• How do I get started with Eugenie?


    • Identify the assets and processes you wish to track.
    • Deploy Eugenie either on-premise or through cloud platforms
    • Connect your data sources to Eugenie such as SCADA, from the end-point
      sensors/edge devices, ERP, or third-party data services.
    • Start getting insights from Eugenie’s machine learning algorithms on the various
      consumption platforms.
    • If your systems do not include the above-mentioned data sources, contact us
      and we’d help you to develop an alternative.
  • Do I need to perform any customizations to fetch data from the legacy systems?


    No. Eugenie can extract data from SCADA or any other systems
    through MQTT protocols. Eugenie can also connect with historians
    such as OSIsoft PI systems. You don’t need to create API or software
    for Eugenie.

  • How much machine historical data is needed for Eugenie?


    While historical data is preferable, it is not mandatory for Eugenie to
    generate insights. The algorithms of Eugenie can start understanding
    the machine behavior, based on the condition monitoring data as well
    as the SOPs of the OEM’s.

  • Are any specific hardware or storage required to deploy Eugenie?


    Absolutely not. Eugenie’s ecosystem is hosted on the cloud. In a few cases,
    where on-premise deployment is needed, we can deploy Eugenie on an edge server.

  • Can we try Eugenie with a sample/test dataset?


    From our experience, we have observed that POCs and short-term engagements usually
    give insufficient results. Our patented products have helped several global companies in
    achieving operational reliability fairly quickly. The value of a system like Eugenie can be
    realized to the fullest by applying it in your daily operations. The data-to-decision journey
    of operations will lead to concrete value in terms of ROI, OpEx, and asset
    performance. The question to be considered is:
    What are we losing while we are busy testing Eugenie?

  • What is your uniqueness?


    • Flexibility: Eugenie is built as a Lego-blocks with micro-service
      where modules are exposed as a service. This makes deployment
      extremely speedy and agile.
    • Data-robust: It supports different data formats from IoT sensors such
      as structured time-series, Audio, Video, etc. Also, Data connectors for all
      databases (SQL, NoSQL, Cloud) are available.
    • End-to-end secured platform which includes auto-ML for anomaly detection.
      Eugenie’s Unsupervised ML algorithms can detect unknown unknowns with
      high accuracy.
    • Explainable AI for increasing trust by bridging the gap between humans and
      algorithms with transparency of insights.
    • Model accuracy: Patented deep AI algorithms, offering unparalleled accuracy
      across a wide range of machinery.
  • What kind of trends and anomalies can Eugenie spot?

    Eugenie processes the asset data captured from the industrial IoT networks in
    the form of a time series of sensor readings, which are then used to monitor the
    asset condition remotely. Our machine learning models utilize a large amount of
    data to learn the patterns corresponding to the behavior of the assets. These models
    can then be used for different tasks like generating forecasting, rare events detection,
    causality analysis, etc.
    Eugenie utilizes a unique approach involving neural network architecture in a streaming
    data pipeline for univariate and multivariate time-series data.

  • How is Eugenie licensed?

    Eugenie is priced on the basis of the number of assets connected with it.
    This license provides an unlimited number of users with the generated visual insights.

  • Is there any limit for the number of assets that can be connected with Eugenie?


    No. Eugenie’s operational reliability applications automatically track and learn
    the patterns and detect anomalies. Be it one asset or hundreds of assets, the
    approach is uniform.

  • What sort of issues can be solved by Eugenie?


    • Proactive prediction of machinery failure and performance based on asset tracking and accurate anomaly detection
    • Better reliability and reduced dependence on redundancy by giving sufficient alerts in case of suboptimal asset performance
    • Reduced OpEx and GHG emissions by streamlining maintenance efforts and costs.
    • Remedial steps through Explainable AI framework to ease the data-to-decision process
    • Empowering decision-makers by intuitive visualizations and human-centered insights.
    • Ready for a digital transformation ?

      Get in touch with us