Weightless AI
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Weightless AI

AI integration in weeks instead of months. Plug and play Endpoint and Edge AI technologies that are friendly to existing infrastructures, rapidly deployed, and quick to provide business and operational value. Frictionless AI Integration!

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What Makes our Technology “Weightless”?

Under the Hood

As the name implies, MicroAI’s core engine (AtomML) is extremely small and lightweight.
There is no smaller AI engine available on the market today. Our technology differentiators include:

  • Eliminates the need for the data exploration phase of potential AI project deployment
  • tunningFully automatic tuning of the AI model(s) to be deployed
  • tunningDynamic, automated, feature engineering
  • Eliminates the heavy burden of collecting and synthesizing historical data
  • Creates baseline asset performance models based on real-time data ingestion
  • Eliminates the need to store large amounts of data at the edge/endpoint or on the cloud
  • Provides developers with the option to…
  • Utilize AtomML’s closed data ingestion feature that automatically fetches data from specified asset endpoints
  • Create their own data ingestion code
  • Real-time streaming and analysis of data greatly reduces or eliminates cloud-based data storage
  • Elimination of data storage reduces vulnerability to cyber attack
  • Low memory requirement reduces overall “weight” of AI solution

Rapid Customization and Deployment

A common barrier for companies looking to utilize AI to optimize operational performance is…. time. Deployment of existing technologies can take months, during which time there can be multiple disruptions to daily operations. Those disruptions cost money and can negatively impact bottom lines for extended periods of time.

MicroAI’s endpoint (AtomML) and edge (AtomML+) technologies were designed to reduce the time required to customize AI-enabled products to meet specific customer needs and then to deploy those products into the customer’s machines, factory, factories, networks, etc.


Rapid Evaluation of AI Model Impact

By utilizing MicroAI’s AIStudio platform, users can dynamically evaluate the capabilities of MicroAI’s AI technologies on their asset data. Various AI models can be quickly tested to determine a model that will deliver maximum value to a particular use case.
AIStudio simplifies and expedites the process of evaluating how MicroAI’s technologies can bring value to asset performance optimization use cases. AI model evaluation that is…

  • Faster:

    Complete assessment in hours instead of days/weeks

  • Simpler:

    Easier to use, requiring no extraneous expertise

  • More flexible:

    In-built flexibility to accommodate specific assessment requirements

  • More dynamic:

    Real-time model assessment vs time-lag limitations

  • Less costly:

    Ability to evaluate AI models in a virtual environment significantly reduces cost

  • Less Risky:

    Ability to thoroughly evaluate before making an investment

Deployment Model


Deployment Time

Frictionless Integration

MicroAI’s plug-and-play technology can be quickly integrated with existing customer applications and platforms. An ecosystem of intelligence is created without having to scuttle expensive assets and/or to procure new supporting solutions. Depending on the scope of integration, this can save the enterprise 10’s to 100’s of thousands of dollars.

  • Ultra-low requirements for storage, memory, and CPU power
  • Seamless integration into existing low spec hardware
  • Ability to leverage existing devices and machines without additional hardware investment

Reduced Drain on Customer Resources

Many initial AI deployments fail due to lack of internal AI expertise required to manage deployment and integration processes that are too dense and complex. MicroAI has developed technologies that provide self-contained development tools and processes that significantly reduce the resource burden on the client. Operations are not faced with the dilemma of having to divert existing resources or adding new resources to support the AI integration effort.

Faster Time-to-Benefits

  • Optimized machine/factory performance
  • AI-enabled predictive maintenance
  • Transition to digital manufacturing
  • Improved network quality of service (NQoS)
  • Higher OEE scores
  • Reduced cost and higher margins