Sovereign Edge AI

Edge-Native SLMsSovereign AI for Air-Gapped and Regulated Environments

Fine-tuned small language models deployable at the edge — inside hospital networks, classified government systems, or disconnected industrial facilities.

Compliance & Security

Data never leaves client perimeter
FIPS 140-2 compliant cryptography modules
UK Government NCSC cloud security aligned
US DoD CMMC Level 2 architecture-compatible
<50ms

Inference latency on standard server

96.1%

Domain task accuracy vs GPT-4o

0.7B

Average model parameter count

Architecture

Designed for Environments Where Data Cannot Leave

Sovereign Deployment

Deploy inside your network perimeter with no data leaving your jurisdiction. Fully air-gapped operation on customer-controlled infrastructure.

Domain Fine-Tuning

Pre-trained on curated domain corpora for healthcare, finance, legal, and industrial settings, with further RAFT customization to your workflows.

Edge Hardware Optimization

INT4 and INT8 quantized models optimized for NVIDIA Jetson, AMD EPYC, Intel Gaudi, and standard x86 servers without GPU acceleration.

Federated Learning

Continual improvement via federated learning pipelines without centralizing sensitive data, compliant with GDPR data minimization principles.

Deployment

From Assessment to Production in 8 Weeks

1

Assess & Select

We evaluate your hardware, use case, and compliance requirements to select the optimal model variant and quantization level.

2

Domain Fine-Tune

RAFT fine-tuning on your proprietary documentation establishes domain accuracy before deployment.

3

Secure Deploy

Model is deployed within your sovereign perimeter — air-gapped network, private cloud, or approved on-premise infrastructure.

4

Monitor & Improve

Federated monitoring and incremental fine-tuning cycles keep your model accurate as your data evolves.

FAQ

Technical Questions

Ready to Deploy Sovereign AI?

Request a technical briefing. We'll walk through your infrastructure requirements, compliance constraints, and optimal model configuration.