Though data connectivity is quite unlimited, today there is a need for intelligence to be on the edge of the network. In many cases, the data is preferred to be processed as soon as it enters the network and analyzed, and decisions are made locally and instantaneously. This is especially useful for applications that cannot afford to wait till the cloud infrastructure comes back with decisions. Though the cost of edge computing for artificial intelligence and machine learning is more, the cost of cloud infrastructure and data charges are brought down, and system can work even in remote-offline locations. The to-and-fro data bandwidth is conserved with only the processed data pushed to server.
Monitor key parameters of the devices, detect risks in advance and prevent failures using prognostic and predictive models
Track objects and activities, extract patterns, detect anomalies and take logical decisions in a real-time manner
Process images, run ML models and algorithms to classify and detect contents and act as per business requirement at the edge
Create engaging user experience using Natural language processing, conversational AIs and recommendation engines
Our engineers help deploy advanced AI algorithms on resource limited embedded systems to infuse intelligence at the edge
Run tailor made machine learning models and cutting-edge algorithms that keeps getting better by re-training and updates
Provide vision to the eyes of the system - cameras - using our video analytics to improve quality, performance, and security