Chatbot and Virtual Agents

How Serverless Architecture Helps Chatbot Development

Building a chatbot traditionally requires four steps: - Identify the data sources that will allow the bot’s training, - Create and train the bot thanks to a machine-learning model, - Add an API layer and choose a secure hosting environment, - Package the features into an app, a virtual assistant, or a web discussion for the end-user. Cloud providers propose chatbot APIs that include training models and back-end services, covering roughly steps 2 and 3 above. Thus, serverless tech provides a means to quickly deploy an advanced chatbot application to your website without having to spend a lot of time and effort into developing a chatbot from scratch.  The Amazon Lex chatbot is one of the best serverless chatbot implementations that run on top of the Lambda functions. It allows choosing either Node.js or python to develop the APIs and additional specific components to add to the chatbot.  Lex takes care of all the standard functionalities you would expect from the chatbot, including the comprehension of user inputs, replying with answers, and responding with error handling prompts. Now all a business must do is add any other extra feature like validating user inputs and presenting business-specific information to the users.  For instance, a banking chatbot can adapt Lex and add a specific feature that lets users input their account numbers and get their outstanding balance details. The new functionality can be implemented as a custom microservice triggered by a Lambda script.

Benefits Of Using Serverless for Chatbot Implementation

There is a rising demand for chatbots across all business sectors. About 57% of all companies across the globe already make use of a chatbot or have plans to implement one shortly. Chatbots provide a better cost-effective alternative to basic customer service queries by automating customer interactions and business transactions. In addition, they allow for lesser manual labor and better quality of service. And the current advancements in AI and machine learning have made chatbots more powerful and capable of answering queries accurately.  There is one more reason why chatbots have quickly risen to mainstream prominence. Now they are easier to develop and deploy at a rapid pace thanks to serverless architectures.  Serverless architectures provide additional benefits such as: - Better scalability You can enable automatic scaling by making use of serverless infrastructures. In serverless, there is simply no limit on the number of requests that a chatbot can handle.  - Reduced costs Serverless chatbots are based on a pay-as-you-use payment model. It means you only must pay as much as you use and not commit to expensive investments in resources.  - Easy integration Creating a traditional chatbot means developing custom data connectors to your third parties, existing products, and data sources. Using a well-tested cloud service will take off this extra effort required as it provides you with several in-built features and easy integration with any other service.


Main serverless chatbot APIs:  - IBM Watson Assistant - Google's Dialog Flow - Microsoft's LUIS - Amazon's LEX


Digital virtual assistant at Liberty Mutual | Liberty Mutual has created an Alexa skill with the help of the Watson™ Assistant and the Apache OpenWhisk serverless framework. This voice assistant application integrates the Watson Assistant chatbot implementation from IBM Cloud Functions and Alexa, the voice service from Amazon.  This is an excellent example of using the serverless framework to enhance further a voice assistant program where users can have an actual conversation with the Watson chatbot.  The IBM cloud functions that invoke the Watson Assistant service are triggered by the specific voice input from the user: "Alexa, ask Watson."  On receiving this input, Alexa invokes the IBM cloud functions, which pass it to the Watson Assistant and send back the response from Watson to the Alexa device. The Alexa device will then reply to the user. 

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