The Technology Behind Physical AI
Edge-first inference engines, privacy-first architecture, and real-time streaming protocols that power the XF Platform.
XF Platform Architecture
Four layers working together to deliver real-time Physical AI.
1. Inference Engines
Edge-optimized models running on NPUs, GPUs, and CPUs. TensorRT, ONNX Runtime, TFLite.
2. Sensor Fusion Layer
Multi-sensor integration: Camera + GPS + IMU + LiDAR + Radar. Kalman filters for data fusion.
3. Streaming Protocol
Real-time event streaming fallback. Works offline with 4-hour battery backup.
4. AI Agent Layer
Autonomous decision-making agents for predictive alerts and automated responses.
Core Technologies
PyTorch
Model development and training
TensorRT
NVIDIA GPU optimization
ONNX
Cross-platform model deployment
ROS2
Robotics middleware
OpenCV
Computer vision processing
Rust
High-performance edge runtime
Kafka
Event streaming
TimescaleDB
Time-series data storage
Kubernetes
Container orchestration
Privacy-First Architecture
Metadata-First Storage
We store behavioral metadata (attention scores, risk events), not raw video or biometric data.
Edge Processing
All AI inference happens on-device. No cloud dependency for core functionality.
Zero Face Storage
XF Campus and XF Classroom never store facial images or biometric templates.
Compliance
GDPR, FERPA, CCPA, ISO 27001 compliant by design.