Driving Innovation by Sonatus

This episode of The Driving Innovation Podcast explores how in-vehicle edge AI is reshaping the automotive industry and introduces Sonatus AI Director, an end-to-end orchestration platform that makes large-scale AI deployment in vehicles practical and cost-effective.
Steve Stoddard, Product Manager for AI Products at Sonatus, explains how AI Director helps automakers optimize, deploy, and manage diverse AI models on today’s ECUs and vehicle hardware—enabling smarter, adaptive, and personalized driving experiences.
The discussion highlights Sonatus’ ecosystem partnerships, spanning silicon providers, cloud platforms, and AI model vendors, to accelerate a wide range of embedded AI features. Together, these collaborations mark a major step forward in bringing AI from concept to production, advancing the vision of truly intelligent, software-defined vehicles.

Creators and Guests

Host
Sanjay Khatri
Sanjay Khatri is Head of Product Marketing at Sonatus, Inc., a leading provider of software solutions accelerating the shift to AI-enabled Software-Defined Vehicles (SDVs). He brings more than 20 years of experience in product management and marketing for cloud and edge software across Fortune 500 companies and early-stage startups. At Sonatus, Sanjay defines product positioning and go-to-market strategy for solutions that help automakers deliver intelligent, adaptive vehicles that evolve with changing market demands. His background includes leading strategy and execution across Connected Car, In-Vehicle Infotainment, and IoT technologies. Sanjay holds a Bachelor’s degree in Computer Engineering and an MBA, along with certifications in AI/ML, telecommunications engineering, and content strategy. He is a frequent writer and speaker on the future of automotive software, edge AI, and the rapidly transforming mobility ecosystem.

What is Driving Innovation by Sonatus?

Dig into wide-ranging technical topics about modern vehicle technology with industry leaders, hosted by Sanjay Khatri, Head of Product Marketing, Sonatus.

Welcome to another episode
of Driving Innovation,

The podcast that explores
products and solutions that are

driving the automotive
industry towards AI-enabled

software-defined vehicles.

We have a special
episode for you today.

We're going to be discussing
our latest product Sonatus AI

Director.

There's been a lot of
discussion around AI and the

automotive industry.

Now a lot of the
innovations around AI

have focused on cloud-based
implementations.

Think of your chat bots
or your voice assistants

and yet there's still a lot
more potential for AI directly

inside the vehicle to
make vehicles a lot more

intelligent, adaptive, more
fun to drive for end customers.

And that is really what edge AI
in the vehicle is intended to do.

And in fact, that is the
challenge that AI Director

is addressing.

So let's get started.

With me today is Steve Stoddard,

Product Manager for AI
Products here at Sonatus.

Steve is going to go over
the launch of the AI Director and

what its goals are, and what
it is actually delivering.

Steve, welcome back
to the podcast.

Thank you, Sanjay.
Glad to be here.

I feel like I need to give you
some sort of a t-shirt or a mug

for your three-peat appearance.

So good to have you
here. Thank you.

So let's get right to the point.

What is AI Director and why are
we launching this product now?

Sure.

AI Director is a platform
for OEMs to develop, deploy,

execute, and manage AI
models in their vehicles.

And there's three main reasons that
AI is ready to run in vehicles now.

The first is that end customers,

as well as OEMs themselves,

kind of are demanding
customization,

more personalization,

really sort of AI-driven
features in their vehicles

because it's with
us everywhere today.

A second reason is that
with the proliferation of

software-defined vehicles and
data, it's really everywhere,

and the data costs
really become,

exorbitant if everything
is going back to

the cloud in order to
run AI models there.

Along with that
is data security.

Everything that leaves the
vehicle is a potential threat,

potential leak, and running AI
models in the vehicle is a more

secure way to do that
when it's possible.

And the third reason is really
that all the technologies are

starting to come together today
to really make this possible

where just a few years
ago, it didn't really,

exist and wasn't possible
as the way it is today.

Yeah.

So there's a lot
more potential for

AI-enabled intelligence
inside the vehicle.

I can see how, you know,
similar to how, you know,

other appliances and devices
around us are becoming more

intelligent and more catering
to our personal needs.

The car or the vehicle can
also do the same thing.

So help me understand or help
us understand a little bit

about what AI Director
is doing that,

for example, other solutions
aren't doing in the market today.

Sure.

One of the problems we
see is that, actually,

there are many different tools
for building, developing,

and deploying AI models,

but they're tied to the
history of AI development and

development of the
Internet in general.

So a lot of cloud-based
applications focus on building

models that are gonna deploy
in data centers where there's

virtually infinite compute
available in order to actually

execute during the inference
time for these models.

Another issue is that the data
which is needed in order to

build these models currently
lives in the vehicle.

That's improving, of course,

as more data is coming
back to the cloud today,

but that's one of the
challenges that we've seen with

our customers and the ability
to actually deploy their models.

And the third major area is
the fact that these models are

going to run in vehicles.

So it's non-standardized
hardware.

Every vehicle is a
little bit different.

The different

ECUs in the vehicle have
different capabilities and

access to different
data sources.

And so there really is a need
to be able to optimize these

models to run in vehicle as
well as to feed those models

with the right data at at
the right time from the different

ECUs.

Sure.

So what you're describing
is, essentially,

a tool chain kind of to end
manage the end-to-end life

cycle from development,
training, development

optimization, all the way to
actually situating it in the

car, and then let
it do its magic

inside the car.

Well, let's talk
about that magic.

What are some of the use
cases that we envision that

automotive companies are going to
want to do with AI embedded

inside the vehicle?

Yeah. Absolutely.

So a lot of different use cases
really can benefit from AI.

One of those is just
general personalization.

So all kinds of features where
you want to customize the way

the vehicle's operating
for the specific user.

Another example comes
with virtual sensors.

Of course, today,
cost is everything.

And with software-defined
vehicles,

it's possible to deploy
sensors into the vehicle

virtually through these AI
models and eliminate some of

the hardware costs as well.

Another area that we've
typically seen a lot of benefit

comes in vehicle cybersecurity.

As I mentioned earlier,

some of the challenges of
removing data from the vehicle

into the cloud in order
to run these models,

all of that need is eliminated
once you're able to run these

in vehicle and protect
it much better that way.

BMS models or battery
management systems is another

area as vehicles are
becoming more electrified.

It's typically a
challenge for OEMs today,

particularly as they're making
that transition from ICE into

EVs.

But, these are typically much more
software-defined vehicles as well,

and a lot more data goes into
properly managing those battery

systems and making sure they're
operating efficiently and

charging at their
optimal values as well.

So these are all ripe
opportunities for,

AI models to run a vehicle.

Sure.

So we know we've obviously
heard a lot about the amount of

immense amount of
compute resources that,

you know, running AI requires.

And while vehicles are getting
a lot more powerful and capable

from an electronics point of
view, you still don't have,

you know, the high-end GPUs and
NPUs inside the the vehicle.

How has AI Director or
how will AI Director,

overcome these so that you can
actually make it practical to

actually deploy AI
models like the ones that

you described, into current
generation of vehicles?

Yeah. Absolutely. It's
a very good question.

So traditional AI and
automotive has been focused on

ADAS or autonomous
driving, of course.

And this is an area that's
very well saturated.

It's very well understood.

But these other different
domains, as I mentioned before,

don't have the same level of
development that has gone into them.

One of the things that we see
with AI Director and many of

our model partners is that
there's a variety of types of

models that people are
developing and want to be able

to deploy to the vehicles.

So this can

range from everything from very
simple sort of physics-based

models, maybe linear
regression types of things,

all the way up to neural network
or even LLM-based models.

So depending on the type of
model as well as the type of

hardware that's in the vehicle,

it's possible to optimize those
models for that hardware and

deploy it to the right place
at the right time so that the

right data can feed
into those models,

to provide the benefit
that they need to provide.

And you mentioned a couple
of the use cases, and,

I think you mentioned a
couple of, vendors as well.

I know we're partnered with,
several of them right off,

the bat, and I'm sure
we'll be adding more.

But can you talk a little bit
about some of the partners that

we've partnered
with, in this launch?

Yeah.

So it's a very important
ecosystem to bring together

everybody all the steps that
are needed in this process.

We've actually worked with
AWS on the cloud side,

particularly to implement,

integrate with our model
development workflows.

Of course, there's many great tools
out there that exist today to train

clean data, train models,

and then prepare them for
ultimately bringing into our

system to deploy
into the vehicles.

And so AWS SageMaker Studio is
a very excellent tool for that

that we've worked
with extensively.

On the silicon side
and the hardware,

we have a very great
relationship with NXP,

and we partner with them
to work closely around

optimization, specifically
to make them it easier to,

optimize those models for
deployment into NXP hardware.

On the model side,

we have a number of vendors
that we've been working with.

One of the use cases is
a headlight leveling.

Out of the EU, there's a

new regulation that's
going to be required,

for headlight leveling
starting in 2027.

And a company called Compredict
has developed a virtual sensor

to enable that capability,

without having to have additional
hardware to support that.

So that's a really cool application
we've been working with.

Another one is on the BMS
battery management side,

a company called QNovo.

They work with vehicle
health battery management and

detecting health
of vehicle cells,

how they're performing and predicting
failures and things like that.

So that's a new model that,

a model they've been working on
that we've launched with as well.

And, another vendor on the
cybersecurity side, VicOne,

we've partnered with.

They have a very innovative
new LLM based model that,

we've been working with to
deploy into our hardware as well.

And so another, model that we're
demonstrating is an engine anomaly detection

model, which we've developed
internally here at Sonatus.

Again, using a lot of the,

optimization tools
through both AWS and NXP.

NXP. Okay.

So a good mix of partners,

good variety of different
model vendors and so forth.

So that really shows the
diversity of of the types of

models that, AI director
can manage and handle.

I think, bringing, AI
models closer to the edge,

closer to the user, I think
is an important endeavor.

And, we will look forward to

additional discussions around
AI Director and some of its

derivative solutions
in the future episodes.

Again, thank you for coming by
and being so generous with your

time as a three-peat guest.

And thank you. Yeah.

And congratulations on, on the
launch. Thanks so much, Sanjay.

Appreciate it.

That wraps it up for this
episode of Driving Innovation.

To learn more about AI Director,

get some more details on the things
that we discussed here today,

go to the product
page on sonatus.com

and also check out the more
detailed solution brief that

you can download.

And thanks again for joining
us on this episode of Driving

Innovation and we look forward to
seeing you again in the future.