Embedded Intelligence that Optimizes Machine Performance
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AtomML™

MicroAI AtomML is an Edge-Native AI platform that lives directly on the MCU or MPU of a device or machine. AtomML provides deep observability into the performance, health, and security of IT and OT assets. Operational Excellence at the endpoint.

Our Tech: Closed-Loop Asset Observability

  • I/O Layer
  • Auto - Tuning
  • Health Scores
  • Fault Detection
  • Alerts
  • Root Cause
  • Corrective Action
I/O Layer
Auto - Tuning
Health Scores
Fault Detection
Alerts
Root Cause
Corrective Action

I/O Layer

Live data is leveraged from a variety of devices...

Auto - Tuning

Fully automatic tuning of the AI model(s)...

Health Scores

Real-time, on-demand, health scores provide...

Fault Detection

Embedded ML algorithms learn the normal operating...

Alerts

The embedding and training of intelligent workflows...

Root Cause

High-speed processing of historical asset performance...

Corrective Action

Through accurate identification of root cause...

Customized Asset Observability

AtomML embeds personalized intelligence into individual devices and machines within the asset ecosystem. Operating within the asset itself, AtomML provides deeper–and more efficient– observability into the performance, health, and security of the device or machine. Benefits of this endpoint visibility include:

Small Footprint

AtomML is small enough to live, train and inference on a micro-controller (MCU) or micro-processor (MPU) eliminating the need for extraneous hardware and minimizing cloud dependance.

Endpoint Intelligence

Proprietary algorithms analyze times-series data from machine and device sensors to deliver deep insights into the behavior of critical assets.

Predictive Maintenance

AtomML provides the learning and asset observability required to evolve from planned (inefficient) maintenance routines to predictive routines that are more productive and less disruptive.

Rapid Alert and Mitigation

AtomML utilizes multidimensional behavioral algorithms to produce recursive analysis, training, and processing, providing real-time performance alerts and workflow-enabled notifications and mitigations.

Asset-Centric Cyber Security

AtomML embeds and trains advanced security algorithms directly into a device, machine, or process. AtomML learns the normal state of device behavior and provides early-stage detection of profile deviations caused by cyber intrusion. Edge-Native AI security that delivers:

  • Asset-Specific Security Insights

    AtomML embeds security learning and protocols that are customized for the specific device or machine.

  • Local Monitoring and Processing

    Processing critical data at the endpoint eliminates security risks associated with cloud data transfer and storage.

  • Improved Precision

    Endpoint security provides more precise analysis of current asset state as well as actionable predictive analytics.

  • More Robust and Less Costly

    AtomML provides asset cyber protection that is more hardened, more predictive, more rapid, and less costly than other solutions available today.

Improved OEE

Many Industry 4.0 initiatives are geared toward improving OEE (overall equipment effectiveness). The manufacturing and industrial automation segments have struggled to surpass the 70% OEE mark. AtomML is the Industry 4.0 solution to improved OEE.

  • A 15% improvement in OEE can equate to a 17% increase in productivity. An operation producing $60M worth of products can increase their output to ~ $70M.
  • Improved OEE equates directly to a reduction in asset maintenance costs. Unnecessary maintenance is eliminated via the implementation of predictive maintenance.
  • Higher OEE scores translate to improved quality of the products being produced. Machine and device performance are more reliable and more predictable.
  • Production costs are reduced. This results in improved product pricing as well as healthier bottom lines.
industry4.0

Rapid and Cost-Effective Deployment

AtomML has a tiny footprint, is hardware agnostic, is common code based, and can be deployed onto virtually any type of device or machine. AtomML requires no data labelling or expensive pre-training. AtomML can be deployed in several ways, including:

Embedded in the MC chip directly from the semiconductor supplier

Embedded as new over-the-top (OTT) firmware once the device OEM has taken delivery of the device MCU

As a unique, purpose-built, OTT machine module solution

Example Deployment Model

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