# GenAI Work Order Management System POC Implementation ## Project Overview NTT Data worked with a large infrastructure company that manages housing complexes across the U.S. to implement a proof of concept for automating their work order management system. The existing system was largely manual, with approximately 70 employees processing 1,500 work orders daily from a total of over 500,000 annual maintenance requests. This manual approach led to inconsistencies and potential errors in work order categorization and management. ## Technical Implementation Details ### Security and Infrastructure - Implemented the POC in a secure, third-party environment - Utilized a privately hosted LLM owned by NTT Data - Designed for eventual deployment within the client's firewall - Focus on security and risk mitigation through private deployment ### Knowledge Base Development - Incorporated extensive existing documentation: - Built prompt engineering system to guide LLM in understanding organizational context - Designed for future extensibility with custom application for policy updates ### Core Functionality - Automated classification of maintenance requests into appropriate categories - Determination of urgency levels for requests - Identification of special handling requirements - Generation of reasoning explanations for classifications - Audit trail creation for accountability and process improvement ### LLMOps Considerations ### Model Selection and Deployment - Choice of private LLM deployment over public models - Focus on security and data privacy - Infrastructure designed for behind-firewall deployment - Consideration of scaling requirements for full production deployment ### Knowledge Integration - Systematic approach to converting company documentation into LLM context - Development of prompt engineering strategies for accurate classification - Design of update mechanisms for ongoing knowledge base maintenance ### Monitoring and Improvement - Built-in explanation generation for classification decisions - Audit trail capabilities for tracking model decisions - Planned iterative improvements based on performance analysis - Framework for comparing model performance against human operators ## Production Deployment Strategy ### Phased Implementation Approach - Initial use as training and optimization tool for human operators - Gradual transition to more automated processing - Continuous monitoring and validation of model outputs - Integration with existing work order management systems ### Quality Assurance - Comparison of model outputs with human operator decisions - Tracking of accuracy and consistency metrics - Validation of classification reasoning - Monitoring of special case handling accuracy ### Future Scalability - Design for handling increasing work order volumes - Planning for knowledge base expansion - Integration capabilities with other enterprise systems - Framework for ongoing model improvements ## Lessons Learned and Best Practices ### Technical Insights - Importance of balanced business and technical approach - Value of private LLM deployment for sensitive operations - Need for transparent decision-making processes - Significance of knowledge base quality and maintenance ### Implementation Strategy - Benefits of focused POC scope - Importance of quick iteration and learning - Value of partner expertise in implementation - Need for patience in validation and refinement ### Risk Management - Security considerations in LLM deployment - Data privacy protection mechanisms - Audit trail importance for accountability - Gradual transition strategy to minimize disruption ## Critical Success Factors ### Business Integration - Alignment with existing processes - Clear value proposition demonstration - Measurable performance improvements - Stakeholder buy-in and support ### Technical Excellence - Robust security implementation - Reliable classification accuracy - Scalable architecture design - Maintainable knowledge base structure ### Change Management - Operator training and support - Clear communication of system capabilities - Gradual transition planning - Performance monitoring and feedback loops ## Future Considerations ### Scaling Opportunities - Expansion to additional maintenance categories - Integration with other business systems - Enhanced automation capabilities - Performance optimization strategies ### Continuous Improvement - Regular knowledge base updates - Model performance optimization - Process refinement based on learnings - Integration of user feedback and experiences The case study demonstrates a pragmatic approach to implementing GenAI in a production environment, with careful consideration of security, scalability, and practical business value. The focus on a private LLM deployment and thorough knowledge base development shows the importance of tailoring AI solutions to specific business contexts while maintaining security and control.