AI search, GEO, and the
law-firm marketer's playbook.

Wondering whether your firm shows up when clients ask an AI assistant for a lawyer? Ten questions you can answer yourself — each mapped to something AI engines actually check — that tell you where your firm's AI-search foundation is solid and where it's quietly costing you referrals.

Every legal marketing agency now says it does AI search. Most are running an SEO playbook with a new label. Here's what genuinely differs between the two disciplines — the corpus, the unit of success, the measurement, and the compliance surface — and the questions that tell you which one you're actually buying.

The GEO category is new and every provider claims to do "AI." Seven concrete tests that separate a real GEO partner from a repackaged SEO retainer — measurement you can check, built-in compliance, legal-specific expertise, honest mechanics, low-risk terms, visible work, and self-honest claims.

A large share of the highest-intent immigration searches in the US are made in Spanish — and AI assistants answer in the language they're asked in. Most firms are invisible to that demand. Why machine translation doesn't fix it, the notario compliance trap, and what genuinely closes the gap.

Generic law-firm GEO advice misses what's specific about immigration: clients search in many languages, intent is urgent and high-stakes, and the law changes constantly. What actually gets an immigration firm cited by AI search — and the compliance tripwires that are sharper in this vertical.

Most AI-visibility reporting is a screenshot, a traffic chart, or a percentage with no denominator. This is our full method instead — the six engines we track as separate rows, why the prompt set is frozen, why a person still runs a manual pass every month, the numbers we refuse to report, and how to run a lighter version of it yourself.

A week-by-week walkthrough of month one — the 50–100-prompt baseline, entity consolidation, the content-gap map, the compliance log, and the first PROOF Report. Specific enough to picture the engagement; specific enough to audit the method.

SEO checklists tell law firms to add ten kinds of schema. For AI search, a small subset does the real work — and structured data is still lawyer advertising. What to prioritize, what to skip, and the compliance tripwires.

Most marketing optimizes what AI engines display. The smarter work optimizes what they retrieve. Entity consolidation is the single highest-correlation signal for AI citation rate — and the work most agencies don't do because it's invisible to traditional SEO dashboards.

The retrieval architecture in plain language, the five signals that actually move citation rates for legal queries, and what everyone fixates on that doesn't.

Avvo, Justia, FindLaw, Martindale, Google Business Profile, the state bar — ranked by what actually moves AI-search citations, not backlinks. Plus the consistency rule that beats any single listing, and the ratings-and-badges compliance trap.

Avvo, Justia, JD Supra, Google Business Profile, and state/local bar. Five citation sources AI engines disproportionately pull from for legal queries — ranked by leverage, with operational checklists.

The most-referenced and least-quoted document in legal-vertical AI marketing. The guidance is more specific than the industry's general interpretation suggests — and the compliance work that protects the firm also lifts its AI-search citation rate.

How AI-generated content can quietly cross state-bar advertising-rule lines, why generalist GEO agencies don't catch the violations, and the practitioner checklist for catching them before they ship.

"What does it cost?" is the question most GEO agencies dodge. The honest answer is a range, because cost is scope against your firm's baseline. What actually drives the price, the sensible way to buy, and the pricing red flags to watch for.

A quantitative study of 200 California firms across 4 AI engines, 60 days, 50 buyer-intent prompts. Pre-registered methodology, publishable either way. Q4 2026.

We pre-registered a timeline for PROOF Series #1 and committed to disclosing any deviation over 30 days. The deviation happened. Here is the disclosure: data collection moves to September 1 – October 30, publication to Q4 2026. Nothing about the methodology changes.