Agent Project Manager receives a mandate for a conversational application. He then proceeds to select his team members, reflecting on their role in the upcoming project. Also, there is a cat.

My Zoom meeting is interrupted by the doorbell. It rings four times, following the usual pattern.

I know what that means. Time for a new mandate.

I apologize to my fellow agents, exit the session and rush to the door.

As expected, no one is there, but I notice a small package on the ground. I pick it up and go back inside. The envelope is bare. …


We said it before, and we’ll say it again: error handling is a crucial element of the conversational UX for chatbot. In a previous post, we identified two primordial characteristics of good error handling. Firstly, it should be contextual, by avoiding generic messages (like “I’m sorry, I didn’t understand that”) and ensuring that error prompts are always relevant in the context of the dialogue. Secondly, it should be progressive, which consists of giving different error messages if the bot doesn’t recognize the user query multiple times in a row, each time escalating towards more exhaustive answers. …


An overview of essential discourse patterns, part 2

This is a continuation of our description of essential discourse patterns for chatbots. If you haven’t read it already, part 1 (which was about error handling and error messages) is right here. In this section, we will take a more varied approach and talk about counter proposals, contextual constraint checks and digression.

Counter-proposals

A conversation with a chatbot doesn’t have to follow a simple question-answer structure. For example, bots can offer suggestions to the user: this paves the way to even more complex interactions. …


An overview of essential discourse patterns, part 1

Here at Nu Echo, we’ve been involved in the conversational space for quite some time now. One of the things we learned is that while creating a simple chatbot may take a few days (or even just a few minutes), creating one that is truly conversational requires a lot more time and expertise.

The purpose of this article is to present a list of the most important discourse patterns required to build what we consider a good conversational chatbot. This list is not exhaustive, but even then, it was quite long, so we decided to split it in multiple parts…


And we’re glad we did!

Building a chatbot is far from a trivial task: better make sure you use the right tools for the job. In this article, we explain why we think Elixir is a good candidate.

At Nu Echo, for a couple of months now, we’ve been working on a conversational, task-oriented chatbot. The project uses multiple technologies, but what holds everything together is a certain amount of Elixir code. My colleague Dominique Boucher already talked a little about that language in a recent article on the importance of programming languages in innovation.

Today, I want to approach the subject from another angle, and expose some of the reasons we believe Elixir was (and still is!) a wise choice to build our chatbot. Before we started working on the chatbot, we chose Elixir not only because…


A developer’s approach to the workspaces

Our team recently worked on the development of a chatbot, using Watson Conversation for natural language understanding (NLU) and a portion of dialogue management (not all of it, because our application needs custom code on our end to generate some dialogue elements). The chatbot is a proof of concept we are currently developing in our Omnichannel Innovations Lab at Nu Echo.

The tools provided by Watson gave us a way to have a working prototype really fast, but it wasn’t very long before we hit a wall in terms of productivity once confronted to a number of core requirements in…

Guillaume Voisine

Software developer at @nuecho

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