“Over the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t.”
Erik Brynjolfsson & Andrew McAfee (MIT), Harvard Business Review 2018
QUOMATIC.AI is a company applying years of research and highly sophisticated artificial intelligence methods for transforming data to knowledge. We are your experienced partner in AI and help saving costs, optimising processes, boosting profits and driving innovation in a fast-moving environment
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AI Power through Crowd Sourcing
“Artificial intelligence will have more influence on mankind than the invention of computers.”
An amazing team is able to do amazing things
Dr. Franz JuenCEO
Expert in software design and development, machine learning, data warehouses and business intelligence
Dr. David StrieglCEO
Dr. Ulrich BodenhoferCHIEF AI OFFICER
Expert in deep learning, support vector machines, statistical learning, rule-based machine learning and computer vision
Univ.-Prof. Dr. Sepp HochreiterAdvisory Board
Head of the Institute for Machine Learning and Linz Institute of Technology (LIT) AI Lab at the Johannes Kepler University of Linz. He developed the popular long short-term memory (LSTM), which is considered as a milestone in the timeline of machine learning.
Development of a novel AI-based risk score for heart valve surgery that outperformed established risk models. The approach serves as a role model how machine learning/artificial intelligence helps to exploit electronic health records for the benefit of patients.
AI-based prediction of allergic reactions to non-steroidal anti-inflammatory drugs (NSAIDs) from genotypes of patients. This model helped for identifying candidate genes that are associated with those allergies.
AI-based prediction of antibiotics resistances from the genotypes of Pseudomonas aeruginosa, a common pathogen in hospital-acquired infections. The results served as the basis for further investigation of advanced antibiotic resistance mechanisms.
Fully automatic quality control system of prints on optical digital media using a multi-method approach that combines computer vision, fuzzy logic, and machine learning.
Machine learning-based prediction of paper quality from a paper mill's complex set of parameters. The resulting model allowed for optimizing the paper production process beyond hitherto available expert knowledge.