“Deepassist” is the name of an artificial intelligence that answers both written and telephone customer enquiries
Being among the top five in natural language technologies
worldwide is a great success for Deepassist. Founded by
Roland Fleischhacker, it is a major player when it comes to
AI in text and speech recognition.
The AI is pre-trained and immediately usable for every channel
a customer may have. A real-world example shows how Deepassist
radically accelerates customer service: The City of Vienna’s “Stadt
Wien - Wiener Wohnen” is one of the largest property management
companies in Europe, where 550,000 customers are serviced around
the clock, dealing with about 1,000 different issues. Deepassist
supports the agents in real time – which greatly reduces the handling
time and significantly increases the resolution rate after the
first call or email. In addition, training new employees now only
takes a few days instead of weeks
CEO Roland Fleischhacker explains what is behind this groundbreaking
technology and where it is already successfully in use.
What does Deepsearch do in conjunction with Deepassist?
Roland Fleischhacker: We have a product called Deepassist, and
the name says it all. The term ‘deep’ has stood for artificial intelligence
applications for some time. When we invented the name
Deepsearch in 2010, this metaphor did not yet exist. Our objective
is for a machine to be able to interpret texts on a human level.
Language in the broadest sense, whether written or spoken, is an
omnipresent thing in both private and business environments. If
language can be interpreted in an automated way, a lot of applications
arise. Documents can be better classified and retrieved. This
applies to email, but also to telephone conversations. It supports
communication and, if necessary, also automates it. Here, ‘deep’
stands for AI and ‘assist’ for the fact that we want to help companies
support processes with it.
© deepassist
How can you help companies?
Fleischhacker: In many different ways. For example, with emails
that are read and understood by the AI and forwarded to the right
person for processing. That is our simplest use case.
Wie erkennt das deepassist?
Fleischhacker: It reads the text and transforms the most important
elements – what is it about and the most important information –
into a defined format. This makes it readable and understandable
for other systems, which then process it. For example, if someone
wants to cancel their mobile phone contract, there is a customer
number, a contract number and the date of the desired contract
cancellation. Then the information is transferred to a mail or ticketing
system. This system takes this information in a structured form
and starts a certain workflow. A further step is if the AI has already
identified exactly what is involved and all the required information
is already available. This information is then not forwarded to a
person but processed automatically.
So incoming texts are answered automatically?
Fleischhacker: Yes. We often even have to include a time delay,
because the customer would not understand why their request is
dealt with and answered immediately, within a few seconds
Does that work with all languages?
Fleischhacker: In theory, yes. We have developed our own technology
that works completely differently from 99 percent of other
systems that are based on neural networks. Our AI is semantic and
pragmatic, and does not react to static patterns but actually understands
what it is about. The system can also read between the
lines. We are currently patenting this technology for the American
market.
Where can this type of AI be used?
Fleischhacker: Mainly in customer service, but also in employee
service. Especially in customer service, customers are often not able
or willing enough to communicate exactly what they want. They
often can’t put their request into words, but only explain symptoms
or beat around the bush. Here, our system hypothesises what the
customer actually wants to communicate. This works not only with
emails, but also with calls in real time.
What an empathically talented person can normally do, the
machine can also do?
Fleischhacker: Exactly. It prepares the information, and an employee
can then proactively approach the customer. This significantly
improves the customer experience if the system has already told
you what the customer wants without him saying it.
Can Deepassist also have conversations, or does it only provide
answers?
Fleischhacker: We don’t do conversations, but we can connect our
system to a chatbot that is capable of having conversations
How high is the error rate with Deepassist?
Fleischhacker: There is a kind of glass ceiling in recognition, which
is about 95 to 98 percent.
© deepassist
Are there sometimes wrong answers?
Fleischhacker: No, it’s more likely that the answer is ambiguous.
If a telephone call is not clear, it often requires a query. But we have
included many expressions from the Viennese dialect in the system,
so that we also understand dialect-specific words. We also
have to recognise when someone means something different from
what they say. For example, many people still talk about a standing
order when making payments, although they mean a SEPA mandate.
Can Deepassist be the solution to the shortage of employees at
call centres?
Fleischhacker: Yes, because at the moment you can hardly get
staff. The customers’ expectations are growing, and the tasks are
becoming more and more complex. Our system brings great advantages,
especially for high-frequency and highly repetitive enquiries,
particularly via email. These are, for example, complaints, changes
of account numbers and the like. Manual processing of such emails
takes between seven and nine minutes, in our case two CPU seconds.
How long does Deepsearch need to train a
Deepassist application to make it usable for a
customer? How long does the programming
process take?
Fleischhacker: Customers don’t want long,
expensive or risky products. That’s why one of
the basic premises in the development of Deepassist
from the very beginning was that it
should be very simple, very quick to implement
and transparent. The customer service of a
bank is completely different from that of an investment
bank, an energy supplier or an online shop. We call
this domain language. So, I have to recognise what the customer
is saying, i.e. understand the domain language, and
I have to know the solution processes that trigger certain
requirements. We deliver this in the form of
industry solutions.
industry solutions.
We have pre-programmed systems
that not only understand
what the customer is saying,
but also a solution catalogue
of process templates
that varies from
customer to customer.
At Stadtwerke
Hamm, a customer
in Germany, the
implementation of
our industry solution
for energy
suppliers took
only five days.
Other providers
can only do that in
months. So, there
are ready-to-use industry
solutions, such
as for facility services of
large property management
companies with 15,000 or more
residential units and public transport,
such as railways or airlines.
We are currently working working
on industry solutions for insurance companies
and retail banking.
Can individual industry solutions be transferred
from one country to another?
Fleischhacker: Yes. There are certain topics that
are cross-sectoral, such as address changes or
changes in bank accounts. As far as languages
are concerned, it’s more a matter of culture.
Italians speak much more when
calling a call centre, make
more small talk than people
from other countries.
This makes it
more difficult because
the longer
a conversation
goes on and it’s
not about the
actual subject
of the call, the
more hypotheses
the system
makes about
what it might actually
be about. Then
the system needs more
context to be able to identify
what it is really about. We
have developed a system
based on semantic building
blocks – similar to Lego. So,
there is a box full of building
blocks and we deliver a model.
Individual parts of it can be rebuilt
individually for the customer
with the same building blocks.
Which markets is Deepsearch focusing
on?
Fleischhacker: Today it is the German-speaking
world. 2023 is the preparatory year
for us to go beyond this region.