Nu Echo has been using Google Dialogflow for some time now and would like to take a moment to share our thoughts about using the platform for chatbot and intelligent virtual agent projects within enterprise organizations. In this blog post, we will describe what Dialogflow is good for, its current limitations, and how we work around those limitations to get the most out of our development. We will also explore our plans for the future as part of our IVA Solutions practice. This article will solely focus on an engineering perspective to kick off this series but please watch for future posts which will touch on dialogue management and conversational aspects to be considered for great IVA development and more!

What makes it great!

Browsing around Dialogflow’s website, you can easily find the benefits. Some are accurate, others are less evident as true benefits or are lacking as of yet. Let’s start by considering what some of the true benefits are that we have been able to experience and take advantage of:

Quick and easy to start building: Yes! You can truly create your first bot within a matter of minutes. I think my mother did one in, like, 16 minutes, while cooking her famous apple jelly. You login to the console, create an agent project, create one or two intents, and then try it out within the console. Instant reward. A few more clicks to activate the Phone Gateway integration and then you can, yes, call in (assuming your bot is en-US for now). Small dopamine rush.

Built on Google infrastructure: From within a project, you can tap into the rich Google Cloud ecosystem. Conversation logs get pushed over Stackdriver, fulfillment is only a few Google Cloud Functions or Firebase Functions away, security and containment are directly handled as part of IAM, while speech-to-text and text-to-speech relies on Google Cloud respective APIs. And frankly, although we are using some beta features, the platform is quite stable.

Easy to scale: Seriously! This is an additional benefit to the point above. No need to worry about any sort of viral effect your bot might experience. There’s not even a dial or knob to tweak or turn on. It’s essentially all done behind the scenes. Coupled with Google Cloud Functions for serverless fulfillment stage and you’re golden.

Strong natural language understanding (NLU) capabilities: So far so good on this front. We definitely have yet to push the limits but its context-based approach appears to be robust. More on that to come in future posts.

Where it might fall a little short…

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