Next-Generation of Edge and Embedded Intelligence
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MicroAI Technology

MicroAI AtomML™ is a self-correcting, semi-supervised learning engine that aggregates data from PLCs, IoT sensors and other existing sources, to tune itself to create a 360-degree view of asset behavior – delivering performance improvements and security enhancements to any IoT device or machine.

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Key Capabilities and Benefits

cyber

Protection from
Cyber Threats

  • Detection of unauthorized smart asset access
  • AI-enabled assessment of abnormal network behavior
  • Automated analysis of abnormal data variability
  • Self-learning to improve communication and authorization protocols

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Smart Asset Health &
Performance Management

  • Real-time asset performance monitoring enables proactive response to performance anomalies.
  • AI-enabled predictive analytics provide insight into potential future failure.
  • Optimization of maintenance schedules to avoid unnecessary machine servicing.
  • Automated fine-tuning of asset production rates to attain an optimum balance between productivity and asset utilization.

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How It Works

MicroAI’s unsupervised learning engine evolves by fine tuning itself in order to deliver the most optimal performance improvements and security enhancements to the machine or device.

To provide the most insight, an AI algorithm must be able to ingest numerous forms of sensor data (e.g. acceleration on the x, y, and z axes) MicroAI AtomML™ can concurrently analyze as many data types as needed to deliver critical alerts with the highest correlation to the specific event.

When assets are sensitive to environmental fluctuations, such as magnetic field disturbances, operators and technicians require high frequency analytics to alert them of any issues. MicroAI AtomML™ delivers this seamlessly.

In mission-critical operations, actions must be taken on a split second basis to avoid a catastrophic event. MicroAI AtomML™ processes all the asset data locally enabling immediate event related alerts and faster response time.

MicroAI AtomML™

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MicroAI AtomML™ provides AI and ML developers with a revolutionary approach to AI training of smart devices and machines. AI training that occurs right on the device/machine MCU.

MicroAI AtomML™ brings big infrastructure intelligence into a single piece of equipment or device. Unlike traditional AI-driven asset management solutions that rely on edge-based microcontrollers, MicroAI AtomML™ is deployed directly onto smart devices and sensors.

The advantages of training IoT assets right at the device MCU….


  • Flexibility & Scalability

    Deploy on a single asset (MCU) or scale across an entire asset ecosystem


  • Speed & Agility

    Data processed locally—not in the cloud—provides rapid, real-time, data-sampling


  • Security

    Real-time visibility into asset health and self-learning that enables rapid threat-response


  • Cost

    Asset model training occurs directly on the asset MCU—no need for costly new hardware

See MicroAI AtomML™ in Action

APM Tutorial

Security Tutorial

Closing the Loop

Accessing the MicroAI™ Cloud Infrastructure

MicroAI’s Cloud provides the infrastructure to access data from any connected source and execute complex algorithms that use data science, machine learning and artificial intelligence. The integrative capabilities work together to deliver analytics that optimize the performance of every asset while automating sales, service and core internal workflows.

Accessing the MicroAI™ Mobile Application

MicroAI’s Mobile Application brings Next Generation Optimization into the hands of your Technicians, Salespeople and Supervisors. By curating analytic output from the EDGE and CLOUD, MicroAI™ MOBILE empowers your field force to address the right task at the right time to improve the customer experience. With MicroAI™ MOBILE, stakeholders optimize their daily operations with more visibility and confidence than before.

Interested in how MicroAI can benefit you?

 MicroAI AtomML brings big infrastructure intelligence down into a single piece of equipment or device.

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