We’re opening internal access to FPL’s AI-native cloud platform.
The platform brings together GPU and CPU compute substrates, with a focus on on-premises deployments and sovereign AI. We’re starting with internal use: putting the infrastructure to work, learning from real workloads, and improving the experience before opening it more broadly.
Our goal is straightforward: make AI infrastructure useful without asking teams to give up control of their computing environment.
More Than a Place to Run a Model
An AI application needs more than an inference endpoint. It needs the software around the model: services, data preparation, integrations, storage, and the everyday operations that keep a system usable.
That’s why we’re approaching this as a computing platform, not just a collection of GPUs. GPU acceleration and CPU-based services both belong in the picture. We want teams to build around the needs of their applications, with the right compute for each part of the job.
For FPL, that work connects directly to the tools we build for manufacturing, engineering, and software development. We’re interested in AI that can become part of a working system, not remain an isolated demonstration.
On-Premises by Design, Sovereignty as a Priority
Cloud should describe a way of operating infrastructure, not a requirement to put everything in someone else’s data center.
Our focus is on bringing that operational model to infrastructure organizations control. On-premises deployment matters when a team needs to work close to its equipment, integrate with existing systems, or make deliberate decisions about where its data and workloads live.
Sovereign AI is part of that same direction. To us, it starts with practical questions: Who operates the infrastructure? Where does the workload run? Who can access it? How dependent is the system on a single outside provider?
Those questions should shape an architecture from the beginning. They aren’t answered by putting a label on a model, and this announcement isn’t a claim of blanket regulatory compliance. The deployment requirements and responsibilities need to be understood for each organization.
Starting Internally
Internal access gives us room to test the platform against our own needs before making broader availability commitments. We’re using this stage to learn what is useful, where the operational friction is, and what needs to improve.
This is not a public self-service launch. We’re not announcing general availability or a release date here. We are opening the conversation with people who care about the same problems: running AI on their own terms, connecting it to real work, and building systems they can continue to understand and operate.
Let’s Talk
If you’re exploring on-premises AI, CPU/GPU infrastructure, or sovereign deployment options, we’d like to hear what you’re building and what constraints you’re working with.
Contact Future Present Labs or email [email protected] for more information and to discuss your requirements.