EP 11: From Islands to Outcomes: Making Additive Work at Scale

In this episode of The Additive Advantage Podcast, Shon Anderson sits down with Andre Wegner, CEO of Authentise, for a wide-ranging conversation on workflow orchestration, contextual data, AI, and the future of scalable additive manufacturing.

Andre shares how Authentise evolved from focusing on IP protection and distributed manufacturing into building systems designed to connect the entire additive workflow — eliminating the “island approach” that continues to limit scalability across the industry. Together, Shon and Andre explore why disconnected machines, software, and data systems create unnecessary friction and why the future of manufacturing depends on continuity across design, engineering, production, and quality.

The conversation dives into agile factories, reverse engineering at scale, automation in aerospace and defense, and the growing importance of capturing the “why” behind engineering decisions. Andre also explains why context will become critical for AI-enabled manufacturing systems and why manufacturers need strategic partners instead of simply searching for the next machine.

Along the way, the two discuss lessons learned from building companies in additive manufacturing, why curiosity may become one of the most important workforce skills of the future, and how manufacturers can begin thinking differently about additive as a strategic capability rather than a standalone tool.

If you’re interested in where additive manufacturing, automation, AI, and digital workflows are heading next, this episode delivers an honest and forward-looking conversation from two leaders deeply involved in building that future.

About the Show
The Additive Advantage Podcast explores what it really takes to turn additive manufacturing into a scalable, performance-driven business capability. Hosted by Dani Mason and Shon Anderson, the show features real conversations with leaders accountable for outcomes — not hype.

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About the Hosts
Hosted by Dani Mason and Shon Anderson, industry leaders with deep experience in technology and additive manufacturing.

Transcript

SPEAKER_01 0:00

I think the industry is still only at 1% of where it should be. And I I I stick to that. I I don't I honestly have no idea how we can be padding ourselves in the back. We've got still got so much work to do.

SPEAKER_00 0:09

Welcome to the Additive Advantage Podcast. I'm Danny Mason, joined by Sean Anderson. This is where additive manufacturing meets accountability, and where we talk about turning pilots into production and ideas into outcomes. Let's jump in.

SPEAKER_02 0:23

Today's conversation goes beyond machines and materials into something far more foundational, how additive actually works at scale. For this episode, we're joined by Andre Wagner, CEO of Authentize. They're a leader in flexible AI-powered workflows in the most agile manufacturing and engineering settings. Authentize has taken a little different approach than most in the industry, focusing less on hardware and even beyond traditional software, more on orchestrating the full life cycle of additive production. From workflow continuity to data capture and project-based delivery, this conversation challenges some core assumptions holding the industry back. And I put Andre on the spot about what he would do if he was someone who had just heard what we discussed. In this episode, we'll dig into what it really takes to move additive beyond an isolated use case toward an integrated outcome-driven manufacturing system. And as you'll hear later in the episode, we're also going to be putting our producer, Nicole Dietz, to work on a huge future guest request. And I will hold her accountable for a progress update. So, Nicole, consider this your official introduction. Let's get into it. Welcome to another episode of the Additive Advantage podcast. This is Sean Anderson. Um, joined today by uh Andre Wagner from Authentize. And for those of you who tend to think of Authentize as primarily a software company, I think you're going to be impressed by what you hear today. Andre and I have gotten to know each other over the past few months, and as we've shared this passion for making additive really perform at scale, discovered that uh it isn't just one of us that's crazy. Maybe we're both crazy and how we're looking at this. So eager to jump in and explore that with you this morning. Thanks for joining, Andre. Thanks for having me. It's good to be here. So um, what would you want to share with our audience as we start out about your background and um about your company before we dig into what it takes to make additive work at scale?

SPEAKER_01 2:16

Well, um, I think we'll get into some of the facets of it, but suffice to say that you and I share a similar passion on um the need to remove uh uh additive from a sort of island approach to a more cohesive one. Um we shared that and and your background certainly speaks to that. So it's great to be changed exchanging those ideas with you. My personal pet peeve um is twofold and um authentizes sort of a reflection of that. Firstly, I don't feel like we're we have a cohesive um operation in in additive and and we build a digital infrastructure to support that initially, and and we'll get into how we're expanding that now. And and the second is I I don't think we're capturing nearly enough data. It's a sort of side story that I maybe we'll touch on at the end.

SPEAKER_02 3:03

Yeah, I think that'll be a recurring theme that runs throughout because of course, if you don't have any data when you think about manufacturing, you know, quality decisions, supply chain decisions, obviously in a in a non-additive context, you wouldn't ask anyone to make any decisions about those things without data. But for some reason, in additive, people forget. So where I would love to start this conversation is maybe going back a little bit to the beginning of how Auth and Ties came to be, you've taken quite a bit different path than most companies in the additive space as you've been building Auth and Ties, definitely less about hardware. And I'm gonna say something maybe slightly provocative. I think even less about software, but more of a focus on production workflows and orchestration. What, where did you get the vision for that? Or how did you come to believe that that was the right approach?

SPEAKER_01 3:54

Um, it came organically, I think. The um the initial uh mission of Orphan Ties was to enable distributive manufacturing. And we thought initially the only thing that was required was intellectual property protection. And we were very thoroughly wrong with that. So after three years of of banging our heads against the walls, even though the news media were very excited about us um, you know, enabling streaming for additive, we uh then started listening really closely to the customer. And and I think that's where this um focus on orchestration has really come from. You know, the the customer. Initially, Rico in Japan have got a hat tip them, and and um more and more uh companies around the world are are beginning to understand that if they want to do this at scale, they have to um start thinking about the the the systems, not as an individual system, but uh how they connect and increasingly also how they connect to their original intent. Our job, Sean, you and I, I think we shared this before, is to reduce the time it takes to turn an idea into a part. If we're gonna do that, we can't you know manually exchange every step of the way data one in one form to another form. So we need we really need cohesive, cohesive approach. You know, the vision of enabling distributed distributed manufacturing is still still holds true. It's just that we found that instead of intellectual protection, we need more um continuity and integrity um to facilitate that mission and vision.

SPEAKER_02 5:21

Agreed. It's it's always been interesting to me that you know people talk about additive as speeding up just the time to do the physical production and and forget about everything that it takes to you know do the design, validate the product with a customer, all of those, all of that intent. I love the word that you use there. You know, we think about design intent. Oftentimes we need to think about production intent. And I know that's a topic we're gonna get more into in a little bit based on some of the interesting products you guys have been launching. One of the things that struck me in our first conversation is you, it sounds like have a view of not just making parts, but viewing these additive initiatives as projects. Tell me how how did you come to see this as opposed to just, well, we're just trying to make this widget. It's more of a turnkey project to deliver an outcome. Where did that idea come from?

SPEAKER_01 6:12

Yeah, I it's an interesting, I'm not sure what the exact origin is. I can tell you that we've always believed that we need to take ownership. And ultimately, you know, we were building workflow software. Um, and our business model is kind of unique in just for selling software. We uh we take a big gamble, so we charge relatively low fees at the front, and then we grow with the customer success. So we really attach ourselves to the customers' KPIs. So we we take ownership of the customers' KPIs as well. But there's no better way of doing that than actually to own the infrastructure. And so without directly competing with the customers, what are what can we do to facilitate that? So we've been moving ahead in sort of taking more and more ownership. And we became the first software company to become a prime to the DoD. We believe the first software company to become prime to the DoD uh three or four years ago in a project for the Air Force uh um in California. And so um that's really when we started saying, hey, we can not only deliver a final part to you, but we can deliver it with all the data attached. So we had a competitive advantage um to the to the final customer, and to us, we could say, hey, if no if anybody knows how to use the software to his full potential, then it's probably us. Um so there, you know, it's just a question of taking ownership and not saddling the customer with uh my favorite new phrase of the cognitive load of making it work. You know, that's really, really my job. Um, yeah, I I think that's where where it's going. We'll also we'll see there's a few avenues where this can be realized in production environments, but also in reverse engineering areas basically where the market has failed to provide uh real solutions that the customer wants. And and I think that they've left them left the door wide open for us.

SPEAKER_02 8:05

Outstanding. It's amazing how if you just listen well enough to the customer, they'll tell you what to do. You touched on something right there at the end, Andre, about um, you know, maybe some gaps in the market. I'm curious, when you look at the current state of additive, and we just came out of Rapid TCT, where do you think the industry is still missing the mark in terms of, you know, maybe incomplete offering or wrong view of what customers are interested in? Where's the gap remaining?

SPEAKER_01 8:32

Yeah, it's um I mean we we've alluded to that. I I think that the OEMs are still very much um island makers and they fail to understand exactly how their tools fit in with the rest of the workflow and ultimately deliver a final part, which is all the customer really wants. They think that the job of producing that um production system is really a job for automation system integrators, but I think that's the wrong approach. A, you leave quality on the table, there's you know, or or uh productivity opportunities on the table. We've explored how getting data from one um one system, like a metal additive system, could influence surface finishing parameters later on in the process. So definitely a more integrated view of of the data schemes makes sense. Uh and the second is that if we just leave it as a system integrators, we uh we will lose agility. You know, industry 4.0 has basically meant we're we're gonna build these highly sophisticated production systems just for one use case, and that you know, that system's gonna run forever. We've seen that trend emerge over the last 50, 60 years in the automotive sector and other sectors um in the pursuit of um efficiency and productivity. And now we're facing this world where we need much more agility and we're like, ah, maybe that was the wrong approach. So uh bottom line is I I I think we've left um a lot of um uh room um for improvement in in the fact that we've we've seen the additive systems and and the individual post-processes as uh individual islands rather than a cohesive system. Um very very briefly, I think the same is also case in it, also the case in reverse engineering and and other engineering processes, where the demand is clearly there to produce, you know, millions or reverse engineer millions of designs ready for additive. Um, and yet we're we're hand-holding them from one station to one digital station to another and not thinking about cohesive holes. So really the island approach is what uh what's um leaving us behind.

SPEAKER_02 10:48

Agreed. Uh I'm curious when you think about going from just printing a part or I'll use that phrase, island approach. Uh, by the way, I'm gonna steal your phrase cognitive load, because you know, as a country boy from South Dakota, I say people don't care how the sausage is made, and avoiding the cognitive load sounds way more sophisticated. That's modern, doesn't it? Uh or that just reflects the fact that I like food a lot. Who knows? But um so I'm curious when you as you're thinking about building a company and you have this mindset shift from printing a part to project-based additive, there are a lot of implications on the competencies you need in the organization, the mindset of the organization, et cetera. Talk me through how your view has shaped what you've built into Auth and Ties and maybe some of the challenges and some of the victories that have gone along with you know building the company differently.

SPEAKER_01 11:44

Um Yeah, I mean, some of it was um intentional, and I would say some of it was pure love. Um a couple of things happened. The first is that we're not venture-backed. Um, you know, we we build a company that's uh that has investors, and we have uh, you know, a largely built the company on the back of the dollars that we made, which meant that we have a lot more freedom. We don't have venture capitalists jumping down our back saying, hey, you must do X. You know, recurring revenues has been the leitmotif until recently of all the VCs, and that's distracted people from doing things that might bring new dollars, but not recurring dollars, right? Um, and so we were lucky um in part. I mean, I I used to run a venture capital fund and I I I instinctively knew that a venture was not the right approach for a company in the industrial space because this it's a signal poor environment and sales cycles are super long. So that's one thing. The second thing is that um I used to also work in the microfinance industry where hybrid structures are quite common. So you have microfinance banks that are banks and then also have a charitable arm. Um and I I started in a similar vein thinking about well, there's a lot of innovation funding, but I know innovation funding might distract my commercial aims. So let me build an organization that is separate, that just focuses on innovation funding, uses the same infrastructure, but uh it is for all intents and purposes separate. So that's what we did, and we have a great business working with governments around the world on innovation projects. And that's been going on for about five or six years. So the transition to project work was not really that difficult because A, we didn't have anybody shouting at us that we we we shouldn't do it, and B, we had a really competent team that was familiar with uh launching complex projects and executing them, right? Which are what innovation projects are. So we we just transitioned that that approach to um to commercial to the commercial side, and especially in the defense market, that um that goes without saying. The third ingredient, I think, is that we've had a few incredible customers who've trusted us from an early stage. I mean, the the DOD taking uh a punt on us to um to work on a you know multi-million dollar project. Uh people had to put their their names on the line for that. And I do appreciate that. And and so we've worked with some really incredible people and organizations along the way.

SPEAKER_02 14:12

It's amazing how um and you have to take a man's word for it when he says, Well, I used to work as part of a VC organization and I recognize it wasn't the right fit. We always say that customer dollars are better funding than investor dollars, right? Because you investor dollars validate that you made a compelling pitch. Customer dollars validate that you actually solved a real problem. And uh better implications there. I mean, I had to learn it the hard way, Sean. But I learned it in the end. Well, you you did learn it in the end. I don't know if our industry has learned it yet, but uh certainly not if the number of hype cycles go speak to anything. Yeah, I think you know you have a lot of companies out there just hoping that there's one more hype cycle left. I'm curious when you think about this project-based additive model, what types of customers or applications do you see as best suited for that model for where the industry is today?

SPEAKER_01 15:08

You know what? Um I'm not entirely um certain that this requires a fully fledged application-driven model. Uh, I do think that you know there's an obvious fit um to deploy factories for for applications that are single purpose, that um require uh a specific fit, missile shells or um uh suppressors. You can also think about highly flexible factories, and obviously we have many of those. Uh a line makes a million parts a day, you know, unique parts a day. So I don't I think flexibility is um is also interesting, especially in in certain environments. So, you know, we are involved in a German uh project for um lights out metal additive manufacturing, uh led by a company called ZF, and uh this is public. They are interested in the long tail of parts, right? And so they're going in it without the idea of a specific application that needs to be run. Um there's also projects that are being um requested from uh defense departments around the world in in the areas of contested logistics. Again, you're dealing with environments that are multi-purpose. So it you know, it it fills the whole the whole thread of different applications.

SPEAKER_02 16:37

Interesting. Uh can you tell us a little bit more about that project you mentioned in Germany where you talk about the long tail focus on the parts? When you think about the, you know, we've talked a lot about the reverse engineering focus on the front end of things, and there are other players in the space. You see what Worth Additive is doing, you know, launching their DIS, where, you know, their core business is really the long tail but traditionally manufactured. And now they're wading into the you know digital inventory concept, again, thinking about the, you know, what I would call consumption over time. What can you share with us about that project and what is motivating my the impression I got is that's a an agile or flexible factory by design. How does how does the demand signal get connected to what's being built in that project today based on the long-term view? Does that make sense?

SPEAKER_01 17:30

Yeah, I think so. And if I'm not answering the question correctly, you can always throw something at me. The the intent behind the project really steered the project in a very different way. So it's not only focusing on automating the process, the additive process, it's it's also automating things that hadn't previously been automated, like support removal. It's doing that for unique shapes. So you're generating toolpaths separately every time. If you're thinking that backwards, you're also gonna have to think about orienting, supporting, um uh adding barcodes, tool tooling points, you know. So you have a lot of um, you know, build preparation work that's going on that also has to be automated. Because if you're gonna want to have a high throughput factory that has this flexibility, you have to keep on thinking in different ways. So I that's the intent drives a lot of design decisions that are that wouldn't normally be considered in a normal automation project. If you have like um, you know, when carbon went out and started making the shoe, they had just the midsole in mind, and bam, you know, this is all we've got to do. Please help me automate this process. I, you know, and an agile factory thinks differently. Understood.

SPEAKER_02 18:48

Um as you're building authentic, so this workforce is a topic that comes up a ton on this podcast. And so I anytime we have a business leader on, I try to at least touch on it a little bit. Of course, they all view it through a little bit different lens. And I'm curious, based on what you've shared with us about Authentize, what are the skills that you believe are hardest to find right now in the additive space as you're building your company?

SPEAKER_01 19:14

Hmm. That's really interesting. I I think the deep domain knowledge is still really valuable. So if I if you know, I I just spoke to a chap who's been the technical data package focal for the Navy for 20 years. And, you know, that's just that's gold. Speaking to people like that, understanding what their real challenges are, trying to drive home, you know, what the latest tools can do to help them and really work in a collaborative manner to address that. You know, getting rid of the the grind that they don't really want to uh engage with. And that really brings me to like a a higher level point. I think that and I've I've seen it in my own company right now. Um if you're if you have an interest, if you if you want to break it down, if you want to dive deeper, if a curiosity is part of your whole market, so you know, you're gonna be fine and no matter what the changing environment is. At orthontize we're heavily basing our our products on AI. That doesn't mean that the use of AI throughout Authentize is, you know, uh uh all at the same level. It's uh really interesting to see how curiosity differs with different people and how the uh as a result the adoption of of of AI tools might differ with different people and you have to make a real effort. So that's a that's a skill. Curiosity is a learned um is a learned skill and has a lot to do with your background and and the way you were brought up. Um yeah, it's something that that you can still learn, and it I heard that people do.

SPEAKER_02 20:49

I love that. That's the first time I've heard someone talk about curiosity as a skill, but I would absolutely agree it's vital to building a company that can evolve and remain agile as customer needs evolve. I want to go back to something you shared earlier. We talked about you know hardware OEMs oftentimes kind of as islands, but let's look at it through more of a software lens. Talk to me about, you know, when you think about current tools, so from MES systems to workflow software, data management, you've got engineering, you know, workflow and design um uh tool sets in the tech stack. Where does all that fall short when you think about the software side? What's missing there?

SPEAKER_01 21:34

A couple things. Traditionally, the the thing that people have mostly complained about is that they are all islands the same way the equipments are. You know, you're you're you're talking about your cow tool, your simulation tool, talking about your support uh generation tool and slicer, you're you know, and then you're you've got an in process monitoring tool, maybe. Um, you know, there's dozens of I call them heavy math algorithms that sit along that pipe that need to be integrated. And that's really what the workflow systems are there to do. A lot of them, you know, just stopped innovating, basically, and stopped doing that. The the other thing is that there's a whole slew of data, Sean, that I'm I think has even more value that we haven't that we've largely ignored, primarily, I think, because we didn't think it was possible to use it. And that's the contextual collaboration data that that we generate every day, the emails that you and I send, and the teams or Slack messages, uh, the random drive, Google Drive draft documents that we've got, and anything, basically, the transcripts on the video calls that we have. Um, and that all that information is is used um and it's becoming increasingly valuable because we realize how important context is for AI. Um, but we've always dismissed it. So not only do we have algorithmic islands, we also have data islands. We have these pieces of information that we never previously thought to utilize. And so the software market is really, really poor in its evolution because it hasn't managed to combine those two things and is still claiming success. Um, where you know, I we won an award at Rapid, and you know, I got up and I'm like, I I think the industry is still only at 1% of where it should be. And I I I stick to that. I I don't I honestly have no idea how we can be behind ourselves in the bat. We've got still got so much work to do.

SPEAKER_02 23:29

I I think you touched on something there that you know everyone, everybody has to talk about AI now, or you know, you feel like you're irrelevant, except most people talk about AI and they're still using it to plan their vacation with their wife, you know, saying what are the technologies. Yeah. But you touched on something that is so important because you know, I don't care if you're a grok, claude, chat, whatever, context is everything. And if you don't give, regardless of the model, if you don't give it context, you will not get a very good answer. And I love the fact that you call you're calling out, Andre, that if we don't start capturing context around decisions of why was the part designed this way? Why didn't we do that? How come we chose this, you know, whether it's surface finish, machining pro, whatever it is. But all of the whys, if we don't start capturing that now, AI is going to continue to give us junk answers for another 10 years. It's astonishing to me.

SPEAKER_01 24:29

Thank you. Um, it's it's a cross-eyed will die on and um it's something we've invested significant resources in. I can't believe we're you know, we're we're going the one of the reasons the industry is the size it is today is because a lot of the traditional parts won't be reverse engineered because it's just simply too difficult to do it because we don't have those Y's recorded properly. And so for every part we have to go back to first principles, which means a million-dollar effort in most cases. And uh or more than that. And the, you know, the the traditional platforms, think about the commercial airlines, just won't do that. They won't certify parts again, right? So we're stuck waiting for new platforms to arrive, which barely ever do, right? And as a result, our market, our total total addressable market is much smaller than it ought to be. And yet, despite that, despite that constraint, now when we engineer a new product today, or or even reverse engineer a new product today, we're still not making any effort to capture the wine. Any. Knowing for sure that there will be a new manufacturing capability available to us in six months' time, 12 months, 24 months, there will be something. It's insane to me that we are, it's like Einstein's, you know, the definition of insanity is you keep on banging your head and saying wall. It's true. Like that's what we're doing. Um, so yeah, it's something that we we are, you know, Whisper, the the new product we we released um is absolutely kind of what one of the things that we're really trying to address. But I I acknowledge it's a hard road to climb because it it's a behavior change. It's people acknowledging that information that they consider not to be valuable is suddenly valuable, and we need to find methods of capturing it.

SPEAKER_02 26:14

Well, people forget that it's valuable until you want to reverse engineer a part. And you know, one of our applications, people will be talking to a customer and be like, well, why is why does the part have this feature? It doesn't seem I can't tell what the purpose is. Why, well, you know, the guy that designed that, he retired like 20 years ago, and you know, we didn't make any notes on the drawing.

SPEAKER_01 26:37

If you're lucky if you have the drawing, yeah.

SPEAKER_02 26:39

Yeah, if there is a drawing, but you know, even if there is a drawing, you don't get 50 pages of emails attached to it that explain, you know, why why is this curve this radius? And why because it would seem you know intuitively obvious to do it differently. Why didn't you? There must be a good reason. And what I see happen oftentimes is you know, we use the phrase Dfam as an excuse to throw out original design intent. It's like, well, we don't know what that feature was really for. We defammed it, which is code for changed it in a way that might make it not work. Too often. Yeah.

SPEAKER_01 27:14

Uh again, just yeah, I mean, we this this is part of making it a more cohesive system, right? Is that we we stop a tradition, a well-trodden tradition, of throwing things over the wall. You know, the engineer is done, goes to the manufacturing engineer. Um, don't see it anymore. You know, it it's if you really want to reduce the time it takes to turn an idea into a part, you can't have those walls anymore. You have to make it a single journey. And that also means understanding what the previous people did and what the next people have to do.

SPEAKER_02 27:46

One of the things that's interesting to me about your new product, Whisper, um, and I'd be interested to see if you share this view, but in business, my experience is most of the issues happen at the junctures. You know, you just talked about the engineer throws it over the cube wall to the manufacturing engineer, and ideally you'd eliminate the wall and you'd eliminate those junctures. But what I've found is rarely does the problem happen within a function. Not never, but you know, seldom. I mean, the best when things on both yeah, if we have to transition across those junctures, that's where things go wrong. And it would seem to me like you know, we need Whisper to apply to, you know, strategic execution across junctures in every context, not just manufacturing. I mean, and not to blow up your product roadmap, but maybe you guys need to need to uh tackle that on a grander scale.

SPEAKER_01 28:37

I, you know, uh, I I believe uh you know, obviously people are working on context engines, much larger companies are working on context engines, and I think that in the manufacturing and engineering space, we may have a niche where it makes sense to have a kind of specialized tool set. But having said that, um, the the concept is still so novel that it certainly might apply. And, you know, we we're fairly embedded in the defense industry, and so we do get asked from time to time, hey, you know, can you can you have a look at this problem set? So uh I'll go wherever somebody says, I acknowledge that this is an issue and that you know we need to start working on it at this point in in the company. But yeah, it is it is a problem across industries. And my expectation is that it will be addressed in the next few months and years as people realize how valuable context is as a result of using AI for the first time.

SPEAKER_02 29:32

It it is key, not only obviously in manufacturing, but when I look at all of, you know, people say we're gonna leverage AI. And, you know, when you look at what companies are doing, one of the things that's different, I'll call it about this workspace disruption, is usually when there's a workforce disruption, it kind of starts at the front line and slowly the wave ripples its way up in an organization and kind of upper middle management generally isn't impacted too much. AI, completely the opposite. If you're the, you know, if you're the delivery driver who actually hands a product to someone, or you are the lineman that climbs a telephone or climbs an electrical pole to maintain electrical service, ChatGPT ain't gonna do that. But the you know, executive vice president of such and such that basically takes data from down here and turns it into a pretty report to send up to the senior executives, that woman or that man's job is becoming superfluous. And the problem is without a context engine, you know, unfortunately, the dis some of the decision making that used to happen in that woman or in that man's head before the data got fed up, now that's gone. And now we're just we're feeding data. What does it mean? Don't know. Why does the data say this? Don't know. And I think when people and organizations want to say, well, AI is too immature. I'm not getting good output, you know, the first thing you have to do is look at, well, what are you telling it?

SPEAKER_01 31:01

If you're giving it any context. I mean, 10 years ago when I was when I was teaching at Singularity, yeah, I I used to say engineers would be turned into constraint managers, right? And constraint is another way of of defining context, right? And and so that it's become clear that to me, this evolution doesn't mean that we are going to be, you know, making people redundant en masse, not necessarily, at least, um that you know, they're not necessarily superfluous. I'm working harder with the aid of AI than I ever ever have before. So if I'm anything good, I think possibly not. But what I do, but I do think that there's a a context that's gonna make better decisions, allow the AI to take grunt work off our hands faster, and allow the creative work of deciding where those constraints should lie to fall back to the the managers. And that's really what drives us in our industries, defining the strategic vision, right? Shoran, you and I are the same way, um, and then letting the the system execute it. And so my hope is that we can get there faster as a result of context.

SPEAKER_02 32:12

I pray that you are right, and I think it's it's a great opportunity to revisit the concept of best and highest use, right? Best and highest use of a person is not coming through Excel generating some report, right? Or tweaking the graph on the slide deck for the management meeting, right? That's best and highest use of nobody. Those are the things that should be automated. And then, yes, you do need a human brain involved in, you know, taking that output and discerning meaning, charting it against strategy, et cetera. But companies that fail to build context into and capture context in their workflows will be decades behind in their ability to harvest value from AI. That's my prediction.

SPEAKER_01 32:56

No, I'm I I I I'm 100% with you. And I I I mean, we we may be biased because we work in the manufacturing sector that um is read to be agile and and not limited by legacy, but I have yet to encounter somebody who's really throwing themselves against changing the status quo, right? This is not about you know everybody that I met is like, oh, you want to take away my ground work? Yeah, please take away my grant work. Like I don't don't like doing this work, right? I've tried to get everybody in my in my team to create a um a piece of software at least once a month, even the commercial people. Because, you know, I I think that understanding that now we can think about the kind of constraints that we're under in a different way. We can remove them. Uh, we can change our mental model of what it's possible to do as a result of AI really changes the playing field. I'm hopeful that nobody will have to go through dot sort of excel sheets anymore, but I I still see it happen. And when it does happen, I spend an hour coding up an automation and being like, might want to consider this in the future.

SPEAKER_02 34:02

Yeah. Well, I I can I can guarantee you that especially in large organizations, and this is this is the kind of back to the people side of technology, when leaders don't consider how to implement technology for the benefit of their people, the people are, you know, it's human nature, they're gonna dig their heels in. And so you have, you know, the person who crunches data out of some Excel sheet, you know, crunching it even harder to demonstrate how valuable it is now because they haven't been shown the benefit and their future role, like, hey, if we could get, again, maybe it takes an hour, maybe if we've never done it before, maybe it takes us a day, right, to write up an automation that'll comb what we want to comb out of this, out of this data. But oh my gosh, even if we had to spend an entire day, if we only did that once, you know.

SPEAKER_01 34:48

Yeah, and it, you know, there's a there's a really this this goes back to the days of RPA, um, and the and the fact that there's there's always been a move towards automating all kinds of different things. We have to be careful. We obviously don't want to automate something that takes a minute once every three months um with a day's worth of labor. But yeah, it it it's it's a thing that's been going on. It's now a powerful tool in everybody's hands to address those automation opportunities. Um, but we do have to make sure that everybody understands that that tool exists and uses it wisely, for sure.

SPEAKER_02 35:21

Agreed. So I want to get back a little bit to you know, let's let's pretend for a minute that you are listening to this podcast, and you know, you run manufacturing for some company, and your boss has said, Hey, Andre, we need to be leveraging additive better. And you just heard these two guys, incredibly handsome guys, intelligent, go on and on about how we need to think differently about additive. We need to, you know, think of it more on a project basis, you know, reduce the cognitive load. You've heard all this good discussion. Where would you start if you were that manufacturing leader?

SPEAKER_01 35:57

What would you go do based on what you've heard in this podcast today, as opposed to maybe the old approach of, well, I guess I'm gonna call Stratasys and hope that they have a machine that can make my I'm assuming at this point, you know, the conversation has moved on from how do we leverage additive in the organization and you're thinking strategically about how to make um additive core to your business, right? We've been through 30 years of this industry. If you're still interested, you're still coming to Justratics and whoever and thinking, hey, you know, well how what do I do with this technology? And you you you may want to start there, you know, start tools, jigs, and fixtures. But you every business is being pushed in a completely new way, especially in the last few years, right? Missile stocks are running out, and um, you know, car manufacturers have to shift their production locations and all kinds of things are happening. And so there are areas in your business that are very, very stressed. And the question is how can you leverage additive to to make that happen? And don't be afraid of, oh, but additive is expensive, oh an additive is this. No, how the ask yourself how can you make that happen? I'm very interested in projects going on at you know, the the missile companies right now, really identifying the the technology that works and automating it to the hilt to making sure that that technology can deliver to their specific use cases. It's phenomenal. Like the amount of effort and energy being put into that because somebody stands behind it with a vision that says, there's a need here. I know this technology can work, it's gonna require work to get that in place, but we're not afraid of doing to do that. So, really, what you've got to do instead of going to a machine vendor, you've got to go find yourself a partner. I uh had a friend who worked for a uh one of Alliance competitors, um Clear Aliner company, and built you know an Align competitive uh competitive um uh manufacturing outfit in Kentucky, Tennessee. Um I don't know, you know, uh out of nothing. And you'd see him walk around with this guy who was uh for all intents and purposes a a reseller, but really just a partner, a project delivery partner who'd help him understand what automation opportunities were required, who'd make him who'd help him think through the the project, uh understand his aims, um, help bring and surface new technologies to his attention. Um so you know, really the reality is find yourself a partner, somebody that you trust that you can work with that is gonna help you deliver this thing. Doesn't want all the cake for himself, but is happy to share it um and and wants just wants to work with you on an outcome-based basis. That's what I would say you should be looking for. But it really depends. If you're still looking for the how do I use additive in my business, probably not the right way of going about it in the first place.

SPEAKER_02 39:04

You know, what I would normally tell people is that no matter where you are, picking a partner before you are too wrapped around the part is a good idea because it could prevent five years of wasted money and time. That's you know, we to you I I hear you loud and clear, Andre, with your comment about well, hopefully people are thinking about how to use additive as a strategic advantage, not just how do we make some parts, but the majority of companies that we talk to, I I would say there's a there's a top, you know, 15% that are thinking about additive as a strategic capability. And then there's the middle, I would say 65% under the bell curve who maybe tried this, failed, and have now slipped back to can we just find a project that this can actually perform on?

SPEAKER_01 39:48

Yeah. Yeah, it's challenging, isn't it? Especially if you don't necessarily have a well, you know, you and I know that if you we're running companies, we're constantly distracted by a thousand different issues, right? And so trying to make a technology work in a specific area is always going to be hard uh to justify. But yeah, it's a two well to put it this way. I I I think that um many people, just the same way people aren't considering the um the the contextual collaborative data as valuable. I think people are dismissing additive opportunities out of hand quite often because they don't, you know, they've had those negative experiences and they they don't think that it's truly scalable. They just want one little example, as you point out. But really, you know, this technology has arrived and it's possible to derive significant values. Think about all the you know, I I started last year predicting that there'd be five use cases, uh, you know, of million parts each, um, that would that would arrive. And and really there's been more than that. Just think about the suppressors and and so forth that have really come to the market lately. It is possible to drive real value with these tools, not just marginal value. And if you want to persuade your C-level that that that this is a tool that can really last, then you have to th start thinking big, is my view, rather than and then those marginal opportunities. So that's where the projects come in, right? And that's where we generate value with the customer.

SPEAKER_02 41:20

Love that. I think I think there was some great advice in that. Kind of coming back to a little bit of your your leadership journey. What's something that you've learned in building uh a company in this space that you think others could benefit from? Stay a flu. Yeah, that's important.

SPEAKER_01 41:39

That's an important point. Um, we're really um we're really truly blessed with a um with partners, um, both internal and external, that have seen the value um and have uh worked with us. I can just think about the the team that we won the the work that we won the the award for at Rapid was uh work that we've done in the reverse engineering space is largely based on the feedback that we've received over the years from Boeing and the and the support we've received over the years from Boeing, um Boeing Global Services. And that's been really interesting, really understanding where their pain points are, listening really deeply, but their willingness to tell us and to pay us to address those pain points has been um uh really, really important. So you know, having both a team both uh partners that are willing to share, um, and a team that is willing to go continue to go on journeys um and address those pain points is is the most important thing that I've learned. Um, you know, I started this company as uh effectively a solo founder, but started with others, but then they all left. I you know, it felt like a lonely journey for a long time. But now with these partners and these, you know, the internal partners that we have, it's a completely different ball here. It's really uh uh really interesting. So lessons I've learned while building in this space, I think, you know, find yourself your right partners, come back to my earlier point.

SPEAKER_02 43:11

A lot of wisdom there. And you touched on something that we found to be so important, which is you know, a partner that will help bring a problem to you, but then is willing to write a check to have the problem solved. And that's you know, uh one of the one of the measuring sticks we use here is you know, how big of a problem is this? If they're not willing to write a decent sized check, we're not working on the right problem.

SPEAKER_01 43:36

I think that's uh yeah. It's been it's been it's been interesting, you know. One of the things that we we did when we transitioned from from IP protection um to sort of workflow approach was to say, look, if somebody wants a feature badly enough in our software, they'll pay us for it. So we estimate that eighty five percent of all our features will be paid for by external customers, and that's being really As a result of, you know, us saying, no, we're not going to build it yet. We know that this is important. We're not going to build it yet. Because we want somebody to guide us and tell us exactly how it should be built. You know? And too often we build things that that just um just weren't ready, um, weren't ready for. So I I think listening, be having customers that are willing to pay for it is is a very important indicator for for how important that that feature really is, whether it's some new machine or a software feature.

SPEAKER_02 44:28

Yeah, it's that how do you how do you validate, I'm gonna use a generic term, but product market fit or feature market fit before you build it.

SPEAKER_01 44:36

You get somebody to pay you to do it, right? And that was also the interesting thing with Whisper, right? We did we did it with a group of partners that were we put on a steering committee and we said, You believe in this problem, right? Yeah, they did. Well, pay us some money in advance and pay us more when we when we built for it. And this was for this first time I've ever done that for a completely new product. You know, they had no reason to believe in us whatsoever. It's fascinating to see that it's actually working. And I think, Sean, we can we can speak from like a a high chair about this stuff because we've been in the market for a while, you and I. It's it's easy now to generate the trust necessary to make those kinds of or get people to make those kinds of bets because we've been we've been around for a while. We can tell people we know what we're doing, and you know that we can deliver and we will deliver on those promises. Otherwise, people wouldn't put their name down for, you know. And these are people's professional features as well, right? They're gambling on you. Nobody's ever been fired for hiring IBM. Now it turns out that they trust authentic enough to not be fired.

SPEAKER_02 45:36

Yeah, you know, and it's amazing you talk about your story, and you know, maybe we'll do a I think it would be interesting to do uh another podcast and simply talk about the importance of partnerships in building a business, but you talked about Boeing early on, you know, B9 Creations. We had a moment where we had a customer uh write us a multiple six-figure check to build them something to a specification we'd never built a product to. Uh, and at the time, I think we were a 12-person company. And that, you know, finding someone who believes in you enough to say this is a big problem, or in this case, it was a big opportunity. We need this, we're trusting you to deliver this. And then when you deliver and you hit it out of the park, um, it inspires not only confidence in the customer, but the confidence in your team to say yes to things with big unknowns, you know, like this focus group around Whisper, right? To say, hey, we all we see this need for a con I love that phrase, context, Angela. I'm stealing a lot of your language today, Andre, by the way. What we're shouting here, yeah. But, you know, to say we all agree that leveraging AI requires an enormous amount of context. Can we find people who are committed enough to having this problem solved to write checks to pay us to do it? And that's you know, that's how companies are built. I always tell people the the number one way a small technology company goes out of business is by making products people don't buy. And when you can get customers to pay for it before you build it, you can solve that problem or inoculate yourself against that potential uh disease in advance.

SPEAKER_01 47:11

Well, small companies have many disadvantages, right? Um so we have to find advantages that that compensate to that.

SPEAKER_02 47:18

Absolutely. And you know, I always tell people that that we're uh exploring partnerships with is you know, the advantage that you have working with someone like a B9 Creations, or I'm guessing similar to your company, is if you've got a big enough priority, you can make it my priority. Right? If you're calling Stratasys, you're probably not gonna get Yoav to significantly change what's on his to-do list for the next 90 days. But if it's a if it's solving a problem that we believe is key to the future of the industry and seeing manufacturing thrive in, you know, this very hostile global environment. Um, you know, if if the customer's committed enough, we'll we'll make the whole company committed to it. Um it just has to be strategically, you know, the old phrase strategically aligned and tactically defined.

SPEAKER_01 48:09

Yeah, good phrase. See, that's one I'll I'll take from you. Yeah.

SPEAKER_02 48:13

Excellent. Well, Andre, this has been fantastic. Yeah, I've really enjoyed it. Thank you. I don't know how many how many podcasts you consume on a on a regular basis. I learned something over the weekend. This is a little off-the-beaten path, but you know, we're gonna give Nicole a to-do item. You know, I watched the uh Theo Vaughn podcast with Ella Langley, and I'm convinced that for additive advantage to get to the next level, if we could get Ella Langley on here, I think we could open open this podcast up to a whole new realm of viewers and start teaching a lot more people about 3D printing.

SPEAKER_01 48:47

Um so we're definitely an aspect of having a broader audience, that's for sure.

SPEAKER_02 48:52

Yes. I don't know how much American country music you listen to, Andre, but if you haven't checked out Ella Langley, uh somebody listened to this now. Yep, yep. You're gonna have to fire up Spotify and and check it out. The uh the podcast she did with Theo Vaughn was pretty hilarious. Um, you know, you see a lot of artists who you know, they cut an album in a studio and you have auto-tune and all these things. She's in the in the podcast venue with him and she's just singing and uh love the authenticity and the bravery. Um and yeah, it was pretty cool. So anyway, future additive advantage episode with Ella Langley. Nicole's gonna work on that. Um well, have a great week, Andre. Please stay in touch. And I I've I've made a couple of notes here about maybe a future episode we need to come back and do together.

SPEAKER_01 49:39

Well, it sounds like you've got your your rooster all all sorted, roster all sorted there for a while at least. It's been really fun, uh, Sean. Thank you for for the chat. Really appreciate it. Outstanding. Have a great day, you too. Bye.

SPEAKER_00 49:52

What a cool conversation to be able to talk with someone whose philosophy about project-based additive looks similar to ours. And one of the things that really stood out to me was his idea about shifting from printing parts to delivering projects. Do you think the industry is ready for that shift?

SPEAKER_02 50:08

Well, I don't know if the industry is ready. I think customers are ready and actually demanding it. Because when you think about, you know, implementation of whether it's additive or you're going to stand up a new milling line, anything like that, it's really a project to bring that capability online. And I think when you when you wrap around what it takes to execute a project, so clearly defined, you know, scope, schedule, budget, expectations, all those things. And then this concept that, you know, you might have a project team that is different than the run it when it's done team. I think it's another great example of how we don't need to reinvent the wheel to make this work. We need to take some best practices from other places. Andre, as he spoke about this, he talked about, you know, at the end being able to kind of, you know, hand the thing over to the customer. Parts are coming out, systems are automated, QAQC processes are in place. I mean, that's what people want. And I'm going to steal his phrase. You know, they don't want to deal with the cognitive load of what it takes to make all that happen. Again, I think I don't know if the industry is ready or not. Customers are demanding it.

SPEAKER_00 51:12

Aaron Powell Yeah, that that brings a point up too when you talk about being able at the end to hand over a whole process. He talks a lot about this island problem between machines and software and and data. Where do you see that breaking down kind of in the real-world additive environments that we interact with?

SPEAKER_02 51:30

Well, you know, it it's interesting that when we started out, I I asked him before we started recording, I said, are you okay if I say that I don't really think of authentizes as a software company? Or am I gonna, you know, trouble anyone if I make that statement? But I loved his perspective because, you know, he talks about the islands of hardware, materials, you know, software, or even we got into, you know, the generically we tend to say, well, if it's software, you know, it's I hate the word ecosystem, but you know, it's this ecosystem. We talk about MES systems, you know, slicers, all the different pieces of software, which are really still islands because they're not integrated. There isn't an overarching workflow. And until you put that together, you know, I think part of his point, and certainly my belief, is that's where additive has the opportunity to outperform traditional manufacturing. Because if we're honest, you go into most factories, that entire workflow in a traditional manufacturing context is also largely made up of islands, even though a lot of that technology has been around a long time. For most people, if you think about going all the way from the system you use to order your incoming raw materials to the system that your customer uses to place an order and receive whatever that finished product is going out the door, that's not integrated. Now, there are certain parts of it you could make an argument, maybe don't need to be, but there are a ton of parts of that that should be integrated. Again, here's an opportunity for additive where realistically, most of the time, additive is not going to be the easiest way to do something. But if you can also, you know, leverage additive, whether it's for the flexibility of distributed production, all those traditional reasons, and have it be the most integrated production capacity on your floor, that's a tremendous opportunity. And I thought he really keyed in on that when he said, you know, he said, actually, I asked him, you know, pretend you're the leader of a manufacturing organization who's been tasked with implementing an additive. And he said, Well, gee, Sean, I hope nobody's thinking of it like that anymore. I hope people are are thinking about, well, how do we better integrate additive into our strategy? And we had a nice little back and forth over. I think those of us that live in the industry would hope everyone's there. In reality, I think most people are not there because that hasn't been their experience. Again, to your point about islands, oftentimes they've had trouble just getting one island stood up successfully. And then, you know, how many projects have you seen where it's like, ooh, we got the part designed the way we want it? Hey, we think we even found a material that were worth will work. And it prints on this printer. Now we want to make a hundred thousand of them and it doesn't. It doesn't work at scale.

SPEAKER_00 54:01

Yeah.

SPEAKER_02 54:01

So um, again, real opportunity in my mind to bring technology to the production floor in an integrated fashion. And it just happens to be additive because that's where so many people are investing right now.

SPEAKER_00 54:14

Mm-hmm. Yeah. I he made another good point too, to focus on the context and the why behind the decisions. Why do you think, or do you think, you know, companies are or aren't doing this? And what are kind of the consequences of not just the connecting of islands, if you will, but understanding the context of why you made the decisions you did.

SPEAKER_02 54:33

Oh my gosh. I would love to say that everybody is working on this, Danny, but that would probably be that would probably be a little bit Pollyanna-ish of me. Hopefully the motivating factor would be this. If you work in a manufacturing company today and you haven't heard this conversation, I'll send you a dollar. Just message me with your email and your name and your address at the end of the episode. I'm gonna send you a dollar if you can honestly say you've never heard this conversation. Hey, here's a part. We need to move the tool to a different molder or we need to set up a different, you know, we're gonna move this from this milling line to this milling line. Does anyone know where the drawing is? Well, hey, good news. We found the drawing. Does anybody know why we designed it this way? Like, why is this feature like this? Because I could reduce my setups if I could eliminate this radius. I could have one less setup in the production process. Well, you know, Bill designed that, and Bill retired 10 years ago. We could probably call him, but he's on this fishing boat off the coast of Florida. Now, again, maybe there's a few variations in the version that you've heard, but I will send you a dollar if you can tell me you're in a manufacturing company and you've never had a conversation like that. The point of all this is context is so important. And it's part of why, in my mind, oftentize isn't just a software company. Software is certainly the, I will say the medium that they use to do what they do. But when you think about, you know, we're, I think at this point we're comfortable hearing people talk about large language models, AI engines. He talked about a context engine. When you can document, you know, why is this feature this way? I mean, do we really need this radius here? Because it requires an extra setup. And that costs me more money in terms of what I machine off, labor. I need to know why that's there. Otherwise, I'd quit doing it. When we can document those decisions all the way through the design and pre-production process and, you know, first article and then the revisions that get made, and we're capturing all of the back and forth, whether it's in Slack, Teams, email, et cetera. And you think about, you know, AI agents now taking meeting notes. We have the ability today, not at some distant point in the future, but today to capture all of that. And then five years from now, when that question comes up, they're like, oh, well, Rachel designed that part. She's moved on to another job, but let's leverage our context engine to figure out why that radius is there. Well, if you, you know, getting value out of AI is all about giving it context. If I just upload, you know, a scan of this part and say, why is this radius here? I'm going to get a garbage answer. But if I built a context engine, I have the opportunity to reap tremendous benefits in the future, but it's a little bit like planting a tree. You know, if I don't plant it today, I won't reap the benefits in, you know, five or 10 years. Same thing with this context engine concept. And it's one of the areas I think Andre and his team are way out in front of most companies, which is where they need to be. But if you want to get better value out of AI, figure out how to give it more context.

SPEAKER_00 57:27

Yeah. What a what a great spot to end on. The idea of are you powering the context engine of your business? I love that. Incredible conversation.

SPEAKER_02 57:35

So we do have a couple of other things. Nicole, can you give us, is there anything that you can share about us getting Ella Langley booked for the additive advantage? She's shaking her head. I don't does that mean does that mean there's nothing we can share, just nothing we could say publicly? I think, I think she just means we can't say anything publicly yet. But anyway, we'll keep you updated. I don't know why country music and additive manufacturing haven't really partnered together up till now, but we're gonna do our best to break that barrier down.

SPEAKER_00 58:02

Follow the Additive Advantage podcast on Apple and Spotify and watch full episodes on YouTube. If this was helpful, leave us a five star review on Apple or rate us five stars on Spotify. It helps the show grow. And you can follow us on LinkedIn for new episodes and updates. Thanks for listening.