Leading the Way: Awards for Product Innovation

2019 CES Innovation Awards Honoree

2019 MWC Global Mobile Awards Shortlisted

2020 AIconics Awards
Shortlisted

2021 CES Innovation Awards
Honoree

2024 CES Innovation Awards
Honoree

Our Edge AI Solutions

AutoML

Accelerate the pace of edge AI development & scale

Fully Automated No-Code Tool
Intuitive UI that enables developers to build models in minutes

Wide Range of ML Methods
Supports regressors, decision trees, & neural networks

Optimized for Embedded Applications
Optimized for low latency, power, and memory

Engineered AI

Comprehensive end-to-end AI development experience

Full-Service AI Delivery
Full AI development cycle: Data pre-processing through model development & deployment

Industry Experts
All required expertise on staff: Hardware, Software, AI, & Embedded Engineering

Infrastructure Optimization
Experience building and scaling AI models across all types of environments

Real-Time Machine Health Monitoring

Complete AI-Powered CbM system

Hardware
Wide range of supported sensor devices and connectivity devices

AI-Powered CbM Models
Multi-Class anomaly detection models to gain more insights from sensor data

Reporting Dashboard
Custom dashboard to provide increased visibility into operations

What is Edge AI?

Edge AI refers to the deployment of artificial intelligence algorithms and models directly on edge devices, such as sensors, gateways, and other IoT (Internet of Things) systems, rather than relying on expensive centralized cloud servers for data processing and analysis. Benefits include:

Scalability

Edge AI supports distributed computing architectures, allowing AI models to be deployed across a network of edge devices. This scalability is beneficial for applications spanning large geographical areas or involving many edge devices.

Real-time Decision Making

By processing data locally, Edge AI enables real-time decision-making capabilities. This is critical in applications where immediate action is required, such as in industrial automation.

Privacy and
Security

Keeping data on the edge device can enhance privacy and security, as sensitive data does not need to be transmitted over networks or stored in centralized cloud servers.

Bandwidth
Efficiency

Edge AI reduces the amount of data that needs to be transmitted to the cloud. This  optimizes bandwidth usage  and reduces associated resource costs.

Cost
Efficiency

By processing data locally, Edge AI can reduce costs associated with cloud computing resources and data transfer fees.

Partnerships That Drive Success