This thesis concerns the task of designing and building a system around a natural-language understanding (NLU) bot to help automate email communication, using cloud computing technology. Focus lies on a serverless environment within the Amazon Web Services (AWS) infrastructure. The purpose of the system is to make the process of requesting bid information connected to public procurement efficient and scalable. Unique email addresses were employed to easily keep track of email threads connected to specific requests, and a storage solution was developed in Amazon Simple Storage Service (S3) to store inbound attachments connected to each procurement request. The NLU bot was trained on existing email data and deployed to classify inbound email content. The classification was then used by the system to determine adequate responses.
Templates were used for outgoing emails, which required a large amount of data in order to be useful. The thesis initially sought to develop a fully automated system but that goal was modified to require human approval of outgoing emails to closely monitor the accuracy of the NLU as well as to give a user-friendly experience. This decision resulted in a system that required more time and work from the user but that provided better reliability and accuracy. Improvements of the template matching mechanics would provide better template suggestions for outgoing email and is a main focus for future work. In hindsight, more time should have been invested in training the NLU bot in order for it to produce better classifications, which would have made it possible to more fully test the capabilities of the system.