Chatbot Development Using AWS Lex and Lambda

Abstract: The proliferation of cloud-native services has created new possibilities for building intelligent conversational interfaces without the overhead of maintaining dedicated infrastructure. This paper presents the design, implementation, and evaluation of a customer-facing chatbot constructed using Amazon Lex V2 for natural language understanding and AWS Lambda for serverless fulfillment logic. The proposed system achieves an intent recognition accuracy of 94.2% across a test corpus of five hundred utterances, with an average end-to-end response latency of 310 milliseconds. The architecture integrates seamlessly with a suite of AWS backend services—DynamoDB, Simple Notification Service, and Simple Email Service—enabling context-aware, multi-turn conversations for a retail order-management use case. Comparative analysis against equivalent implementations on Google Dialogflow and IBM Watson demonstrates competitive accuracy and markedly lower operational cost at moderate traffic volumes. The paper discusses architectural trade-offs, cold-start mitigation strategies, slot validation patterns, and directions for future enhancement through sentiment analysis and personalization layers.

Keywords: Amazon Lex, AWS Lambda, serverless computing, natural language understanding, conversational AI, chatbot, cloud computing, intent recognition.