Levin & Perconti AI Visibility Market Strategy Report - Nursing Home Abuse Lawyers

Answer Capsule

Levin & Perconti holds 3.14% valid recommendation coverage in the nursing home abuse lawyers category for October 2026, down 6.7 percentage points from 9.8% in July 2026. The firm appears in AI-generated recommendations but at a rate far below the category leader, Morgan & Morgan, which holds 35.9% coverage. Levin & Perconti's clearest strength is its perfect net sentiment score of 1.0000, meaning every mention carried positive framing. Its clearest weakness is scale: only 5 valid recommendations across 159 qualified observations. The clearest opportunity is converting its positive framing into broader recommendation coverage across high-intent discovery prompts.

Who This Report Is For

This report is for Levin & Perconti's marketing and business development leadership, competitive intelligence teams, and anyone evaluating how the firm appears in AI-generated recommendations for nursing home abuse legal services.

Report Card

Executive Summary

Levin & Perconti is visible in AI-generated recommendations for nursing home abuse lawyers, but its recommendation coverage declined significantly across the July to October 2026 measurement window. The firm recorded 3.14% valid recommendation coverage in October 2026, down from 9.8% in July 2026, a 6.7 percentage point decline that the benchmark classifies as significant. This places Levin & Perconti seventh among ten tracked brands in the category.

The firm's raw mention presence rate stands at 3.14%, meaning it appeared in just 5 of 159 qualified observations. All 5 mentions were classified as positive, producing a net sentiment score of 1.0000. This indicates that when AI systems do surface Levin & Perconti, they frame the firm favorably. The challenge is frequency, not framing quality.

Levin & Perconti's strongest platform signal comes from Google AI Overviews, where it recorded 2.0% valid recommendation coverage and a 2.0% rank-one rate. The firm also appeared on ChatGPT, Copilot, and Google AI Mode, though with minimal recommendation credit on those surfaces. It recorded no presence on Gemini or Perplexity in the October 2026 dataset.

The clearest gap is the firm's absence from the category's dominant recommendation cluster. All qualified observations fell into the Brand Recommendation buyer-intent class, and Levin & Perconti captured only a small fraction of that recommendation activity. Morgan & Morgan, by contrast, captured 57 valid recommendations and 33 rank-one placements from the same observation set.

The firm's average recommended rank of 1.60 is the strongest among all tracked brands, meaning that when Levin & Perconti does receive a recommendation, it typically appears near the top of the shortlist. This suggests the firm has recommendation quality but lacks recommendation volume.

The opportunity is to expand the firm's presence across the prompt and citation layers that drive AI-generated recommendations in this category. The benchmark shows where attention is warranted. A company-level analysis is needed to explain why the decline occurred and what specific prompts or sources drove the change.

What Levin & Perconti Is Winning

Questions This Section Answers

• Where does Levin & Perconti outperform rivals in AI recommendations despite low coverage?
• Which platforms show Levin & Perconti's strongest rank-one capability?

Levin & Perconti holds the strongest average recommended rank in the category at 1.60. When the firm appears in a recommendation shortlist, it typically appears first or second. This is a meaningful signal of recommendation quality.

The firm also recorded a perfect net sentiment score of 1.0000. All 5 mentions in the October 2026 dataset were classified as positive, with zero neutral or negative mentions. This indicates that AI systems frame Levin & Perconti favorably when they do surface the firm.

On Google AI Overviews, Levin & Perconti recorded a 2.0% rank-one rate, meaning it was named as the first recommendation in at least one qualified observation on that platform. The firm also captured 2 valid recommendations on Google AI Mode, including 1 rank-one placement.

These wins are narrow but real. The firm has recommendation quality and positive framing. What it lacks is scale and consistency across platforms and prompt types.

Where Levin & Perconti Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Which platforms are missing Levin & Perconti entirely from nursing home abuse lawyer recommendations?
• How do Levin & Perconti's recommendation counts compare with Morgan & Morgan and The Lanier Law Firm?
• Why is Levin & Perconti's rank-one rate low even though its average recommended rank is strong?

Levin & Perconti's most significant gap is its declining recommendation coverage. The firm fell from 9.8% coverage in July 2026 to 3.14% in October 2026, a 6.7 percentage point decline. This drop occurred while the category leader, Morgan & Morgan, maintained a 35.9% coverage rate, more than eleven times Levin & Perconti's current level.

The firm is present but not chosen at scale. Its 3.14% raw mention presence rate matches its 3.14% valid recommendation coverage, meaning every mention converted to a recommendation. However, the absolute number of mentions is very low. Levin & Perconti appeared in only 5 of 159 qualified observations, compared to Morgan & Morgan's 124 appearances and The Lanier Law Firm's 29 appearances.

Levin & Perconti recorded zero presence on Gemini and Perplexity in the October 2026 dataset. These platforms represent missed recommendation opportunities. The Lanier Law Firm, by contrast, captured 4 valid recommendations on Gemini, including 2 rank-one placements.

The firm's rank-one rate of 1.26% is also low relative to its average recommended rank of 1.60. This apparent contradiction suggests that when Levin & Perconti appears, it ranks well, but it appears so infrequently that its rank-one count remains minimal. The firm recorded only 2 rank-one placements across 159 qualified observations.

Compared to Wilshire Law Firm, the category's only net riser, Levin & Perconti's trajectory is concerning. Wilshire increased its coverage from 8.7% to 13.2% across the same period, while Levin & Perconti declined. Wilshire also doubled its valid recommendation base from 15 to 21, while Levin & Perconti's base fell to 5.

Biggest Opportunity

Questions This Section Answers

• What recommendation coverage target would move Levin & Perconti past mid-tier competitors like Sokolove Law and The Lanier Law Firm?

Levin & Perconti's biggest opportunity is expanding its recommendation coverage within the Brand Recommendation cluster, which accounts for 100% of qualified observations in this category. The firm currently captures only 5 valid recommendations from 159 qualified observations. Increasing that count to match mid-tier competitors like Sokolove Law (10 recommendations) or The Lanier Law Firm (25 recommendations) would represent a meaningful improvement in recommendation-stage visibility.

The path to that expansion runs through the prompt and citation layers. The firm's perfect sentiment score and strong average recommended rank indicate that AI systems view Levin & Perconti favorably. The gap is in how often the firm is surfaced in response to high-intent prompts. Building the owned answer layer and citation architecture that AI systems retrieve for nursing home abuse queries would directly address this gap.

Competitive Landscape

Questions This Section Answers

• Where does Levin & Perconti sit in the ranking of nursing home abuse lawyer brands by top-three and rank-one rates?
• Why is Levin & Perconti's average recommended rank 1.60 the strongest in the category despite its low mention volume?
• Which brands lead the nursing home abuse lawyer category on top-three rate and average recommended rank?

Morgan & Morgan holds dominant recommendation power in the nursing home abuse lawyers category, with The Lanier Law Firm and Wilshire Law Firm positioned as the strongest challengers. Levin & Perconti sits in the lower tier of tracked brands, with recommendation coverage well below the category leaders.

Average recommended rank covers rank-eligible recommendations only.

Levin & Perconti's 3.14% top-three rate places it in a tie with Sokolove Law, but its 1.26% rank-one rate is lower. The firm's average recommended rank of 1.60 is the strongest in the category, indicating that when Levin & Perconti does appear in a shortlist, it typically appears near the top. The challenge is that the firm appears in far fewer shortlists than the category leaders.

Prompt Evidence

Questions This Section Answers

• Which specific prompts triggered AI recommendations of Levin & Perconti, and on which platforms?

Google AI Overviews / Brand Recommendation Prompt: "nursing home abuse lawyers" Result: Levin & Perconti appeared as a rank-one recommendation in at least one qualified observation on this platform.

ChatGPT / Brand Recommendation Prompt: "personal injury attorney" Result: Levin & Perconti received 1 valid recommendation on ChatGPT, ranking second in the shortlist.

Copilot / Brand Recommendation Prompt: "workplace injury attorney" Result: Levin & Perconti appeared in the response but did not receive rank-one placement.

Google AI Mode / Brand Recommendation Prompt: "Who is the best personal injury lawyer in Houston?" Result: Levin & Perconti received 1 valid recommendation on Google AI Mode, including 1 rank-one placement.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit Map Levin & Perconti's current recommendation footprint across all six AI platforms, identifying which prompts trigger the firm's appearance and which competitor recommendations displace it.

Phase 2: Recommendation Readiness Plan Prioritize the prompt clusters and platform surfaces where Levin & Perconti has the clearest path from mention to recommendation, focusing on Google AI Overviews and Google AI Mode where the firm already shows rank-one capability.

Phase 3: Owned Answer Layer Buildout Develop firm-controlled content that directly addresses the high-intent prompts AI systems use for nursing home abuse lawyer recommendations, including practice-area pages, attorney profiles, and case result summaries structured for AI retrieval.

Phase 4: Citation / Authority Layer Development Strengthen the public evidence layer that AI systems cite when generating recommendations, including legal directories, review platforms, and news sources that appear in the category's top-cited domains.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor Levin & Perconti's recommendation coverage, rank-one rate, and sentiment across all tracked platforms to measure progress and identify emerging gaps.

Why This Matters

Questions This Section Answers

• Why does Levin & Perconti's perfect sentiment score fail to translate into nursing home abuse lawyer recommendation volume?
• What does the gap between Levin & Perconti's recommendation quality and coverage mean for high-intent client acquisition?

AI-generated recommendations are becoming a primary discovery channel for buyers seeking legal services. When someone asks an AI system for the best nursing home abuse lawyer, the response shapes their shortlist before they ever visit a website or make a call. Levin & Perconti's perfect sentiment score and strong average recommended rank show that AI systems view the firm favorably. But favorable framing without consistent recommendation coverage means the firm is being passed over at the decision moment.

The gap between Levin & Perconti's recommendation quality and its recommendation volume points to a specific remediation path. The firm does not need to change how AI systems perceive it. It needs to ensure AI systems surface it more often in response to high-intent prompts. That requires targeted work on the prompt, page, and citation layers that drive AI-generated recommendations.

Core Metrics

Sentiment Score

Sentiment Score = (positive mentions × 1 + neutral mentions × 0 + negative mentions × -1) / total mentions

Levin & Perconti's sentiment score is 1.0000, calculated as (5 × 1 + 0 × 0 + 0 × -1) / 5. This is the highest possible sentiment score, indicating that every mention of the firm in the October 2026 dataset carried positive framing.

This matters because unclassified mention counts are misleading. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equal. Counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility. Levin & Perconti's perfect sentiment score means the firm's challenge is not perception but presence. Share of voice is a diagnostic metric, not a business KPI. It tells you how often you appear, not how often you are chosen.

Sentiment by Platform

Methodology

1. This report is a benchmark-based analysis of Levin & Perconti's AI visibility and recommendation coverage in the nursing home abuse lawyers category. It is not a client implementation case study.
2. The reporting month is October 2026, with comparison data from July 2026 as the baseline period.
3. The benchmark tracked six AI platforms: ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, and Google AI Mode.
4. The October 2026 measurement began with 656 prompt-surface observations and 480 unique questions. Of those, 597 mentioned a tracked brand or competitor, 308 were relevant, and 159 qualified as the public benchmark denominator.
5. The competitor universe includes ten tracked brands: Levin & Perconti, Morgan & Morgan, The Lanier Law Firm, Wilshire Law Firm, Senior Justice Law Firm, Sokolove Law, Pintas & Mullins, Schenk Nursing Home Abuse, Garcia & Artigliere, and Nursing Home Law Center.
6. All qualified observations fell into the Brand Recommendation buyer-intent cluster. The Pricing & Value and Multi-Brand Comparison clusters contained zero qualified observations in the current dataset.
7. Stage 0 extraction produced the raw prompt-surface observations that were then filtered through relevance and qualification stages to produce the 159 qualified observations used for all brand-level percentages.
8. A mention is defined as any appearance of Levin & Perconti in an AI-generated response, regardless of whether the firm was recommended.
9. A valid recommendation is defined as an appearance where Levin & Perconti was included in a recommendation shortlist with positive sentiment and rank eligibility.
10. Brand-level percentages use the 159 qualified observations as the denominator, not the 656 raw prompt-surface observations.
11. The benchmark does not measure market share, revenue, organic search rankings, social media sentiment, or causality from metric movements.
12. Small-count movement: Levin & Perconti operates on 5 valid recommendations in October 2026. Percentage movement at this sample size is real but should be read alongside the absolute counts.

See How AI Is Recommending Your Brand

The public benchmark shows where Levin & Perconti stands in AI-generated recommendations for nursing home abuse lawyers. A company-level AI visibility audit can show why the firm's recommendation coverage declined and which specific prompts, platforms, and citation sources offer the clearest path to recovery.