Physical AI & Sovereign AI Resources
Reference architectures, checklists, and sizing guides for perception infrastructure at the edge and governed AI applications on private infrastructure. Request access to any of these.
Available Resources
Sovereign AI Platform Overview
Comprehensive introduction to the Enfuse.io sovereign AI platform. Covers Runtime layer, App Factory, MCP services, and deployment patterns for regulated enterprises.
MCP Services Reference Sheet
Technical reference for Model Context Protocol services: DocuFlow (RAG), VoxSovereign (Audio), Panopticon (Vision), Cloudberry (Data Foundation).
On-Prem LLM Deployment Checklist
Step-by-step checklist for deploying large language models on private infrastructure. Hardware requirements, security configuration, and operational readiness.
Compliance Framework Matrix
Mapping of sovereign AI capabilities to compliance frameworks: ITAR, FedRAMP, HIPAA, SOX, GDPR, and industry-specific regulations.
Physical AI Reference Architecture
Perception infrastructure blueprint: multi-sensor capture, calibration and synchronization, edge inference on NVIDIA Jetson, fusion and tracking, and central GPU workloads.
Edge Perception Deployment Checklist
Field readiness checklist for perception deployments: sensor rig calibration, thermal and power headroom, on-device model profiling, and fleet update paths for disconnected sites.
Air-Gapped Deployment Guide
Technical guide for deploying sovereign AI in completely disconnected environments. Signed offline artifacts, secure update mechanisms, and offline operation patterns.
Jetson & GPU Sizing Guide
Sizing method for edge and central compute: Jetson Orin class selection per sensor load, and B200/H200-class server sizing for training, digital twins, and inference.
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