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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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