AWS re:Invent 2025 - TDK SensEI built scalable IoT platform with AWS for sensor insights (NTA204)

TDK SensEI: Transforming Industrial Operations with AWS

Overview of TDK SensEI

• TDK SensEI is a next-generation edge intelligence platform that combines advanced sensors, AI, and machine learning to deliver real-time actionable insights for manufacturing and industrial environments.
• The company was formed just over a year ago with the mission of bringing real-time intelligence and predictive, AI-driven technologies to factory management and manufacturing solutions.
• TDK SensEI has a global footprint across North America, Asia, Japan, and other parts of the world, and focuses on four key market areas: manufacturing, smart buildings, logistics and distribution centers, and the energy sector.

Evolution of Industrial Automation

• The industrial automation space has evolved from condition-based monitoring, which focuses on detecting problems as they occur, to predictive maintenance, which aims to detect problems before they occur to prevent downtime.
• The latest focus is on prescriptive or "agentic" maintenance, where the system not only predicts problems but also analyzes and provides solutions, and can even orchestrate the response automatically.

TDK SensEI Platform Architecture

• The TDK SensEI platform leverages a combination of on-premises and cloud-based components to deliver its capabilities:
  • On-premises: Gateway devices connected to sensors in the factory, collecting data
  • Cloud: AWS services for data storage, machine learning model training, and more
• Key AWS services used include:
  • IoT Core and Greengrass for edge computing and device connectivity
  • SageMaker for machine learning model development and deployment
  • Timestream for time-series data storage
  • Cognito for identity management
  • CloudWatch and EventBridge for logging, monitoring, and security

Security and Compliance

• Security and compliance are critical for TDK SensEI, as the platform is deployed in sensitive manufacturing environments.
• The architecture is designed with a "security-first" approach, leveraging AWS services like CloudWatch, EventBridge, GuardDuty, and Config to ensure comprehensive device provisioning, tenant isolation, and compliance monitoring.
• All connectivity from the factory to the cloud is done over a single, encrypted MQTT protocol, with no inbound connectivity from the cloud to the factory.

AI and Machine Learning Capabilities

• TDK SensEI uses AWS SageMaker and Step Functions to automatically build and train machine learning models for sensor data, without requiring manual configuration by customers.
• The models are deployed directly on the edge devices, allowing them to detect anomalous behavior in real-time.
• The platform also leverages "agentic AI" capabilities, where intelligent agents can analyze the data, recommend solutions, and even orchestrate the response to predicted failures, such as:
  • Identifying the parts needed for a repair
  • Checking parts availability in the ERP system
  • Scheduling maintenance with the appropriate technicians
  • Automatically placing orders for missing parts

Business Impact and Use Cases

• TDK SensEI's platform is designed to have a significant business impact, including:
  • Reducing unplanned downtime in manufacturing and industrial operations
  • Optimizing maintenance costs by proactively addressing issues before failures occur
  • Creating digital twins of operational data to enable better decision-making across industries
• The platform is applicable to a variety of industries, including:
  • Manufacturing: Improving equipment reliability and reducing maintenance costs
  • Smart buildings: Optimizing building systems and energy efficiency
  • Logistics and distribution: Enhancing supply chain visibility and resilience
  • Energy: Optimizing asset performance and reducing operational expenses

Availability and Roadmap

• TDK SensEI is now available on the AWS Marketplace, making the solution accessible to customers facing these industrial automation challenges.
• The company is focused on continuing to evolve the platform, moving beyond just prediction to creating automated solutions and orchestrating responses to predicted issues.