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Safety Detectives Interview with CloudThrill CEO Kosseila Hd

Our Founder & CEO, Kosseila Hd, recently sat down with SafetyDetectives to share CloudThrill’s vision on why the real AI revolution lies in infrastructure rather than just apps, and what that means for organizations building and owning their private, scalable AI.

I. Why aren’t proprietary AI platforms the solution for a private AI audience?

Because they come with too many risks. Security breaches have already exposed customer data, and even with APIs your information can pass through third-party processors (i.e, snowflake). On top of that, you give up full control since interactions are often used to train their models, which is a non-starter for sensitive business data. In fact, even U.S. court orders have required OpenAI to retain deleted chats.

Then there’s cost and transparency. Proprietary platforms operate as black boxes with shifting pricing models tied to token usage (you pay for input, output and even thinking tokens). That makes it hard to predict expenses or even understand how decisions are being made. It’s no surprise that highly regulated sectors , from banks to healthcare, have restricted or outright banned ChatGPT and similar platforms.

At CloudThrill we take the opposite view: companies should own their AI stack. By building on Open-Source models and local inference, organizations can maintain privacy, stay compliant, and keep full control over their data and costs. That’s the path to a truly private AI practice.

II. What challenges do you see for organizations adopting AI?

• Skills gap:
  Everyone wants AI, but few teams know how to operationalize it securely and at scale. Many organizations also lack internal expertise or even clarity on the right use cases to pursue. And don’t forget this technology is barely two years old. It jumped from research papers to production almost overnight.
• Cost surprises:
  GPU bills skyrocket without optimization. What starts as a small pilot often turns into sticker shock when workloads scale, especially if no long-term budget is planned. CIOs are not PhD students in machine learning, and they often need extra guidance just to evaluate which GPU series or chip combination makes sense for their workloads. Without that, companies end up overspending fast.
• Data governance:
  Handling sensitive data in AI workflows without breaking compliance or sovereignty rules is still new territory. Think of it like a SOC2 equivalent for AI. Not every department can freely share data, yet decision makers still need access to high-level insights. Do you create a fine-tuned model for each vertical? A global RAG system? Or manage access at the application level?

These are the kinds of unresolved questions organizations face today. It is up to the AI professional community to turn those challenges into opportunities by helping customers filter signal from noise, while building AI infrastructure that is cost-efficient and compliant without cutting corners.

III. What do you believe are the smartest moves in the AI industry right now ?

I think one of the smartest moves we’re seeing in AI right now is companies focusing less on the hype around consumer-facing tools and more on building hybrid enterprise-grade AI services. Take a look at Cohere for example, they’re not out there chasing consumer buzz with splashy model releases or tiered subscriptions. Instead, they’re quietly closing big deals with telcos and financial institutions like Bell , The Government of Canada and RBC.
Their model is about combining deep neural network expertise with the real, business-critical data of large enterprises, and giving themselves (and their customers) the time to iterate, make mistakes, and perfect solutions before productizing them at scale. A striking resemblance to how the likes of SAP became the backbone for ERP systems: not flashy, but deeply embedded and indispensable. That’s the kind of execution that hits all the boxes, sustainable, enterprise-first, and transformative in the long run.

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