  This paper presents key principles and solutions to the challenges faced in
designing a domain-specific conversational agent for the legal domain. It
includes issues of scope, platform, architecture and preparation of input data.
It provides functionality in answering user queries and recording user
information including contact details and case-related information. It utilises
deep learning technology built upon Amazon Web Services (AWS) LEX in
combination with AWS Lambda. Due to lack of publicly available data, we
identified two methods including crowdsourcing experiments and archived
enquiries to develop a number of linguistic resources. This includes a training
dataset, set of predetermined responses for the conversational agent, a set of
regression test cases and a further conversation test set. We propose a
hierarchical bot structure that facilitates multi-level delegation and report
model accuracy on the regression test set. Additionally, we highlight features
that are added to the bot to improve the conversation flow and overall user
experience.
