Author: ontonixqcm

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!
by ontonixqcm August 9, 2025 Number of comments0
Complexity Engineering

Complexity-based Augmentation of Control Systems

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.

by ontonixqcm May 27, 2025 1
Complexity Engineering Medicine

Ontonix Releases Multi-Processor Version of QCM/Artificial Intuition

On May 27th, 2025, Ontonix launched a Multi-Processor version of its QCM/Artificial Intuition technology, enhancing capabilities for large-scale problem-solving, including infrastructure and traffic monitoring. This architecture enables real-time data analysis through parallel processing, particularly for molecular simulations and image processing, significantly improving computational efficiency.