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AI-Powered SALI LMSS Taxonomy Automation Tool
  1. case
  2. AI-Powered SALI LMSS Taxonomy Automation Tool

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AI-Powered SALI LMSS Taxonomy Automation Tool

darly solutions
Legal
Business services
Financial services

Legal Taxonomy Interoperability Challenges

Law firms and corporate clients face significant manual effort in mapping legal definitions to SALI tags due to lack of universal taxonomy standards. Existing SALI LMSS database requires hours of manual processing, creating inefficiencies in billing and matter classification workflows.

About the Client

Legal technology innovation firm focused on improving billing and taxonomy interoperability for law firms and corporate clients

Automation & Accuracy Enhancement Goals

  • Automate SALI tag mapping with 99.1% accuracy using AI
  • Reduce manual tagging time by 90%
  • Achieve sub-1 second response times for search queries
  • Maintain 85%+ user satisfaction through intuitive design
  • Enable secure processing of sensitive legal documents

Core System Capabilities

  • Natural language search bar with real-time results
  • Interactive table view of SALI tags with reliability scores
  • CSV export functionality for results
  • API integration for secure data processing
  • Dynamic query refinement with include/exclude parameters

Technology Stack Requirements

React
Node.js
Next.js
OpenAI GPT-4

External System Connections

  • ChatGPT API
  • SALI LMSS database API
  • GitHub repository sync

Performance & Security Standards

  • 99.1%+ accuracy in tag mapping
  • Sub-1 second average response time
  • 10% or lower application error rate
  • Zero-authentication access with secure data transmission
  • 90%+ task success rate in usability testing

Business Transformation Outcomes

Enables law firms to reduce manual taxonomy mapping costs by 90%, improves billing alignment between firms and clients, enhances SALI standard adoption through user-friendly AI interface, and establishes brand authority through innovative legal tech solution.

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