• Identify the assets and processes you wish to track.
• Deploy Eugenie either on-premise or through cloud platforms
  (https://bit.ly/3x0EpMp)
• 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.

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.

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.

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.

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?

• 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.

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.

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.

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.

• 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.