Artificial Intuition – Best Application Scenarios
Over the past twenty years, Ontonix has explored and worked on hundreds of applications of its QCM-powered Artificial Intuition technology,Continue Reading
is powered by Quantitative Complexity Management
Over the past twenty years, Ontonix has explored and worked on hundreds of applications of its QCM-powered Artificial Intuition technology,Continue Reading
Complexity-based augmentation in control systems introduces an adaptive layer that allows real-time feedback, adjusting to the complexity of tasks and environments. Unlike traditional systems with fixed rules, this approach enhances performance and safety in uncertain scenarios. It aims to keep system complexity below critical thresholds to avoid instability. By integrating real-time complexity analytics, QCM control becomes proactive in predicting failures. Its dual role as a monitor and controller promises greater resilience for modern high-tech systems and products.
A New Paradigm in Early Anomaly Detection. Artificial Intuition identifies problems in complex, critical systems and helps solve them. BeforeContinue Reading
First of all, let’s clarify what Artificial Intuition can do: Three key point need to be stressed: The “intuition” inContinue Reading
“Telemetry is the in situ collection of measurements or other data at remote points and their automatic transmission to receivingContinue Reading
The best way to put QCM/Artificial Intuition to work is to OEM the technology. We provide our computational engine withContinue Reading
In the framework of the European Horizon Project AFFIRMO, grant 899871, Ontonix has developed a Risk Stratification tool which providesContinue Reading
Numerous articles have been appearing about Artificial Intuition being the newset form of AI, actually fourth generation AI. One suchContinue Reading
A recent pilot application has demonstrated our unique QCM-based Artificial Intuition capabilities in producing anomaly pre-alarms as well as diagnosingContinue Reading
Artificial Intuition is beyond mainstream AI, beyond Machine Learning. We don’t need to learn from examples to recognize anomalies. MoreContinue Reading









