LLMs accelerate Adyen's support team through smart-ticket routing and support agent copilot

Adyen, a financial technology platform, is enhancing the efficiency and satisfaction of its support teams by implementing innovative solutions using large language models (LLMs). To address the challenge of ticket response times, Adyen established a team of Data Scientists and Machine Learning Engineers at their new Tech Hub in Madrid to develop a smart ticket routing system and a support agent copilot. These tools aim to direct tickets to the appropriate support person swiftly and aid agents in providing faster, more accurate responses. By leveraging LangChain, Adyen built a flexible framework that allows for easy customization and experimentation with various LLMs. This approach has improved ticket routing accuracy and reduced response times by dynamically analyzing ticket themes and sentiments and utilizing a vector database for efficient document retrieval. The integration of these technologies into a microservice architecture hosted on Kubernetes has resulted in more efficient support operations and increased agent satisfaction.

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