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LegalAI implementation case study

Legal – High-Volume Personal Injury Firm: Intake-to-Litigation-Prep AI System

AI agents for 24/7 intake qualification, retainer workflows, records ordering, and demand-package drafting support for a PI firm.

This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.

Key takeaway

PrimeAxiom automated the full intake-to-production pipeline so AI agents handled repetitive legal operations while attorneys supervised strategy.

The engagement

From operational friction to a connected AI system

01 · Context

Business overview

Industry: personal injury. Size: 85 legal staff. Complexity: high marketing volume, contingency fees, strict bar advertising rules. Systems: case management, call center, medical records vendors.

02 · Challenge

Core problem

Manual intake scripts, lost web leads after hours, records chaos, copy-paste demands. Bottlenecks at paralegal assignment and insurer comms. Revenue impact when statute deadlines loomed.

03 · Approach

Solution

PrimeAxiom deployed multi-agent automation: bilingual intake, conflict checks, fee agreement e-sign, records bots with provider-specific portals, and demand drafting with verified fact extraction from case DB.

04 · Impact

Results

Qualified intakes +31% without headcount; records cycle time −40%; demand first-draft time −55%; attorney review hours reallocated to court appearances.

System design

The full automation system

The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.

Unified matter creation from omnichannel intake, AI scoring for case viability, automated conflicts and retainer flows, records orchestration with status agents, demand workspace with RAG over case documents.

Workflow

How the automation runs

1. Trigger: call, web, chat, referral. 2. AI extracts facts, runs conflict check. 3. Matter structured; retainer sent. 4. Retainer signed triggers records strategy. 5. Records requests, chasers, PDF merges. 6. Attorneys review demands; negotiate. 7. Outcomes improve viability model.

Intelligence layer

AI agents and decision logic

Intake Qualification Agent, Conflict Agent, Records Chaser Agent, Medical Chronology Agent, Demand Draft Agent.

Under the hood

Technical architecture

SOC2-minded stack, encrypted docs, CM APIs, vector store per matter, LLM with retrieval grounding.

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