AEOGeoAI Research Report · July 2026
Miami Legal AI Search Visibility Study 2026Analysis of 120 Law Firms Across ChatGPT, Claude & Gemini
An Independent Analysis of AI Citation Patterns Across 120 Miami-Dade Law Firms.
An Independent Analysis of AI Citation Patterns Across 120 Miami-Dade Law Firms.
ChatGPT, Claude, and Gemini almost never failed to answer local legal questions in Miami. They answered by naming competitors while omitting the tested law firm.
The competitors that do get named are overwhelmingly large institutions — not comparable independent law firms. See Section 3.
This report is part of the larger Miami AI Search Visibility Study (515 businesses across health, legal, and real estate). It documents observed AI citation patterns for the legal sector specifically, tested during July 2026.
This study measures AI citation presence — defined as whether a law firm is:
This study measures AI citation presence rather than Google rankings, website quality, reputation, or business tenure. For businesses evaluating their own standing, the free AI Search Visibility Checker applies this same methodology on demand.
These findings should not be interpreted as measurements of:
Instead, they measure only AI citation presence — whether ChatGPT, Claude, or Gemini named a specific business by name — during the study period in July 2026.
Each law firm was queried individually across three AI systems using a standardised prompt. We tested whether the AI system included the named business in its recommendation-style answer. See also the full AEOGeoAI methodology page and the Miami AI Search Visibility Study for the combined cross-sector methodology.
This study measures observed output behaviour rather than internal model architecture. No conclusions are drawn regarding the proprietary ranking or retrieval mechanisms of ChatGPT, Claude, or Gemini beyond the responses produced during testing.
"best [category] in [city] FL"
Business selected
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Prompt generated ("best [category] in [city] FL")
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Query sent independently to ChatGPT, Claude, and Gemini
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Response collected from each model
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Automated secondary classifier scores each response
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Citation score assigned (0 / 1–34 / 35+) per model
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Highest score across the three models used as the business's headline result
Each business's category was used directly, or reduced to its primary field where a compound category was listed. Example: a Miami business lawyer received the query "best business lawyer in Miami FL".
A binary name-match alone treats "AI discussed the right category and named three competitors, but not this business" identically to "AI has never heard of this business or its category" — both would score zero. This study distinguishes the two. A raw score of 35 or higher indicates the business's name was actually matched in the response — a genuine citation. Scores of 1–34 indicate category-adjacency. A score of 0 means no relevant signal was found at all.
The 35-point threshold marks the point at which the classifier identified the business's own name as explicitly present in the response text, rather than only its category or general attributes being discussed. AEOGeoAI has not published a formal statistical derivation of this specific cutoff; it functions as a categorical boundary within our scoring system rather than a value derived from this dataset.
| Score | Meaning |
|---|---|
| 0 | No relevant signal — category not discussed |
| 1–34 | Category-adjacent — topic/competitors discussed, business not named |
| 35–59 | Genuine citation — business named, limited prominence |
| 60–79 | Genuine citation — consistently included |
| 80–100 | Genuine citation — prominently featured |
Law Firms were identified from commercially sourced contact lists, used only to identify candidate businesses to query — not to influence results. Commercial contact lists were used solely to identify businesses for inclusion; they were not used as sources for scoring, ranking, or classification. No business paid for inclusion in this study, and no business was excluded based on its citation outcome.
| Parameter | Value |
|---|---|
| Report ID | AEOGEOAI-MIA-LEGAL-2026-001 |
| Dataset ID | MIA-LEGAL-120-v1 |
| Sample size | 120 law firms |
| Top categories tested | General law firm (44), Personal injury (24), Real estate attorney (10), Criminal defense (9), Immigration (8) |
| Top cities | Miami (46), Coral Gables (14), Fort Lauderdale (14), Plantation (11), Hollywood (6) |
| Test period | July 2026 |
| Rate control | 4-second delay per query |
| Tooling | AEOGeoAI visibility scoring system with automated secondary classifier for category-adjacency and competitor extraction |
Individual business names are not published in this report. The underlying dataset is structured as follows:
| Column | Description |
|---|---|
| Business | Business name (withheld in this published report) |
| City | City used in the query |
| Category | Category used in the query |
| Claude score | 0–100 |
| Gemini score | 0–100 |
| ChatGPT score | 0–100 |
| Classification | True zero / Category-adjacent / Genuine citation |
| Competitors named | De-duplicated list of businesses named in place of the tested business |
The query format, scoring thresholds, and category-adjacency detection logic are documented in this section. Independent researchers with access to ChatGPT, Claude, and Gemini can reproduce the general approach using the identical query format and thresholds described above. Full row-level data is available on request — see the FAQ below.
Limitations: AI outputs are probabilistic and may vary across time and model updates. Results represent a snapshot of observed behaviour in July 2026 and should not be interpreted as permanent or universal. This study only tested ChatGPT, Claude, and Gemini — it does not include Google AI Mode or Google AI Overviews specifically.
Scope of these findings: this study describes the observed sample of 120 Miami-Dade law firms and is not intended to estimate AI citation prevalence across all law firms in Florida.
Scoring definitions used below are documented in Section 1: Methodology.
The highest score achieved on any model (Claude, Gemini, or ChatGPT), per business, was used as that business's headline score. Across all 120 law firms:
| Statistic | Value |
|---|---|
| Mean (highest score per business) | 12.68 |
| Median (highest score per business) | 12.0 |
| Standard deviation | 3.02 |
| Minimum | 1 |
| Maximum | 22 |
These figures describe the observed sample only. No confidence interval is reported, since the sample was not drawn as a random probability sample of all Miami-Dade law firms — it reflects the commercially sourced contact list described in Section 1.
| Outcome | Meaning | Businesses |
|---|---|---|
| True zero | No relevant signal — category not discussed at all | 0 (0%) |
| Category-adjacent, unnamed | Category and often competitors discussed — business never named | 120 (100%) |
| Genuinely named | The business itself was named by at least one model | 0 (0%) |
The 100% figure is the sharper finding. These law firms aren't falling outside AI's field of view — the opposite is true. ChatGPT, Claude, and Gemini are actively discussing their exact category and, in the large majority of cases, naming their competitors in the same response. The business itself is simply omitted from the answer.
None of the 120 law firms tested were genuinely cited (score 35+ on any model) during the study period. This represents the most uniform outcome observed across the report series — every law firm received either a category-adjacent or true-zero response, and none was named directly by ChatGPT, Claude, or Gemini in the tests performed for this study.
Every one of the 120 law firms tested received at least a category-adjacent response — none returned a complete absence of signal.
Because our detection method captures category-adjacent responses rather than discarding them, we can extract exactly which businesses get named in place of the tested law firm — using only names that actually appeared in the model's response. This is the same entity-signal gap covered in our AI Search Optimization Guide.
Counts represent the number of distinct law firms in this study whose ChatGPT, Claude, or Gemini responses named this organisation at least once. Each business contributes at most one count per organisation, regardless of how many of the three models named it — so counts cannot exceed the 120 law firms tested.
| Business named | Type | Times cited | % of businesses |
|---|---|---|---|
| Greenberg Traurig | AmLaw 100 firm | 46 | 38.3% |
| Shutts & Bowen | Regional firm | 40 | 33.3% |
| Akerman | AmLaw 200 firm | 36 | 30.0% |
| Holland & Knight | AmLaw 100 firm | 33 | 27.5% |
| Baker McKenzie | Global firm | 17 | 14.2% |
| Gunster | Regional firm | 15 | 12.5% |
| Bilzin Sumberg | Regional firm | 12 | 10.0% |
| Podhurst Orseck | Regional firm | 11 | 9.2% |
| Carlton Fields | Regional firm | 9 | 7.5% |
| Gerson & Schwartz | Regional firm | 9 | 7.5% |
| Berger Singerman | Regional firm | 6 | 5.0% |
| Shiner Law Group | Regional firm | 5 | 4.2% |
| Morgan & Morgan | National firm | 5 | 4.2% |
| GrayRobinson | Regional firm | 5 | 4.2% |
Across the sampled responses, AI systems most frequently recommended larger institutions. When an independent law firm isn't named, the fallback is almost never "a different independent competitor" — it's an institution with far deeper third-party coverage.
Where AI names a specific alternative, the qualities it associates with them cluster around a small set of themes:
| Attribute | Times mentioned |
|---|---|
| Personalized service | 10 |
| Strong presence in Miami | 8 |
| Litigation experience | 13 |
| Full-service firm | 6 |
| Strong reputation | 6 |
| Long-established | 6 |
| Large international firm | 5 |
| Strong in real estate law | 5 |
None of these are differentiators unique to large institutions — these are exactly the kind of claims an independent law firm's own website already makes. The gap isn't in what independent law firms offer; it's in whether AI has independent, third-party confirmation of it.
See Full Methodology → for how these scores were generated.
100% of 120 law firms received a category-adjacent response. 0 of 120 achieved a genuine citation (score 35+) on at least one model. 0 of 120 achieved citation on more than one model. Mean highest-model score across the sample was 12.68 (median 12.0, standard deviation 3.02). These are the directly measured results of this study.
The observed citation patterns are consistent with systems that rely heavily on externally confirmed entities drawn from multiple indexed sources, though this study did not have access to any model's internal retrieval process and cannot confirm the underlying mechanism directly. This is an interpretation of the observed findings above, not an additional measurement.
Signal gap vs ranking gap. This does not look like a ranking problem. It looks like an entity visibility problem — these AI systems surface entities with sufficient external confirmation signals, and large institutions simply have vastly more of that confirmation than any independent law firm.
0 of 120 law firms in this dataset achieved citation on more than one model. Within this dataset, any observed citation behaviour appeared to be model-specific rather than consistently reproduced across all three systems.
This legal dataset is one of three sectors tested in Miami using identical methodology and detection thresholds.
| Sector | Tested | Category-adjacent | Genuine citation |
|---|---|---|---|
| Legal (this report) | 120 | 100% | 0% |
| Health | 298 | 99.3% | 0.7% |
| Real estate | 97 | 99.0% | 0% |
The pattern replicates closely across all three Miami sectors, despite health, legal, and real estate having nothing in common structurally. See the full Miami AI Search Visibility Study for the complete cross-sector analysis of all 515 businesses.
Law Firms without AI citation presence risk reduced discovery in AI-native search, while their most likely "competitor" in a ChatGPT, Claude, or Gemini answer is a large institution with a permanent structural advantage in third-party coverage.
Third-party citation signals are associated with a higher likelihood of AI citation presence but do not guarantee inclusion in any specific AI-generated answer. AI model outputs are probabilistic and change over time.
For full detail behind these answers, see Full Methodology → and the AEOGeoAI methodology page.
AEOGeoAI.
Miami Legal AI Search Visibility Study 2026.
Report ID: AEOGEOAI-MIA-LEGAL-2026-001.
Version 1.0. Published July 2026.
https://aeogeoai.net/miami-legal-ai-visibility-study
@report{aeogeoai_miami_legal_2026, title = {Miami Legal AI Search Visibility Study 2026}, author = {{AEOGeoAI}}, year = {2026}, month = {7}, institution = {AEOGeoAI}, url = {https://aeogeoai.net/miami-legal-ai-visibility-study}, note = {Report ID: AEOGEOAI-MIA-LEGAL-2026-001} }
| Version | Date | Changes |
|---|---|---|
| 1.0 | July 2026 | Initial publication |
We publish structured entity articles about Miami-Dade law firms on verified local publications already indexed by AI systems, creating additional third-party entity references that may improve the likelihood of AI citation over time.
View Miami Legal service and pricing →From $299 · One-time publication · No retainer · Miami-Dade & Broward Counties