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.

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Asset-Specific Machine Learning

MicroAI AtomML is an Edge-Native AI, self-correcting, semi-supervised learning engine that aggregates data from device and machine sensors to create a behavioral profile of the asset and then actively monitors for abnormal performance and cyber-security intrusions. Advantages to this endpoint approach include:

Lower Cost

AtomML processes data at the edge, vs in the cloud, reducing overall data handling cost by 70 to 80%.
Predictive Analytics

Predictive algorithms minimize maintenance costs while optimizing asset health scores, uptimes, and productivity.
Enhanced Security

AtomML learns the normal state of a device or machine and actively monitors for abnormal behavior induced by cyber-attack.
Speed and Agility

By processing asset data locally, AtomML allows for rapid data sampling rates for real-time monitoring of asset performance without the need for transmission of data to the cloud.

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.

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

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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.

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: