Energy · Finance · Health · Information Technology · Software · Sustainability
Imagine all the data that electrical assets produce on a daily basis. In case of a steel factory, ~ 26,000 data points per second can be generated and if you do the maths, well, this is a lot! At Jungle, we built a machine learning pipeline, which leverages these data jungles and creates a digital version of such assets. This way, we understand how those assets should and therefore should not behave, meaning our pipeline models normality and flags deviations from this normality. With our predictive intelligence, customers can quickly zoom in on where asset health problems or optimisation opportunities exist in their wind portfolio or factory. We only use the data that our customers already have, clean and standardise it, so it can be easily used in our ML pipeline. We handle data automated and at scale, enabling customers to quickly derive value from our product. When we find health or performance issues, we provide actionable insights, that are designed to empower our customers' teams to do the work they are already doing even more effectively. Our product serves as a strategic tool for engineers to collaborate in, investigate issues with and prioritise operation and maintenance efforts. As our team is composed of many electrical engineers with strong knowledge of machine learning, we can assure, that we speak our customers' language and that our insights are meaningful and contextual.Something looks off?