Syndigo on Why AI Commerce Is Failing Without Better Product Data

From SD-WAN to AI Traffic: How Enterprise Networks Are Evolving

What happens when AI workloads begin to overwhelm the network infrastructure originally designed for human browsing and SaaS consumption? In this episode of IT Infrastructure as a Conversation, I’m joined by Jamie Pugh from Globalgig to discuss why enterprise connectivity is rapidly becoming one of the biggest blind spots in the AI era. While much of the industry conversation focuses on GPUs, models, and data centers, Jamie explains why the network itself is now under growing pressure from entirely new traffic patterns driven by AI systems communicating with other AI systems. We explore how enterprise infrastructure was largely built around human behavior, employees accessing applications, downloading files, and consuming cloud services. AI changes that model completely. Today, agents are constantly interacting with tools, inference engines are querying massive data stores, and cloud environments are exchanging huge volumes of east-west traffic across regions in real time. Jamie explains why many SD-WAN architectures and broadband-heavy deployments were never designed for these sustained, burst-heavy workloads. The conversation also examines the growing importance of cloud on-ramps and why many organizations discover bottlenecks only after deploying AI-enabled services into production. Jamie shares how asymmetric broadband connections, fragmented carrier relationships, and static connectivity models can quietly introduce latency, resilience, and observability problems that directly impact AI performance and user experience. One of the most interesting parts of the discussion centers on how dependent modern workflows are becoming on AI tools. Jamie talks candidly about using platforms like Claude, Perplexity, and ChatGPT throughout his working day and why losing connectivity now feels less like a temporary inconvenience and more like losing access to an essential member of the team. That shift in expectation is forcing infrastructure leaders to rethink resilience, automation, and real-time observability across hybrid and multi-cloud environments. We also discuss programmable networks, predictive routing, network-as-a-service fabrics, and the growing move toward centralized control planes that can dynamically adapt to changing AI traffic patterns. Jamie explains why enterprises need to stop thinking purely about north-south traffic and start preparing for a future dominated by east-west communication between clouds, data centers, agents, and inference platforms. There is also a valuable conversation around governance, security, and data sovereignty as organizations increasingly bring AI inference closer to private infrastructure rather than relying entirely on public models. Jamie argues that networking, security, and AI strategy teams can no longer operate in silos if businesses want to scale AI safely and effectively. If your organization is building toward an AI-first future, this conversation offers a timely look at the infrastructure challenges many enterprises are only beginning to recognize.

Syndigo on Why AI Commerce Is Failing Without Better Product Data

What if the biggest problem in AI-powered commerce isn’t the AI at all, but the data feeding it? In this episode of IT Infrastructure as a Conversation, I spoke with Tarun Chandrasekhar, Chief Product Officer at Syndigo, about the hidden infrastructure powering modern commerce and why product data has suddenly become one of the most strategic assets inside every retail and consumer brand. As AI shopping assistants, conversational commerce, and agentic retail experiences rapidly move into the mainstream, many companies are discovering a hard truth. Their product information systems were never built for an AI-first world. Tarun explained why decades of fragmented product records, disconnected systems, inconsistent metadata, and siloed workflows are now becoming major blockers to reliable AI-driven discovery and personalization. This episode offers a fascinating look at why “single source of truth” projects continue to fail across enterprises decades after organizations first started chasing them. Tarun argued that this is less a technology problem and more a people-and-process problem, where organizational handoffs and disconnected ownership models continue to create friction across data pipelines. We also explored the rise of agentic commerce, AI readiness scoring for enterprise data, and how companies are now being forced to treat product data as infrastructure rather than simply marketing content. Tarun shared how smaller brands sometimes leapfrog larger enterprises by moving faster, adopting AI-native workflows more easily, and avoiding decades of technical debt. We also discussed Syndigo’s acquisition of OneWorldSync and how ratings, reviews, and product syndication data are increasingly interconnected within AI-powered commerce ecosystems. The long-term vision is a world where product data continuously improves through feedback loops between customers, retailers, AI systems, and manufacturers. If you work in retail technology, AI infrastructure, enterprise data management, supply chain systems, or digital commerce, this episode offers a valuable behind-the-scenes look at the systems quietly shaping the future of how products are discovered, trusted, and purchased online. Useful Links * Syndigo Acquires 1WorldSync to Lead AI-First PXM [https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsyndigo.com%2Fnews%2Fsyndigo-acquires-1worldsync%2F&data=05%7C02%7C%7C3a182cdc4b9d4cc0df1608deaf754cd4%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C639141115547703940%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=b4S8PkPSzO2C7a37vPJwWCbxwCVaMzQHyxvX7c7eflo%3D&reserved=0] * Syndigo [https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsyndigo.com%2F&data=05%7C02%7C%7C3a182cdc4b9d4cc0df1608deaf754cd4%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C639141115547757695%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Y5I3RxiCbNq60Ob%2Befi7MqPULu83AqHjKnN6BXQMuMI%3D&reserved=0] * Connect with Syndigo’s Chief Product Officer, Tarun Chandrasekha [https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.linkedin.com%2Fin%2Ftarun-chan%2F&data=05%7C02%7C%7Cd78ed05ca2194f00fbbd08de997fa96a%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C639116970746763467%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=4EDf73O3cGWuTwn6X0aMDsH5OzRYjfEOUhkV6SoD0Fg%3D&reserved=0]r

Why Infrastructure Needs A Survivability Layer: HyperBUNKER And The Shift To True Offline Recovery

In this episode, I’m joined by Imran Nino Eškić and Boštjan Kirm from HyperBUNKER, two leaders whose perspective has been shaped by more than 50,000 real-world data loss and ransomware cases. This is not a conversation about theoretical security models or another incremental backup feature. It’s a discussion about what actually survives when production systems, identity layers, and cloud replicas have all been compromised. For years, infrastructure has been designed around availability, scale, and performance. Recovery was treated as a process that would work when needed. But as attackers have grown more patient and methodical, they now target recovery paths first, quietly mapping environments and neutralising backup systems long before an incident becomes visible to the business. That shift forces a new architectural question for infrastructure leaders. Where is the layer that remains reachable when everything connected has been taken down? We explore why so many environments that claim to be air-gapped or immutable still rely on credentials, control planes, and automation, and how those dependencies create hidden single points of failure. Imran and Boštjan explain how HyperBUNKER introduces a physically isolated survivability layer into modern infrastructure, using a hardware-enforced, one-way ingestion process and a double air-gap design that removes the network from the vault entirely. No IP address, no inbound ports, and no authentication surface to attack. This leads to a wider conversation about infrastructure governance, cyber insurance, and regulatory pressure. Insurers are increasingly focused on whether a final, untouchable copy of critical data exists, because the largest financial losses now come from failed recovery rather than the initial breach. That reality is pushing offline recovery out of the basement and into board-level architecture discussions. We also tackle the practical challenge every organisation faces. If only a small percentage of data can be placed in a fully isolated vault, how do you decide what keeps the business alive? That decision, as we discuss, cannot sit with IT alone. It requires operational and executive alignment around what the company must have to restart after a catastrophic event. This episode reframes resilience as an infrastructure design principle rather than a security feature. It asks where a survivability layer should sit alongside cloud platforms, backup software, and existing controls, and why the future of Infrastructure as a Service may depend as much on guaranteed recovery as it does on uptime. If your architecture assumes that recovery will always be there when you need it, this conversation may change how you think about your entire stack.

How To Simplify Storage, Virtualization, And Recovery At Scale

What happens when the infrastructure your business depends on becomes too complex, too expensive, or too slow to recover when something goes wrong? In this episode of Infrastructure as a Service, I sit down with Tvrtko Fritz to talk about how EuroNAS has evolved from “NAS for the masses” into a platform helping organizations simplify storage, virtualization, backup, and scale-out infrastructure. What stood out to me in this conversation was how much of that journey was shaped by real customer pain, from complex deployments to the growing pressure of managing modern environments without a team of specialists. We also get into one of the biggest infrastructure talking points right now, the VMware shift. Tvrtko shares why many customers are not moving because they want to, but because they feel they have to, and how EuroNAS is helping reduce that friction with migration support, VM import tools, and a more predictable licensing model. It is a practical look at what organizations are really facing when they need a plan B but cannot afford disruption. Another part of the conversation I found especially interesting was how recovery speed is becoming just as important as backup itself. Tvrtko explains how instant backup and recovery can change the experience from hours of downtime to seconds, whether that means restoring a full virtual machine or pulling back a single file. We also talk about simplifying Ceph deployments, reducing setup times from days to minutes, and why infrastructure teams increasingly need solutions that let them focus on applications and outcomes rather than wrestling with storage architecture. If you are rethinking your virtualization strategy, looking for more predictable infrastructure costs, or trying to understand how to make enterprise storage less painful to manage, this episode is packed with useful insights. After listening, do you think the future of infrastructure belongs to platforms that hide complexity rather than expose it, and what would make you confident enough to make a switch? Share your thoughts.

PuppyGraph at IT Press Tour: Zero-ETL Graph Analytics on Your Existing Data

What does “infrastructure” mean when your data stays exactly where it is, yet suddenly behaves like a graph? I met Weimo Liu, CEO and co-founder of PuppyGraph, during an IT Press Tour presentation, and I wanted to bring his story to Infrastructure As A Conversation because this is a data infrastructure conversation at its core. Weimo’s pitch is simple to say and harder to pull off: keep a single copy of data in your lake or warehouse, skip the ETL pipelines, and still run graph queries with subsecond performance. Weimo’s background explains why this is more than a clever demo. He worked at TigerGraph, then on Google’s F1 team, and PuppyGraph sits right between those worlds. In our conversation, he walks me through how they treat graph queries as a set of node and edge operations that can be optimized, parallelized, and evaluated in a vectorized way, which is how they keep performance predictable when workloads get real. We also get into the practical details infrastructure teams care about. PuppyGraph is a read-only engine, which changes the trade-offs around concurrency, governance, and operational risk. Instead of copying data into a separate graph store and building a second set of controls, you can query relationships where the data already lives, then write results back into the lake for other engines to consume. The upside is simpler architecture and less duplication. The compromise is that you are not getting transactional graph updates, and Weimo is clear about why that is acceptable for the OLAP-style workloads his customers run. From there, the use cases start to make sense fast. Cybersecurity teams with logs sitting in object storage, fraud detection scenarios where latency matters, and internal AI chatbots that struggle with too many tables and brittle SQL generation. Weimo has a sharp analogy for that last part, text-to-graph queries behave more like a train on rails, which can help AI stay inside defined relationships and reduce messy answers. If you are building modern data platforms and you are tired of pipelines multiplying, this episode is a thought-provoking look at what happens when graph analytics becomes a query layer rather than a destination system. And it all started with a dog-themed name and a surprisingly cheap domain.