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LORIN PRICE: Today we are here with Taylor from Quantiphi,
who is the practice lead for Public Sector and Higher
Education.
We're very excited to explore some of the solutions built
by Quantiphi.
Before we begin, Taylor, can you tell us
a little bit about Quantiphi?
TAYLOR CYR: Yes, of course.
We've been around six years now.
And we focus primarily on AI and machine learning solutions.
We found that we're able to provide a lot of value
for customers by helping them with their data strategy
as well because that's one of the fundamental building
blocks of any AI or ML initiative.
We have been a Google Cloud Partner since 2016.
Obviously, with that AI machine learning background,
we've been very close with Google Cloud.
We have 325 GCP certified professionals,
which is about a quarter of our workforce.
We've actually been the 2017 and 2018 Machine Learning
Partner of the Year, and are a Contact Center AI launch
partner as well.
LORIN PRICE: That's awesome.
Congrats on the ML Partner of the Year
and for being a CCAI launch partner.
Can you tell us a bit more about CCAI
and what it means to be a launch partner?
TAYLOR CYR: Yeah.
So there's three components to CCAI.
There's a topic modeler, which uses
keyword clustering to identify high-frequency topics.
There's Dialogflow, which is the NLP
backend, kind of the virtual agent, so to speak.
And then if there is an instance where
it needs to be transferred to a live agent,
there's functionality called Agent Assist
that uses a real-time keyword search from the conversation
between the live agent and the customer
to pull up suggestions from the knowledge base
and help them answer questions quickly.
As a launch partner, we help customers
through the entire lifecycle of the deployment.
So that's everything from helping
them design conversation flows, programming intents,
building integrations with the knowledge base
CRM systems, anywhere else that user data might exist.
And then, also, building that Agent Assist
functionality as well.
LORIN PRICE: Great.
Thanks for sharing more about that.
Can you talk more in depth about the types of technologies
that Quantiphi uses?
TAYLOR CYR: Yeah.
So because we've been using Dialogflow
since before it was even actually called Dialogflow,
we've built a lot of custom machine learning models
that help you build, manage, and deploy any of these Dialogflow
or Contact Center AI solutions.
One of them is intent identification.
So that takes your historical call or chat logs,
understands the ways that users are
asking questions, and programs that context directly
into the virtual agent.
Another involves the ingestion of text.
So think a knowledge base, or a FAQ section on your web page.
It pulls out questions and answer pairs there, and again
makes it directly programmable into the virtual agent.
We also work a lot with the NLP sentiment analysis
that exists in Dialogflow, taking it
a step further in what we call emotion analysis.
So trying to understand when users
are having these negative feelings,
identifying that earlier, and helping you intervene before it
starts becoming too negative of an impact
on customer satisfaction.
Lastly, we deal a lot with integrations.
So having your users or constituents reach out to you
on whatever platform they feel comfortable is very
important to creating value for the people that
will be interacting with the solution,
whether that be voice, chat, messenger platforms, SMS,
Dialogflow has all those integrations built in
and very easily accessible.
LORIN PRICE: Can you talk about some of the specific solutions
that Quantiphi has built with CCAI?
TAYLOR CYR: Yeah.
So when a lot of people hear Contact Center AI,
they think of the traditional call center.
While it's a great tool for that,
we found that the NLP capabilities behind Dialogflow
are actually useful in a lot of other situations as well.
There was some work that we did in conjunction
with Google Cloud and Penn State World Campus
where we used the NLP capabilities of Dialogflow.
It identified four different types
of student requests, where advisors were spending up
to 5,000 hours per year really just pulling
different pieces of information, determining
a student's eligibility to change major, campus,
something like that.
So we use Dialogflow to recognize
the intents of those users, provide a template in response
to the advisor, that can then be personalized based
on the student, all while pulling all of that data
that they had to spend 15 minutes
gathering, putting it right in front of them,
and giving them the opportunity to add value with their time,
rather than just searching for data.
LORIN PRICE: What are some of the challenges
that you've encountered when building these solutions?
TAYLOR CYR: Yeah.
So the first one that comes to mind
is designing conversation flows.
Not always do customers understand
what they want to automate and how they want
the agent to say it.
So we found that, with using the topic modeler,
you cannot only find a lot of the high-frequency topics,
but you can dig down a level, understand within those topics
what people are talking about, and help structure
the conversation flow of the virtual agent using that.
We've also seen intent clashing a lot.
So as you continue to add intents to the bot,
it becomes more and more likely that there's
going to be similarities between an existing intent.
So we've created an automated solution testing
tool that goes through and tests those intents every so often,
and gives you an early indication if there's
going to be problems with a previous intent
so that you can intervene and fix that before it ever
sees the light of day.
LORIN PRICE: Well, this has been very interesting.
Thank you so much for being here with us today.
TAYLOR CYR: It was my pleasure.
LORIN PRICE: If you'd like to learn more about Quantiphi,
you can visit quantiphi.com.
Thanks for watching, and stay tuned for more episodes
of "Stack Chat."
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