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PRIYANKA VERGADIA: Welcome to "Deconstructing Chatbots."
I'm Priyanka Vergadia, and today's episode
is a special one because we are going
to visit Spring ML's office in Pleasanton, California
and sit down with Dhruv Hemmady, their expert at managing
conversational AI projects.
Stay tuned because we will talk about real-world use
cases of conversational AI, how some of them
have come to life, starting from the idea
to the finished experience.
Without further delay, let's go and chat with Dhruv.
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DHRUV HEMMADY: My name's Dhruv Hemmady.
I'm a technical project manager at Spring ML.
Spring ML is a Google Cloud Premier
Partner in advanced analytics and machine learning.
We work on AI and ML use cases across computer vision,
chatbots, predictive modeling, increasingly data migration
as well.
We founded the company about four years ago now--
started as a small local team of less than 10 employees.
We've grown exponentially since then.
We're about 100 folks all over the globe--
four different offices.
And it's been an exciting journey.
We feel like we're just getting started in a lot of ways.
So the founders started with a core belief
that machine learning and artificial intelligence
will really transform the way enterprises do business
in the future.
And that's really our mission is to help them incorporate AI
and ML across their business.
Google is probably the leader in AI and ML,
and we've leveraged a lot of the best Google technologies,
from TensorFlow to AutoML to Dialogflow
for building chatbots, and really helped customers
accelerate the time that they see impact.
We've seen use cases, both B2B as well as consumer chatbots,
spanning a number of different industries, from retail
to banking to pharmaceutical.
One that we did recently for a big pharma company--
helping their product managers easily
retrieve information about different drug studies
as well as regulatory hurdles.
We also worked on a cool consumer bot for a retailer
in helping them provide a gift recommender for the customers.
I think the biggest learning, as I see it,
is that design is fundamental to building
any successful chatbot.
Unlike traditional IVR systems, chatbots
can handle non-linear conversations.
And so really working with business stakeholders
to figure out what the ideal conversation flow looks like
will be essential to building that bot that
works for the users.
PRIYANKA VERGADIA: Thanks, Dhruv, for having me
here today, at Spring ML office in Pleasanton.
It's such a nice day today.
DHRUV HEMMADY: Absolutely.
Really nice.
PRIYANKA VERGADIA: So what do you mean by conversation flow?
How do you design a conversation?
DHRUV HEMMADY: We ask a number of different questions
initially, right off the bat.
First, we want to understand what are the key business
objectives of the chatbot.
What is your workflow like?
Will a human be part of the process,
or should the bot be able to handle all questions?
Then we move to some more tactical types of things.
So how many unique types of questions
do you want the bot to handle?
Are most of those questions static?
Is there a general response for a question,
or are they dynamic questions?
So is it dependent on the specific information
that the user provides?
PRIYANKA VERGADIA: So like connecting
to a back-end database or something.
DHRUV HEMMADY: Absolutely.
PRIYANKA VERGADIA: Once you have kicked off a project,
what do you see as some of the implementation recommendations
that you provide to users?
DHRUV HEMMADY: So if you're a new customer,
we recommend that you start with a four-week POC just to kind
of showcase the art of what is possible--
maybe throw in some static and dynamic questions
for some variety.
We start with a week of requirement gathering,
followed by a couple weeks of agile development.
We'll take your feedback into account.
And finally, a week of user acceptance
testing, just to make sure that users are comfortable,
and everything is working effectively.
PRIYANKA VERGADIA: What happens after maybe a month or so?
DHRUV HEMMADY: Sure, so after about a month,
that's when we move into a production phase.
So that'll be a couple more months
where we add more questions, both static and dynamic.
We may add a user interface, and then
we may release the bot through multiple channels,
including Google Assistant and Google Home,
but others like Facebook Messenger and Slack.
PRIYANKA VERGADIA: So Dhruv, you've talked about,
a little bit, in terms of how to implement the chatbots.
You've talked about some examples.
Can you show me a back-end infrastructure,
an architecture, of an implemented chatbot?
DHRUV HEMMADY: Absolutely, let me show you.
PRIYANKA VERGADIA: Awesome.
All right.
We are with Dhruv, and he's going
to walk us through how some of these chatbots work.
DHRUV HEMMADY: Yeah, so let's take a look at one
that we did for a coffee shop.
PRIYANKA VERGADIA: Ooh, coffee.
I love coffee.
Can we get donuts too?
DHRUV HEMMADY: Absolutely.
That's what the chatbot will do for you.
So this chatbot basically helps the coffee shop automate
their customer service.
PRIYANKA VERGADIA: Awesome.
DHRUV HEMMADY: So as you can see,
we have aggregated some of the most popular items
at the bottom here, just to allow users to easily select
one of the items.
So for example, if you want to order a couple donuts,
you can select the item at the bottom.
And as you can see to the right, we're
basically showing you your order in real time,
so you can see how much you're paying.
PRIYANKA VERGADIA: And the front end,
is it created with App Engine?
DHRUV HEMMADY: It is, yes.
So this is deployed on App Engine.
PRIYANKA VERGADIA: OK.
DHRUV HEMMADY: And one other item that's kind of cool
is you can select what's most popular this week.
So for that, we're able to aggregate overall data
over the past week and figure out
what was the most ordered item.
PRIYANKA VERGADIA: Oh, that is amazing.
DHRUV HEMMADY: Would you be interested to see
what that was?
PRIYANKA VERGADIA: Sure.
DHRUV HEMMADY: OK.
So we click on that.
Oh, wow.
It looks like some more glazed donuts.
PRIYANKA VERGADIA: [LAUGHS] I think
I've had enough for today.
DHRUV HEMMADY: Enough for today?
OK.
PRIYANKA VERGADIA: Yeah.
So that-- actually, the coffee shop
demo itself was pretty enlightening to understand
how the front end would work.
Can you show me an example which talks
about a little bit of back end, behind the scenes stuff?
DHRUV HEMMADY: Absolutely.
So we actually did a chatbot recently
for a Google internal team--
PRIYANKA VERGADIA: Perfect.
DHRUV HEMMADY: --for Google Collections.
PRIYANKA VERGADIA: OK.
DHRUV HEMMADY: And really, what this chatbot does is it
helps Google customers easily look up their account
information, invoice details, details
about their account in general.
For example, if you're a customer at Telemart,
you can easily look up information specific
to Telemart, and only once you're
authenticated as a Telemart costumer.
PRIYANKA VERGADIA: OK.
So what does the front end look like for that one?
DHRUV HEMMADY: So for that one, we go to the bot.
So as you can see, this is a main Google Help page.
And to the bottom left, we have a section
for Google Collections.
PRIYANKA VERGADIA: Yeah.
DHRUV HEMMADY: So you can click on that,
and then you have to log in with your details.
And again, only if you're a Telemart customer are
you authenticated to see Telemart invoices.
So in this case, we'll use those email details.
And now we're on the main Google Collections page.
PRIYANKA VERGADIA: And I see the chat window that can come up.
DHRUV HEMMADY: Absolutely.
So it's a pop-up at the bottom right.
So if you click on the pop-up, you
can now start using the chatbot.
PRIYANKA VERGADIA: Oh, perfect.
So before we actually start to use the chatbot with Telemart
as an example, can you show some of
the back-end interconnections between the Dialogflow
interface and the front end and what's
in the behind the scenes?
DHRUV HEMMADY: Absolutely.
So basically, users are interacting with the web
UI deployed on App Engine.
Now that App Engine layer sits on top of Dialogflow.
And Dialogflow is where all of the user questions
are built in.
Now, Dialogflow integrates to a couple of applications.
It integrates to BigQuery.
In this case, BigQuery is where all of our data is stored,
all the information around account details and invoices.
And then the actual documents themselves--
they're stored in Cloud Storage.
And what that does is basically allows
us to email those documents back to the user
after they ask questions.
PRIYANKA VERGADIA: OK.
So are you going to show the example where
we see the email coming out?
DHRUV HEMMADY: Absolutely.
Let's take a look.
Say I wanted to find my latest invoices--
so it asked for our company name.
So we're at Telemart.
Then as you can see, the chatbot responds
with the three latest unpaid invoices,
so the invoice numbers, the due date, as well as the amounts.
And then if you go to your email,
you can see that those just arrived.
PRIYANKA VERGADIA: Oh, great.
DHRUV HEMMADY: You can take a look at the copies.
PRIYANKA VERGADIA: That is perfect.
Can we also look at Dialogflow interface
and some of the intents that were created in there?
DHRUV HEMMADY: Yes, absolutely.
So as you can see, each of these items
here are different intents.
So on that latest invoices example, if you click on it--
so we fed in some different training phrases.
So those are different ways that a user
can ask a particular question--
you know, recent invoice, latest unpaid invoices.
And as I mentioned before, the cool thing about Dialogflow
is that it has the ML and AI capability to learn over time.
So if you ask something similar, it should know that that's--
latest invoices is what, in fact, you're asking for.
PRIYANKA VERGADIA: Well, awesome.
That was great.
Thank you so much for giving us all that information,
starting from how you started to design the bot,
up to the point where how you created
these integrations with BigQuery as well as with GCS.
Thanks, Dhruv, for walking us through that amazing demo.
DHRUV HEMMADY: You're very welcome.
PRIYANKA VERGADIA: Well, I hope you enjoyed the tips
and tricks shared by Dhruv today,
to bring your conversational AI use case to life.
We cannot wait to hear more about your specific use case
in the comments below.
If you liked this video and would
like to see more such content, don't
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