Tully Rinckey Appears in 15.6% 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

  • Tully Rinckey was mentioned in 19 of 122 benchmark answers, but none of those mentions became a recommendation.
  • The first priority is fact verification, including approved company facts and any cited third-party pages that may need review.
  • The firm’s federal employment pages were the most cited on its own site and should be the first pages briefed for improvement.
  • Observed buyer questions point to federal, military, and local legal topics that can guide new or revised content briefs.

Answer Capsule

In November 2026, Tully Rinckey appeared in 19 of 122 AI answers included in the Bankruptcy Lawyers benchmark (15.57%). It received 0 recommendations (0.00%), with 0 Top 3 placements and 0 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 Tully Rinckey mentions, recommendations and placements compare with its Bankruptcy Lawyers peers?

Tully Rinckey recorded 19 mentions (15.57%) and 0 recommendations (0.00%) across 122 answers included in the benchmark. Top 3 placements numbered 0; first-place recommendations numbered 0. Framing among its mentions was 0 positive, 19 neutral and 0 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

Upsolve

77

63.11%

19

15.57%

9

7.38%

9

7.38%

2

DebtStoppers

11

9.02%

5

4.10%

3

2.46%

3

2.46%

3

Allmand Law

6

4.92%

0

0.00%

0

0.00%

0

0.00%

3

Cibik Law

0

0.00%

0

0.00%

0

0.00%

0

0.00%

3

Fears Nachawati

0

0.00%

0

0.00%

0

0.00%

0

0.00%

3

Heupel Law

0

0.00%

0

0.00%

0

0.00%

0

0.00%

3

John T. Orcutt

8

6.56%

0

0.00%

0

0.00%

0

0.00%

3

Sasser Law Firm

4

3.28%

0

0.00%

0

0.00%

0

0.00%

3

The Semrad Law Firm

6

4.92%

0

0.00%

0

0.00%

0

0.00%

3

Tully Rinckey

19

15.57%

0

0.00%

0

0.00%

0

0.00%

Tully Rinckey appears in a meaningful share of the benchmark answers, yet none of those appearances is an endorsement. Being named is not the same as being chosen, and for a firm competing for client attention the gap between the two is the whole story. The neutral framing suggests the answers treat the firm as one option among several rather than a preferred one.

That pattern sets the right ambition. The near-term goal is not to chase the leader's volume but to convert some existing mentions into affirmative recommendations. A firm that is already visible has a shorter path to endorsement than one that is absent, so the work should focus on what makes an answer willing to recommend, not simply on being named more often.

What changed since last month

Questions This Section Answers

  • How did Tully Rinckey counts and coverage differ across the available monthly snapshots?

Metric

Jul 2026

Aug 2026

Sep 2026

Oct 2026

Nov 2026

Mentions

0 / 141 (0.00%)

0 / 146 (0.00%)

0 / 166 (0.00%)

0 / 114 (0.00%)

19 / 122 (15.57%)

Recommendations

0 / 141 (0.00%)

0 / 146 (0.00%)

0 / 166 (0.00%)

0 / 114 (0.00%)

0 / 122 (0.00%)

Top 3 recommendations

0 / 141 (0.00%)

0 / 146 (0.00%)

0 / 166 (0.00%)

0 / 114 (0.00%)

0 / 122 (0.00%)

#1 recommendations

0 / 141 (0.00%)

0 / 146 (0.00%)

0 / 166 (0.00%)

0 / 114 (0.00%)

0 / 122 (0.00%)

From October 2026 to November 2026, Tully Rinckey's mention count rose from 0 to 19, while its recommendation count stayed at 0. The recommendation rate was 0.00% and 0.00% respectively.

The monthly record shows a clear shift: after several months with no recorded mentions, the firm appeared in the November sample. Recommendations did not move with it. The two measures are separate, and a rise in one does not imply a rise in the other.

The samples differ between months, so the change should be read as an observation about this selected set of answers rather than a trend line. What matters for planning is the pattern itself: visibility can arrive without endorsement, which means the firm's task is to earn the recommendation, not just the mention.

AI recommendation trend

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

0%0.03%0.05%0.08%0.1%Jul 2026Aug 2026Sep 2026Oct 2026Nov 20260.00%Jul 2026: 0.00%0.00%Aug 2026: 0.00%0.00%Sep 2026: 0.00%0.00%Oct 2026: 0.00%0.00%Nov 2026: 0.00%
  • Tully Rinckey0.00%

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 Tully Rinckey?

AI answers contained 0 conflicting claims about Tully Rinckey: 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.

Before setting targets, the client needs a clear picture of where it stands and what, if anything, is being said about it that needs checking. A visibility comparison against the firms that do earn recommendations would show which questions and services the firm is close to winning and where it is simply present.

A companion list of claims to fact-check would separate reputation risk from visibility work. The captured answers contained no conflicting claims about the firm, so this list may be short. Even so, confirming that is worth doing once, with a named owner, so later work rests on checked facts rather than assumption.

Step 2. Make the website consistent

Questions This Section Answers

  • Which recorded claim differences about Tully Rinckey 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.

No disputed claims surfaced for this company in the captured answers, so there is nothing to correct on the evidence shown. That is a clean starting position, not a reason to skip verification. The firm's public pages should still be checked against approved facts before any brief is written.

Where a claim does appear later, the first step is to resolve the entity, the date and the source behind it. Different dates can make two statements look contradictory when they are not. Only after that check would a correction be warranted, and only with current primary-source confirmation.

Step 3. Align the external narrative

Questions This Section Answers

  • Which cited third-party pages are relevant to fact review for Tully Rinckey?

Cited page selected for review

Citation appearances in all captured answers

Answers mentioning the company

Conflicting claims linked to this page

https://bbb.org/us/ny/latham/profile/employment-lawyers/tully-rinckey-pllc-0041-134501076/complaints

2

2

0

https://glassdoor.com/Reviews/Tully-Rinckey-PLLC-Reviews-E579066.htm

2

2

0

https://reddit.com/r/LawFirm/comments/14pr542/anyone_have_any_feedback_on_tully_rinckey_good/

2

2

0

http://bbb.org/us/ny/latham/profile/employment-lawyers/tully-rinckey-pllc-0041-134501076/customer-reviews

1

1

0

https://bbb.org/us/ny/latham/profile/employment-lawyers/tully-rinckey-pllc-0041-134501076/customer-reviews?page=13

1

1

0

https://glassdoor.com/Overview/Working-at-Tully-Rinckey-PLLC-EI_IE579066.11,29.htm

1

1

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 cluster around employer-review and complaint sites, which is where prospective clients often look before choosing a firm. The selection reflects relevance to the company in the captured answers; it does not establish that any page shaped those answers. Current contents still need checking before any request is made.

The sensible work is a focused review of the most-cited pages, paired with evidence-backed requests where a genuine inaccuracy is confirmed. Publishers control their own pages, so the agency can prepare and submit well-founded requests but cannot promise a change. Requests and responses should be logged separately from confirmed edits.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed Tully Rinckey 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://tullylegal.com/legal-services/federal-employment-law/

13

1

0

https://tullylegal.com/legal-services/federal-employment-law/merit-systems-protection-board-mspb/

4

1

0

https://tullylegal.com/washington-dc/federal-emploment/

3

0

0

https://tullylegal.com/legal-services/federal-employment-law/disability-retirement/

2

0

0

https://tullylegal.com/resources/articles/how-long-does-a-security-clearance-last/

2

0

0

http://tullylegal.com/wp-content/uploads/Guide_to_Obtaining_Medical_Discharge.pdf

1

1

0

https://tullylegal.com/

1

1

0

https://tullylegal.com/albany-trust-and-estates/

1

0

0

The company's website was cited 48 times across 29 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 own site is already the most-cited source in the answers, which gives the client real leverage. The federal employment pages carry most of that attention, so they deserve the first briefs. A brief should state the approved facts, the audience questions the page should answer, and the specific services it covers.

Because a citation is not an endorsement, the briefs should aim at clarity and usefulness rather than at chasing mentions. Pages that are cited but never accompany a recommendation are the most interesting to review: they may be answering the wrong question, or answering it without giving an answer engine a reason to prefer the firm.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for Tully Rinckey?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

how to get medically discharged from the army

1

1

0

how to prepare for a divorce as a woman

1

1

0

how to upgrade military discharge

1

1

0

iep explained

1

1

0

labor lawyer buffalo ny

1

1

0

law firms in washington dc

1

1

0

law firms rochester ny

1

1

0

lawyer for federal employees

1

1

0

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

The recorded questions point to two audiences: federal and military employees navigating discharge and clearance issues, and people seeking local counsel in specific cities. These are observations from the answers, not search-volume data, so they should guide content topics rather than forecast demand.

Before proposing new pages, the agency should check whether the firm already covers each question well. Where coverage exists, the brief is about sharpening it; where it does not, a new page may be justified. Either way, the content should answer the question directly and give a clear reason to choose the firm.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed Tully Rinckey 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 list and a shortlist of pages and sources, each with an owner and a review status. This groundwork matters because later briefs and requests depend on checked facts rather than assumptions.

The second month turns that into approved page briefs and justified publisher requests, with confirmed edits, no-change decisions, requests and responses recorded separately. The third month adds buyer-question content briefs and a fresh scorecard. Delivered work is tracked apart from measured answer outcomes, since visibility can move for reasons outside the agency's control.

Methodology

Questions This Section Answers

  • How are Tully Rinckey 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 122 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 323 captured answers and 2477 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

141

6

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

Aug 2026

146

6

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

Sep 2026

166

6

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

Oct 2026

114

6

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

Nov 2026

122

6

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

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

The report covers a single month, and the exact dates the answers were collected have not been verified. Visibility rates rest on answers that met the benchmark's inclusion rules, and a company counts as recommended only when an answer both endorses it and uses positive wording. Mentions and recommendations are separate measures and should not be combined.

An answer can name several firms, so company counts cannot be added to find a number of distinct answers. The monthly samples differ, so comparisons show how recorded results changed, not why. Cited pages and any disputed claims still need independent verification.

Our first three priorities

The first priority is verification. Before any brief or request, the agency should confirm the firm's approved facts and check the most-cited external pages, so later work rests on evidence rather than assumption. This is quick work, but it protects everything that follows.

The second is a set of page briefs for the federal employment pages that already attract citations, aimed at turning presence into preference. The third is buyer-question content for the federal, military and local-counsel topics the answers surfaced, checking existing coverage first. Together these give the client a clear path from being named to being recommended.

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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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