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Accelerating Production-Ready Intelligence at the Edge
Our edge AI design partner ecosystem extends our full-stack strategy by combining silicon, software, AI models and deployment expertise. These partners help customers move from data to production-ready embedded intelligence faster, with lower risk and optimized performance.
Capabilities Our Edge AI Partners Offer
AI Model Development and Optimization
Sensor Fusion and Domain Algorithms
Production Deployment Engineering
Edge Analytics Platforms
Edge-to-Cloud Lifecycle Management
Full-Stack Edge AI Partnerships
AI Model and Embedded Intelligence Partners
- Model creation, optimization and deployment to silicon
Edge Analytics and Fleet Management Partners
- Dashboard, lifecycle, remote monitoring and scaling
Edge AI Design Partners
221e
AI Model and Embedded Intelligence
221e delivers sensor-fusion AI that combines multi-sensor data with embedded Machine Learning (ML) to enable real-time context awareness, anomaly detection and motion intelligence directly at the edge, without relying on the cloud.
Vedya Labs
AI Model and Embedded Intelligence
Vedya Labs is an AI software and systems engineering partner that specializes in the delivery of optimized, production-ready, on-device edge AI solutions to help semiconductor vendors, OEMs and system companies deploy real-time intelligence on embedded devices.
WGTech Solutions
AI Model and Embedded Intelligence
WGTech Solutions provides end-to-end edge AI engineering services, including model development, optimization and deployment, helping customers move AI from prototype to production on embedded hardware.
Avnet®/IOTCONNECTTM
Edge Analytics and Fleet Management
Avnet’s /IOTCONNECT™ provides a secure edge-to-cloud platform for the creation, deployment and security of edge AI solutions at scale, enabling remote monitoring, analytics and lifecycle management of deployed devices.
Stream Analyze
Edge Analytics and Fleet Management
Stream Analyze offers a lightweight analytics and AI platform that enables real-time data processing and ML inference on resource-constrained edge devices to support applications like predictive maintenance and condition monitoring.
Machine Learning (ML) on the Edge for Speed and Security
Running Artificial Intelligence (AI) models on the edge involves processing data locally within embedded systems, which reduces latency and enhances privacy by minimizing data transmission to the cloud. This approach doesn’t require Internet connectivity and enables real-time decision making, making it a great solution for applications that require immediate responses and high reliability.