Personal Injury Law

    Automating Client Intake & Case Qualification for a High-Volume PI Firm

    How we increased signed-case conversions by automating intake and qualification workflows.

    AI
    Legal Tech
    Workflow Automation
    Intake Automation
    72%
    Intake Processing Time
    Reduction in average time from inquiry to qualified review.
    28%
    Signed-Case Conversion
    Increase in retained clients from inbound inquiries.
    420+ / month
    Administrative Hours Saved
    Paralegal hours reallocated to case preparation.

    The Challenge

    The firm was losing qualified leads due to slow intake response times and inconsistent manual screening.

    Our Solution

    Implemented an AI-powered intake and qualification system integrated with their case management software.

    The Outcome

    Reduced intake processing time by 72% and increased signed-case conversion rate by 28%.

    United States
    45 Employees
    SECURE & COMPLIANT

    Overview

    The firm was losing qualified leads due to slow intake response times and inconsistent manual screening.

    Primary Constraints:

    • Strict confidentiality requirements
    • Existing legacy case management system
    • Limited internal IT resources

    How We Solved It

    1

    Workflow Audit

    Mapped the full intake lifecycle from first contact to signed engagement letter, identifying 9 manual touchpoints.

    2

    AI Qualification Engine

    Built a scoring model to assess case viability based on injury severity, liability clarity, and jurisdiction.

    3

    System Integration

    Integrated AI intake workflows with the firm's existing case management platform via secure APIs.

    4

    Human-in-the-Loop Deployment

    Maintained attorney oversight for final approval while automating data collection and preliminary screening.

    Architecture

    The system uses a secure AI intake layer connected to the firm's CMS via encrypted APIs. Incoming submissions trigger automated parsing, scoring, and routing. A human-in-the-loop approval mechanism ensures compliance and final validation before client onboarding.

    Methodology & Stack

    Azure OpenAI

    To ensure data residency and enterprise-grade security controls.

    Python Workflow Engine

    For orchestrating intake logic, scoring, and multi-step routing.

    Secure API Integrations

    To sync structured intake data directly into the firm's CMS.

    The Results

    The automation eliminated manual email back-and-forth and standardized qualification logic across all practice areas. Attorneys now receive pre-qualified, structured case summaries within minutes of submission. The firm increased case throughput without hiring additional intake staff, directly impacting monthly revenue.

    • Average response time reduced from 20 hours to under 3 hours.
    • Lead leakage reduced by 35%.
    • No additional administrative hires required during 12-month growth phase.

    Lessons Learned

    Standardizing qualification criteria before automation is critical.

    Clear attorney oversight builds trust in AI-assisted scoring.

    Response-time improvements directly correlate with conversion lift.

    Security & Compliance

    All client data is encrypted in transit and at rest. Access is restricted via role-based controls. The system maintains a full audit log of AI scoring decisions for internal review.

    Out of Scope

    Final legal strategy decisions and engagement letter approval remain attorney-controlled.

    Related Impact

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