The primary and secondary sectors are industries traditionally accessible to automation. Through machine learning, the extension of both human capabilities and the physical means of production can significantly accelerate innovation, optimize workflows and increase productivity.
Next, we list our solutions for Manufacturing and Agriculture enterprises.
It uses machine learning and real-time data gathered from equipment to
notify the company's personnel when something is starting
to go wrong. That way the machinery can be fixed before there is a catastrophe. This method
goes beyond standard maintenance, using the actual condition of
an equipment to determine when repairs should be performed.
Using Computer Vision algorithms, we can automatically inspect parts, components, and products
identify defects by matching patterns to images of defects that it has previously analyzed and
classified. This technology can be also used for identifying a wide variety of objects or waste
and separate them from the rest of the sorting.
Computer vision can be used to analyze aerial images of land
to review soil cover in great detail. It can generate analytics to monitor
climate change, it can be used to prevent deforestation, provide insights to understand
the impacts of urbanization, or identify opportunities to address global environmental
For staying competitive in today’s world, organizations need to constantly reconfigure
supply chains and continually manage the changing demand and supply. Intelligent decisions
in inventory management can drive large cost saves and avoid missing selling opportunities.
In both manufacturing and agriculture industries, smart telemetry can be used to address technological challenges. Some examples are enabling farmers to sustainably lower costs and improve yields, intelligently detecting inefficiencies in manufacturing processes, or analyzing massive amounts of data to provide insights for decision-making.