AI Is Referring Lawyers—Is It Referring You?

Author: Paul J Bruemmer

This article is designed to help plaintiff trial lawyers optimize their firm’s website and brand visibility for AI models (ChatGPT, Perplexity, Gemini) and traditional search engines (Google, Bing). Recommendations are strategic and tactical—with expert-sourced guidance, including citations.

Embrace “LLM Noise” as Brand Insights

LLMs (large language models) generate inconsistent responses for the same query—this isn’t a flaw but an opportunity. These variations reveal how AI perceives and narrates your firm’s brand over time (Waikay.io, Genie Jones via X).

Tactic: Run structured prompts like:

“What makes Firm X effective in personal injury cases?”
“How do clients describe Firm X?”

Track recurring themes and narrative shifts—those are your brand’s resonance signals.

Track Brand Entities and Citations

AI models reference sites they “trust.” Measuring how often and in what context your firm is mentioned (entities), and which sources are cited alongside, reveals where to invest in credibility (Waikay.io).

Tactic: Create an AI-brand visibility dashboard tracking:

Frequency of your firm as an entity
Domains cited in proximity
Shifts in sentiment and positioning

Optimize Website for AI-Powered Discovery

Drawing on Aleyda Solís’s AI Search Content Optimization Checklist, here’s how to structure content:

Chunk-Level Retrieval (Usman Ishaq)

LLMs retrieve bite-sized content chunks, not full articles. Each section must be self-contained with clear H2/H3 headings.

Tactic: Break pages into logical sections like:

“Our Track Record”
“Client Testimonials”
“Typical Case Timeline”
“What Sets Us Apart”

Answer Synthesis Optimization (Aleyda Solís)

LLMs synthesize multiple sources into a single answer. Format your content like Q&A for citation readiness.

Tactic: Use structured FAQs:

“What compensation can I expect after a car accident?”
Include bullet‑point answers followed by detailed explanations

Citation-Worthy Content

LLMs favor structured, factual, and trustworthy content (Facebook, PPC Land).

Tactic:

Include verifiable data and client outcomes (e.g., “75% of our cases settle pre-trial”).
Cite authoritative sources like CDC stats and legal precedent.

Topical Breadth & Depth

Ensure deep coverage of the entire personal injury spectrum with strong interlinking (PPC Land, Aleyda Solís).

Tactic: Build topic cluster hubs:

“Traumatic Brain Injury”
“Workplace Incidents”
“Slip & Fall Cases”

Multi-Modal Support

Incorporate graphics, timelines, and videos to improve semantic richness (Aleyda Solís).

Tactic:

Add infographics (e.g., “Timeline: From Injury to Compensation”)
Embed short client-experience videos

Authoritativeness Signals

Use metadata and schema markup to demonstrate trust (Aleyda Solís, PPC Land).

Tactic:

Add Person, LawFirm, Review, Award schema
Embed key trust signals directly into HTML

Personalization-Resilient Content

Focus on evergreen, factual content with user-centric design (Authoritas, X).

Tactic:

Use localized, accessible language (“Injured in San Diego?”)
Avoid jargon-heavy or overly academic prose

AI Crawlability (Dan Hinckley)

As Hinckley notes, ChatGPT-User bots fetch only raw HTML (LinkedIn). They skip JS, CSS, and images.

Tactic:

Keep critical content in HTML source
Use server logs to verify what AI bots access

Monitor & Measure AI Visibility

Track Entity Mentions (Authoritas, Waikay.io)

Use monitoring tools to evaluate brand citations across LLMs.

Tactic:

Log mentions across ChatGPT, Gemini, Perplexity
Compare citation frequency with competitors

Quarterly SEO + GEO Reviews (GoldenComm)

Review AI search visibility and align with brand strategy (GoldenComm).

Tactic:

Run recurring prompt audits
Update FAQs and trust signals quarterly

AI-generated visual that blends technology and legal practice

Strategic Recommendations for Trial Lawyers

Objective	Strategy	Tactic
Build authoritative presence	Publish factual, citable case studies	“Client received $250K after rear-end collision (2024).”
Dominate AI-covered areas	Develop topical hubs	Clustered FAQs on “Comparative Negligence in CA”
Increase citations	Reference trusted third-party sources	CDC injury stats, bar association guidelines
Track narrative shifts	Analyze and adjust LLM outputs	Monitor prompts quarterly and refine structure

GEO: Generative Engine Optimization (Andrew Holland)

Unlike traditional SEO, GEO targets AI-driven buyer intent, not just informational queries (LinkedIn).

Strategy (Holland): Write for Buyer Queries

Create localized, semantically structured service pages
Use schema markup and user-centric headers
Reflect actual client language

Tactic: Test AI prompts across ChatGPT, Gemini, and Perplexity. Note which competitors appear and reverse-engineer their format.

Citations:

Dan Hinckley, Board Member and Co-Founder at Go Fish Digital
- Source: LinkedIn Post (1 week ago)
- Insight: ChatGPT-User bot retrieves only raw HTML—not JavaScript, CSS, or images—highlighting the need to embed key content directly in the HTML source.
- Additional Insight: Demonstrated method for clustering 25,000 GSC keywords using Google Vertex AI to uncover trusted topics and authority gaps.
Usman Ishaq, Semantic SEO Specialist | Website Strategist
- Source: Multiple LinkedIn Posts (1 week to 4 days ago)
- Insight: LLMs extract content in reusable chunks like definitions, frameworks, steps, comparisons, and attributed quotes. Recommends shifting from keyword-heavy formats to semantically structured content.
- Additional Insight: Encourages aligning headers with user intent and structuring content as topic graphs for better retrieval and citation.
Andrew Holland, Director of SEO – GEO/AI Search Optimization Specialist
- Source: LinkedIn Post (2 weeks ago)
- Insight: Differentiated GEO (Generative Engine Optimization) from traditional SEO. Emphasized the importance of optimizing for buyer-intent queries rather than informational citations. Advocated for AI readiness testing and product/service-level visibility in AI search contexts.

Additional Citations:

Mastering the New Digital Landscape for 2030 and Beyond