Examples of how Artificial Intuition generates Early Warnings

Our technology allows us to produce early warnings of disruptions in manufacturing processes, supply chains, traffic systems, machinery, even stock market crashes, major economic crises or large losses in trading desks. By processing real-time data from any of these systems, we are able to provide early warnings, hours of even days ahead of time.

Our system does not require hundreds of examples from which to learn in order to recognize a crisis or a disruption. This is because our technology goes beyond conventional Artificial Intelligence, and Machine Learning in particular.

The following examples illustrate how our QCM-based Early Warning System is able to identify anomalies before they actually materialize. We illustrate three cases from the electronics industry and manufacturing, finance and medicine.

Note that in none of these cases we resort to Machine Learning. Our QCM system doesn’t require hundreds or thousands of examples of an anomaly in order to spot it.

It is important to remark that in all the cases illustrated below, we use the same identical algorithm, without any application-specific tuning. The tool is always the same one.