Avnet /IOTCONNECT Physical AI Implementation – Edge to Cloud

Why Physical AI - Why Now

Physical AI systems that perceive their environment through sensors, reason with cloud-trained models, and act through actuators and robots is the foundational technology behind the global automation wave. Organizations struggle to bridge the gap between cloud-based AI development and real-world physical environments: powerful edge infrastructure, robust cloud pipelines, and validated simulation capabilities are all required simultaneously.

Avnet /IOTCONNECT Physical AI Implementation Services solves this end-to-end.

What Avnet Delivers

Avnet's services bring together AWS cloud AI services, and the Avnet /IOTCONNECT platform into a single, cohesive architecture.

1. Physical AI Assessment & Architecture

AWS Well-Architected review aligned to Physical AI workloads Reference architecture: edge (Greengrass v2), connectivity (/IOTCONNECT + IoT Core), cloud (SageMaker, Bedrock, Kinesis, SiteWise) and Digital twin

2. Edge AI Inference Deployment

Industrial edge platform procurement, configuration, and deployment Model optimization for edge (Greengrass ML components, on-device inference runtimes) Computer vision pipelines: defect detection, leak detection, object tracking, occupancy monitoring Local inference with store-and-forward resilience for disconnected environments

3. Cloud AI Training & MLOps Pipeline

AWS SageMaker Pipelines: model training, versioning, auto-retrain on drift Amazon Bedrock GenAI for incident summarization and operational intelligence Continuous loop: edge → IoT Core → Kinesis → SageMaker → OTA model push via /IOTCONNECT

4. /IOTCONNECT Platform Implementation

Full tenant setup: entity hierarchy, device templates, attribute mapping Fleet onboarding for PLCs, gateways, cameras, sensors, and AMRs KeyScaler PKI with X.509 auto-rotation, OTA firmware, Device Twin, rules engine Multi-tenant hierarchy for brand/site/region management

5. Application & Dashboard Development

Operational dashboards, Real-time alerting via Lambda, Step Functions, EventBridge, SNS/SES, integration via API Gateway / AppSync

6. Managed Services & Continuous Optimization

Ongoing device ops, certificate rotation, and ML model drift monitoring SLA-backed managed support with Avnet's global team Compliance-grade audit trails via CloudTrail

Proven Physical AI Use Cases

Smart Factory – Production Intelligence — Real-time edge AI on machine and line telemetry for throughput, quality, and OEE — Manufacturing Fluid / Leak Detection — Thermal + RGB vision fusion detecting leaks and anomalies before failures escalate — Energy & Industrial Smart Warehouse – Asset & Worker Tracking — Spatial awareness of assets, vehicles, and personnel for safety and flow optimization — Logistics Building Automation – Energy Management — AI-driven sensing and control of HVAC, lighting, and power across facilities — Commercial Buildings Smart Factory – 21 CFR Compliance — Validated, audit-ready production monitoring in regulated environments — Life Sciences Connected AI Drone Telemetry — Aerial sensing and autonomous inspection with live edge-to-cloud telemetry — Agriculture Vision Defect Detection — Camera-based inference catching defects at line speed, at the edge — Manufacturing

Technology Stack - Examples

Edge: Advantech industrial edge servers · Greengrass v2 · FLIR Thermal + RGB cameras · Hailo-8L NPU · Industrial PLCs (OPC-UA, Modbus, BACnet, EtherNet/IP) · STM32 / Infineon sensors

Connectivity (/IOTCONNECT): AWS IoT Core · AWS IoT Greengrass · Amazon Sidewalk · LoRaWAN · MQTT/TLS · BLE 5.x · Kinesis Video Streams · KeyScaler PKI

Cloud AI & Data: SageMaker Pipelines · Amazon Bedrock · AWS Lambda · Kinesis · IoT SiteWise · Timestream · DynamoDB · S3 · RDS

Applications: QuickSight · CloudWatch · Cognito · CloudFormation · Security Hub · EventBridge

Engagement Model

Engagements are scoped and priced as private offers through AWS Marketplace, supporting AWS. Engagements begin with a 2–4 week Physical AI Assessment, followed by phased implementation sprints.