Bots: An Understanding of Time

Some modern applications must understand time, because the messages they receive contain time sensitive information. Consider a modern Service Desk solution, that may have to retrieve tickets based on a date range (the span between dates) or a duration of time.
In this blog post, I’ll explain how bots can interpret date ranges and durations, so they can respond to natural language queries provided by users, either via keyboard or microphone.
First, let’s consider  the building blocks of a bot, as depicted in the following view:

The client runs an application that sends messages to a messaging endpoint in the cloud.… [Keep reading] “Bots: An Understanding of Time”

Getting Started with Adaptive Cards and the Bot Framework

This article will provide an introduction to working with AdaptiveCards and the Bot Framework. AdaptiveCards provide bot developers with an option to create their own card templates to suit variety of different scenarios. I’ll also show you a couple of tricks with node.js that will help you design smart.
Before I run through the example, I want to point you to some great resources from which will help you build and test your own AdaptiveCards:

  • The schema explorer provides a breakdown of the constructs you can use to build your AdaptiveCards.
[Keep reading] “Getting Started with Adaptive Cards and the Bot Framework”

5 Tips: Designing Better Bots

Around about now many of you will be in discussions internally or with your partners on chatbots and their applications.
The design process for any bot distils a business process and associated outcome into a dialog. This is a series of interactions between the user and the bot where information is exchanged. The bot must deliver that outcome expediently, seeking clarifications where necessary.
I’ve been involved in many workshops with customers to elicit and evaluate business processes that could be improved through the use of bots.… [Keep reading] “5 Tips: Designing Better Bots”

Adding Bot to Microsoft Teams

If you are following up on my previous blog posts about Bots and integrating LUIS with them, you are almost done with building bots and already had some fun with it. Now it’s time to bring them to life and let internal or external users interact with Bot via some sort of front end channel accessible by them. If you haven’t read my previous posts on the subject yet, please give them a read at Creating a Bot and Creating a LUIS app before reading further.… [Keep reading] “Adding Bot to Microsoft Teams”

Using a Bot Framework to build LUIS enabled Bots


In this post, we are going to build a bot using Microsoft Bot framework and add intelligence to it to extract meanings from the conversation with users utilising Microsoft cognitive service named LUIS. The last post discussed details about LUIS, give it a read before you continue on reading. This post assumes you have a basic understanding of Language Understanding Intelligent Service (LUIS) and Bot Framework, further details can be read about them at LUIS and Bot Framework.… [Keep reading] “Using a Bot Framework to build LUIS enabled Bots”

How LUIS can help BOTs in understanding natural language

Since bots are evolving, you need a mechanism to better understand what user wants from his/her language and take actions or respond to user queries appropriately. In the days of increasing automation, bots can certainly help provided they are backed by tools to understand user language both naturally and contextually.
Azure Cognitive Services has an API that can help to identify what user wants, extracts concepts and entities from a sentence (user input) using an intelligent service name Language Understanding Intelligent Service (LUIS).… [Keep reading] “How LUIS can help BOTs in understanding natural language”