The AI agent resolved the customer’s issue. How much did it cost?
An AI agent responds to the customer in seconds. But every step in the chain comes at a cost. Gartner estimates that the cost per agent workflow will more than quintuple by 2028, and that open models will handle 90 percent of the steps by 2030. Here’s how to calculate the cost per resolved case.
Top-Notch AI for Defense and Medtech: Why Regulated Industries Are Going It Alone
NVIDIA is connecting GPU capacity to Nordic data centers. For the defense, medtech, and automotive sectors, this shifts the focus to the most important question: Where is the data located, and who controls it?
AI Consultant in Gothenburg: How Kärcher Used AI to Make Technical Manuals More Accessible
How can AI make technical manuals more useful? Read about Aixia’s collaboration with Kärcher and what companies in Gothenburg can learn from the project.
From Model to Production: LLMOps Will Be the Most Important MLOps Shift of 2026
Here’s how to take language models from pilot to production with LLMOps: monitoring responses—not just servers—model operations, policy-as-code, and distributed responsibility in a managed service.
Liquid Cooling and Energy Requirements for 2026: The Data Center Hall Meets AI Density
An AI rack now consumes more power than an entire row of traditional IT equipment. Here’s what has changed, why liquid cooling has become the standard, and why electricity—not cooling—is the real bottleneck.
Cohesity Now Protects AI Agents: Agent Backup Is the Next Step in Cyber Resilience
AI agents take action, not just respond—and most recovery plans fail to address what happens when an agent makes a mistake. Cohesity Agent Resilience protects the agents’ memory, configuration, and the systems they interact with.
From Pilot to Production: The MLOps Pitfalls When Scaling AI
Most AI pilots never make it to production. Here are the five most common MLOps pitfalls—traceability, monitoring, model operations, resource sharing, and ownership—and what it takes to avoid them.
GPU Computing in the Nordic Region: Where Should the AI Infrastructure Be Located?
Where should computing power be located? A public cloud, an on-premises data center, colocation, or a combination? Here’s how to make that decision—and what the defense, medtech, and automotive industries should consider.
AiQu and MLOps: How to Stop Wasting Expensive GPUs
AiQu is Aixia’s MLOps platform that helps organizations efficiently manage, schedule, and optimize AI workloads.
You’ve built a RAG. Congratulations. Now the hard part begins.
Building a RAG application is easy. Getting it to produce reliable results at scale—that’s the hard part.
