for legal & immigration · AI Discovery Intelligence
When someone asks ChatGPT, Claude, Gemini, or Perplexity whether they need a lawyer for a matter, the best firm for it in your city, or whether they qualify, the engine answers - naming a competitor, a directory, or no one. The intake call that used to start with that question now starts, and often ends, in a chat window.
28 Labs measures how the engines recommend your practice - in every market you operate in - shows who is named above you, and gives you the moves to win it, across all four engines.
4 engines · per-market query set · every read confidence-tagged
Measured from the outside. We do not measure training-data exposure, and we never present a claim we cannot source.
The queries that bring clients
per market
A firm's clients ask hundreds of meaningful questions before they call. We score each by intent and run the set across every market you serve, not the general-explainer long tail.
The shift
When someone asks an engine who to call or whether they have a case, it answers off your property - you do not know if it named you, a competitor, or a directory.
The client's shortlist - and sometimes the decision not to call at all - now forms inside a chat window before they search your name.
We measure how the four engines recommend your practice in each market you serve, show where you stand and who is named above you, and hand you the moves to change it.
The board-level risk
A firm's moat is being the practice clients trust enough to call. Answer engines now sit in front of that decision in every market you serve. The exposure is specific, and it is measurable.
"Do I need a lawyer for {matter}" gets answered directly - and sometimes talks the client out of calling at all. The first question that used to open an intake now resolves inside a chat window.
Engines lean on legal directories and review sites, naming aggregators or competitors ahead of the firms themselves - so the client's shortlist forms around third parties, not you.
"Do I have a case / how much does it cost" - the questions that precede an intake call - get answered without you, sometimes inaccurately, in a market where the answer should have come from your firm.
"Is {firm} reputable" gets decided by reviews and forums the engine trusts. A trust verdict is rendered about your practice, in each market, without you in the room.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another firm's numbers to make the point.
The query universe
A firm's clients do not ask four questions. They ask hundreds, across a journey, in every market you serve. The work is not to track every query. It is to score each by commercial intent and run the high-intent set across every market, mapped to where the client is in the decision.
Eligibility, cost, and best-firm queries carry the intakes. Open-ended research like general "what is {area of law}" carries almost none. We weight what you track to where the intakes are - so the read you get is the one that matters to the practice, in each market, not a vanity average across the long tail.
How we keep it honest
No one can prove a single AI recommendation caused a single lead. Anyone who claims they can is selling certainty that does not exist. We treat it the way marketing-mix modeling treats a channel: we estimate the contribution from converging signals and we label the confidence of every read, so your analysts can audit any claim back to the evidence behind it.
When a read is directly observed in your own data, we say so. When it is inferred, we say so. When it rests on industry intelligence, we say that too. You never get a confident-sounding number with nothing under it.
Directly observed. The signal is present in the AI-engine output itself. We saw it happen.
Inferred. Multiple independent signals converge on the same read, but no single source confirms it outright.
Industry intelligence. The read rests on category benchmarks and external patterns, not your own data. Treated as directional.
What winning takes
A firm wins when it becomes the source the engines trust to answer the matter, in each market it serves. That is structural. More blog posts will not do it. Being the authoritative, reachable source for what clients actually ask will.
We diagnose where the engines already trust you, where they trust a competitor instead, and the specific moves that shift that trust toward you - in every market you serve. The output is a build plan your teams can act on, not a list of keywords.
Start here
A 60-minute working session. We walk a sample audit, show you how the per-market recommendation read would shape up for your practice, and scope a live run across the markets you operate in.
No obligation. We do not measure training-data exposure, and we tell you what we can and cannot see before you commit.