Edge AI Deployment & Integration — Intelligence Where It's Needed
End-to-end AI inference pipelines on edge devices (Jetson, RPi, custom NPUs). Integration with XF products or third-party hardware.
The Problem
97% of CIOs have Edge AI on their roadmap, but most lack the expertise to move from pilot to production. Cloud-first architectures fail in bandwidth-constrained environments.
What We Deliver
Edge Deployment Architecture
Complete inference pipeline design for your hardware targets (Jetson, RPi, custom NPUs)
Optimized Inference Pipeline
ONNX/TensorRT optimization for maximum performance and minimal latency
Real-Time Alert Streaming
Low-latency event streaming over constrained networks (3G/4G)
Dashboard Integration
Unified monitoring and control interface for fleet management
Technology Stack
ONNX
Model format conversion
TensorRT
NVIDIA GPU optimization
Jetson / RPi
Edge hardware platforms
Custom NPUs
Specialized accelerators
MQTT / Kafka
Event streaming
Docker
Containerized deployment
Deployment Models
Cloud-Connected Edge
Edge inference with cloud sync for analytics and model updates
Fully Offline Edge
Complete autonomy with no cloud dependency (defense, oil & gas)
Hybrid Architecture
Edge for real-time, cloud for batch processing and retraining
Multi-Device Fleet
Centralized management for 100s-1000s of edge devices