Wildes & Weinberg Appears in 81.9% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Mark HuntleyBy Mark HuntleyFounder and CEO
10 minutes read

Key Takeaways

  • Wildes & Weinberg is mentioned in 81.93% of benchmark answers, but only 24.10% of those answers recommend it.
  • The main issue is the gap between being named and being endorsed, especially in the latest monthly sample.
  • The website review should start with the homepage and New York office page, which carry most citation weight.
  • Buyer questions are local and comparison-driven, such as best immigration lawyer in New York and top immigration lawyers NYC.

Answer Capsule

In November 2026, Wildes & Weinberg appeared in 68 of 83 AI answers included in the Immigration Law Firms benchmark (81.93%). It received 20 recommendations (24.10%), with 16 Top 3 placements and 15 first-place recommendations. A mention means the company appeared in an answer; a recommendation means the answer affirmatively endorsed it.

Our plan compares the company with its competitors, checks conflicting AI claims against reliable sources, and identifies website pages, third-party sources and buyer questions to address. The historical results show how the figures changed across available months. Different monthly samples limit those comparisons, and conflicting AI claims require verification before corrections are proposed.

Current AI visibility

Questions This Section Answers

  • How do Wildes & Weinberg mentions, recommendations and placements compare with its Immigration Law Firms peers?

Wildes & Weinberg recorded 68 mentions (81.93%) and 20 recommendations (24.10%) across 83 answers included in the benchmark. Top 3 placements numbered 16; first-place recommendations numbered 15. Framing among its mentions was 36 positive, 32 neutral and 0 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

Wildes & Weinberg

68

81.93%

20

24.10%

16

19.28%

15

18.07%

2

Cyrus D. Mehta & Partners

14

16.87%

4

4.82%

4

4.82%

4

4.82%

3

Spar & Bernstein

12

14.46%

3

3.61%

3

3.61%

3

3.61%

4

Barst & Mukamal

0

0.00%

0

0.00%

0

0.00%

0

0.00%

4

Bretz & Coven

3

3.61%

0

0.00%

0

0.00%

0

0.00%

4

Transparent Justice Law Firm

0

0.00%

0

0.00%

0

0.00%

0

0.00%

Wildes & Weinberg is the firm the answers reach for most often in this benchmark, and it is also the firm they endorse most often. That combination matters more than reach alone: being named in most answers but recommended in only a fraction of them would signal a weaker position than the raw mention count suggests.

The gap between mentions and recommendations is the practical story here. The firm is present in the conversation far more than it is put forward as a choice, so the useful work is understanding what separates the answers that endorse it from those that merely name it. A scorecard that tracks mentions, recommendations and placement separately would give the client a clearer picture than a single visibility figure.

What changed since last month

Questions This Section Answers

  • How did Wildes & Weinberg counts and coverage differ across the available monthly snapshots?

Metric

Jul 2026

Aug 2026

Sep 2026

Oct 2026

Nov 2026

Mentions

62 / 70 (88.57%)

51 / 55 (92.73%)

47 / 50 (94.00%)

40 / 46 (86.96%)

68 / 83 (81.93%)

Recommendations

61 / 70 (87.14%)

50 / 55 (90.91%)

43 / 50 (86.00%)

40 / 46 (86.96%)

20 / 83 (24.10%)

Top 3 recommendations

51 / 70 (72.86%)

46 / 55 (83.64%)

23 / 50 (46.00%)

23 / 46 (50.00%)

16 / 83 (19.28%)

#1 recommendations

27 / 70 (38.57%)

39 / 55 (70.91%)

12 / 50 (24.00%)

8 / 46 (17.39%)

15 / 83 (18.07%)

From October 2026 to November 2026, Wildes & Weinberg's mention count rose from 40 to 68, while its recommendation count fell from 40 to 20. The recommendation rate was 86.96% and 24.10% respectively.

The monthly record shows a firm whose presence in answers has been consistently high while its endorsement pattern has moved sharply. The most recent month pairs a higher mention count with a much lower recommendation count than the month before, and the placement figures sit well below the earlier months.

These are separate samples, so the movement should be read as a change in what was recorded rather than as a trend with a known cause. The honest reading is that the endorsement position weakened in this sample while reach held up. That makes the recommendation gap the thing to watch, not the mention count.

AI recommendation trend

Observed recommendation rate (%), Jul 2026 to Nov 2026

0%25%50%75%100%Jul 2026Aug 2026Sep 2026Oct 2026Nov 202687.14%Jul 2026: 87.14%90.91%Aug 2026: 90.91%86.00%Sep 2026: 86.00%86.96%Oct 2026: 86.96%24.10%Nov 2026: 24.10%
  • Wildes & Weinberg24.10%

Separate selected monthly samples. Missing points are unavailable, not zero. Provider/prompt equivalence is unverified; rate movement does not demonstrate causation.

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Step 1. Measure

Questions This Section Answers

  • What should the agency measure and verify first for Wildes & Weinberg?

AI answers contained 0 conflicting claims about Wildes & Weinberg: 0 critical, 0 high, 0 medium, 0 low. Of these, 0 were rated critical or high priority; the critical claims are included in that total. These ratings help prioritize fact-checking. They do not establish that a company or publisher made an error.

No conflicting claims about the firm were recorded in this benchmark, which is a cleaner starting position than many firms have. The absence of disputes does not mean the answers are accurate, only that nothing in this sample contradicted itself about the company.

The useful first step is a visibility comparison that sets reach against endorsement, paired with a short list of claims worth checking against primary sources. That gives the client a defensible basis for deciding where to spend effort, rather than reacting to a single month's movement.

Step 2. Make the website consistent

Questions This Section Answers

  • Which recorded claim differences about Wildes & Weinberg should be verified before proposed page edits?

No results are available for this part of the analysis.

Both columns quote AI answers, not verified company facts. Resolve the entity, date and source before deciding whether a correction is warranted; different dates can explain an apparent conflict.

This part of the analysis returned no results, so there are no disputed topics to work through and no page briefs to propose on that basis. The client should treat this as an open item rather than a clean bill of health.

Where claims do surface later, the entity, date and source need resolving before anyone decides a correction is warranted. Different dates can make two statements look like a conflict when they are not. Any proposed correction would rest on current primary-source verification, not on an AI answer alone.

Step 3. Align the external narrative

Questions This Section Answers

  • Which cited third-party pages are relevant to fact review for Wildes & Weinberg?

Cited page selected for review

Citation appearances in all captured answers

Answers mentioning the company

Conflicting claims linked to this page

https://google.com/searchviewer/10?svid=CAwSGxIZCgNwdnESEkNnc3ZaeTh4ZEdvNFltSm5iZxgK

38

36

0

https://google.com/searchviewer/10?svid=CAwSHRIbCgNwdnESFENnMHZaeTh4TVdJM00zQnJNbkp1GAo

27

25

0

https://prizant-law.com/

17

17

0

https://google.com/searchviewer/10?svid=CAwSHRIbCgNwdnESFENnMHZaeTh4TVdJMWVYRnVhelI1GAo

18

16

0

https://nyc.gov/site/hra/help/legal-services-for-immigrant-new-yorkers.page

15

15

0

https://nycbar.org/get-legal-help/article/immigration-law/

14

14

0

These pages were selected because they relate to the company in the captured answers. Their current contents still need checking before any correction request. A page linked to a conflicting claim is not necessarily its cause, and the same answer may contribute to more than one row.

The cited pages skew heavily toward search-viewer links and legal-help resources rather than the firm's own material. That pattern is worth reviewing, but a citation is not evidence that a page shaped an answer, and none of these pages carried a conflicting claim about the firm in this sample.

The proposed work is a focused source review: check what each selected page currently says, confirm whether the firm is described accurately, and only then decide whether a publisher request is justified. Requests should be evidence-backed and framed as a query, not an assertion of error.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed Wildes & Weinberg website pages should receive review briefs?

Company website page

Citation appearances in all captured answers

Benchmark answers citing the page

Recommending answers citing the page

https://wildeslaw.com/

43

43

13

https://wildeslaw.com/offices/immigration-office-new-york/

20

20

6

https://wildeslaw.com/immigration-resources/latest-immigration-news/our-representation-of-mohammad-gulab-heroic-afghan-villager/

6

4

0

https://wildeslaw.com/news-events/in-the-news/michael-wildes-secures-safe-passage-for-hero-mohamed-gulab-who-saved-navy-seal-marcus-luttrell/

5

3

0

https://wildeslaw.com/services/immigration/o-visas/

2

2

2

https://wildeslaw.com/about-the-firm/

1

1

0

https://wildeslaw.com/about-the-firm/the-john-lennon-case/

1

0

0

https://wildeslaw.com/immigration-resources/latest-immigration-news/o-1-visa-approved-for-influencer-and-content-creator-2/

1

1

0

The company's website was cited 81 times across 10 distinct page addresses; the table shows 8 pages selected for review. Zeros in the answer columns mean no matching answers were recorded. A cited page is not automatically a page that accompanied a recommendation.

The firm's homepage carries most of the citation weight, with the New York office page a clear second. The remaining pages appear far less often, and several were cited in answers that did not recommend the firm at all.

Page briefs should start with the homepage and the office page, using approved facts and the questions buyers actually ask. The visa service page is a natural candidate because it was cited in recommending answers despite appearing rarely. Each brief should confirm existing coverage before proposing new content.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for Wildes & Weinberg?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

immigration law firms nyc

2

2

1

immigration lawyer new york city

2

2

1

top immigration lawyers nyc

2

2

1

best immigration lawyer in new york

2

1

1

best immigration lawyer in nyc

1

1

1

best new york immigration lawyer

1

1

1

best o1 visa lawyer

1

1

1

citizenship attorney nyc

1

1

1

These are recorded answer observations, not search volume, verified demand, or proof of missing website content.

The recorded questions are overwhelmingly local and comparison-driven: people asking for immigration firms in New York, top or best lawyers, and a smaller set around citizenship and the o1 visa category. The firm appears in most of these answers and is recommended in most of them.

These are answer observations, not search volume, so they should guide content briefs rather than justify a demand forecast. The sensible move is to check whether the site already answers these questions well, then brief pages or sections that speak directly to them in the firm's own words.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed Wildes & Weinberg work and judge completion?

Period

Deliverable

Completion criterion

Days 1–30

Baseline scorecard, scoped fact-review register and page/source shortlist

Each record has an owner, evidence requirement and review status

Days 31–60

Approved page briefs and justified publisher requests

Separate confirmed edits, no-change decisions, requests and responses

Days 61–90

Buyer-content briefs and a fresh scorecard

Record work completed separately from measured answer outcomes and comparability limits

The first month establishes the baseline: a scorecard, a scoped fact-review register and a shortlist of pages and sources, each with an owner and a review status. Nothing gets corrected in that window; the point is to know what is being looked at.

The second month turns confirmed findings into approved page briefs and, where justified, publisher requests, with confirmed edits and no-change decisions recorded separately. The third month produces buyer-question content briefs and a fresh scorecard. Completed work will be reported apart from measured answer outcomes, since the two are not the same thing.

Methodology

Questions This Section Answers

  • How are Wildes & Weinberg answer metrics and citation populations defined?

This report covers 2026-11. The exact dates when the original AI answers were collected have not been verified. The visibility rates use 83 answers that met the benchmark's inclusion rules. A company is recommended only when the answer both endorses it and uses positive wording. Top 3 and first-place counts are part of those recommendations, not extra recommendations.

An answer can mention or recommend several companies, so company counts cannot be added to find the number of distinct answers. The net sentiment score is positive minus negative mentions, divided by all company mentions; it describes answer wording, not customer satisfaction.

The wider citation analysis covers 86 captured answers and 660 appearances of cited URLs. Citation appearances across all captured answers differ from the number of benchmark answers citing a page and the number that also recommend a company. The current contents of cited pages and disputed company claims still need independent verification.

Monthly sample coverage:

Reporting month

Answers included

AI services covered

Services

Jul 2026

70

2

Google AI Mode, Google AI Overviews

Aug 2026

55

3

Gemini, Google AI Mode, Google AI Overviews

Sep 2026

50

5

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews

Oct 2026

46

5

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews

Nov 2026

83

5

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews

The answer samples differ between months. These comparisons show how the recorded results changed; they do not establish why they changed.

The monthly samples differ in size and in which AI services were covered, so comparisons describe what was recorded rather than why it changed. Answers can name several firms, so company counts do not add up to distinct answers. A recommendation requires both an endorsement and positive wording.

Citation appearances across all captured answers are a wider measure than benchmark answers citing a page, and neither shows that a page influenced an answer. The current contents of cited pages and any disputed claims still need independent verification.

Our first three priorities

First, close the recommendation gap. The firm is named far more often than it is endorsed, and the most recent sample widened that gap. A scorecard that separates reach from endorsement, reviewed against the questions buyers actually ask, would tell the client where the firm is being described but not chosen.

Second, verify before correcting. With no conflicting claims recorded and no consistency results available, the immediate task is a fact-review register and a check of the cited pages, starting with the homepage and the New York office page. Any publisher request should follow that verification.

Third, brief content around the local comparison questions, confirming what the site already covers before proposing anything new. Progress should be judged by work completed and by later scorecards, not by promised visibility gains.

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Understanding AI search visibility.

AI search experiences create answers by pulling information from many places online and summarizing it into a single response.

What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.

About The Author

Mark Huntley

Mark Huntley

Founder and CEO

Mark Huntley, J.D. is founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.

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