Even if you don't have a chatbox on your website, you have definitely encountered one before. Facebook, eBay, Domino's Pizza, and Universal Studios are some of the big names that have their own chatbots. With the advancements in AI, mundane tasks like customer service can easily be handled by a chatbot. Some people reject the merit of chatbots stating that communicating with them feels very impersonal. According to the 2018 State of Chatbots Report by Salesforce, 69% of consumers said they preferred communicating with chatbots.

Chatbots have gained extreme popularity in recent years with the advancements in Machine Learning and natural language processing. The internet works like a platform that creates a web of information that helps the bots to gather this information and respond accordingly. This makes them utterly useful! Everything today works on data and information, for instance, machine learning or its advanced version i.e. With the increased online data and information, companies are now relying on cloud computing as a source of data storage or other services that could be provided using a remote/virtual computer. Before we get into all the technological stuff, let's have a general idea of what is a chatbot.

With technologies like Alexa from Amazon, Home from Google, and Siri from Apple it's clear that human conversation is becoming a genuine alternative to traditional user interfaces. Throughout the year, I started building a deeper Chatbot example using Watson Conversation, and working with Stefania Kaczmarczyk. This is the first of a series of posts that looks at Watson Conversation holistically in the context of the software design and engineering of a Chatbot example. In this first post I'll introduce the concept with a short video clip, and review how we built the dialog using Watson Conversation. Most of all I wanted to experiment with a Chatbot that worked with personal data, accessed from a database.