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AWS QnABot is a multi-channel, multi-language conversational interface (chatbot) that responds to your customer's questions, answers, and feedback. The solution allows you to deploy a fully functional chatbot across multiple channels including chat, voice, SMS and Amazon Alexa.
This repo contains a sample application to show how to build a voice interface for patient outcome reporting (PRO) by leveraging NLP capabilities provided by Amazon Lex and its integration with AWS's cloud contact center offering, Amazon Connect.
This project deploys an AWS Lex V2 chatbot for analyzing IoT data stored in an S3 bucket. The infrastructure is defined using Terraform, and the project includes a Lambda function for chatbot fulfillment.
This app is a serverless, micro service-driven web application created completely using AWS cloud services. The main application of this chatbot is to provide restaurant suggestions to its users based on the preferences provided to it through conversations.
This creates an AWS Chatbot to give users their investment portfolio based on their risk tolerance level i.e. conservative, moderate, or aggressive. With the use of machine learning, the tool will be created to different portfolios based off that.
A chatbot (Slack integration) that suggests user what and where to eat based on their current location with the use of AWS Amazon Lex, DynamoDB and Lambda. The chatbot employs Yelp API to find nearby restaurants, keep showing their food categories and pictures along with sassy comments when the user says ‘no’, then give chosen restaurant details…