July 16, 2025 • 18 mins
Interested in being a guest? Email us at admin@evankirstel.com
Imagine a world where engineers no longer waste hours on mundane translation tasks after designing a product. That's exactly what Ross Meyercord, CEO of Propel Software, is helping create through cloud-native solutions that are revolutionizing manufacturing.
Manufacturing software has been built on the same stack for 30-40 years, but Propel's cloud approach creates what Ross calls "almost an unfair advantage" - they can deploy innovations before competitors even finish planning. This speed is powering real-world AI applications that solve tangible problems for engineers and product developers.
Most engineers feel their job is done once they've created a CAD file, but the rest of the company can't work with that format. Propel's AI agents now automatically handle part numbering, bill of material structuring, and change order analysis, freeing up valuable engineering time. For highly-compensated professionals, these time savings translate directly to bottom-line benefits.
The transformation extends beyond design efficiency. By building on the Salesforce platform, Propel creates a closed-loop system connecting product data with real-world performance metrics. When quality issues arise, manufacturers can quickly trace problems to specific factories, revisions, or serial numbers. This visibility accelerates fixes and improves future designs.
Despite these advances, many manufacturers still struggle with digital transformation. The majority of manufacturing data remains trapped in on-premises systems, making AI implementation challenging. Moving to the cloud is just the first step – companies must then ensure data quality and integration to realize the full benefits. As Ross advises, manufacturers should start by assessing what data truly matters before selecting a modern platform that can leverage emerging AI capabilities.
Ready to transform your manufacturing processes with AI? Visit Propel in Redwood City, California to see firsthand how cloud-native solutions are creating the future of product development.
More at https://linktr.ee/EvanKirstel
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Hey everybody.
Great topic today, as we talkabout how AI and digital are
reshaping manufacturing.
Ross, how are you?
Speaker 2 (00:09):
I'm great, evan, great to spend time with you
today.
Speaker 1 (00:11):
Well, great to talk to you Really timely and
important topic Before that,maybe introduce yourself.
Your journey with Propel andyour growth has been really
impressive.
What's the big idea and thesecret sauce behind all that
momentum?
Speaker 2 (00:27):
Yeah, so I am Ross Meyerquard, ceo for Propel
Software.
We just had our 10-yearanniversary and, yeah, which
we're really excited about,we're coming up on becoming a
teenager here as we work withdouble digits and Evan, our
whole premise for our solutionis we are looking to provide
(00:54):
digital cloud nativecapabilities to folks that are
building physical products, andall the front end software that
goes into that process is,remarkably, really the same
software, built on the samesoftware stack that's been
around for 30 or 40 years, andso our idea was you know, not a
(01:15):
particularly novel one broadly,but in this space it was novel
was hey, let's take thissoftware to the cloud, let's
bring in modern technology andsee if we can help drive
innovation in this space.
And of course we have, and itprovides us really almost what I
think of as like an unfairadvantage relative to our
competitors, because we have theability to ideate, design and
(01:40):
deploy software out to ourcustomers before our competitors
, frankly, even have gotten offthe drawing board, so we can
bring that capability to marketso much faster and really
helping.
You know, as we know, with allthis age of big data and AI,
getting to market faster withnew solutions right now, of
course, is what the game's allabout.
Speaker 1 (02:01):
Indeed, and AI is clearly moving beyond the hype
cycle, especially in our sort ofB2B markets.
What are some of the real worldAI use cases or applications
you're seeing making the biggestimpact in your arena product
development, supply chain andbeyond?
Speaker 2 (02:19):
So in our world, you know what the engineers do is.
You know, at the end of the daythey are looking to get their
design from their heads, ofcourse, onto the computer, and
they do that via CAD solutions.
And you know, to an engineer alot of their job feels like it's
done at that point, oncethey've created that CAD file.
But no one else in the companyknows what to do with a CAD file
(02:42):
.
You know they don't speak CAD,they don't have access to it, et
cetera.
So our solutions in productlifecycle management really help
translate that CAD design intousable product information that
the rest of the enterprise cantake advantage of.
But that translation process andgetting all that data available
can feel a bit mundane andlaborious to the average
(03:06):
engineer.
It's not what they want tospend their time doing.
So what we're doing is we'reintroduced a number of AI agents
that really look to automate,minimize and in some cases
eliminate the work required forthose engineers to bring those
products forward, and so,specifically, this gets in into
bowels a bit of engineering.
(03:27):
But there are things like whenyour CAD file may have dozens
and dozens of new partsavailable and bringing, getting
those parts added to the systemand part numbering and the
structuring, the bill ofmaterial.
We have agents that can now cando that automatically, given a
CAD file.
You know you can have sometimesthese change orders which are
(03:48):
dozens of pages long of all thechanges you're looking to
introduce in a product and ouragents can look at that change
order and tell the approverhere's what's different, here's
what's changing and, by the way,here's what we think you should
do relative to this in terms ofapprove change et cetera.
And so these types of thingsnow really taking a lot of time
(04:10):
out of the cycle for engineersspent on the system, and these
engineers are relatively highlycompensated individuals in the
enterprise.
So we can help save time forthese folks.
This is real bottom linebenefit for companies.
Speaker 1 (04:26):
I bet.
And so these industrialmanufacturers you work with are
under tremendous pressure weread about that every day in the
market to innovate faster,integrating your product data
and how that data then flies orflies, flows through the entire
(04:57):
supply chain.
Speaker 2 (04:58):
Sometimes it flies, but it definitely flows through
the full supply chain.
But then there's also thisfeedback loop, evan, that the
products in market and it'seither doing great or it's not.
You're having product issues,you're having huge growth spikes
.
How do you bring that data backinto the enterprise and bring
that back all the way intoengineering?
And so one of the things thatPropel did is also novel is we
(05:23):
have built our product on top ofthe Salesforce platform, of the
Salesforce platform.
So as part of that, we have theopportunity to connect our
product data into everythingthat Salesforce CRM knows about,
which includes all of thecustomer support cases where
those assets are in the field,et cetera.
So we have this ability to, inliterally real time, connect the
(05:46):
information, what's happeningin the field, back into the
engineering process to reallyjust shorten that awareness
cycle of what the heck is goingon in the field for bringing new
products to market.
But then, on the occasion whena product has issues, whether
it's a safety issue or just aquality issue the ability to
(06:07):
quickly get that back.
Quality issue the ability toquickly get that back.
Which series of products thatwere on which revision that had
which serial numbers and theycan quickly isolate that down to
the factory is built in therevision of the design to impact
that, to of course, fix thedesign but also then get the
word back out to the field onwhere you have issues.
(06:29):
So that closed loop has beenincredibly powerful.
Speaker 1 (06:33):
Amazing.
From idea all the way to thecustomer.
That's right.
It's amazing.
You're connecting all thosedots and yet you know
manufacturers are still facingchallenge.
Is it because they're not usingPropel, or are there other
aspects of the business that arechallenging when it comes to
digital transformation?
What's holding them up?
Speaker 2 (06:50):
yet you know it's as I mentioned at the front end of
this, evan, the vast majorityyou know the corpus of the data
is still in their own datacenters.
It is proprietary data that isin this on-prem environment and,
as we all know, once you're,when your data is sitting in
on-prem, the ability to connectthat across the data sources, to
(07:13):
leverage analytics, to leverageAI, to tap into that data is
really impacted.
And so part of what this nextwave of change is, and what we
believe Propel is really leadingthe charge on, is bringing all
of this data to the cloud.
And bringing to the cloud, ofcourse, is really table stakes,
(07:35):
but for a lot of manufacturersit's still a step.
But once you get to the cloud,then you have to make sure that
the data is good, because weknow bad inputs is not going to
really help your AI that much.
So really making sure that youhave good quality around that
(07:56):
data, you understand thecurrency of the data, you
understand where that data issitting in your supply chain, so
just really investing the timethen to make the data good and
then, at the end of the day, wehave to then integrate that data
and bring the data together.
So it's a journey for a numberof these companies and we've
seen some customers which arereally at the forefront of this
(08:18):
and seeing the benefits.
But every company we talk to isin the process of this journey.
There's really, at this point,the decision has been made that
this is where they have to go.
It's just the speed at whichthey're getting there.
Speaker 1 (08:37):
Interesting and how do you think about all the
sensitive data that's flowingthrough these systems, including
yours, and balancing cloud andautomation with IP protection
and security and encryption andall that kind of thing?
Speaker 2 (08:50):
Yeah, it's a huge concern, Evan, and rightly so.
That's you know the power ofthe modern tools, especially the
.
You know the CHEP, gpts and theother LLMs of the world are
incredibly powerful and, as weknow, if you just you know, in
the free versions, if you juststart pumping data in, it's now
(09:11):
their data.
They're using that to continueto build their models and so,
you know, trying to wire thisstuff together yourself is you
know it's a tough journey andit's one that I think you know
can introduce.
You know data privacy, datasecurity concerns.
(09:31):
So what our approach would beand what I'd recommend to any of
your listeners out there ischoose a platform that's
pre-wired and built for theenterprise and leverage that
platform.
There are several great onesout there.
As I mentioned, we leverageSalesforce's platform, the
AgentForce platform I mentioned.
(09:56):
We leverage Salesforce'splatform, the agent force
platform, which is a rock solidplatform where the data is never
exposed to public Internet.
It's your data staysspecifically in your org, but
you get the power of all thelarge language models and all of
the IP that sits around thatthat can enhance the data and
really drive the agents andreally drive the agents.
So you know that my big beliefis you got to embrace the
technology, but go in eyes wideopen and make sure you are
(10:16):
working with a package solution.
You have a lot of confidence in.
Speaker 1 (10:21):
Yeah, salesforce is doing amazing work.
Maybe Mark Benioff is evenlistening or watching, but you
know, let's talk aboutautomation, doing more with less
something Salesforce is eventalking really loudly about or
watching.
But you know, let's talk aboutautomation, doing more with less
, something Salesforce is eventalking really loudly about.
What are the biggestopportunities to streamline
workflows, decision-making, youknow, doing more with the same
headcount you have.
What are your customers askingor demanding of you?
Speaker 2 (10:46):
Yeah, and it's, you know, to me what we're hearing
the most is can you just help ustake out the they've largely
taken out non-value added tasksin the processes, but now
there's the moderately valuedtasks that people currently have
(11:08):
to do but they don't want to do.
And that, to me, is a reallyimportant segment of work in
this idea of the AI agentsworking alongside of the people
or on behalf of the people, andI think ultimately they'll be
agents which will be performingwithout people in the future,
but right now there's so muchvalue to be driven from just
(11:31):
having that agent work alongsidethe folks.
And so you know, like Evan, wehave In our customer base today
people that have to developwithin our quality module kind
of training quizzes that as youroll out new processes to the
shop floor, people have tovalidate that.
Yeah, I've, I understand theprocess change.
(11:52):
I took a little quizlet tovalidate that, this change, and
sometimes you know this thesecan be based off of 100, 200,
500 page process changedocuments.
And so today someone has toread that document like what was
it?
What are we changing?
And then create a quiz fromthat.
And there's people today who dothis job and they tell me, ross
(12:16):
, this is really not fun, but Ihave to do it.
So we have an agent now, evan,that goes, reads that document
and you tell it how many quizquestions you want and it'll go
create those quiz questions foryou.
It creates the right answer andthree valid wrong answers,
three functionally correct butwrong answers, and prints it
(12:41):
back to the user and says whatdo you think?
And the user says, like good,or I like these two questions,
but give me three new ones.
You go through that process andin a matter of minutes you
develop this quiz that we hearfrom our customers.
Sometimes I spent four, eight,20 hours doing this in the past,
and so these types of thingswhere we can drive this time out
(13:02):
of a process to me are just soexciting.
Speaker 1 (13:07):
Really is, and clearly you're helping change
the way products are built.
What about AI changing the wayproducts are sold and supported,
marketed?
What do you see as far asthat's concerned?
Speaker 2 (13:20):
Yeah, you know one of the things you know AI will
continue to help drive theproduct development cycle
shorter and shorter, and so asthe cycles get shorter and
shorter, it tends to mean thatthe average shelf life of that
product you know, the sellabletime for that product is
decreasing.
(13:41):
So you know, everyone's justmoving faster in this process,
and so the ability to have totake the information from
engineering and push that out tothe rest of the supply chain in
a rapid fashion, but alsoaccurate fashion, is really
important.
So things like, hey, supplychain procurement you know,
(14:05):
here's the new chipset we'reusing, this is the new resins
that we're deploying.
This is, hey, because of thistariff thing, we're sourcing
from this different location.
So all of these rapid changesinto the procurement cycle, or,
hey, marketing, we need newcollateral, we need new content,
we need new safety documentsand being able to push that
(14:27):
information to marketing, andeven some of the solutions we
have Evan now will create thatmarketing material for the
marketer directly from theproduct designs and so these
kinds of things to really helpfacilitate that the entire
enterprise can work as fast aswhat engineering is now cranking
out.
Speaker 1 (14:48):
Fantastic.
So I'm in Massachusetts.
We do a lot in the medicaldevice manufacturing and design
world.
Speaker 2 (14:54):
We have a lot of great customers with you there
in Massachusetts.
Speaker 1 (14:56):
Oh great.
So any advice to a manufacturerwho maybe hasn't embarked on
this leading edge AI infusedapproach how to get started.
Maybe they've been around for adecade or two and have their
products and things are kind ofchugging along.
How do you get started on thisjourney?
Speaker 2 (15:13):
Yeah, evan, you know I think there's a step one,
there's almost a step zero, andI think the step zero is really
just trying to get your armsaround the data you have today.
Companies that have been aroundfor decades typically have data
which is decades old andthey're often those old legacy
(15:35):
products.
The data is not particularlygood.
In fact, you may like yourmodern product design, may talk
about the chipset and you have arequired chipset you have to
identify, but maybe your productfrom 25 years ago didn't have a
chip in it and so that partwill bomb when you try and load
it up into the new design.
So this idea of really thinkingthrough what data do we need,
(15:59):
you know, from a regulatory andcompliance and customer support
perspective, or you know from aregulatory and compliance and
customer support perspective, oryou know, design and
reusability, what data do weneed, and really focus on that
and the rest of your data.
You don't have to throw it away, but archive it someplace, kind
of get it out of your modernprocessing systems and really
focus on your core data.
I'll call that step zero.
(16:21):
Step one is you know we'd loveyou to choose Propel, but choose
a solution that can take you toa modern architecture.
You know that's cloud based, etcetera.
And until you kind of move upto that modern architecture
stack, you can not takeadvantage of all of the goodness
(16:41):
which is coming out relative toAI, analytics and everything
else.
That trying to retrofit that ontop of those old legacy on-prem
solutions is just a losingcause.
And so you know it's.
It's sometimes.
We're a system of record.
You know it's like changing outyour email system or your ERP
system, not something you liketo do that often, but once in a
(17:04):
decade, maybe every other decade, it's something you got to do
because the technology haschanged so much that it's time
to really step up and moveforward with this, taking
advantage of all thecapabilities these modern
architectures can provide.
Speaker 1 (17:19):
Great advice.
So we're here in the dog daysof summer a little quieter, but
there's still a lot going on.
Where can people see you ormeet you, either this summer or
in the fall?
Any events or meetings.
Speaker 2 (17:33):
So we're headquartered in Redwood City,
which has one of the funniestsayings it is voted best by
government test, which I guess.
In the fifties, the governmentlooked at all the different
weather patterns across thecountry and the Redwood city,
(17:54):
california, was deemed to havethe most moderate weather across
the entire country.
So anyway, come visit us inRedwood City.
We'd be happy to host you inour headquarters.
Speaker 1 (18:05):
Yeah, beautiful town, not too many Redwoods.
You have to go a little furthernorth.
Speaker 2 (18:08):
That's true, but nonetheless it's a great town.
Speaker 1 (18:10):
Thanks for joining.
Really great insights sharedand thanks everyone for
listening, watching, sharingthis episode and also check out
our new TV show now on FoxBusiness and Bloomberg at
techimpact.
Hey everybody.
Great topic today, as we talkabout how AI and digital are
reshaping manufacturing.
Ross, how are you?
Speaker 2 (00:09):
I'm great, evan, great to spend time with you
today.
Speaker 1 (00:11):
Well, great to talk to you Really timely and
important topic Before that,maybe introduce yourself.
Your journey with Propel andyour growth has been really
impressive.
What's the big idea and thesecret sauce behind all that
momentum?
Speaker 2 (00:27):
Yeah, so I am Ross Meyerquard, ceo for Propel
Software.
We just had our 10-yearanniversary and, yeah, which
we're really excited about,we're coming up on becoming a
teenager here as we work withdouble digits and Evan, our
whole premise for our solutionis we are looking to provide
(00:54):
digital cloud nativecapabilities to folks that are
building physical products, andall the front end software that
goes into that process is,remarkably, really the same
software, built on the samesoftware stack that's been
around for 30 or 40 years, andso our idea was you know, not a
(01:15):
particularly novel one broadly,but in this space it was novel
was hey, let's take thissoftware to the cloud, let's
bring in modern technology andsee if we can help drive
innovation in this space.
And of course we have, and itprovides us really almost what I
think of as like an unfairadvantage relative to our
competitors, because we have theability to ideate, design and
(01:40):
deploy software out to ourcustomers before our competitors
, frankly, even have gotten offthe drawing board, so we can
bring that capability to marketso much faster and really
helping.
You know, as we know, with allthis age of big data and AI,
getting to market faster withnew solutions right now, of
course, is what the game's allabout.
Speaker 1 (02:01):
Indeed, and AI is clearly moving beyond the hype
cycle, especially in our sort ofB2B markets.
What are some of the real worldAI use cases or applications
you're seeing making the biggestimpact in your arena product
development, supply chain andbeyond?
Speaker 2 (02:19):
So in our world, you know what the engineers do is.
You know, at the end of the daythey are looking to get their
design from their heads, ofcourse, onto the computer, and
they do that via CAD solutions.
And you know, to an engineer alot of their job feels like it's
done at that point, oncethey've created that CAD file.
But no one else in the companyknows what to do with a CAD file
(02:42):
.
You know they don't speak CAD,they don't have access to it, et
cetera.
So our solutions in productlifecycle management really help
translate that CAD design intousable product information that
the rest of the enterprise cantake advantage of.
But that translation process andgetting all that data available
can feel a bit mundane andlaborious to the average
(03:06):
engineer.
It's not what they want tospend their time doing.
So what we're doing is we'reintroduced a number of AI agents
that really look to automate,minimize and in some cases
eliminate the work required forthose engineers to bring those
products forward, and so,specifically, this gets in into
bowels a bit of engineering.
(03:27):
But there are things like whenyour CAD file may have dozens
and dozens of new partsavailable and bringing, getting
those parts added to the systemand part numbering and the
structuring, the bill ofmaterial.
We have agents that can now cando that automatically, given a
CAD file.
You know you can have sometimesthese change orders which are
(03:48):
dozens of pages long of all thechanges you're looking to
introduce in a product and ouragents can look at that change
order and tell the approverhere's what's different, here's
what's changing and, by the way,here's what we think you should
do relative to this in terms ofapprove change et cetera.
And so these types of thingsnow really taking a lot of time
(04:10):
out of the cycle for engineersspent on the system, and these
engineers are relatively highlycompensated individuals in the
enterprise.
So we can help save time forthese folks.
This is real bottom linebenefit for companies.
Speaker 1 (04:26):
I bet.
And so these industrialmanufacturers you work with are
under tremendous pressure weread about that every day in the
market to innovate faster,integrating your product data
and how that data then flies orflies, flows through the entire
(04:57):
supply chain.
Speaker 2 (04:58):
Sometimes it flies, but it definitely flows through
the full supply chain.
But then there's also thisfeedback loop, evan, that the
products in market and it'seither doing great or it's not.
You're having product issues,you're having huge growth spikes
.
How do you bring that data backinto the enterprise and bring
that back all the way intoengineering?
And so one of the things thatPropel did is also novel is we
(05:23):
have built our product on top ofthe Salesforce platform, of the
Salesforce platform.
So as part of that, we have theopportunity to connect our
product data into everythingthat Salesforce CRM knows about,
which includes all of thecustomer support cases where
those assets are in the field,et cetera.
So we have this ability to, inliterally real time, connect the
(05:46):
information, what's happeningin the field, back into the
engineering process to reallyjust shorten that awareness
cycle of what the heck is goingon in the field for bringing new
products to market.
But then, on the occasion whena product has issues, whether
it's a safety issue or just aquality issue the ability to
(06:07):
quickly get that back.
Quality issue the ability toquickly get that back.
Which series of products thatwere on which revision that had
which serial numbers and theycan quickly isolate that down to
the factory is built in therevision of the design to impact
that, to of course, fix thedesign but also then get the
word back out to the field onwhere you have issues.
(06:29):
So that closed loop has beenincredibly powerful.
Speaker 1 (06:33):
Amazing.
From idea all the way to thecustomer.
That's right.
It's amazing.
You're connecting all thosedots and yet you know
manufacturers are still facingchallenge.
Is it because they're not usingPropel, or are there other
aspects of the business that arechallenging when it comes to
digital transformation?
What's holding them up?
Speaker 2 (06:50):
yet you know it's as I mentioned at the front end of
this, evan, the vast majorityyou know the corpus of the data
is still in their own datacenters.
It is proprietary data that isin this on-prem environment and,
as we all know, once you're,when your data is sitting in
on-prem, the ability to connectthat across the data sources, to
(07:13):
leverage analytics, to leverageAI, to tap into that data is
really impacted.
And so part of what this nextwave of change is, and what we
believe Propel is really leadingthe charge on, is bringing all
of this data to the cloud.
And bringing to the cloud, ofcourse, is really table stakes,
(07:35):
but for a lot of manufacturersit's still a step.
But once you get to the cloud,then you have to make sure that
the data is good, because weknow bad inputs is not going to
really help your AI that much.
So really making sure that youhave good quality around that
(07:56):
data, you understand thecurrency of the data, you
understand where that data issitting in your supply chain, so
just really investing the timethen to make the data good and
then, at the end of the day, wehave to then integrate that data
and bring the data together.
So it's a journey for a numberof these companies and we've
seen some customers which arereally at the forefront of this
(08:18):
and seeing the benefits.
But every company we talk to isin the process of this journey.
There's really, at this point,the decision has been made that
this is where they have to go.
It's just the speed at whichthey're getting there.
Speaker 1 (08:37):
Interesting and how do you think about all the
sensitive data that's flowingthrough these systems, including
yours, and balancing cloud andautomation with IP protection
and security and encryption andall that kind of thing?
Speaker 2 (08:50):
Yeah, it's a huge concern, Evan, and rightly so.
That's you know the power ofthe modern tools, especially the
.
You know the CHEP, gpts and theother LLMs of the world are
incredibly powerful and, as weknow, if you just you know, in
the free versions, if you juststart pumping data in, it's now
(09:11):
their data.
They're using that to continueto build their models and so,
you know, trying to wire thisstuff together yourself is you
know it's a tough journey andit's one that I think you know
can introduce.
You know data privacy, datasecurity concerns.
(09:31):
So what our approach would beand what I'd recommend to any of
your listeners out there ischoose a platform that's
pre-wired and built for theenterprise and leverage that
platform.
There are several great onesout there.
As I mentioned, we leverageSalesforce's platform, the
AgentForce platform I mentioned.
(09:56):
We leverage Salesforce'splatform, the agent force
platform, which is a rock solidplatform where the data is never
exposed to public Internet.
It's your data staysspecifically in your org, but
you get the power of all thelarge language models and all of
the IP that sits around thatthat can enhance the data and
really drive the agents andreally drive the agents.
So you know that my big beliefis you got to embrace the
technology, but go in eyes wideopen and make sure you are
(10:16):
working with a package solution.
You have a lot of confidence in.
Speaker 1 (10:21):
Yeah, salesforce is doing amazing work.
Maybe Mark Benioff is evenlistening or watching, but you
know, let's talk aboutautomation, doing more with less
something Salesforce is eventalking really loudly about or
watching.
But you know, let's talk aboutautomation, doing more with less
, something Salesforce is eventalking really loudly about.
What are the biggestopportunities to streamline
workflows, decision-making, youknow, doing more with the same
headcount you have.
What are your customers askingor demanding of you?
Speaker 2 (10:46):
Yeah, and it's, you know, to me what we're hearing
the most is can you just help ustake out the they've largely
taken out non-value added tasksin the processes, but now
there's the moderately valuedtasks that people currently have
(11:08):
to do but they don't want to do.
And that, to me, is a reallyimportant segment of work in
this idea of the AI agentsworking alongside of the people
or on behalf of the people, andI think ultimately they'll be
agents which will be performingwithout people in the future,
but right now there's so muchvalue to be driven from just
(11:31):
having that agent work alongsidethe folks.
And so you know, like Evan, wehave In our customer base today
people that have to developwithin our quality module kind
of training quizzes that as youroll out new processes to the
shop floor, people have tovalidate that.
Yeah, I've, I understand theprocess change.
(11:52):
I took a little quizlet tovalidate that, this change, and
sometimes you know this thesecan be based off of 100, 200,
500 page process changedocuments.
And so today someone has toread that document like what was
it?
What are we changing?
And then create a quiz fromthat.
And there's people today who dothis job and they tell me, ross
(12:16):
, this is really not fun, but Ihave to do it.
So we have an agent now, evan,that goes, reads that document
and you tell it how many quizquestions you want and it'll go
create those quiz questions foryou.
It creates the right answer andthree valid wrong answers,
three functionally correct butwrong answers, and prints it
(12:41):
back to the user and says whatdo you think?
And the user says, like good,or I like these two questions,
but give me three new ones.
You go through that process andin a matter of minutes you
develop this quiz that we hearfrom our customers.
Sometimes I spent four, eight,20 hours doing this in the past,
and so these types of thingswhere we can drive this time out
(13:02):
of a process to me are just soexciting.
Speaker 1 (13:07):
Really is, and clearly you're helping change
the way products are built.
What about AI changing the wayproducts are sold and supported,
marketed?
What do you see as far asthat's concerned?
Speaker 2 (13:20):
Yeah, you know one of the things you know AI will
continue to help drive theproduct development cycle
shorter and shorter, and so asthe cycles get shorter and
shorter, it tends to mean thatthe average shelf life of that
product you know, the sellabletime for that product is
decreasing.
(13:41):
So you know, everyone's justmoving faster in this process,
and so the ability to have totake the information from
engineering and push that out tothe rest of the supply chain in
a rapid fashion, but alsoaccurate fashion, is really
important.
So things like, hey, supplychain procurement you know,
(14:05):
here's the new chipset we'reusing, this is the new resins
that we're deploying.
This is, hey, because of thistariff thing, we're sourcing
from this different location.
So all of these rapid changesinto the procurement cycle, or,
hey, marketing, we need newcollateral, we need new content,
we need new safety documentsand being able to push that
(14:27):
information to marketing, andeven some of the solutions we
have Evan now will create thatmarketing material for the
marketer directly from theproduct designs and so these
kinds of things to really helpfacilitate that the entire
enterprise can work as fast aswhat engineering is now cranking
out.
Speaker 1 (14:48):
Fantastic.
So I'm in Massachusetts.
We do a lot in the medicaldevice manufacturing and design
world.
Speaker 2 (14:54):
We have a lot of great customers with you there
in Massachusetts.
Speaker 1 (14:56):
Oh great.
So any advice to a manufacturerwho maybe hasn't embarked on
this leading edge AI infusedapproach how to get started.
Maybe they've been around for adecade or two and have their
products and things are kind ofchugging along.
How do you get started on thisjourney?
Speaker 2 (15:13):
Yeah, evan, you know I think there's a step one,
there's almost a step zero, andI think the step zero is really
just trying to get your armsaround the data you have today.
Companies that have been aroundfor decades typically have data
which is decades old andthey're often those old legacy
(15:35):
products.
The data is not particularlygood.
In fact, you may like yourmodern product design, may talk
about the chipset and you have arequired chipset you have to
identify, but maybe your productfrom 25 years ago didn't have a
chip in it and so that partwill bomb when you try and load
it up into the new design.
So this idea of really thinkingthrough what data do we need,
(15:59):
you know, from a regulatory andcompliance and customer support
perspective, or you know from aregulatory and compliance and
customer support perspective, oryou know, design and
reusability, what data do weneed, and really focus on that
and the rest of your data.
You don't have to throw it away, but archive it someplace, kind
of get it out of your modernprocessing systems and really
focus on your core data.
I'll call that step zero.
(16:21):
Step one is you know we'd loveyou to choose Propel, but choose
a solution that can take you toa modern architecture.
You know that's cloud based, etcetera.
And until you kind of move upto that modern architecture
stack, you can not takeadvantage of all of the goodness
(16:41):
which is coming out relative toAI, analytics and everything
else.
That trying to retrofit that ontop of those old legacy on-prem
solutions is just a losingcause.
And so you know it's.
It's sometimes.
We're a system of record.
You know it's like changing outyour email system or your ERP
system, not something you liketo do that often, but once in a
(17:04):
decade, maybe every other decade, it's something you got to do
because the technology haschanged so much that it's time
to really step up and moveforward with this, taking
advantage of all thecapabilities these modern
architectures can provide.
Speaker 1 (17:19):
Great advice.
So we're here in the dog daysof summer a little quieter, but
there's still a lot going on.
Where can people see you ormeet you, either this summer or
in the fall?
Any events or meetings.
Speaker 2 (17:33):
So we're headquartered in Redwood City,
which has one of the funniestsayings it is voted best by
government test, which I guess.
In the fifties, the governmentlooked at all the different
weather patterns across thecountry and the Redwood city,
(17:54):
california, was deemed to havethe most moderate weather across
the entire country.
So anyway, come visit us inRedwood City.
We'd be happy to host you inour headquarters.
Speaker 1 (18:05):
Yeah, beautiful town, not too many Redwoods.
You have to go a little furthernorth.
Speaker 2 (18:08):
That's true, but nonetheless it's a great town.
Speaker 1 (18:10):
Thanks for joining.
Really great insights sharedand thanks everyone for
listening, watching, sharingthis episode and also check out
our new TV show now on FoxBusiness and Bloomberg at
techimpact.
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