Syndigo on Why AI Commerce Is Failing Without Better Product Data

[00:00:00] So a huge thanks to Denodo for supporting the Tech Talks Network, helping us produce more than 60 interviews a month. And when it comes to trusted data products, it all starts with the right foundation. And trusted data products start with Denodo because they can help you create, manage and deliver business-ready data products faster with secure real-time access across all of your data sources.

[00:00:26] And you can learn more by simply visiting Denodo.com. What happens when the product data behind every online purchase becomes as important as the product itself? Well, today I'm joined by the Chief Product Officer at Syndigo. We're going to have a conversation that takes us behind the scenes of modern commerce and into the infrastructure that most consumers never see.

[00:00:55] Because my guest leads Syndigo's product organisation and its product experience management business line. And he's someone that works with thousands of brands, retailers and distributors that all depend on accurate product information. So they can sell across physical stores, websites, marketplaces, apps and now AI-powered shopping experiences. But what we don't talk about enough is the infrastructure that makes all this possible.

[00:01:24] And why data has moved from being just content on a page to becoming commerce infrastructure. Because if a customer is asking a chatbot for the best running shoe for knee pain or using AI shopping agents to compare products, the brands with incomplete, inconsistent or outdated product data, they could quickly disappear from the answer.

[00:01:48] And my guest will explain today why many companies built systems for e-commerce search, SEO, product detail pages, but they're now discovering that AI-driven commerce needs far deeper product attributes, stronger governance, richer metadata and better handoffs between teams. So I want to learn more about the hidden complexity behind a simple product listing,

[00:02:12] whether it be from ERP and PLM systems to supply chain, marketing, ratings, reviews, global sizing, regional language differences and syndication across retailers. The list is long. But my guest will share how Syndigo's recent acquisition of OneWorldSync will also help change the conversation around product data standards, ratings, reviews and global commerce infrastructure.

[00:02:38] So as AI changes how people discover and buy products, is your brand ready for a world where the shelf is no longer a web page, but a conversation? And on that note, it's now time for me to officially introduce you to today's guest. So a massive warm welcome to the show. Thank you for joining me today. For everyone listening, can you tell them a little about who you are and what you do? Sure.

[00:03:07] So my name is Tarun Chandrasekhar, and I lead the product organization and the PXM business line for Syndigo. So PXM stands for product experience management, and I'll drive into what that really translates into for customers. But Syndigo is a software company. We work with about 15,000 brands and 3,500 retailers and distributors. And the best way I describe this is that we provide the infrastructure

[00:03:36] that ensures that product data gets to the right place. So if you're a brand and you're building products, physical products, you want to make sure that everything about that product is accurate, whether you're selling it in store, your own stores, whether you're selling it in an Amazon, Walmart, Target, on your own .com website in a third-party marketplace, because if that information is not accurate, either your returns go up or you get sued because your allergy information is off.

[00:04:02] You want to make sure that to reduce the friction in that sale, your product information stays accurate. That's product experience management at the most basic level. And we sell software that allows you to optimize all elements of that. And in two variables, we tend to help optimize. The way I describe this is speed to market. What is the fastest way I can get a skew to a shelf? Because nobody is making money until that skew hits the shelf,

[00:04:32] that it gets to a store. The brands don't make money. Distributors don't make money. Retailers don't make money. So how do we optimize that? And time is the primary element there. Once it gets to a store shelf, physical or digital or increasingly agentic, how much more money can I make? How many more eyeballs can I get on that product? How can I build the trust about the product? So those are the different product lines. I love that. And of course, when we're talking about this world,

[00:05:01] AI is absolutely everywhere at the moment in e-commerce, whether it be recommendations and search to automated product descriptions and even conversational shopping assistants or concierges. So how much of this current AI challenge that people in e-commerce are coming across, how much of that actually comes down to poor product data rather than weak AI models? Because I think very often we focus on the AI models. But one phrase I hear a lot at tech conferences,

[00:05:29] no data, no AI. And it almost reminds us of the age-old saying of garbage in, garbage out. But what are you seeing here? And how much of those challenges come down to poor product data? I think the failure mode is almost always the same. You know, companies invest in the model, but they skip the foundation. You cannot build reliable AI applications on top of messy, inconsistent, incomplete data.

[00:05:55] The real hardship that customers are facing, companies are facing today is this, especially in the commerce space. In the last 20 years, we used the page ranking algorithm and SEO, search engine optimization, as a workaround to bad data. Because what we said was, hey, I know that I don't know whether this is a single record or multiple records, or is this unique or not. And I don't know about the governance and the provenance of this data,

[00:06:24] but I do know that these 25 attributes really matter to whether you're going to show up on page number one or page number seven on Google. So I will invest and get that right. Which was great when you're surfacing this product on an e-commerce website, when you're on a product detail page, when you're on amazon.com, on Google. But 60% of customers today have started their shopping journey within a chatbot. They're like, Chachi PD, Gemini, Claude, you pick your poison.

[00:06:54] And a lot of times they're starting a journey where they don't know they need a product. They're like, oh, I have knee pain when I run. That will translate into, when was the last time you changed your shoe? You run a thousand miles on these shoes. You need to go swap the shoe out. That leads to a shopper product discovery conversation, right? In that situation, AI is not looking at, hey, what the, oh, is this like a marathoner who's been running?

[00:07:21] Which sports star is wearing this shoe does not influence whether it's going to recommend a shoe to you. It's going to use it based on, what do I know about Neil, right? What do I know about Tarun, right? What pace does he run at? What kind of process does he do? What kind of, what's his weight? It'll take all that into consideration and marry that with product data, which is complete. It could go back to what is the cushioning in that insole? Hey, there's the carbon plate. Is it nylon plate?

[00:07:50] What kind of material is used in the construction of the shoe? What is the toe width that you want, the shoe box width that you need for wide toes or not? And all that information is not on your product detail page all the time. It may be sometimes, it may not be. So you get this entire volume of what we're calling AI enabling product data attributes that were all sitting upstream in your enterprise that were being measured for different reasons.

[00:08:19] You may actually have all that to measure and build the right size of the box in which the shoe needs to be shipped. And so, yes, their supply chain is using those attributes, but you're not bringing it up into your product marketing because you never needed to. And now you have to connect all those dots and bring that in. So it has become super crucial. And the companies that actually invested in getting their foundation right are seeing that leg up today

[00:08:48] where they're like, hey, I get it. I did that work of connecting these dots internal to my organization. The companies took shortcuts of scrambling right now to say, what other shortcuts can I take to catch up or leapfrog at this point? Yeah, so true. And, of course, you work closely with brands trying to operationalize AI at scale. And I'm curious, what are companies discovering when they realize that their existing product information systems

[00:09:16] were never designed for AI-driven commerce? Are there any trends in the kind of things that they discover and they come to you and ask for help with? Yeah, so a couple of things that happen, right? So the first, I should probably not say this and maybe I'll get in trouble with it. I don't think so. It's a public company. I worked for BP for seven years, okay? In my career, as I mostly have been on the product side, but about seven years I went to the buyer side and said, I'm going to implement data management systems

[00:09:45] and understand why people don't use software the way I think they should use. And it was a great, phenomenal learning for me. But the biggest thing that sticks to my mind is that it took me months to find out categorically how many wells this BP owns. Think about it for a second, right? It's core to the existence of an oil company. Oil wells are we drill. But to be able to reliably tell you, I could give you 10 different answers.

[00:10:13] I could not tell you the one answer as to how many. So if I go back to a problem, coming back to brands and retail, the biggest problem is we don't know the state of our data. We know state of silos or slices of the data. Anything to do with ERP and finance, highest importance. You want to make sure you don't screw up your payment processing. You're going to get that right, right? But then progressively through different functions,

[00:10:41] they were always assigned different weightage and different reliability. So people don't know the state of the data. And suddenly they're being forced to say, everything is important because you don't know what AI is going to need. So surface all of that. So we've been actually working with a lot of our customers. We offer this thing called an agentic readiness assessment where we're essentially coming back and saying, here's the spec. We think that these are the things that matter for an AI to surface your data right.

[00:11:11] And then we'll map you to it and come back and say, where do you fall on that spectrum? And we come up with the score. And it's interesting because when we go in, most customers are like, I'm going to rate between a 60 and 80 on your score of 100 as to how AI ready is my data. And we're consistently finding companies small and large at about 30 to 40 today.

[00:11:35] And it's because they were all geared towards e-commerce enablement and not towards agentic enablement. And that gap is vast. It's not small. And I think as consumers, when we hear about the digital shelf, it sounds incredibly simple on the surface. But of course, behind every product listing, there are massive infrastructure, governance and syndication challenges.

[00:12:01] So for everyone listening, can you help them understand the hidden operational complexity powering these modern retail ecosystems that we all almost take for granted as consumers? Yeah. So let's take maybe an example. Let's pick up what product would we like. Let's do a shirt. Let's do a polish shirt. Let's do a polish shirt. The journey is going to start. I'm a brand. I could be Tommy Hilfiger, Florian, whoever it is. I'm a brand.

[00:12:25] I'm going to start with typically the question, the first question that a brand has to think through is where is that product born? And it's either in the ERP system or it's in the PLM or the product lifecycle management system. I'm a product guy, I think, in design terms. So I think in the PLM side, because I'm like, there's a dean in the eye of David Lauren or whoever. Somebody's designer just looks at this. This is the kind of photo shot I want to build.

[00:12:53] And they may be hand drawing it, but eventually it gets into a PLM system where they start looking and designing what the field will be, what materials it will be made of, what sizes, what the cut, the style, everything. And the information about that design is in the product lifecycle management system. Way before the product is ever built or even like the first samples are built. So if you think about the inception of data, it starts there. There is a lot of theoretical design data there. None of it may come back to life.

[00:13:23] But eventually somebody goes and builds a product, tests it out, says, yes, this should be born now. And then it moves from them into classically some variation of a product master, an item master, product master. And there are lots of three-letter acronyms in this industry across MDM, PXM, PIM, where all this makes it. But eventually it makes it into a system of Rappler that says, this is my polo shirt.

[00:13:51] It comes in these colors, these styles, these skews, right? A slim fitting, comfortable fit, plus sizes, petite. These seven colors, which will all be, you know, I think in primary colors, my kids make fun of me. I can't differentiate with two shirts. But they'll come up with the colors and they'll come up with the sizing.

[00:14:13] And now sizing is interesting because sizing is different in UK versus China versus India versus US. But the skew may still be the same. It may still be the same product. And you're saying, hey, this is a large in Asia, but I'm going to sell the same product as a medium in the US. And that's how do you manage that complexity? So most sophisticated product experience management systems will handle those contexts and support it where you don't have multiple products you're creating.

[00:14:43] Just because you're selling them in different regions. Just because you're having to write down descriptions in different languages. Or you're writing product descriptions to appeal to different personas. Because you could be selling a shirt that a 16-year-old will want and a 48-year-old will want. And you want to be able to appeal to them with slightly different things that matter to them. And you want to manage all of that in a single environment that allows you to handle that complexity. So just think about that.

[00:15:12] In that world, you're starting to understand what geographies are selling. Now, you could be a manufacturer who has multiple brands. And you're selling the same product to multiple brands and multiple geographies. Because in the US, you're selling it under this brand name. In Europe, you're selling it in a different brand name. You want to be able to handle all of that attribution as well. So there are sophisticated workflows. And I say sophisticated loosely because there are complex workflows is what I mean.

[00:15:40] Which allow any manufacturer, any brand to manage that information. And I haven't even gotten to what types of information. This is just the complexity of the business that they work in. The systems that this has to go through. And now let's talk about attribution. You're talking about your primary attribution, if you think about it, is your core identity. Is it a GTIN number? Is it a SKU? What is the name of the product?

[00:16:10] What is the title? The core description? Something that's going to be fairly immutable across wherever you go. That doesn't change much, right? The core identity of the product will stay like that. But then this could also include variant hierarchies, which is what I said, color, style, SKU. This is the way I structure my product, the model number of the SKU. But then you get to the marketing attribution.

[00:16:36] Marketing attribution, long descriptions, feature benefit bullets, short summary, intended use of the statement. All of the images, because you want your hero images, the one that shows up with Tiger Woods wearing that polo shirt. You could have alternate angles. You want lifestyle images, video 360. You keep going down and then you work on your SEO optimization. Keywords, structured queries, phrases that want to surface when somebody's searching for it in Google.

[00:17:06] And all of this is just towards the marketing attribution, right? But you also want to make sure that the product is ready for supply chain. Does this go in cases? Does it go in callics? Does it go in each? And what is the size and dimension for that? Because you get that wrong and your entire truck that you're bringing is now you're going to end up with two boxes extra that didn't fit. And that could lead to dollars lost in that journey because you didn't think this through.

[00:17:34] So small changes, small errors in data have big impact. So we spoke marketing, we spoke supply chain, ERP. The best way I've always described this is you are managing product information for multiple purposes. How much am I making with this? Which is ERP enrichment is basically about everything about this product better be right so I can make my money.

[00:17:58] Supply chain is everything about the product better be right so I can ship this product accurately and not lose money. And then marketing, everything better be right so that I can actually appeal to right audience and make money on the ship. There are about seven such vectors that we cover, but these three are the three that probably best explain what we're doing. All of this I just said, it still happened within the brand.

[00:18:25] And we haven't even talked about after that. This has to go somewhere. It could go to Ralph Lauren's stores and ralflaren.com, but it also needs to go to Bloomingdale's and Macy's and H&M. So you start thinking through that, the journey of making sure the product makes it across the physical product and the information about the product such that it's also available on macy's.com. And what shows up on macy's.com is exactly what shows up on ralphlauren.com. It should not be different.

[00:18:55] You shouldn't be like, oh, that color is fuchsia and not pink. And now suddenly you're getting returns going up because somebody doesn't understand it. You mentioned some big names, some big brands and retailers there. And on the flip side of this, smaller brands, they're the ones that often struggle to compete with enterprise retailers because they have such huge data and infrastructure resources.

[00:19:17] So I'm curious, is AI helping level the playing field or is it widening the gap between companies with mature data operations and those still playing catch up? Or do smaller businesses have less technical debt and are able to catch up? What are you seeing here? So it's an interesting question. And the answer isn't as linear that small brand versus large. It almost goes back to the verticals that play in. So let's kind of separate it out, right? Think of food and grocery, consumer package goods, retail apparel.

[00:19:47] I'll just take these three examples. Wherever there is complexity of use cases, I have multiple. Hey, I sell this product not just by itself, but in a kit or a bundle, right? I need to now manage that. That complexity is where AI is really helping small brands scale up. So it's not like a volume complexity. It is a breadth of the different types of attributes that you're able to manage with AI.

[00:20:13] That's where small brands are able to leapfrog large organizations and say, hey, I can now for the first time handle this complexity at scale without having to add more human resources. Let's be very clear here. We've always been able to manage these complexities. We just threw bodies at it.

[00:20:31] And the last 10 years have not been kind to this industry because what used to be a team of 15 people is now a team of two people who have to manage 78 different channels where this product has to be sold, right? So AI has given a lease on life to a lot of these grants to say, hey, I said that we're not going to hire more people and we'll just make do with who we have. I did not have a way forward.

[00:20:57] And now I suddenly do have a way forward that allows me to actually compete and win in this market. So much so that most large retailers, most large brands, enterprises, they are less worried about what the other large brands are doing. They're looking at what the mid-market and smaller brands are doing and how are they able to innovate with AI. And they're learning from there to become more agile and nimble.

[00:21:28] And for as long as I can remember, we have often heard people talk about having this utopia of a single source of truth for enterprise data. But in reality, almost every organization is still dealing with fragmented systems, inconsistent product records, disconnected teams. But here we are now. Why has solving this problem remained so difficult? It is true. 50 years, we've had the same issue. We were trying to solve this on mainframes and RDBMS system.

[00:21:56] Since the technology may have changed, this problem statement has not changed yet. It's difficult because when you run a business, you're not thinking about data as an infrastructure. You're thinking product data as content. It's like, hey, it serves its purpose. I want to get the unique identifier right because I want to identify this as the same product. When I scan a barcode, I get the right information. That's all I care about.

[00:22:25] And so there is a body of people inside the company who are only focused on that. There's a body of people who are only focused on getting the description right because they were hired to get the product description right. And that interconnectivity, which here's how I personally feel about it. I think this is not a technology problem or even a process problem. This, at the heart of it, is a people problem. And the people problem is not that people don't want to. They have the best intentions, right?

[00:22:54] But like Jeff Bezos says, good intentions don't matter. Mechanisms do. Who is the mechanism that allows you as somebody who is only focused on supply chain or only focused on product marketing, only focused on e-commerce to be able to do your job, but still deliver information in a manner that makes everybody else's job easier. That, those pipes, those bridges, we talked about it. We never really invest. Those were never considered.

[00:23:24] No C-suite discussion, no board level discussion talks about that pipe because at the heart of it, data management is janitorial work, right? It's not sexy. It's not AI. It's not analytics like it was 10 years ago. It is the boring stuff. And the only time you bring it up, the only time you invest in it is when your sewer pipes break. That's when you worry about it.

[00:23:52] You're not looking at it proactively saying, if I built that. But I'll go back to military strategy, right? People talk about strategy and tactics, and you'll see the best generals talk about it. Wars are well-won based on logistics. They're based on supply chain. They're based on, hey, am I able to supply my army as I move stage by stage by stage? That's how wars are won and lost. They are not won on strategy and tactics. They're won on logistics. I firmly believe in that.

[00:24:20] And this is where I put that data strategy. For companies that think through and say, data is my logistical challenge that I want to address. And if they can do that when they're starting out, that's beautiful. But who does? Everybody who's starting out is in the business of trying to scale up, make money quickly. But there is a point in time when you're hitting that scale. You were a mid-market company, and you're going to become a large company.

[00:24:47] That's the time for you to kind of pause, step back and say, do I have the pipes to be able to scale up? Such few companies do that. But the ones who do absolutely kill it in the market. So if you go back and probably do a study of everybody who was able to scale up and succeed in product, in commerce, you will probably find that one point when they said, hey, I'm going to go back and rebuild that.

[00:25:14] And it doesn't, I'm being suspicious here, that doesn't mean a digital transformation project. That doesn't mean you just hired somebody and said, hey, you know what? We're going to throw away everything we had. That just goes back into getting on a board and saying, okay, where is my product born? Like, is it what countries do I sell in? What brands is it going to be part of? Is this going to be like a three-level hierarchy or a seven-level hierarchy? Just map it out on a piece of paper for all your care.

[00:25:42] And that helps you drive positions and alignment across departments. The challenge exacerbates the challenge that you correctly pointed out. Why are people not able to solve this problem when these departments, these functions are not aligned on handoffs? Right. So I'm a triathlete. I do Ironmans. And when I do, I'm not a fast one. I'm super slow.

[00:26:07] But the saying that goes in triathlons is that the races are not won during the swim or the bike or the rebound. The races are won during the transition when you switch from a sport to another. And it's the same thing for business. You win in business when it comes to the handoffs between these functions. And how easy and frictionless is that handoff across the different departments? And that pipe that we were talking about is core to that thesis.

[00:26:35] And as you said right at the beginning of our conversation, AI-driven discovery is changing how consumers find products, whether it's search engines, marketplaces, voice assistants, or AI shopping agents. I know I, as an individual, my searching methods have completely changed in the last 12 months. So how do brands need to rethink product content and metadata for a world where humans might no longer be going to search as the primary interface? They're going to be using different methods.

[00:27:04] For those people listening, what should they be doing to prepare for this? I say prepare. It's already out there. So what should they be doing? So it goes. And there are two elements to AI-based shopping or what we call agentic commerce, right? The easy way or the right now way to think about agentic commerce is we define four types of formats. Physical commerce in stores, right? Digital commerce on websites.

[00:27:32] Social commerce, you want to buy in TikTok and Instagram directly. And then agentic commerce where you're now starting your shopper journey inside a chatbot. Sometimes you're finishing the shopper journey inside the chatbot too because as you're seeing what OpenAI is doing with Shopify integration, you're able to now start buying certain products directly. Walmart's enabling it directly inside OpenAI. You'll see a lot more of this behavior happen.

[00:27:56] Having said that, I'll define two parts to it. Every brand, every distributor, every retailer absolutely has to make sure that their product shows up when somebody is having a conversation with a chatbot. And that's a much harder thing to do because if AI gets confused about that product, if that product data is not accurate, if that product data is not complete,

[00:28:27] AI is making decisions in microseconds saying, hey, Tarun wants to run. He has knee pain. Should I recommend Nike or Adidas? The question is going to be disanswered really quick. And if the information it finds about Nike is not complete or conflicting, it'll ignore it and go to Adidas because it's starting to hit that time commitment as well. Right? So step one is to make sure that when your product surfaces, it is more complete than it has ever been.

[00:28:55] Whether you have to force it with hand, whether you build your pipes, like I said, doesn't really matter. But make sure that data is complete, the data is correct across multiple channels. Just because it's correct on Nike.com doesn't matter if Macy's has got their own information. AI will not surface it. It's not like it would show up and say, hey, I don't know. I got completed. It just would ignore it and move to the next product. So that's super important to us.

[00:29:20] The second element is, which is where we don't know how this place will pay out. Because if you saw in October, OpenAI announced the Agentic Commerce Protocol saying, hey, instant checkout. You can buy directly within OpenAI. But by March, they were like, we're going to enable discovery, but we're not directly going to source and say instant checkout. We're going to have retailers create MCP servers and connect to them. And then that's reasonable.

[00:29:48] So it's been a little bit of a one step forward, one step back, one step forward, one step back. Nobody knows how this will land. But one thing is for sure, product discovery, just the ability for people to find your product has changed. And that is going to require most of our brands to start. So if I go back and say, what should these companies be doing today?

[00:30:10] They absolutely have to first make sure that they treat their product as infrastructure, not just content, which is a mind shift shift, right? Someone, there has to be some accountability for this charter. Look, if you're just going to say, yep, I already managed data being sent to Amazon and Walmart, and I'm just going to do it there as well. So it's kind of the same thing, but not really.

[00:30:35] But you need one more level of reflection to say, there is a certain body of work we have to do to become AI ready. And are we investing in that today? Which is timely because every board is like, what are you going to do to support AI in your world today? There is budget for it. And we need to start using that. I do think that the other thing that we should look at is around speed of using AI tools internally to accelerate.

[00:31:04] We don't talk enough about this today, but we call this agentic product experience management in our platform. But it has reached a certain maturity where, again, whether it came to discovering bad data, profiling it, fixing it at bulk, right? Writing agents that can connect multiple workflows and accelerate through that. That journey has also improved. So use the right tools. Use the right technology.

[00:31:34] The hard part here is not the availability. Everybody's building AI tools today. It is the inertia. It is the inertia of a business process that is 100 years old today, where a brand or a manufacturer, a distributor, a retailer is sitting and saying, this is how I work. I have these seven people. It's almost like a little assembly line. Everybody has to do their part. And in that journey, they're like, oh, I have a 17-step process.

[00:32:02] In step number 13 and 17, I can use AI. That's going to give you a little bit. But if you just take a step back and say, is there a completely different way to solve this problem using the right tools today? You're going to see that whole 17-step process may now become a three-step process. But you're now able to solve your bad data problem at scale without having to go invest and ask your board for another $10 more million to go and do XYZ. That problem gets solved.

[00:32:31] And before you join me on the podcast today, I was reading about Syndigo's acquisition of OneWorldSync, which felt like a major move in the product data management space. So I'm curious, how has this changed the conversation around product experience management, data syndication, and indeed global commerce infrastructure? What does this mean? The best way to describe this, right? So OneWorldSync, and there are a couple of product lines we acquired from OneWorldSync I'll talk about. The primary one was GDSN syndication, right?

[00:33:01] So if you think of GS1 standards, which is where a G10 number comes from industry standards for consumer packaged goods, food, grocery, multiple verticals, where you're saying, hey, if you have your product information structured this way, then tens of thousands of brands and retailers can consume it directly without you having to do more. It's a great standard. It makes real sense to do that.

[00:33:26] OneWorldSync was the largest GDSN data pool, is the largest GDSN data pool. It's most number of trading partners use that standard to connect to each other. Syndigo was the number two there because we, in addition to all the other product softwares, have had a GDSN business as well. For us, we realized what was happening for a lot of our customers were they were buying a license to Syndigo and then they were buying a license to OneWorldSync.

[00:33:53] And then they were trying to fiddle around and try to make these things work together to connect the dots. So going back to, again, that conversation of reducing friction in a speed-to-market play, we realized there's an opportunity for us to actually connect these to ourselves and make sure that when you think about your product experience journey, when it comes to master data management, when it comes to enriching a product and adhering to a standard,

[00:34:19] if we take on the cost of making that integration first party, it provides a tremendous benefit to our customers who are now not having an IT project to connect these pipes and rapidly increase that value proposition for our brand, which says, hey, you know what? I don't have to worry about connecting these dots. I can just do this. So which is huge, again, in this era of AI.

[00:34:45] Primary GS1 attributes, I would say, are split across logistics and marketing. So you marry those two together and you suddenly, the shipment of product and product information from a brand to a distributor or retailer. Second element that one will think brought us was Power Reviews. Power Reviews is a company that manages ratings and reviews for brands and retailers. So think about it, right?

[00:35:09] This is the second element of the KPI that we help, which is building trust in a product. Reviews have become increasingly important. You want to make sure as you're a consumer, you're not shopping without looking at reviews today, right? You're absolutely saying, hey, I want to understand what's going on with this product. How many people have liked it? The people who liked it, there is bias there. I will not disagree with this because what happens is if you think somebody didn't like that product,

[00:35:36] you're first going by saying, why did they not like it? And you're already mentally justified in saying, is that person like me or is that person not like me? Oh, OK, that's a use case I'm never going to have. I can ignore it. Or no, wait, that person has exactly left me. That will truly impact whether I'm going to buy it or not. So if you think about that world, that world also sits downstream of where we are today because we're making the best product information available at every shelf.

[00:36:04] And then there is a signal that's coming back from a customer that says, great, going back to the same Nike shoe example. Hey, you know what? I like this Nike shoe a lot or I don't like this Nike shoe because the fit is too tight or I like it a lot because it's very cushiony. Whatever it be, you can start. If you analyze all those reviews at scale with AI, we're able to now extract our very important information that should actually be a part of my marketing copy that could help me sell

[00:36:34] more if I did it early. But those two worlds are so far apart in a customer. One part of that brand is focused on getting product out to Amazon, Walmart, Target. One part is just reviewing those things and making sure that nobody returns the products or is just showing higher rankings so that you're processing them. But nobody's connecting those two dots. So that was the other reason for us for this acquisition to say, hey, by buying these two,

[00:36:59] we can actually help brands say everything that you learn from that review can automatically and agentically become a part of your product record so that you are able to now make more money with that product because it's highlighting things that are clearly valuable. And I think that is a fantastic thought-provoking moment to end on. But for anyone listening that would like to carry on this conversation with you, we've only scratched the surface of it today. For people wanting to learn more about you, your team, what you're doing, read about some

[00:37:27] of the things that you're putting out there as well. Where would you like me to point everyone on listening? So the best way to think about this right now is if you just go to syndigo.com today, we have two or three things. There is directly all the information about what we're doing in agentic commerce. What are we doing with agentic PXM or product experience management? The two big charters we have. We have a huge presence on LinkedIn. Hit me up on LinkedIn. Feel free to ask me any question that you want to directly from there.

[00:37:58] There is a Q&A post on our website. And there's a communities channel from One World Sync and Syndico, which allows you to just interact with us around. Awesome. Well, I'll have links to everything that you mentioned there. And I think as a consumer, that digital shelf that I mentioned at the beginning sounds so simple. But I, for one, I've just loved learning today about behind every product listing. There is this massive infrastructure, governance and range of syndication challenges.

[00:38:26] And taking a look under the hood at some of the tech that makes that possible has been priceless for me. And I'm sure it will be for everyone listening. So I would urge them to check out all the links in the show notes to everything you mentioned there. But more than anything, thank you for starting this conversation today. Well, thanks for having me. This has been absolutely joyous for me. As you can see, I'm pretty passionate about this topic and I can keep rambling on. One of the things I loved about this conversation today was how my guests made something many

[00:38:54] people never think about feel instantly relevant. Every time we buy a shirt, a pair of running shoes or even a grocery item online, we now expect the information to be accurate as standard. We assume the size, colour, ingredients, images, shipping details, reviews and availability are all connected. But as my guests explained today, behind the simple product page sits a huge amount of operational

[00:39:20] work across systems, teams, standards, workflows and data handoffs. And the part that really stood out to me was his argument that product data can no longer be treated as static content. Because in an AI-powered shopping journey, the product information becomes the infrastructure. And if that data is incomplete or inconsistent, an AI assistant may simply ignore that product

[00:39:45] altogether and move to another brand with cleaner, richer and more reliable information. And that creates both an opportunity and a challenge. Because smaller brands may use AI to handle complexity that once required large teams. And larger enterprises may need to rethink old processes that were built for websites, SEO, marketplaces, being number one on the first page of Google, rather than agentic commerce.

[00:40:15] And the movement from design to product record and product record to retailer, from reviews back to marketing copy and internal systems into AI-ready experiences, all these things will collectively increasingly shape who gets discovered and who gets left behind. So as consumers start shopping through conversations rather than search boxes, what happens to the brands whose product data is not ready to be understood by AI? I'd love to hear your thoughts on this one.

[00:40:44] We did cover a lot and it was an interesting angle. So you can get a hold of me just at techtalksnetwork.com. You'll find eight different podcasts, 4,000 interviews and lots of different ways of contacting me. A big thank you to NordLayer for backing the podcast and supporting the kind of real-world cybersecurity conversations that we need more of. Because as someone that records 65 plus interviews a month, I've personally seen a huge increase

[00:41:12] in browser-based attacks over the past year, whether that be phishing, malicious extensions, account takeovers, the list is long. And it's all happening where people spend most of their time, inside the browser. So NordLayer's new business browser, that's built to address exactly that. It blocks malicious sites before they load. It limits risky behaviors like uncontrolled downloads or data sharing.

[00:41:39] And gives you visibility into how your team interacts with web apps. And it also helps you stay compliant by controlling access and enforcing policies without the need to rely on multiple disconnected tools. So for anyone listening that is thinking seriously about reducing risk in SaaS-heavy environments, this feels like a smarter and more focused approach. And you can learn more about it by visiting nordlayer.com slash browser.

[00:42:07] So let me know your thoughts. I'll be back again real soon with another guest. But thank you for listening today. And I'll speak with you again soon. Bye for now.