Curandus, a Boston-based medical startup, decided to check a chatbot for collecting leads on the website. We have created a chatbot based on Amazon Lex, AWS machine learning technology. The chatbot talks to potential clients, doctors and patients.
At the beginning of the conversation, our chatbot determines whether it is talking to a client (pharmaceutical company), doctor or patient. After determining who the interlocutor is, the chatbot launches appropriate conversation scenarios. The entire solution architecture is based on Amazon AWS technologies. The user is talking to the chatbot on the Curandus website.
Talk to our chatbot. Remember to say hi and introduce who you are.
We did not have ready-made chatbot scenarios. The client provided us only with the structure of the data, which the chatbot was to collect from different types of users and deliver in the form of leads. Based on this data, we developed efficient and simple scenarios tailored to the requirements of Amazon Lex technology.
For an experienced development team, implementing a chatbot from the programming side based on Amazon Lex is relatively simple. But designing the chatbot conversation flow correctly can be challenging. A chatbot is not simply a form that specifies exactly what data a user must fill in. The collection of data by a chatbot is done through a conversation, the design of which lies with the UX Designer. In a conversation, we cannot predict exactly how it will go and what exactly the user will give us. Please visit the blog and read more about chatbot design.
Lex, S3, DynamoDB, Lambda, IAM, Cognito, Amplify
Django, React.js, SASS, AWS