Hey everybody.

Great topic today, as we talk about how AI and digital are reshaping manufacturing.

Ross, how are you?

I'm great, evan, great to spend time with you today.

Well, great to talk to you Really timely and important topic Before that, maybe introduce yourself.

Your journey with Propel and your growth has been really impressive.

What's the big idea and the secret sauce behind all that momentum?

Yeah, so I am Ross Meyerquard, ceo for Propel Software.

We just had our 10-year anniversary and, yeah, which we're really excited about, we're coming up on becoming a teenager here as we work with double digits and Evan, our whole premise for our solution is we are looking to provide digital cloud native capabilities to folks that are building physical products, and all the front end software that goes into that process is, remarkably, really the same software, built on the same software stack that's been around for 30 or 40 years, and so our idea was you know, not a particularly novel one broadly, but in this space it was novel was hey, let's take this software to the cloud, let's bring in modern technology and see if we can help drive innovation in this space.

And of course we have, and it provides us really almost what I think of as like an unfair advantage relative to our competitors, because we have the ability to ideate, design and deploy software out to our customers before our competitors, frankly, even have gotten off the drawing board, so we can bring that capability to market so much faster and really helping.

You know, as we know, with all this age of big data and AI, getting to market faster with new solutions right now, of course, is what the game's all about.

Indeed, and AI is clearly moving beyond the hype cycle, especially in our sort of B2B markets.

What are some of the real world AI use cases or applications you're seeing making the biggest impact in your arena product development, supply chain and beyond?

So in our world, you know what the engineers do is.

You know, at the end of the day they are looking to get their design from their heads, of course, onto the computer, and they do that via CAD solutions.

And you know, to an engineer a lot of their job feels like it's done at that point, once they've created that CAD file.

But no one else in the company knows what to do with a CAD file.

You know they don't speak CAD, they don't have access to it, et cetera.

So our solutions in product lifecycle management really help translate that CAD design into usable product information that the rest of the enterprise can take advantage of.

But that translation process and getting all that data available can feel a bit mundane and laborious to the average engineer.

It's not what they want to spend their time doing.

So what we're doing is we're introduced a number of AI agents that really look to automate, minimize and in some cases eliminate the work required for those engineers to bring those products forward, and so, specifically, this gets in into bowels a bit of engineering.

But there are things like when your CAD file may have dozens and dozens of new parts available and bringing, getting those parts added to the system and part numbering and the structuring, the bill of material.

We have agents that can now can do that automatically, given a CAD file.

You know you can have sometimes these change orders which are dozens of pages long of all the changes you're looking to introduce in a product and our agents can look at that change order and tell the approver here'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 of approve change et cetera.

And so these types of things now really taking a lot of time out of the cycle for engineers spent on the system, and these engineers are relatively highly compensated individuals in the enterprise.

So we can help save time for these folks.

This is real bottom line benefit for companies.

I bet.

And so these industrial manufacturers you work with are under tremendous pressure we read about that every day in the market to innovate faster, integrating your product data and how that data then flies or flies, flows through the entire supply chain.

Sometimes it flies, but it definitely flows through the full supply chain.

But then there's also this feedback loop, evan, that the products in market and it's either doing great or it's not.

You're having product issues, you're having huge growth spikes.

How do you bring that data back into the enterprise and bring that back all the way into engineering?

And so one of the things that Propel did is also novel is we have built our product on top of the Salesforce platform, of the Salesforce platform.

So as part of that, we have the opportunity to connect our product data into everything that Salesforce CRM knows about, which includes all of the customer support cases where those assets are in the field, et cetera.

So we have this ability to, in literally real time, connect the information, what's happening in the field, back into the engineering process to really just shorten that awareness cycle of what the heck is going on in the field for bringing new products to market.

But then, on the occasion when a product has issues, whether it's a safety issue or just a quality issue the ability to quickly get that back.

Quality issue the ability to quickly get that back.

Which series of products that were on which revision that had which serial numbers and they can quickly isolate that down to the factory is built in the revision of the design to impact that, to of course, fix the design but also then get the word back out to the field on where you have issues.

So that closed loop has been incredibly powerful.

Amazing.

From idea all the way to the customer.

That's right.

It's amazing.

You're connecting all those dots and yet you know manufacturers are still facing challenge.

Is it because they're not using Propel, or are there other aspects of the business that are challenging when it comes to digital transformation?

What's holding them up?

yet you know it's as I mentioned at the front end of this, evan, the vast majority you know the corpus of the data is still in their own data centers.

It is proprietary data that is in this on-prem environment and, as we all know, once you're, when your data is sitting in on-prem, the ability to connect that across the data sources, to leverage analytics, to leverage AI, to tap into that data is really impacted.

And so part of what this next wave of change is, and what we believe Propel is really leading the charge on, is bringing all of this data to the cloud.

And bringing to the cloud, of course, is really table stakes, but for a lot of manufacturers it's still a step.

But once you get to the cloud, then you have to make sure that the data is good, because we know bad inputs is not going to really help your AI that much.

So really making sure that you have good quality around that data, you understand the currency of the data, you understand where that data is sitting in your supply chain, so just really investing the time then to make the data good and then, at the end of the day, we have to then integrate that data and bring the data together.

So it's a journey for a number of these companies and we've seen some customers which are really at the forefront of this and seeing the benefits.

But every company we talk to is in 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 which they're getting there.

Interesting and how do you think about all the sensitive data that's flowing through these systems, including yours, and balancing cloud and automation with IP protection and security and encryption and all that kind of thing?

Yeah, it's a huge concern, Evan, and rightly so.

That's you know the power of the modern tools, especially the.

You know the CHEP, gpts and the other LLMs of the world are incredibly powerful and, as we know, if you just you know, in the free versions, if you just start pumping data in, it's now their data.

They're using that to continue to build their models and so, you know, trying to wire this stuff together yourself is you know it's a tough journey and it's one that I think you know can introduce.

You know data privacy, data security concerns.

So what our approach would be and what I'd recommend to any of your listeners out there is choose a platform that's pre-wired and built for the enterprise and leverage that platform.

There are several great ones out there.

As I mentioned, we leverage Salesforce's platform, the AgentForce platform I mentioned.

We leverage Salesforce's platform, the agent force platform, which is a rock solid platform where the data is never exposed to public Internet.

It's your data stays specifically in your org, but you get the power of all the large language models and all of the IP that sits around that that can enhance the data and really drive the agents and really drive the agents.

So you know that my big belief is you got to embrace the technology, but go in eyes wide open and make sure you are working with a package solution.

You have a lot of confidence in.

Yeah, salesforce is doing amazing work.

Maybe Mark Benioff is even listening or watching, but you know, let's talk about automation, doing more with less something Salesforce is even talking really loudly about or watching.

But you know, let's talk about automation, doing more with less, something Salesforce is even talking really loudly about.

What are the biggest opportunities to streamline workflows, decision-making, you know, doing more with the same headcount you have.

What are your customers asking or demanding of you?

Yeah, and it's, you know, to me what we're hearing the most is can you just help us take out the they've largely taken out non-value added tasks in the processes, but now there's the moderately valued tasks that people currently have to do but they don't want to do.

And that, to me, is a really important segment of work in this idea of the AI agents working alongside of the people or on behalf of the people, and I think ultimately they'll be agents which will be performing without people in the future, but right now there's so much value to be driven from just having that agent work alongside the folks.

And so you know, like Evan, we have In our customer base today people that have to develop within our quality module kind of training quizzes that as you roll out new processes to the shop floor, people have to validate that.

Yeah, I've, I understand the process change.

I took a little quizlet to validate that, this change, and sometimes you know this these can be based off of 100, 200, 500 page process change documents.

And so today someone has to read that document like what was it?

What are we changing?

And then create a quiz from that.

And there's people today who do this job and they tell me, ross, this is really not fun, but I have to do it.

So we have an agent now, evan, that goes, reads that document and you tell it how many quiz questions you want and it'll go create those quiz questions for you.

It creates the right answer and three valid wrong answers, three functionally correct but wrong answers, and prints it back to the user and says what do 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 and in a matter of minutes you develop this quiz that we hear from our customers.

Sometimes I spent four, eight, 20 hours doing this in the past, and so these types of things where we can drive this time out of a process to me are just so exciting.

Really is, and clearly you're helping change the way products are built.

What about AI changing the way products are sold and supported, marketed?

What do you see as far as that's concerned?

Yeah, you know one of the things you know AI will continue to help drive the product development cycle shorter and shorter, and so as the cycles get shorter and shorter, it tends to mean that the average shelf life of that product you know, the sellable time for that product is decreasing.

So you know, everyone's just moving faster in this process, and so the ability to have to take the information from engineering and push that out to the rest of the supply chain in a rapid fashion, but also accurate fashion, is really important.

So things like, hey, supply chain procurement you know, here's the new chipset we're using, this is the new resins that we're deploying.

This is, hey, because of this tariff thing, we're sourcing from this different location.

So all of these rapid changes into the procurement cycle, or, hey, marketing, we need new collateral, we need new content, we need new safety documents and being able to push that information to marketing, and even some of the solutions we have Evan now will create that marketing material for the marketer directly from the product designs and so these kinds of things to really help facilitate that the entire enterprise can work as fast as what engineering is now cranking out.

Fantastic.

So I'm in Massachusetts.

We do a lot in the medical device manufacturing and design world.

We have a lot of great customers with you there in Massachusetts.

Oh great.

So any advice to a manufacturer who maybe hasn't embarked on this leading edge AI infused approach how to get started.

Maybe they've been around for a decade or two and have their products and things are kind of chugging along.

How do you get started on this journey?

Yeah, evan, you know I think there's a step one, there's almost a step ero, and I think the step ero is really just trying to get your arms around the data you have today.

Companies that have been around for decades typically have data which is decades old and they're often those old legacy products.

The data is not particularly good.

In fact, you may like your modern product design, may talk about the chipset and you have a required chipset you have to identify, but maybe your product from 25 years ago didn't have a chip in it and so that part will bomb when you try and load it up into the new design.

So this idea of really thinking through what data do we need, you know, from a regulatory and compliance and customer support perspective, or you know from a regulatory and compliance and customer support perspective, or you know, design and reusability, what data do we need, 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 modern processing systems and really focus on your core data.

I'll call that step ero.

Step one is you know we'd love you to choose Propel, but choose a solution that can take you to a modern architecture.

You know that's cloud based, et cetera.

And until you kind of move up to that modern architecture stack, you can not take advantage of all of the goodness which is coming out relative to AI, analytics and everything else.

That trying to retrofit that on top of those old legacy on-prem solutions is just a losing cause.

And so you know it's.

It's sometimes.

We're a system of record.

You know it's like changing out your email system or your ERP system, not something you like to do that often, but once in a decade, maybe every other decade, it's something you got to do because the technology has changed so much that it's time to really step up and move forward with this, taking advantage of all the capabilities these modern architectures can provide.

Great advice.

So we're here in the dog days of summer a little quieter, but there's still a lot going on.

Where can people see you or meet you, either this summer or in the fall?

Any events or meetings.

So we're headquartered in Redwood City, which has one of the funniest sayings it is voted best by government test, which I guess.

In the fifties, the government looked at all the different weather patterns across the country and the Redwood city, california, was deemed to have the most moderate weather across the entire country.

So anyway, come visit us in Redwood City.

We'd be happy to host you in our headquarters.

Yeah, beautiful town, not too many Redwoods.

You have to go a little further north.

That's true, but nonetheless it's a great town.

Thanks for joining.

Really great insights shared and thanks everyone for listening, watching, sharing this episode and also check out our new TV show now on Fox Business and Bloomberg at techimpact.