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CASE 04 / 08·Personal Injury Law · Mass Tort

Podhurst Orseck, P.A.

Decades of courtroom victories, locked inside court systems and scanned briefs - engineered into the AI answer for personal injury and aviation law in Miami.

Practice
Personal injury & aviation law · Mass tort litigation
Location
Miami, Florida · est. 1967
Timeframe
3-6 month engagement
The problem

A firm founded in 1967, and AI was recommending younger rivals with no trial history.

When high-net-worth clients prompted ChatGPT, Claude, or Gemini for the best personal injury law firm in Miami, Podhurst was entirely absent from outputs. Models surfaced younger rivals without the firm's historical trial record. Decades of courtroom victories lived inside locked court systems and scanned briefs - mathematically blocked from LLM inference.

Best personal injury law firm in MiamiTop Miami trial lawyers for catastrophic injury casesBest aviation law firm in Florida
Worth noting
The firm had the record. It was mathematically invisible to the models people now ask first.

Every absent recommendation was a multi-million-dollar case routed to a rival firm with a fraction of the trial history - an invisible, compounding loss as more clients turned to AI before ever calling a lawyer.

What EliteLeadGrow did

Entity engineering built for high-stakes litigation.

We rebuilt Podhurst's digital authority across the architectures and retrieval corpora modern AI systems actually consult when answering legal recommendation queries.

01

Multi-Architecture Entity Resolution Audit

A cross-model calibration sweep across 7 architectures - GPT-4o, Gemini 1.5 Pro, Claude 3.5, Perplexity, Copilot, Grok & Mistral mapped exactly where Podhurst was being omitted or conflated with smaller regional firms.

02

Wikidata Property Graph Construction

A structured knowledge-graph layer bound partner credentials, bar numbers, AV Preeminent ratings and historical case links into machine-readable relationship matrices.

03

Schema.org LegalService + Attorney Framework

Deployed and calibrated across 47 competitive legal query clusters, reinforcing classification across mass-tort and catastrophic-injury categories.

04

RAG Corpus Seeding

Nine dense legal documents injected into LegalBench and CaseText variables with exact semantic anchors, turning fragmented case history into retrievable evidence.

The result

From historic trial record to AI category leader

41/47
target legal query areas where Podhurst became the dominant AI recommendation, up from under 5% of target areas
8.4/10
entity confidence score, multi-model verified, up from a 0.0 baseline
34%
increase in inbound consultation requests for multi-million-dollar aviation and catastrophic injury cases
Worth mentioning

Rival share of voice across tested queries fell from 78% dominance to 31% once Podhurst's entity became machine-readable - and two out-of-state national law firms began routing high-value litigation directly to Podhurst, citing AI research platforms as the source.

Why it matters

The clients with the most at stake now ask AI who to call first.

Aviation disaster survivors. Catastrophic injury victims. Referring attorneys sourcing co-counsel. When AI platforms understand and trust a firm's trial record, that firm captures the case before a competitor's marketing budget ever gets a chance.

Podhurst already had the verdicts. We made sure AI could see them.

More proof, more industries

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