This post is an excerpt from our recent solution tutorial with step-by-step instructions showing how easy it is to quickly create a voice-enabled Android-native chatbot with Watson Assistant, Text to Speech, Speech to Text and mobile analytics services on IBM Cloud.
Recently, I introduced you to a new tutorial for a database-driven Slackbot. Today, I am going to discuss security details, how the IBM Watson Conversation service is accessing a Db2 Warehouse service from within a dialog. It uses a serverless setup with IBM Cloud Functions. All the necessary credentials to execute the code and to access the Db2 database are automatically bound. Hence, the function code and the dialog don't need any account-specific changes and are generic.
If you follow my private blog you might remember that I have been using the IBM Watson Conversation service and DB2. My goal was to write a database-driven Slackbot, a Slack app that serves as chat interface to data stored in Db2. I will write more about that entire Slackbot soon, but today I wanted to share some chatbot tricks I learned. How to gather input data, perform checks and clean up the processing environment.
One of the most frequent questions clients ask when visiting a Cloud Garage is "Can you build us a chatbot?" This question is reflective of an industry-wide trend towards more natural language in computerised interactions, and also more automation of interactions currently handled by humans. Today, there are currently more than 33,000 chatbots on Facebook Messenger alone. Many businesses are turning to Watson Conversation to help take out cost and improve user satisfaction. Our Hursley Labs colleague Simon Burns has written an excellent series of articles on how to write great Watson chatbots, which you should definitely go read. Think of this blog as a supplement, with our experiences from the field. To address this pressing question, I’ve compiled a set of considerations for you to address when deciding whether a chatbot is truly the solution to your business needs.
Imagine, you are in a conversation with a chatbot and you feel that the human angle is completely missing because the bot starts it's dialog with a usual "Hi" or "Hello". You may want to personify the conversation by adding the name of the person (who's logged in) to the boring "Hi" or "Hello". Ever thought of this? It's not just personification, How about wishing appropriately based on the time of the day someone invokes your chat application? Also, how about passing values back and forth during a conversation between the nodes or from application to a node?
Chatbots, as a means of customer interaction, are taking the financial sphere by storm as more bots are launched weekly. Established financial institutions, mostly in the retail-banking sector, investment management and wealth management sectors, are jumping on the bandwagon and experimenting with bots. Financial institutions have embraced this trend and experts predict that, within five years, bots will replace conventional online interfaces such as websites or mobile apps.
You build your first chatbot and it is working ok. Did you know that you can make chatbots even more interactive? That you can access conversation metadata and application variables inside the dialog nodes? You can even use predicates to tailor output to the usage scenario. As a follow up from our "Lessons and Tips from a Chatbot Hackathon", let's dig deeper into important features of the IBM Watson Conversation service on the IBM Cloud with Bluemix.