We’re so happy to have you here! As you probably realized from the context, Ekkono is from Varberg. And we’re going from here to the entire world. You see, Edge Machine Learning sees no national borders. It is just as relevant for industrial compressors as it is for washing machines. After making the design of products as robust and energy-efficient as can be, the next step in the evolution is to proactively service them before they have fatal wear, and configure them to run at their best based on where, how, for what, and the individual preferences of the user.

This is what Ekkono enables. The word Ekkono means cognition, and that is what we bring to the Internet of Things (IoT). Ekkono’s unique ability to not only do inference but also training at the edge, onboard the device, enables it to learn the super-local environment and the individual conditions. And we can do this in operations. This enables stone crushers to predict remaining useful life on the crusher cone, heat exchangers to predict when it needs cleaning, calculate remaining range for car batteries, replace hard to deploy temperature sensors in train motors with virtual sensors, and optimize gas burner settings on gas turbines.

The core of Ekkono’s software is an embedded software library in C or C++. It comes with APIs with bindings to Python and C#, and a comprehensive SDK with autoML functionality that helps expedite the implementation of these smart, self-learning and predictive features. Ekkono is built for deployment, which becomes obvious when you see how the machine learning model that has been developed in Python is called using the C/C++ library with only 10-20 lines of code.

For any kind of inquiry, please contact us at [email protected].

We’re Hiring!

Ekkono is hiring a number of new positions. All of them are located in Varberg, and some of them are feasible for remote or hybrid employments.

For current positions, visit Career @ Ekkono.