Sample AI Visibility Teardown

An Austin personal-injury firm, scored.

Demonstration audit. Subject: a real Austin personal-injury firm, anonymized here for privacy — chosen to show the format. Findings are based on public answer-engine synthesis captured June 2026, the same method we run for paying clients (the production version adds direct ChatGPT / Perplexity / Google-AI API probes). This is constructive competitive analysis, not an endorsement or disparagement.

AI Visibility Score

34 / 100 🟠

Component	Score	Notes
Presence (40)	12 / 40	Named in 0 of 5 AI-synthesized answers to core buyer queries; appears only as a raw directory/link listing.
Citation quality (25)	6 / 25	When surfaced, it’s a bare link, not a cited recommendation with detail.
Competitive share (20)	4 / 20	Competitors dominate the synthesized answer (see below).
Crawlability / E-E-A-T (15)	12 / 15	Site loads; family-run / 30+ yrs experience is a strong, citable credential — but it isn’t being extracted by AI.

What we tested (5 of the 10 buyer-intent queries)

“best personal injury lawyer in Austin for car accident settlement”
“who is the top rated motorcycle accident attorney in Austin”
“best personal injury lawyer in Austin TX”
“who should I call after a car accident in Austin”
“[firm name] reviews — is it a good firm”

What the AI actually said (the gap, in their words)

For query 1, the synthesized answer named three competitor firms citing specific results (multi-million-dollar settlements, top Avvo ratings). The subject firm was not named.
For query 2, it again named competitors with extractable proof points (success rates, recognitions). The subject firm was not named.
The pattern: AI answers reward firms that publish specific, extractable facts (dollar results, success rates, named recognitions). The subject firm’s genuine strength — family-run, 30+ years — exists on the site but isn’t structured for extraction, so the AI never cites it.

Root-cause diagnosis

Facts aren’t extractable. “30+ years” and case outcomes are buried in marketing prose, not in citable fact blocks or schema.
No buyer-question content. Competitors implicitly answer “how much is my case worth?” / “who’s the best for X” with structured pages; the subject firm doesn’t, so it’s absent from those answers.
Weak machine-readable E-E-A-T. Attorney credentials and recognitions aren’t marked up where AI crawlers look.

Top 5 fixes (ranked by impact) — what a retainer would deliver

Citable results block — a structured, fact-dense “Results & Recognition” section (specific settlements w/ context, years in practice, bar admissions). Drafted by us.
“Best injury lawyer in Austin for ___” answer pages — 1 per core practice area, answer-first format. Drafted by us.
FAQ schema for the 10 highest-intent questions (case value, timelines, fees). Markup spec by us.
Attorney bio + credentials schema — make E-E-A-T machine-readable.
Review/reputation surfacing — structured testimonials + aggregate rating markup.

Projected outcome (honest framing)

Target: move from 0/5 → 3/5 named presence on core queries within 60–90 days. AI engines are non-deterministic, so we sell measured movement + the done work, not a guarantee.

This is the format your firm’s teardown would take.

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