About us

An Industrial IT company!

We are striving to provide simple solutions for complex problems with state of the art software

With more sophisticated computing, faster networking, and large-scale cloud-storage becoming readily available, Adventis sees artificial intelligence delivered to the edge as the future of IIoT.

We are very friendly in our dealings to the customers, and it helps us retain existing clients and expand customer circle. We always try to improve the quality of our products by exploring innovative ideas.

VISION

4.0

With more sophisticated computing, faster networking, and large-scale cloud-storage becoming readily available, Adventis sees artificial intelligence delivered to the edge as the future of IIoT.

Traditionally, maintenance of industrial equipment has been based upon standard schedules and practices. An IIoT-based system removes the need for a schedule for plant maintenance, repairs, or replacements by dynamically assessing all machines. This eliminates the risk of factory downtime while eliminating the additional cost of unnecessary scheduled inspections. This, for instance, can be applied in oil and gas refineries to survey equipment regularly without human intervention, or to regularly assess moving parts in industrial equipment such as large motors through vibrational analysis. The vast repertory of data collected over an extended period of time allows for learning and predictive capabilities with a fine-tuned perspective on the degradation process of any and all machines. Information such as usage history, remaining useful lifetime, classification models, and sensor data are all critical to industrial predictive maintenance.
Traditionally, maintenance of industrial equipment has been based upon standard schedules and practices. An IIoT-based system removes the need for a schedule for plant maintenance, repairs, or replacements by dynamically assessing all machines. This eliminates the risk of factory downtime while eliminating the additional cost of unnecessary scheduled inspections. This, for instance, can be applied in oil and gas refineries to survey equipment regularly without human intervention, or to regularly assess

moving parts in industrial equipment such as large motors through vibrational analysis. The vast repertory of data collected over an extended period of time allows for learning and predictive capabilities with a fine-tuned perspective on the degradation process of any and all machines. Information such as usage history, remaining useful lifetime, classification models, and sensor data are all critical to industrial predictive maintenance.