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

Answer Capsule

Levin & Perconti holds a narrow but real position in AI-generated recommendations for nursing home abuse lawyers, with valid recommendation coverage of 2.68% in September 2026. The firm appears in AI answers at a rate of 3.36%, meaning it is mentioned more often than it is recommended, and its recommendation flow has contracted sharply from 9.8% coverage in July 2026. The clearest strength is a positive framing profile with no negative mentions, while the clearest weakness is a thin presence that leaves the firm vulnerable to displacement by stronger competitors. The opportunity lies in converting its existing positive references into consistent shortlist placements across more AI platforms.

Who This Report Is For

This report is for marketing, business development, and firm leadership teams at Levin & Perconti who need to understand how AI systems currently surface and recommend the firm in nursing home abuse lawyer searches.

Report Card

Executive Summary

Levin & Perconti holds a marginal position in AI-driven recommendations for nursing home abuse lawyers. The benchmark shows the firm with 3.36% raw mention presence and 2.68% valid recommendation coverage across 149 qualified observations in September 2026. This means the firm appears in AI answers roughly three to four times out of every hundred, and is placed in a recommendation shortlist slightly less often than that.

The firm's recommendation coverage declined from 9.8% in July 2026 to 2.7% in September 2026, a 7.1-point drop that the benchmark classifies as beyond normal variation. With only 4 valid recommendations out of 149 qualified observations, the firm's presence in AI-generated shortlists is now minimal. Its rank-one rate stands at 1.34%, and its top-three rate at 2.01%, indicating that when the firm is recommended, it can appear prominently, but those moments are rare.

The strongest signal for Levin & Perconti is the quality of its mentions. The firm recorded 4 positive mentions, 1 neutral mention, and 0 negative mentions, producing a net sentiment score of 0.80. No AI platform in the tracked set framed the firm negatively. The weakest signal is platform breadth: the firm appears on ChatGPT, Gemini, Google AI Mode, and Google AI Overviews, but has no presence on Copilot or Perplexity in the qualified dataset.

The clearest platform strength is Google AI Mode, where the firm holds 5.26% valid recommendation coverage, its highest rate on any tracked surface. The clearest gap is the absence of any recommendation presence on Copilot and Perplexity, combined with a recommendation rate on ChatGPT of just 3.70%.

What Levin & Perconti Is Winning

Questions This Section Answers

• How strong is Levin & Perconti's sentiment profile across AI platforms?
• Where does the firm earn its most consistent recommendation coverage?

Levin & Perconti's most defensible position is its sentiment profile. The firm recorded zero negative mentions across all tracked AI platforms in September 2026. Every mention of the firm was either positive or neutral, producing a net sentiment score of 0.80. In a category where several competitors hold stronger recommendation positions, Levin & Perconti is not being framed negatively by AI systems.

The firm also shows a narrow but meaningful recommendation pocket on Google AI Mode. With 5.26% valid recommendation coverage on that platform, Levin & Perconti appears in shortlists more consistently there than on any other tracked surface. When the firm is recommended, it tends to appear early: its average recommended rank across all platforms is 2.25, and two of its four valid recommendations placed it at rank one.

These wins are limited in scale. The firm's positive framing is real, but it operates across a very small number of mentions. The benchmark evidence suggests Levin & Perconti is viewed favorably when surfaced, not that it is surfaced often.

Where Levin & Perconti Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Why does Levin & Perconti appear in AI answers more often than it is recommended?
• Which platforms could remove the firm from the category first if prompt conditions shift?

Levin & Perconti's most significant gap is the distance between its mention presence and its recommendation coverage. The firm appears in AI answers 3.36% of the time but is recommended only 2.68% of the time. While this gap is not as wide as some competitors, it indicates that the firm is occasionally mentioned without being placed in a shortlist, a pattern that suggests AI systems recognize the firm but do not consistently select it.

The firm's decline across the benchmark series is the second major gap. Levin & Perconti fell from 9.8% valid recommendation coverage in July 2026 to 2.7% in September 2026, a 7.1-point drop beyond normal variation. The firm went from a meaningful second-tier presence to the edge of the tracked set in two months. With only 4 valid recommendations in September, the firm is now operating at a level where a single prompt change could remove it from the category entirely.

Platform coverage presents a third gap. Levin & Perconti has no presence on Copilot or Perplexity in the qualified dataset. Morgan & Morgan, the category leader, appears across all six tracked surface families. Wilshire Law Firm, the strongest riser, holds recommendation positions on ChatGPT, Copilot, Gemini, Google AI Mode, and Google AI Overviews. Levin & Perconti's absence from two major platforms limits its ability to capture recommendation-stage visibility where buyers may be forming shortlists.

The competitive comparison is stark. Morgan & Morgan holds 34.23% valid recommendation coverage, more than twelve times Levin & Perconti's rate. Wilshire Law Firm holds 14.09%, more than five times the firm's rate. Even Senior Justice Law Firm, which declined sharply in September, holds 10.07% coverage, nearly four times Levin & Perconti's position.

Biggest Opportunity

Questions This Section Answers

• Which platforms should Levin & Perconti prioritize to convert positive mentions into recommendations?
• What should the firm do first to expand its Google AI Mode strength to other surfaces?

Levin & Perconti's clearest opportunity is converting its positive mention profile into consistent recommendation placements on Google AI Mode and Google AI Overviews. The firm already achieves its strongest recommendation coverage on Google AI Mode at 5.26%, and it holds a 2.86% rank-one rate on Google AI Overviews. These are the surfaces where the firm's existing evidence layer appears to resonate most strongly with AI systems.

The path forward is not to chase presence across all six platforms simultaneously. It is to strengthen the firm's position on the Google surfaces where it already earns positive framing, then expand that pattern to ChatGPT, where the firm currently holds only a single valid recommendation. Because the firm has no negative sentiment to overcome, the work is about increasing the frequency and consistency of recommendations, not repairing a damaged narrative.

Competitive Landscape

Questions This Section Answers

• How does Levin & Perconti's recommendation coverage compare with Morgan & Morgan and Wilshire Law Firm?
• Where does the firm's average recommended rank sit against the category leaders?

Morgan & Morgan holds dominant recommendation-stage strength in the nursing home abuse lawyer category, while Wilshire Law Firm has emerged as the strongest challenger. Levin & Perconti sits in the lower tier of the tracked set, with recommendation coverage that places it ahead of only the brands with no valid recommendations.

Average recommended rank covers rank-eligible recommendations only.

Levin & Perconti's top-three rate and rank-one rate are identical to Sokolove Law's, but Sokolove Law holds slightly higher valid recommendation coverage at 3.36%. The firm's average recommended rank of 2.25 is competitive with the category leaders, which shows that when Levin & Perconti is recommended, it appears early in the list. The challenge is frequency, not placement quality.

Prompt Evidence

Google AI Mode / Brand Recommendation Prompt: "nursing home abuse attorney" Result: Levin & Perconti appeared in a recommendation shortlist, earning one of its two valid recommendations on this platform.

Google AI Overviews / Brand Recommendation Prompt: "nursing home abuse lawyers" Result: The firm was named as the first recommendation in one qualified observation, contributing to its 2.86% rank-one rate on this surface.

ChatGPT / Brand Recommendation Prompt: "personal injury law firm" Result: Levin & Perconti received a single valid recommendation, showing presence but limited shortlist conversion on this platform.

Copilot / Brand Recommendation Prompt: "nursing home abuse attorney" Result: No mention of Levin & Perconti was recorded, indicating a complete absence from this surface in the qualified dataset.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map the specific prompts and surfaces where Levin & Perconti appears versus where it is displaced, with emphasis on the gap between its Google AI Mode strength and its ChatGPT weakness.

Phase 2: Recommendation Readiness Plan Identify which owned pages and practice-area content are retrievable by AI systems and which high-intent nursing home abuse queries lack a clear Levin & Perconti answer layer.

Phase 3: Owned Answer Layer Buildout Develop authoritative content that directly addresses nursing home abuse liability, facility negligence, and family recourse questions, structured so AI systems can extract and cite the firm's position.

Phase 4: Citation / Authority Layer Development Strengthen the external source footprint that supports the firm's positive mentions, focusing on directories, legal publications, and case-result coverage that AI systems can retrieve.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor whether the firm's recommendation coverage stabilizes above 2.68% and whether Google AI Mode strength expands to ChatGPT and other surfaces.

Why This Matters

AI-generated recommendations are becoming a primary way families identify legal representation for nursing home abuse cases. When a family asks an AI assistant which law firm handles nursing home abuse claims, the firms named in the response form an effective shortlist before any direct outreach occurs. Levin & Perconti is being mentioned positively, but it is not being recommended consistently enough to capture a meaningful share of those moments.

The evidence shows that presence alone is not enough. The firm's raw mention rate of 3.36% is higher than its recommendation coverage of 2.68%, meaning AI systems sometimes acknowledge Levin & Perconti without selecting it. The next move is targeted correction of the prompt, page, and citation layers to convert positive references into consistent shortlist placements, particularly on the platforms where the firm already holds ground.

Core Metrics

Questions This Section Answers

• What are Levin & Perconti's key recommendation rates and average placement?
• Which platform and cluster produce the firm's strongest recommendation behavior?

Sentiment Score

Questions This Section Answers

• How is Levin & Perconti's net sentiment score calculated and what does it mean for interpreting AI visibility?

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

For Levin & Perconti, the calculation is (4 × 1 + 1 × 0 + 0 × -1) / 5, producing a net sentiment score of 0.80.

This score matters because unclassified mention counts are misleading. A firm can appear frequently in AI answers while being framed negatively or mentioned only as a comparison anchor. Levin & Perconti's positive sentiment score of 0.80 is a genuine asset, but it must be interpreted alongside the firm's small mention count. Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, a neutral reference, and a competitor-displaced mention are not equal, and counting all mentions as wins would overstate the firm's position. Classified sentiment is required before interpreting AI visibility, and in Levin & Perconti's case, the classification shows quality without quantity.

Sentiment by Platform

Questions This Section Answers

• Which platforms show positive sentiment for Levin & Perconti, and where is the sample too small to trust?

Methodology

1. This report is a benchmark-based analysis of how Levin & Perconti appears and is recommended across AI and search surfaces in the nursing home abuse lawyer category. It is not a client implementation case study.
2. The reporting window is September 2026, with July 2026 and August 2026 referenced for trend context where the public benchmark provides historical readings.
3. Six AI surface families were tracked: ChatGPT, Copilot, Gemini, Perplexity, Google AI Mode, and Google AI Overviews.
4. The September 2026 benchmark began with 648 source prompt-surface observations and 488 unique questions. Of these, 283 were relevant to the vertical and 365 were irrelevant.
5. All brand-level metrics use the 149 qualified benchmark observations as the denominator, not the 648 raw collection size.
6. The competitor universe includes 10 tracked brands: Sokolove Law, Garcia & Artigliere, Levin & Perconti, Morgan & Morgan, Nursing Home Law Center, Pintas & Mullins, Schenk Nursing Home Abuse, Senior Justice Law Firm, The Lanier Law Firm, and Wilshire Law Firm.
7. All qualified observations in the public series fall into the Brand Recommendation cluster, covering discovery and consideration intent. The public benchmark does not yet contain qualified observations in the Pricing & Value or Multi-Brand Comparison classes.
8. A mention is defined as any appearance of the brand in a qualified AI response, regardless of framing or recommendation status.
9. A valid recommendation is defined as an appearance in a recommendation shortlist within a qualified AI response. Neutral references, cautionary mentions, and comparison-anchor appearances are not counted as valid recommendations.
10. The public benchmark records change over time but does not by itself establish the cause of that change. Source presence indicates what information was available to AI systems, not proof that a particular source caused a specific recommendation.
11. Small-count movement applies to this report: Levin & Perconti operates on 4 valid recommendations in September 2026, so percentage movement should be read with the absolute counts in mind.
12. Limitations: this public benchmark does not measure market share, revenue, or attributable sales from AI recommendations, nor does it capture every possible AI response a brand could receive.

See How AI Is Recommending Your Brand

The public benchmark shows where Levin & Perconti stands in AI-generated recommendations, but it cannot identify the specific prompts, competitors, or sources driving each result. A company-level AI visibility audit maps those patterns into a prioritized strategy for converting positive mentions into consistent shortlist placements.