Complexity Economics Engineering Medicine Society

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.

More information at

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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