# LLM Implementation for Support Team Optimization at Adyen ## Company Overview and Challenge Adyen, a publicly-traded financial technology platform serving major companies like Meta, Uber, H&M, and Microsoft, faced increasing pressure on their support teams due to growing transaction volumes and merchant adoption. Rather than simply expanding their team size, they sought a technology-driven solution to scale their support operations efficiently. ## Technical Implementation ### Team Structure and Initial Approach - Established a lean team of Data Scientists and Machine Learning Engineers at their Madrid Tech Hub - Focused on two primary LLM applications: ### Technology Stack and Architecture - Primary Framework: LangChain - Development and Monitoring: LangSmith - Infrastructure: ### Smart Ticket Router Implementation - Core Components: - Key Features: ### Support Agent Copilot Development - Document Management: - Retrieval System: ### Production Deployment Considerations - Modular Architecture: - Quality Assurance: ## Results and Impact ### Ticket Routing Improvements - Enhanced Efficiency: - Dynamic Prioritization: ### Support Response Optimization - Implementation Timeline: - Document Retrieval Enhancement: ### Agent Experience Improvements - Workflow Optimization: - Response Quality: ## Technical Lessons and Best Practices ### Architecture Decisions - Use of event-driven architecture for scalability - Implementation of custom LangChain extensions - Integration with existing systems through microservices ### Development Strategy - Focus on modular components - Emphasis on model flexibility - Priority on system reliability and performance ### Monitoring and Optimization - Continuous performance evaluation - Regular model comparison and selection - Cost-effectiveness tracking ## Future Considerations ### Scalability Planning - Architecture designed for growth - Flexible model integration capability - Extensible document management system ### Performance Optimization - Continuous monitoring and improvement - Regular model evaluation and updates - Ongoing system refinement This implementation demonstrates a successful integration of LLMs into a production support environment, showing how careful architecture planning, appropriate tool selection, and focus on practical outcomes can lead to significant improvements in support team efficiency and satisfaction. The use of modern tools like LangChain and LangSmith, combined with robust infrastructure choices like Kubernetes and event-driven architecture, provides a solid foundation for sustained performance and future scaling.