James Theuerkauf, Emily Lane, Bret Schnitker
July 15, 2025
James Theuerkauf 00:05
We also very because we deal with pretty confidential information from our customers, right? We have the entire transaction history, the entire image history. We have very, very strict security and privacy protocols where there's no cross contamination of models across customer bases.
Emily Lane 00:35
Welcome to Clothing Coulture, a fashion industry podcast at the intersection of technology and innovation. I'm Emily Lane
Bret Schnitker 00:42
and I'm Bret Schnitker. We speak with experts and disruptors who are moving the industry forward and discuss solutions to real industry challenges.
Emily Lane 00:51
Clothing Coulture is produced by Stars Design Group, a global design and production house with more than 30 years of experience.
Emily Lane 01:00
Welcome back to another episode of Clothing Coulture. We have an exciting conversation in the technology and innovation side of our conversations today. This is somebody that we met at the Kornit Connections event just a few months ago, and was one of our favorite conversations that we had. So we knew right away that we needed to bring James Theuerkauf to the table. James is the Co-founder of Syrup. This is an amazing company that was founded with the goal of solving the waste epidemic plaguing our industry. They do this by combining AI and all kinds of other amazing technology that I might not actually understand, but hope to get a better understanding of it today.
Bret Schnitker 01:49
Em has a tech curse, don't ger her near any of your software.
Emily Lane 01:55
James has a degree from Harvard, another one from University College in London. We are talking about somebody that is really smart, and I'm just so excited that he has decided to put his intelligence to work in our fashion industry.
Bret Schnitker 02:13
How the hell do you go from Harvard to retail? I mean,
Emily Lane 02:16
it's exactly my first question.
Bret Schnitker 02:18
You could be a doctor, a lawyer. Your parents probably had these grand plans for you. And it's like, I think I'm going to do retail analysis. How did that land?
James Theuerkauf 02:26
Right? You're way too kind. Thank you for the introduction, on I mean, you know, we I prior to going to Harvard business school there, and, you know, I'd worked at McKinsey for a number of years, and I'd worked very closely with apparel and fashion brand manufacturers, and I was just fascinated by both the magnitude of the problem of overstock and understock, and also the the very basic nature of how these extremely consequential decisions are being made, the T Y, l, Y, L, L Y, average curse that our industry lives in, and that was the motivation Bret Emily, to get started here.
Emily Lane 03:12
That's great. Well, we definitely understand those challenges. And in fact, Bret, you're commonly quoted for saying to our clients, especially those that are new to the industry, look, you either over by you under buy, you never buy the right amount.
Bret Schnitker 03:27
That's the first thing that I was told in retail. Like, I become this little kid buyer. I show up and I've got all these spreadsheets, and they're like, you either over by you, under buy, you never buy the right amount,
Emily Lane 03:36
right and so looking at that equation, if you over buy, that's excess inventory. You've got things like close outs and other things of that matter, if you under buy, that's lost revenue opportunity. So I imagine, as you have looked at this equation and are seeking to solve the problem, do you have any sense of like, what that what that lost revenue really looks like for retailers,
James Theuerkauf 03:59
totally and both are extremely expensive, as you mentioned, right the under buyingstock out lost sales in reduction of customer satisfaction, which we can correlate with reduced customer lifetime. Value very expensive, as is the flip side over buyingright? And that then leads to unplanned markdowns or overstock at the end of the season, cash tied up, cash tied up in unproductive inventory. So both are very
Bret Schnitker 04:25
Mountains of clothing in Chile that people are climbing now,.
James Theuerkauf 04:30
Billions and billions of items destroyed every single year or ended that end up in landfills or are burned. So it's really a tragedy, because it's really, really, really hard to get right. Right? I mean, to answer your question, Emily, on what's, what's the the magnitude of of of lost sales and under and under buys and stock outs, it's probably around 20% of revenue for the for the average retailer that they're not measuring today, by and large, right? It's. It's that you're quite hard to measure, because you can measure out of stocks, but they need to understand, how do we, how do we get from out of stocks to lost sales? So there's, there's kind of some math. You can do some with assumption space, but it's probably around 20% of so it's very, very significant.
Bret Schnitker 05:14
And then we've complicated the landscape. When we were in Paris at the AI conference there, there was a, it was a tech group, Israeli based tech group, I guess, based out of London also. And they're like, the big challenge too is that we've complicated the landscape. Because instead of just brick and mortar in the old days, like when I was there, right, you're now omni channel. You've got online you've got multiple stores, you've got multiple warehouses, distribute distribution points globally. So how do you manage the placement of the inventory in those different areas? And it becomes a really complex conversation. And hence, the solution in the past has been just buy more goods to cover more locations, which creates more markdown
James Theuerkauf 05:57
Exactly, exactly right, right? And particularly with, with these, these omni channel networks, becoming ever more sophisticated, per se, where the store acts as a mini warehouse from any fulfillment center with, you know, buy online, pick up in store, buy online, ship from store, buy online, return in store. If you do the combinatorics, you know, it's the complexity doesn't grow linearly, it grows exponentially. And so that's, that's really where, you know, we see AI becoming an incredible force multiplier and supporter for these decisions, because we get ever more sophisticated models that can tackle this ever, ever increasingly complex problem space.
Emily Lane 06:37
Yeah, you know, in looking at all of the various levels of problems you've already addressed, lost revenue. You know, another thing, of course, that we talked initially in our conversation is the problem of dumping the mountains of clothes that are going into these landfills every year. Do you have a sense on the markdowns? Yes, overall, the impact on that side
James Theuerkauf 07:04
totally, for every 10 units of clothing that are produced, three get sold at the originally designed price or a promotional price that's planned, three to four get sold at an unplanned markdown, ie a liquidation or something that's close to that, and then three or four never get sold, ie end up in lqndfill. So it's an industry that you know, in order to sell three, you're producing 10.
Bret Schnitker 07:34
So it sounds like a really crappy ratio, doesn't it?
James Theuerkauf 07:39
Correct. Apples not doing that, right? Apple, Apple's not producing 11 to sell three, right?
Emily Lane 07:45
Wow. Okay, so you've got some complicated math to solve, Tell me about a little give me an overview of how your how your solution.
Bret Schnitker 07:55
Show us the exact formula. So everyone solve this issue.
James Theuerkauf 07:59
I wish it weren't as easy as that. Yes. So what do we do? You can think of us as effectively being an intelligence layer and decision support framework that gives recommendations to brands and retailers. And the way that we do it is by feeding the brain, the Syrup brain, with tons and tons of data, and that data is both first party data from our brand and retail partners, so things like their transaction history, their inventory history, their product attributes, their imagery, their marketing and promo plans, et cetera. And we then complement that with a vast and ever growing library of third party market data. So think of these things like weather and weather forecasts and hyper localized events and and social social media data, so that when we're doing predictions, we're not just relying on history, but we're looking at things like, oh, you know, it's, it's the fourth week of June, and it's hotter in New York than it typically is. And also this event is coming up, and we're seeing a hyper localized trend in a certain color, and you start correlating all of these factors in this very sophisticated, natural neural network of a modeling approach that's then able to give you predictions that follow a probabilistic curve. We're not saying it's one unit you're going to sell. We're going to see the likelihood of you selling one unit is this. The likelihood of selling two is that. And we can use that sort of forward looking view of the world to overlay our customers, business rules, business logic, Target, metrics, etc, etc. So when we're giving them recommendations on how much to buy, how much to rebuy, what to mark down, or what to advertise, or how to allocate to stores. They're following both this, this, this sophisticated science, but they're also following their their particular business rules, any idiosyncrasies in their business, so that. You know, the recommendations that come out are super tailored to some to their needs.
Bret Schnitker 10:04
So you're really breaking that model of history. Talk predicts the future, right, in old retail, it was always you looked at last year and you predicted some percentage of growth and new store, you know, impact, and you know, how are you going to market a little differently, and if there were any unique events, but that was the data we had to go on, and then you kind of threw dice at the table and hoped it worked.
Emily Lane 10:26
Yeah, I like how you're able to combine so many layers of data to really inform it. My question to you is, are are people at all concerned about the invasiveness of that data. I mean, I understand you're using it to do better by the world and do better by business, but do people get a little like, Oh, this is kind of creepy.
James Theuerkauf 10:49
It's a good question. I don't we haven't actually gotten that much of this. And I think part of it is just the consumerization of AI, you know. Think we all use, you know, different llms through different tasks for us, from very basic ones, like, you know, rewrite this email for me to, like, help me with this and that. So it's become such commonplace from a consumer adoption standpoint that, you know, if you have this chat bot that you can, you know, give an email and it rewrites it in four different ways. But then you go to your most important business operations, you're like, Oh, we're still using LY over L, L Y to predict TY. That's nuts. So, so we haven't, we haven't gotten that, that, that much of it. And then we also vary, because we, we deal with pretty, you know, confidential information from our customers, right? We have the entire transaction history, the entire inventory history, we have very, very strict security and privacy protocols where there's no cross contamination of models across customer bases.
Bret Schnitker 11:50
How is that working today? Because one of the big conversations when we were in Paris again, was the learning lessons of people that didn't understand that. Once you put it out on AI, everybody sees it, you know, how do you and that's the big conversation. How are you locking down sensitive information? And so, how is that happening with you? And the other thing too is that, you know, whenever, and this was a big topic of conversation too, is implementation. You know, whenever people implement new models for inventory and everything else. Generally, it's very costly, both in implementation, but it's also costly in terms of the time it takes to implement. It, the challenges with the shutdown of the warehouse, the confusion and all of that. So there was for the for the groups that were really pushing kind of their new products. It was ease of implementation, painless integration. What's happening at Syrup in that respect?
Bret Schnitker 12:46
thats insanely fast?
James Theuerkauf 12:46
Totally, totally, just very briefly, on your how do we how do we do it's based on the deployment and that there is no conf the actual model might be the same for everyone, but it's not learning on like our Gap model doesn't learn from our Abercrombie model, like it might be the same kind of features that we're using, at least the feature stores are the same, but there's no kind of cross data learning in in, in that sense. And then on to your point on an implementation, I think that's absolutely critical. And it's, it's, it's my biggest gripe with, you know, the retail software industry of just how outrageously bad implementation processes are and how expensive, yeah, exactly, expensive, painful, slow involved. It's, it's, you know, it's really frustrating to see. And you know, we often when we quote our implementation times to customers, which on average is three to four weeks, we double that as in when we communicate, because people don't believe us, right? They're like, so
James Theuerkauf 13:45
Exactly, what are you? What are we missing? Like, what are you not doing? That can be that you can be someone's like, no, it's just like the software is built in a better way, like it doesn't take 18 months to do this, but, but it's, it's an industry that, yes, that's, that's been beholden to, you know, systems integrators, very, very, very long implementation times, extremely painful failures and implementation that that are outrageous, kind of in the, you know, the day and age that we live in, in software, where this shouldn't be as hard.
Bret Schnitker 14:23
Well as quickly as things are evolving, you have an 18 month implementation, you're already dated. This, the technology could be already challengingly dated. And that's the reason that, you know, we, we've talked to some billion dollar retailers, and they're using these crazy old systems, and they're just like, we just, we look at the cost from implementation, and we look at the time, and we're just concerned. We spend all of this money, and then all of a sudden, allocation technology is gone, and, you know, it's completely evolved. And so I think it's such a weird landscape today because, you know, with the with the advent of AI. And the evolution actually AI has been around a long time, but the evolution of AI technology is moving so quickly,
James Theuerkauf 15:07
I completely agree. And I think what we're going to see is the future is going to be a lot more modular than it is today, where, you know, we're not going to live in a world where you buy a single suite player that then dominates, you know, your entire process, and then you're stuck without like suite players will continue to exist and will kind of be the foundational elements. But I think it's a democratization of the landscape where it's a lot easier to kind of plug things in and plug things out, and where at the end of the day, you as a vendor have to be measured on value that you're generating, you value that you're generating, great, right? And then you should, you know, you have to earn that position. But you know, being able to be entrenched and impossible to pull out, I think that's that's going to go away in the future.
Bret Schnitker 15:47
Well, you talk about that, I could say the words,
Emily Lane 15:49
democratization
Bret Schnitker 15:50
yeah, right. I say, can't say that word. You know, Python made a big launch because it was this open kind of community of evolution of thought, and there's been a lot of technologies we've been using with that kind of open space landscape. Do you think that that's the future to literally all these different kind of people openly collaborating on on systems, and how do you keep security there at the same time too,
James Theuerkauf 16:16
totally. And I think, I think, I think one is definitely that collaboration, and open source is, is, I think, uncontestably a big trend that the developer communities, it's here to stay, and it's ever growing. I also think that in, you know, in a world where, with, you know, AI, we can, we can create front ends in orders of magnitude faster than we've ever been before. We can create integration pathways much, much faster than we've ever been able to do before, these old moats or stickiness points of you know, we have this integration that no one else has. We have this work for them. Those are they're not going to be moats anymore, because it's really easy and fast to do them, to do them again, right? And so what it really comes down to is, what's the value that your software is generating for the for the for the customer, which is what, whatever, which will all software should be based off. What value are you writing?
Bret Schnitker 17:14
Yeah, where's the ROI? So you talked about how quickly the implementation occurs, and that's, that's where you would expect it to but that makes sense today, right? So, when do they start seeing results? How long does it take to, kind of pull information together, and how quickly do they see, start to see results?
James Theuerkauf 17:33
Yes, pretty fast. In, you know, the the there's a few different use cases that we've got, like, there's, there's two, particular that I read quick ones, a kind of a distribution allocations use case, how do you get product from a place to the right place? In fact, you could be warehouse to store, or could be distribution center, fulfillment center. Could be, could be, kind of any of those. And the view, just because you're doing those daily, typically, or at least once a week, you can see, you can see real impact within a matter of a few weeks. Another use case that we've got is is giving recommendations on which product to advertise on, and that's also immediate. So you also see value really, really quickly. There with a use case, like a buying use case, we're giving recommendations on how much to buy or to rebuy. Typically, it takes a little longer to realize value because you have that lead time production cycle. Yeah, right. So, and
Bret Schnitker 18:22
you've got some anniversaries, that means that both buyers and planners have about two weeks to six months to still have their job, until they're fully you're fully integrated.
James Theuerkauf 18:31
the the I'm quite glad to say that, I I think this is true. I mean, it's definitely the norm that people don't typically lose their job, or, like, there's no reductions inforcue
Bret Schnitker 18:43
they always say the job's evolved,
James Theuerkauf 18:44
but those exactly correct, correct. And I think more than that, you know, what we're really proud of is a lot of the people that we work with and that have really become Syrup super users, have gotten, you know, fantastic promotions, you know, to directors to senior directors, VPS, SVPs, and that's what fills with so much joy, right? Just like it's not, Oh, great. The software will cut 20 of your workforce. You're gonna get a promotion, right? Because you're gonna the core metrics that you're responsible for, that you're measured on, are going to improve. So it's something we're very, very proud of.
Bret Schnitker 19:14
And they get to see sunlight, maybe a vacation once a year, which has never occurred in retail.
Emily Lane 19:21
So does your system kind of trying to envision it in my mind? Does it? Does it kind of look more like a dashboard, like here's all the information, and then they go off and use their whatever tools they're using to report by all those things? Or do you have those folded into your technology as well?
James Theuerkauf 19:39
Yeah, it's the latter. So think of us as, really, as a as kind of a co pilot decision support system that gives you that gives you recommendations. So the core output that we're delivering to you as the you know, merchandiser, planner, buyer allocator, demand forecasters, this is the answer that the system's coming. And coming up with and then there's multiple ways for you to interact with it. Mean you can just outright accept it, and that will then trigger a transfer order or a purchase order into your systems directly. Or you can or you can modify it, right? You can modify inputs. You can say, we're going to like, we're looking to reduce our weeks of supply, or we're looking to increase our weeks of supply. And what does that look like? Or, you know, there's a, there's a store specific promotion that we're doing. What does that mean for the forecast? You can interact with it that way. Or if you just want to overwrite it, you can also always overwrite it, right? The one of our product leaders has this phrase that I they absolutely love. He says, you know, the the Syrup, the AI has no ego. So if you want to change things, you know, like we might, we might think as we they the AI might think that the you're gonna sell 100 units of this, of this Chino pant in New York next week. But if you really think you need 400 then go and send 400
Bret Schnitker 20:58
right? Everything pops up and says, Are you sure?
Emily Lane 21:03
That's interesting. Do you have, have you taken a look at those success rates of those that are like, you know, I'm following my intuition because I know certain things that AI doesn't yet. And have you seen like the success rate of one versus the other?
Bret Schnitker 21:17
He's selling Syrup. So everyone followed their intuition has been fired. Everyone that is bought Syrup followed, Syrup is all promoted to president.
James Theuerkauf 21:25
Exactly, exactly the the I mean, as a general, as a general rule of thumb, this is a little bit like blackjack, right? In the sense that if you're following the rules and the probabilities, on average, you are better off. And so kind of generally find like, if you follow what the science says about you, there is an exception that we found, which is for the real high sellers or for extreme occurrences. We do find this is, call it the top 5% the top 10% of your assortment. We find that subject matter experts and merchandise planners allocators do improve the recommendations. And it's typically because there's something that they know about the product or a or a campaign or an influencer that there's something that the model doesn't pick up. And so by modifying those things, they actually do improve the the model output. The other component that you know, merchandiser, clients, allocators are incredibly important for is actually setting the ground rules for the model right. Actually saying these are the core metrics you should be targeting or visa. And that's indispensable. By setting those up right, we see vast performance improvements. And so it's going a bit from like no need to you managing the outputs, but you manage the inputs right? You manage the rules of the game and the targets, and then let the system do what it's doing, and then override kind of, you know, nuanced or very particular cosmetic components. That's where we see the best.
Bret Schnitker 23:00
So our marketing plans, everything being dumped into Syrup at the front?
James Theuerkauf 23:04
Yeah, if we can and marketing and promo plans are typically the hardest to get, and they all want to impactful.
Bret Schnitker 23:11
Yeah 100%, that's what I wondered. I mean, those would be the things that you would see. That would be the things that would adjust this, because, notoriously, that's the worst organized area. In some organizations.
Emily Lane 23:23
Well, they don't necessarily align. They don't communicate.
Bret Schnitker 23:26
Well the big sales and the big marketing things, again, ancient history, but we could align on we knew that last year we were going to run this big Father's Day sale, but there were all these adjustments that happened on a daily basis, or more organic marketing efforts, and if you didn't communicate them into the system, the system wouldn't know that that would have an impact on sales, therefore that would affect the outcome. Yeah, you talk, you know, there is this massive impact right now that you're talking with Syrup, with the retailer, in one of our seminars that we're talking about. There's this whole upstream component that, you know, manufacturing sides, they never have the financial wherewithal. Technology is always lagging as it goes up there. But we live in an interconnected world where, you know, piece goods, allocations, purchasing, planning, and today, if tariffs continue the way they are. 25% of the world's production is gone, and now all these other factories are getting full and resource allocation in factories. This is a complicated landscape that can impact the ability to fulfill like your model would tell them to fulfill. How far upstream is Syrup going?
James Theuerkauf 24:39
Yeah, and I could not agree more with that assessment. Bret The we've started working with brands and retailers, but we also have a couple manufacturing partners, and MAS, one of the world's largest manufacturers, and S division run by your Bret Ballantine, who you should talk to as well. He's phenomenal. Well, but we work very closely with with him, exactly on this, on this component, right? That, yes, it's great that we can see demand sensors from consumers and from brands, but that the real pot of gold is being able to link that back into your supply chain,
Bret Schnitker 25:16
Your entire supply chain, especially today, exactly because the model could say, go buy it, and you go to the factory and, like, we're booked up for six months.
James Theuerkauf 25:24
Exactly. Versus, if you can then help the factory, say, here the raw materials that we have to pre position here, the manufacturing capacity.
Bret Schnitker 25:32
way in advance. Like, here's the here's the beginning inkling, and start going into knitting and weaving and dying and and, and the biggest complaint, or the biggest challenge that most manufacturers are having is, look, we don't have the resource of the the the investment to be able to do that, but if it's driven by the retailer, and to your point, it sounds like the implementation is not so costly or could be shared by the retailer, the outcome is astounding.
James Theuerkauf 26:03
I could not have said it any better myself. That
Bret Schnitker 26:05
But your accent sounds a lot better. Yeah, if I could say it with your accent, I would be really convincing I'm sure.
Emily Lane 26:14
who's a good fit, like, how big do you have to be?
James Theuerkauf 26:18
Today, So today, we work with its enterprise, brands, retailers. On the smaller end, it's we do work with a couple brands that are 25 to $50 million of revenue. I would say our typical customers probably more $100 million plus, you know, some are north of $10 billion that we work with, probably the the average brand work with 100 million to couple billions, probably,
Bret Schnitker 26:44
and you get, like, what, 5% of every deal. I mean, you get a commission of all the savings
James Theuerkauf 26:50
we, we typically don't price based on on on performance.
Bret Schnitker 26:56
But maybe should that sounds like a really good deal, man, a billion dollar company. And you say, yeah,
James Theuerkauf 27:01
exactly the, I think they've also figured that out, so they'd rather know what the cost is up from. So it's, we typically, you know, it's based on inventory under management. So there's sort of an implicit component there.
Bret Schnitker 27:16
Are you driving this whole upstream model with these larger retailers?
James Theuerkauf 27:20
Yeah, we are. And we're finding, you know, the we started the company five years ago, and we've always had this vision of, you know, this end to end upstream component being where we want to end end up. And a few years ago, there was a lot less receptivity today that than we see today. And you know, we're seeing, we're seeing from both sides, actually, both from the manufacturing side as well as from the brand and retailer side, this desire to what you just outlined, Bret right, of being able to not just look at it from a finished goods perspective, but actually go back to your supply chain and look at the really end to end perspective,
Bret Schnitker 27:53
that's so exciting. Yeah, any good case studies?
James Theuerkauf 27:58
The yes we, we are. I don't think I don't think I can, I can, unfortunately, mention them, probably quite yet, but they are. They were about to publish a couple that that that are going to be really good.
Emily Lane 28:07
Yeah, we'll be on the lookout for that. Okay, silly question. Why the name Syrup?
James Theuerkauf 28:14
Oh goodness, Emily. It's extremely lame. But we, we, we, we started the company at at Harvard, it was COVID, not that much to do in Boston. So we end up going to Vermont a couple times. Also not that much to do in Vermont, wonderful place, but not that much to do. So we ended up going-
Bret Schnitker 28:30
Skiing in the winter.
James Theuerkauf 28:31
That's right, skiing the winter, we end up going to all these syrup makers, or at least a handful of them, and it's their process is amazing, and it's fascinating, but the way that they speak about the process is this combination, or these ones we talked to is a combination of art and science. You know, this art of Syurp making. The signs were like, this is a little like merchandising, right? There's art and science, and we're going to be syrup techs. We're going to bring the technology to this process and, and it was supposed to be a placeholder name, but, you know, then no pun intended, but it's stuck.
Bret Schnitker 28:59
I was going to throw something sticky in there. I'm a dad. You beat me to it. Yeah? So I always say evolution, not revolution. That's what I live by. Revolutions are bloody. You always want to be evolving. What's the next evolution in Syrup technology? What's on the forefront that your you know, your competition can learn from
James Theuerkauf 29:23
I think there is. There's a number of things I completely agree with this evolution not revolution. In fact, it's the same that we say to our customers, right? Like you don't need to turn off this light switch and go from zero to 100 but you
Bret Schnitker 29:36
can just adapt. You always have to be moving Exactly.
James Theuerkauf 29:39
Exactly you know what's worked extremely well for us from a change management perspective. Management perspective, is going actually to, you know, not starting with entire business, but going to a few planners who are really excited about AI, and have them be the first to test this out, see the results, and then they, they, they become the trainers internally, right? And there's been this amazing, amazing process of, you know, ever. Evolution, not revolution. Think for us it's it's similarly like, there's just all of these kind of expansions that we're doing across the product suite, whether it's within the allocation space, within the demand shaping space, or within, within buying with, within upstream. You know there's then. What I've learned in this industry is the further you are from the shore, the deeper you realize the water in for this, there's just so much to be done. And so, yeah, it's evolution on, all, on, all on, all accounts.
Bret Schnitker 30:27
So is this a cloud based model? SAS plugin always updating? Okay,
James Theuerkauf 30:33
okay, yeah, classic, classic, you know, multi, single host, multi tenant, SAS with kind of this core AI brain that's that's also the same for everyone. For its worth,
Emily Lane 30:42
I'm amazed at what you've accomplished in such a short amount of time. I really congratulate you for being such
Bret Schnitker 30:48
He's a Harvard graduate.
Emily Lane 30:49
Yes, it's true,
Bret Schnitker 30:50
that's the requirement.
James Theuerkauf 30:54
It's such a very kind Thank you. Yeah,
Emily Lane 30:56
well, is there anything else we should know before we wrap up this conversation today.
James Theuerkauf 31:02
I mean, I could probably think of, yeah, exactly, but I think we touched on the main ones here. Actually, this is great. Thank you for the conversation.
Emily Lane 31:13
Gosh, of course, we're just absolutely impressed by this technology and what it can bring to our industry with regards to saving costs and proving that purchase, improving that bottom line, and helping people all around make smarter decisions.
Bret Schnitker 31:26
Where, if people are interested and they will be interested, how do they get a hold of you?
James Theuerkauf 31:31
So on your on our website, you can, you know, syrup.tech you can, there's multiple kind of ways to get, also get, get on LinkedIn. James Theuerkauf, surnames, a mouthful, but I'm sure you'll see it in our description here,
Emily Lane 31:45
we'll be able to link everybody up.
James Theuerkauf 31:47
Yeah, exactly,
Emily Lane 31:49
yeah. I really appreciate the conversation today, and I appreciate you being here as well. Don't forget to subscribe to stay apprised of upcoming conversations.
Bret Schnitker 31:58
Thanks very much.
Watch the video here:
