Intelligence
at the Edge
A protocol for distributed machine learning that brings AI capabilities to existing infrastructure without centralized data processing
The Gap We're Filling
Centralized AI systems fail at the edge due to latency, privacy risks, and connectivity constraints.
Legacy Infrastructure
Trillions invested in industrial equipment not designed for AI. Replacement is costly and disruptive.
Data Sovereignty
Centralized systems risk non-compliance with GDPR, CCPA, and industry regulations.
Real-Time Demands
Critical applications require millisecond decisions that cloud systems cannot deliver.
Distributed Intelligence
Collaborative AI across heterogeneous devices without centralizing sensitive data.
How It Works
Federated Learning
Models train locally. Only encrypted updates are shared, preserving privacy.
Hardware Attestation
Cryptographic proofs verify computation integrity on untampered hardware.
Edge-First Design
Processing at data sources eliminates latency for real-time inference.
Design Principles
Privacy-First
Data never leaves the source. Only encrypted model updates are shared.
Protocol-Agnostic
Compatible with Modbus, OPC-UA, MQTT, and LoRaWAN without modification.
Decentralized
No central authority. Proof-of-authority consensus ensures security.
Industries & Applications
Enabling AI at the edge across critical industries.
Manufacturing
Predictive maintenance and quality control with federated learning across factory equipment.
Agriculture
Distributed learning across farms for yield optimization without sharing sensitive crop data.
Smart Cities
GDPR-compliant camera and sensor data processing for traffic and emergency response.
Environmental
Real-time monitoring of air quality, water systems, and climate with edge analytics.
Robotics & Automation
Enable autonomous systems to learn collaboratively while maintaining real-time responsiveness and data privacy.
Fleet Learning
Warehouse robots and delivery systems share insights without exposing facility layouts
Collaborative Robots
Industrial cobots optimize tasks while preserving manufacturing trade secrets
Autonomous Vehicles
Fleet-wide learning with cryptographic verification for safety-critical decisions
Modern Edge Hardware
Optimized for the latest generation of edge AI accelerators with GPUs, NPUs, and specialized AI chips.
GPU Acceleration
NVIDIA Jetson platform from Nano to AGX Orin delivering up to 275 TOPS for vision and robotics.
NPU Efficiency
Intel, AMD, and Qualcomm NPUs with up to 99 TOPS combined for on-device AI inference.
Edge TPU
Google Coral ultra-efficient accelerators delivering 4 TOPS at 2W for vision applications.
Framework Support
AI Frameworks
TensorFlow, PyTorch, ONNX Runtime for cross-platform deployment at the edge
Development Tools
NVIDIA Isaac, Edge Impulse, ROS 2 for robotics and embedded systems
Supported Platforms
Compatible with industry-leading hardware and cloud infrastructure
Jetson • CUDA
Coral • Cloud
Movidius • Core
Ryzen AI
Snapdragon
Cortex • Ethos
Edge Accelerators
GPUs, NPUs, and TPUs for high-performance AI inference
Cloud Integration
Hybrid edge-cloud workflows with major providers
Industrial Systems
Compatible with embedded systems and IoT devices
Enterprise Ready
Built with security, compliance, and compatibility at the core.
- TEE Execution
- Hardware Attestation
- Differential Privacy
- End-to-End Encryption
- Modbus
- OPC-UA
- MQTT
- LoRaWAN
- GDPR
- CCPA
- HIPAA
- ISO 9001
- Federated Learning
- Ensemble Models
- Proof of Authority
- Edge-First
Join the Edge Revolution
We're building the future of distributed intelligence. Connect with us to learn more about deploying AI at the edge.