One question comes up in nearly every initial conversation with a legal tech company considering serious AEO work. It is some version of: “We have run the free grader, we have seen our score, we kind of understand the metrics — but what does a real assessment actually consist of?” The question is fair, and most agencies answer it badly. The honest answer is that a credible AI visibility assessment is not a single number from a single tool. It is a structured read across the six things an AI engine evaluates before it recommends a vendor, drawn from three independent data sources so that no one tool’s bias decides the outcome.
The six questions every assessment answers
An AI engine works through a rough sequence before it names a company. It has to understand who you are, verify that you are credible, confirm your claims, find evidence of your results, see that you answer the questions buyers actually ask, and only then decide whether to recommend you over the alternatives. The Legal Tech AI Visibility Assessment scores a company against exactly that sequence rather than against an abstract checklist.
| Category | The question it answers |
|---|---|
| Entity Definition & Technical Readiness | Can AI understand who you are? |
| Authority Signals | Can AI verify your expertise? |
| Authority Accessibility | Can AI connect and confirm your claims? |
| Proof & Validation | Can AI find evidence of your results? |
| Content Coverage | Are you answering the questions buyers ask AI? |
| AI Visibility & Recommendation Potential | Does AI confidently recommend you? |
The categories are weighted deliberately, because proof and recommendation potential drive an actual recommendation more than technical hygiene does. A company can pass every technical question and still score poorly, because the wider web gives AI engines nothing to corroborate. That pattern — accurate and well-regarded when found, but rarely surfaced — is the most common diagnosis in legal tech, and it is invisible until the assessment makes it legible.
Why three data sources and not one
No single grader sees the whole picture, so the assessment triangulates. Engine-level recognition, sentiment, and share of voice come from the HubSpot AEO Grader. On-site readiness — extractability, technical foundations, entity authority, and off-site citations — comes from an independent AEO scanner. A traditional search baseline, including domain authority and linking domains, comes from Moz. Read together, the three sources cover visibility across the engines buyers are using — ChatGPT, Perplexity, Gemini, and Claude — while grounding the result in the search fundamentals that still feed them. Any one tool can mislead; three rarely agree on a flattering lie.
From score to segment to roadmap
The composite produces an AI Visibility Score, and that score places the company in a program segment — Foundation, Optimization, or Authority and Visibility Growth — rather than a generic proposal. The segment determines the phase of work the company actually needs, sequenced into a 30/60/90-day roadmap the leadership team receives in a working readout. A company that lands in Foundation has an entity-definition and technical problem to solve first; a company in Optimization needs answer-first content, comparison pages, and citation building; a company in Authority is past those and into original research and earned visibility. Matching the work to the score is the point of measuring it.
Why this is a process, not a one-shot fix
The temptation, especially from more sophisticated buyers, is to ask whether the work can be compressed. It cannot, and the reason is mechanical. AI engines need time to discover, validate, and trust new information. Technical improvements can move grader scores within two to four weeks. Visibility gains typically take 30 to 90 days as new content and citations are indexed. Recommendation-level movement — the share-of-voice and confidence that change who gets named — usually takes 60 to 180 days. The roadmap is sequenced to that reality: understand, then earn trust, then earn the recommendation. Running the phases in parallel produces a plan that looks comprehensive but cannot prioritize.
What you walk away with
The output of the assessment is the scored report, an executive readout with your leadership team, and a prioritized roadmap your team can execute directly — with or without continued LTMG involvement. Companies that would rather have us run the roadmap convert to ongoing work, usually several weeks after the readout. That decision is theirs, and it is better made after they have seen exactly where they stand.
Call to action
Ready to see exactly how AI engines understand, trust, and recommend your company? The Legal Tech AI Visibility Assessment scores you across six categories and three data sources and hands your team a prioritized roadmap in two weeks. Book 20 minutes with Cathy to confirm fit and scope.
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