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R. Febo Moniz 27 B, 1150-152 Lisboa, Portugal
Lisboa
Portugal
Employees
Scale: 11-50
Estimated: 29
Engaged corporates
11Added in Motherbase
6 years agoJungle builds scalable AI technology to help people improve the operations of their renewable energy sources.
Jungle develops and applies Artificial Intelligence to increase the uptime and performance of renewable energy sources. Built on existing sensors and data streams, the company’s technology enables solar and wind energy owners to squeeze more out of their assets, accelerating the world’s transition to renewable energy sources.
Why: Operational complexity - such as the one of wind turbine performance - has reached a level that's beyond what our minds can grasp. Our tools enable the world to conquer this operational complexity and give back power to the people who manage it.
How: Through solidified Artificial Intelligence and Machine Learning expertise, the company’s technology leverages massive streams of data to understand the normal behaviour of an electro-mechanical asset, picking up on opportunities for better performance and risks of downtime, and continuously informing its users on what to prioritise.
What: The Dutch-Portuguese deeptech company has productised its services into a web application - Canopy - and is continuously improving it to ensure that the best analyses and visualisations help its users get the maximum energy out of their assets. Jungle operates at large scale - billions of data points per day - providing always-on predictive models, alarms and metrics visualisations for some of the largest and most sophisticated customers in the global renewable energy space.
Interested in joining us? https://jobs.jungle.ai
artificial intelligence, machine learning, data science, prediction & time series modelling, predictive maintenance, predictive intelligence, wind energy, energy distribution, heavy industry, wind turbine health, portfolio optimisation, digital factory, and digital turbine
The AI solutions taking machine performance to the next level
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