We need a new system for the AI era

Why the AI Communications Hub will shape next-generation AI infrastructure

AI has created a new reality

Yet, many overlook the fact that we’re still trying to connect this new technology to yesterday’s infrastructure. We’re using tools never designed for the tasks we now expect AI to handle—securely, flexibly, and with total control.

This creates real daily challenges for public and private enterprises alike.

• Imagine a municipality that wants to use AI to streamline case management or citizen services. Their data sits scattered between internal archives, cloud storage, and external vendors. When the municipality tries to connect this data to a generative AI model, four problems quickly arise:
  The AI returns generic answers because it cannot access relevant, contextual information specific to this municipality.
• The security team hits the brakes, as no one truly knows where sensitive information ends up when the AI model resides beyond their direct control.
• Projects stall because internal (and external) systems don’t speak the same language.
• Trust deteriorates—among employees and residents alike—as AI-driven decisions lack transparency and traceability.

Businesses face identical challenges

Consider a Norwegian export-oriented industrial company looking to use AI to glean insights from markets, production data, and customer feedback. What are the obstacles?

• Data fragments across ERP systems, emails, CRM, and production facilities.
• The AI model lacks the proper context because data doesn’t flow freely.
• Fear of data leakage stalls initiatives, as business-critical data could end up in foreign AI services.
• No ownership of the AI process—forcing the company to conform to a specific AI vendor’s rules and structure.

The outcome is predictable: AI becomes a tool you never fully trust

Equally problematic, AI never achieves its full potential due to inadequate infrastructure. Currently, it seems all systems in your architecture want to offer AI services, resulting in increased fragmentation and diminished control. You quickly lose sight of which tools and LLM models you’re using, who has access to your data, and what it’s actually costing you.

Placing one module at the core—an AI Communications Hub (ACH)—that draws data from multiple sources, provides significantly enhanced control. Control over your architecture, control over your licensing, and most importantly, control over your data. It’s high time we talked about what’s around the next AI bend:

AI Communications Hub (ACH).

ACH is a new category—with humans at the core

With a bit of imagination, it’s hard to see clear limits to where this can take you.

Opportunities expand with AI’s continued evolution—and with an AI Communications Hub (ACH), you’re firmly in the driver’s seat.
Just as CMS (Content Management Systems) were developed to structure and publish content during the early days of the internet, ACH is built to connect your data to AI in the age of artificial intelligence. In other words: AI needs its own infrastructure.

An ACH gives you:

• Full control over data flow: You decide exactly which data AI accesses—and how it’s used, internally and externally.
• Flexibility in AI model selection: Assign tasks to the AI model that fits best—seamlessly switch between ChatGPT, NorLLM, Mistral, or your custom models.
• Flexible storage options: Choose freely whether your data is stored in the cloud, on-premises, or a hybrid solution—and easily move your data whenever needed.
• Centralized AI communication management: Put AI to work exactly where you need it—customer support, knowledge bases, automated research, or business analytics.
• Secure architecture: Keep data safely behind your own firewall, and channel all AI-driven interactions through a single, unified control point.

At TellusR, we believe the AI Communications Hub will become just as fundamental to AI as CMS became to the internet.

Magnus Bakken, Head of Digital at Fritzøe Engros, follows this development closely. He explains that in Fritzøe Engros’s efforts to adopt AI, they’ve encountered exactly these challenges:

• Connecting generative AI safely to business-critical data is difficult without sacrificing flexibility and innovation, he says, adding:
• That’s why I engage with TellusR. This isn’t just about technology—it’s about empowering people with better tools to make safer, more informed decisions based on genuinely valuable insights.
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