Get the power of edge computing in minutes with Arundo Edge Agent. Based on deep experience in field control and SCADA systems and cloud architecture, we have created the easiest way to connect, analyze, and act upon remote field data. Connect in minutes, compute locally, and make better decisions rapidly. Edge Agent manages common edge computing pain points.
Heavy industrial companies in sectors such as oil & gas, shipping, mining and power, often operate assets in remote or intermittently connected environments. These assets generate important data that can significantly improve operations and overall profitability. Unfortunately, it is difficult and expensive to send this data to central cloud data repositories for analytics.
Arundo Edge Agent brings new ease to industrial connectivity, enabling intelligent streaming and edge analytics in just minutes. With deep experience in control systems and cloud platforms, Arundo built the easiest way to connect industrial control systems to a cloud platform. Featuring an easy-to-use interface, out-of-the-box connectors, and the ability to buffer data while offline, Arundo Edge Agent manages all of the typical pain points of global industrial operations in remote or disconnected environments. In addition, Arundo Edge Agent provides immediate access to edge computations on local data, as well as advanced analytics.
With Arundo Edge Agent you can connect from common industrial control system interfaces within less than 5 minutes. You can:
- Buffer offline data for as long as you want
- Easily browse for available tags or import large tag definitions
- Stream data to your cloud environments or Arundo’s Cloud
- Remotely monitor the health of the system, and
- Use a simple viewer to check current streaming data
You can analyze data locally by building compute chains to modify or scale raw data or add virtual sensors to your stream. You can create your own at-the-edge virtual sensors to easily display key performance indicators such as efficiency. You can even push advanced analytics, such as machine learning models, to the edge for offline analytics.