SOVPOWERAI

Sovereign AI
Starts With Power

Self-powered, modular AI Factories for customer-owned AI infrastructure.

WHY NOW

Sovereign AI Is Moving From Policy to Architecture

AI demand is growing faster than conventional data center capacity can be built. For many enterprises, institutions, governments, and regional operators, the bottleneck is not only access to AI models. It is usable infrastructure: powered, local, financeable, controllable, and deployable on a realistic timeline. The following three converging dependencies make sovereign infrastructure not optional but strategically mandatory.

CRITICAL RISK
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HYPERSCALE DEPENDENCY

Enterprises and governments worldwide rely on a handful of foreign frontier providers. Every prompt and every dataset flows through their closed-box systems.

CRITICAL RISK
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ENERGY CONSTRAINT

Traditional grids are at capacity. Sovereign AI requires private, off-grid power generation to bypass infrastructure delays and rising energy costs.

HIGH RISK
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CAPACITY BOTTLENECK

Model access is becoming a commodity, but dedicated compute is not. Real sovereignty is only achieved by controlling the physical hardware layer.

CORE THESIS

Sovereign AI starts with deployable power and compute. Real sovereignty is not only where the model runs — it is whether the infrastructure can be financed, deployed, powered, controlled, and upgraded by the customer.

PRODUCT

The Sovereign Deployment Layer

A SOVPOWER AI Factory is a modular AI data center system that integrates compute, energy generation, cooling, and secure operation into one deployable system — operational within weeks, independent of the local grid.

Air-gap capability is an architectural option for institutions that need AI capacity inside their own legal, operational, and security perimeter.

LAYER 01

SOVEREIGN COMPUTE

High-density compute clusters in standardized modules. No virtualization overhead — pure performance for AI training and inference. Fully operator-controlled to ensure data remains within the owner's legal perimeter.

LAYER 02

SELF-POWERED OPERATION

Integrated on-site power generation creates a structural cost advantage and total energy independence. The facility operates without any connection to the local power grid, bypassing years of infrastructure delays.

LAYER 03

MODULAR DEPLOYMENT

Standardized module footprint deployable on any industrial site within weeks. Scales linearly from a pilot unit to multi-rack regional clusters — without rebuilding infrastructure or modifying the site.

SOVPOWER Modular AI Factories
0 WKS
TIME TO LIVE Contract to operational
0 KW
ENTRY MODULE Per AI factory module
UP TO 0 KW
MAX MODULE Per AI factory module
UP TO 0 GPUs
NVIDIA BLACKWELL (ULTRA) Per AI factory module
MAF VS. TRADITIONAL DATA CENTER
METRIC
MAF MODULAR AI FACTORY
TRADITIONAL DATA CENTER
DEPLOYMENT TIME
✓ Operational within weeks
3–5 years
POWER SOURCE
✓ Integrated on-site power
Grid-dependent
ENERGY COST
✓ Designed for structural power-cost advantage
High industrial tariffs
DATA SOVEREIGNTY
✓ Full — on-premise, air-gap capable, no cloud dependency
Partial or none
MIN. VIABLE SCALE
✓ Pilot-scale deployment before hyperscale capex
Massive upfront infrastructure
ARCHITECTURE

Module Architecture

Two standardized modules form one self-sufficient AI factory. On the left, the Energy Module houses the gas generators and battery system for fully independent power. On the right, the Compute Module holds the AI cluster and its cooling.

Scroll to follow the energy flow — then click the four components to explore how the system works.

Energy Module Compute Module
Scroll to explore
TEAM

The Architects

PHILIPP FEINDERT
President & Co-Founder

PHILIPP FEINDERT

German Diplom-Ingenieur in Electrical Engineering and Information Technology. Eight years at Mercedes-Benz Research, most recently in AI strategy and forecasting. Connects AI strategy, infrastructure architecture, and the energy systems required to make sovereign AI operational.

POWER ELECTRONICSAI STRATEGYSOVEREIGN INFRASTRUCTURE
JAN JUNGE
CEO & Co-Founder

JAN JUNGE

German Diplom-Ingenieur in Electrical Engineering and Information Technology. Ten years at Mercedes-Benz, combining engineering depth with experience in technology transformation, AI-driven systems, and business strategy. He leads the company with a focus on innovation and helping organizations turn artificial intelligence into real business value.

SYSTEM ARCHITECTUREHPCSECURITYINFRASTRUCTURE
CONTACT

Explore Sovereign AI Deployment

Speak with us about self-powered AI infrastructure for public-sector, industrial, education, and regional compute use cases.