Contact Center Voice AI: Low-Latency Intelligence Extraction from Messy Audio Streams — Dippu Singh

Channel: aiDotEngineer

Published at: 2026-04-08

YouTube video id: IEF842ZEU5A

Source: https://www.youtube.com/watch?v=IEF842ZEU5A

Right, hello everyone. Welcome to the AI
engineer 2026 online track.
Um, good morning, good afternoon, good
evening, depending upon your time zone.
My name is Deepu Singh and I lead the
initiatives in the emerging data
technologies and AI architecture at
Fujitsu North America.
Um,
and today I will be
uh, giving you or maybe provide you a
deep dive into a very specific but a
very highly impactful engineering
challenge, uh, which we have encountered
in our uh, contact centers. So, with
that, let me just quickly share my
screen and get this going.
Right, so, um,
um,
this is the topic of the discussion,
VoiceOps-ifying
low latency intelligence extraction from
uh, messy
audio streams. Uh, when we talk about,
you know, generative AI, we often, you
know, focus on
uh, clean text inputs, but if you go
into the real world, specifically in the
customer service
or
or maybe contact centers, specifically,
um, the data does not start as a clean
text, right? It starts as messy, quite
overlapping,
um, sometimes emotionally charged and
and it it may have multi-channel audio
streams also within that, right? And and
today we will explore the technical
architecture which is required to
capture that audio,
process it with
ultra-low latency and use the generative
AI to extract structured, actionable,
um, business intelligence out of it.
Uh, so, with that, let's get started
with this.
So, here is our roadmap for the next
25 minutes. Um,
first we will stage the, um,
um,
stage with what the current challenges
are.
Uh, we have to understand the
operational realities and the intense
human bottlenecks in in the modern
contact centers
to understand why this engineering
matters.
Uh, second, we will walk through the
solution which is provided. I will break
down our high-level architecture into
four key components,
um,
detailing how we move from raw audio to
a structured JSON using advanced
summarization workflows.
Thirdly, we will look at the key
outcomes, specifically like how these
technical implementations they translate
into hard ROI
and operational impacts. And finally, we
will discuss the, um, roadmap ahead.
And being transparent about the current
tech engineering constraints which we
face now and then and where we are
taking this technology and the roadmap
ahead.
So, to engineer a great solution, we
first need to deeply understand the
problem. So, let's look at the current
state of the contact center operations.
So, contact centers, they are the
frontlines of the customer experiences,
but structurally, they are breaking
under the pressure.
If we look at the industry data,
um, over 50% of the contact centers,
they identify
uh, hiring, training, and productivity
as their most critical barriers.
Why? Because the job is incredibly
difficult. When we analyze the reasons
why the operators they leave for the
other professions,
high stress is the number one factor as
part of it.
Now, operators are expected to handle
complex customer emotions, navigate the,
um,
multiple, uh, customer data platforms or
the CRM systems involved into it, and
and also document everything perfectly.
So,
if you think about this, this leads to a
massive retention problem, right? Uh, we
are caught in a negative spiral
um, where understaffing leads to higher
stress for the remaining operators,
um, which leads to a high turnover and
uh, that ultimately implies to more
understaffing, right?
So, to break this cycle, we just
uh, cannot hire more people. We have to
fundamentally engineer the stress out of
the workflow.
And the most glaring inefficiency in
this workflow is something called, um,
after-call workflow or ACW,
uh, which we are going to discuss as
part of this slide.
Um, according to our baseline studies,
the average contact center call it
typically
last about like 6.5 minutes.
However, the average post-processing
time where the operator types up the
notes, summarizes the call, and selects
the disposition codes, it it takes like
almost 6.3 minutes. So, this is nearly
like 1:1 ratio we are talking about.
Um,
and this also implies operators are
spending almost as much time doing
administrative data entry as they are
actually talking to the customers.
Um, furthermore, because the
summarization relies on an individual
operator's memory and the writing
skills, the data quality is highly
inconsistent.
Um, our core engineering mission here
was clear, like use AI to target the
after-call work which is ACW.
Um, if we can mechanize the
summarization and the data extraction,
uh, theoretically, we can reduce the
post-processing time, um,
almost by 50% or even more.
And and this shifts the enterprise, you
know, focus from merely just handling
the calls to actually, you know,
analyzing the
voice of the customers for the business
growth which we are looking out every
now and then.
So, how do we engineer that shift? Let's
deep dive into the technical solution
and the architecture we built to solve
this.
Um,
So, we designed a four-stage low latency
pipeline to transform the conversational
audio into a structured business
intelligence
um, with minimal human intervention. And
it starts with voice capture, which is
tapping into the telephony system to
extract raw, high-fidelity audio
streams.
And that flows into our speech-to-text
engine, STT,
uh, which is responsible for
high-accuracy transcription.
Um, next is the brain of the system, the
generative AI core. This is where we do
the heavy lifting of the intent
recognition and summarization.
And finally, the customer data sync
layer which translates those AI insights
into API calls to
update the,
uh,
uh, customer data or CRM data
automatically.
Uh, let's look at the engineering under
the hood for each of these components in
detail.
So, the first component is the voice
capture. In AI, um,
the typical rule is garbage in equals
garbage out. So, if your audio intake is
flawed, the LLM will hallucinate later
on. Um, we do real-time uh, audio
intake. So, applying noise filters to
strip out the
uh, back office chatter or, um,
you know, anything of those sort which
is creating an attenuation
is important and normalize the audio
level is very very very important for
us. Crucially, if we perform these
channel mapping, uh, we absolutely must
spit split the, uh,
stereo audio to isolate the agent on one
channel, say, on the very left, and the
customer on the other side, which is the
very right, right? So, if if you mix
them into a single mono track, um, like
kind of overlapping with each other, the
AI will struggle to
figure out who said what and and
thereby, you know, ruining the entire
downstream summary.
So, um,
it's important that we have that channel
mapping intact and it separates which
who is what and who is saying what,
right?
Um, finally, we apply a security layer
because sometimes the audio streams can
contain the credit card numbers or maybe
passwords or any anything which is
personally identifiable information,
PII.
So, we utilize buffer management and
early-stage PII masking technique
uh, so that the sensitive data it never
hits the LLM memory banks whenever we
are moving ahead in the channel.
Now, next, the audio it hits the, um
speech-to-text engine for generative AI
or the LLMs to summarize the
um data effectively or your response
effectively, we found that the
speech-to-text, the STT
um accuracy must be above 90%. Uh we
utilize advanced acoustic modeling to
map the
um
phonemes and filter out any regional
dialects. We then apply the language
logic utilizing like domain-specific
dictionaries. Uh just for example, if
it's an insurance agent, the
um speech-to-text engine STT needs to
know the difference between a term life
and a term right. Both of them are very
close to each other, but still there
should be a difference between them.
Uh finally, post-processing is also
vital. Uh we use inverse text
normalization and auto punctuation. Uh
for example, if if a customer says
$5,000, the speech-to-text um engine, it
should must output that into numerical
fashion. And this numerical formatting,
it it drastically improves the LLM's
ability to extract the entities in later
point in time.
Now we reach the generative AI core. We
are not just throwing a raw trask raw
tras transcript at LLM, but we are also
asking it to summarize it. Like we use a
highly orchestrated approach in this
case.
Um in our orchestration layer, we use
specific prompt templates. Our
um setup showed that if if you just ask
an LLM to summarize a call, it outputs a
messy narrative paragraph. So instead,
we use few-shot libraries to instruct
the LLM to output separate bullet
points.
Um one list for customer inquiry uh and
a separate list for operator's action.
Uh then comes the reasoning layer. In
the reasoning layer, we extract the
intent. We provide the
LLM with a predefined list of customer
uh call reasons like
uh cancellation or new application or
any kind of claim status
and instruct it to classify the
transcript and output the reason why it
chose that specific classification.
And finally, the trust layer where we
apply the token optimization to keep the
um latency low and runs the uh automated
hallucination checks to ensure the uh
generated
summary is strict
um and it is grounded in the transcript.
Now the final technical hurdle is
getting this beautiful data back into
hands of the business.
Our API gateway acts as a schema mapper.
It takes the JSON output from the LLM
uh and maps the field like customer
intent or resolution status and and
directly to some corresponding fields in
the um
based on the company CRM system or any
customer data which we have laid out via
some REST APIs. So we don't remove the
human entirely in this. We use a
verification step in between. Uh the
operator, it sees the AI-generated
summary
auto-populated on their screen.
Uh they do a quick visual field
validation, make some minor edits if
necessary, and then just click the
confirm.
Uh simultaneously, this structured data,
it flows into our business intelligence
models
um aggregating the voice of the customer
data for management dashboards and
automatically, you know, flagging the
candidates for new FAQ
um data entries.
Now to tie the architecture together,
this is the linear workflow logic of the
data pipeline.
We take the raw transcript complete um
with the time indexing
uh confidence is scoring and denoising.
We passes we pass it through these
speaker separation because we split the
stereo channels um in the step one. Um
we can easily stitch the dialogue
together logically. Like customer said
X, agent said Y.
Uh then we move to the context deduct
deduction where the LLM spots the
entities like account numbers
um or product name or customer name run
the sentiment analysis and recognizes
the intent. And the final state is the
structured output. So instead of a wall
of text, uh the system, it outputs like
clear uh and a clean JSON schema
matching some predefined uh customer
data or maybe CRM templates which we
have in the enterprise and then
categorize neatly into bullet points.
And and this strict formatting is what
turns an unstructured conversation into
a database-ready asset.
So what happens when we deploy this
architecture in a real contact center
environment? Let's look at the outcomes.
The operational impacts out of the
implementation were quite immediate and
and highly measurable. Um look at the
ACW time
under the manual operation, after
average after-call work was 6.3 minutes.
Uh powered by our AI workflow,
that dropped to like 3.1 minutes, which
is like almost 50% reduction in the
processing time.
Um if you calculate that across 500
seats handling thousands of calls a day,
you are looking at a massive operational
saving, the equivalent of almost
reclaiming dozens of full-time head
counts purely from efficiency
standpoint.
Um [snorts]
the next aspect is of data
entry quality. It it moved from a highly
subjective and a variable to highly
standardized and uniform output.
Uh the inquiry categorization or the
call reason tagging, uh it moved from
being dependent on an operator mode
or memory
um
to um
being a strictly logic-based resulting
in a highly consistent voice of the
customer data set for the management.
And ultimately, by removing these
repetitive administrative burden of
typing out notes, we reduce the
cognitive load on the operators,
thereby stabilizing the operations and
directly combating the stress which is
linked with the staff.
And and this ultimately reduced the
turnover we identified
um
at the very beginning of our discussion.
While the results were fantastic, the in
the engineering work is never done um
and and let's talk about the constraints
we face
uh and our roadmap for the future.
Um
We are currently navigating three main
constraints. First is the um
source to source to text accuracy, STT
accuracy.
The entire generative AI summary, it
relies on the transcript. So if the STT
engine uh
fails to pick up heavy accents or poor
audio quality, the LLM has nothing to
work with. So the STT optimization is a
continuous battle for us.
Uh second is the initial setup cost.
While the long-term ROI is massive, the
initial consumption of the API tokens,
especially running complex LLM
reasonings on long 20-minute uh
transcripts can be costly.
And uh especially during the initial
scaling phases, those are tough ones.
And and we are constantly working on
token optimization techniques to bring
this number down.
Uh the third is the security and
compliance. Um handling PII uh per
any kind of sensitive information in
audio streams is very complex. Um and
ensuring robust masking before the hit
data hits the cloud endpoint is a strict
requirement. Um and uh
because of this, we add some layers and
it ultimately
reduces increases the latency and also
adds some
overhead from architectural standpoint.
So we are still figuring it out how we
can reduce those extra layers and make
it much more robust component-wise.
All right. So to address these
constraints,
and and push the boundaries of what's
possible, our roadmap is currently
broken down into three phases.
Uh phase one, it focuses on explainable
AI.
Um
we want to move beyond just summarizing
calls to actually you know coaching the
operators.
We are engineering the systems to
analyze the audio post call and provide
operators with instant private feedback
on their
either soft skills or empathy level or
any kind of accuracy
from a information standpoint.
The phase two it targets the predictive
staffing by taking the massive amount of
categorized intent data we are now
capturing
we can feed
the same exact data intent data into a
time series analytics.
And this will allow the workforce
management to accurately forecast the
call volume spikes based on those
specific topics and thereby optimizing
the shift scheduling.
The phase three is perhaps the most
important for human well-being combating
the
customer harassment uh
contact center agents they mostly face
an increasingly amount of verbal abuse.
And we are developing
a low latency sentiment and acoustic
analysis that can detect when a customer
becomes abusive.
Ultimately
the system can you know uh trigger some
triggers I mean alerts or something
which is important from notification
standpoint to
supervisor or anyone who is there in the
from the management upper management
standpoint or maybe seamlessly transfer
the call to an AI voice agent just to
protect the human operator
mental health in case of these tough
conversations.
All right. So
uh by applying these rigorous
engineering techniques to the messy
audio data we can definitely transform
the contact centers from
uh
cost centers of like high stress into a
highly efficient and intelligence
gathering engines that protect their
workforces. That's the whole idea of
having this right. Thank you so much for
your time and listening to me.
I have included my QR code which I will
just flash in here uh
so that you can grab me over LinkedIn.
And please feel free to connect if if
you would like to discuss
the architecture or any prompt
engineering strategies related to the
discussion which we had in here and
happy to connect and make things
work things out between us and we can
have more conversations. So thank you so
much for listening to me and have a good
one. Bye.