On this episode of IT Infrastructure as a Conversation, I explore a fresh approach to one of the oldest headaches in enterprise IT: migrating legacy databases without breaking everything.

My guest is Jacek Migdał, co-founder and CEO of Quesma, a startup tackling the messy reality of old data stacks, rigid licensing, and costly, high-risk migration projects. Jacek shares how Quesma’s database gateway acts as a smart proxy, allowing companies to switch data stacks gradually, test changes safely, and avoid the dreaded “big bang” migration that so often fails.

We unpack how Quesma blends pragmatic engineering with AI-driven automation, from SQL extensions that enrich data inside the database to “smart charts” that generate meaningful visualizations without complex BI tools. Jacek also explains why even modern industries like telecom and travel still wrestle with legacy systems and how a flexible, proxy-based approach keeps critical operations online while modernising behind the scenes.

If your team is wrestling with outdated data infrastructure but cannot afford downtime, you will want to hear how Quesma turns risky transitions into manageable, incremental improvements.

This is a candid look at the reality behind today’s data stack promises and a reminder that when it comes to enterprise infrastructure, practical steps often beat grand plans.

In this episode of IT Infrastructure as a Conversation, recorded live at the IT Press Tour, I caught up with Raju Datla, CEO of Fabrix.ai, to talk about a shift that could redefine how IT operations are managed. Formerly known as CloudFabrix, the company has evolved with a sharper focus on what it calls agentic AI: technology that works alongside humans to make smart, controlled decisions at scale.

Raju’s story begins at the Indian Institute of Technology and winds through Silicon Valley, where he has founded several ventures grounded in solving real-world tech problems.

Reducing Noise, Increasing Value

One of the standout achievements we discussed is Fabrix.ai’s ability to reduce alert noise by up to 95 percent. In large environments with millions of daily notifications, that kind of reduction changes how teams work. Instead of chasing false alarms, IT professionals can focus on what matters: stability, uptime, and real outcomes for the business.

The platform does this through a layered architecture Raju describes as the three fabrics: data, AI, and automation. Each plays a role in bringing clarity and action to complex infrastructure environments. Data is unified from dozens of sources. AI makes decisions based on context. Automation executes those decisions while keeping humans involved in key steps.

Strategic Moves and Trusted Partners

Fabrix.ai has not gone it alone. Through close relationships with Cisco, IBM, and Splunk, the company has stayed connected to both market demand and enterprise pain points. These partnerships are not just logos on a slide. They are part of how the platform has been built to handle real-world complexity.

And the results are tangible. Whether it is automating resolution, tracking full alert lifecycles, or offering visual storyboards for better decision-making, Fabrix.ai is helping enterprise teams keep up with a pace of change that is not slowing down.

Agentic AI in Practice

The concept of agentic AI comes up often in this conversation, and for good reason. Unlike systems that simply follow rules or surface alerts, this approach blends autonomy with awareness. It does not just generate insights; it acts on them. And it does so in ways that respect the role of human judgment.

Raju explains that this is not about removing people from the loop. It is about giving them systems that can scale, adapt, and support smart decisions. In that sense, Fabrix.ai is not replacing IT teams. It is extending what they can do.

For leaders wrestling with fragmented tools, alert fatigue, and growing complexity, this episode offers a fresh perspective and a reminder that practical, scalable AI is already here.

Raju’s parting advice to entrepreneurs and IT leaders alike? Solve the problems you care about. Passion always carries more weight than a quick exit plan.

Listen in to learn how Fabrix.ai is helping enterprises bring order to operational chaos, one intelligent decision at a time.

Is AI Infrastructure Broken? A Candid Conversation with Volumez

AI adoption is accelerating, but most enterprises are still stuck in the pilot phase. Cloud costs keep climbing, GPUs go underutilized, and data pipelines struggle to keep pace. If AI is the future, why is the infrastructure built to support it so often stuck in the past?

In this episode, recorded live in Silicon Valley during the IT Press Tour, I sit down with John Blumenthal, Chief Product Officer at Volumez, and Diane Gonzalez, Senior Director of Business Development and Product. Together, we unpack what is really holding AI back and explore how Data Infrastructure as a Service (DIaaS) could change the equation.

We explore:

• Why traditional AI infrastructure models are inefficient and unsustainable
• How DIaaS enables just-in-time, automated infrastructure tuned to each workload
• The role of GPU and data scientist efficiency in determining AI ROI
• How Volumez achieved industry-leading results in the MLCommons benchmark
• Why hybrid and multicloud strategies demand a fundamentally different infrastructure approach

John and Diane share firsthand insights from working at the intersection of data, cloud, and AI infrastructure. They argue that achieving meaningful return on AI investment requires more than hardware upgrades or clever provisioning. It means embracing automation, profiling cloud capabilities in real time, and architecting pipelines that adapt to the specific demands of each phase in AI and ML workflows.

Whether you're building AI platforms, running data science teams, or managing cloud infrastructure, this conversation offers a grounded look at how to make AI actually scalable.

Are you wasting your most valuable resources or are you ready to run AI workloads at full speed with none of the bloat?