“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
SERVICES WE OFFER
AI AND DATA STRATEGY CONSULTING
Discover the true potential of your data with our data science experts and machine learning engineers
AI PROJECTS AND AI SOFTWARE DEVELOPMENT
AI OPERATIONS & MAINTENANCE
WHY WORK WITH US
Highly specialized & experienced in AI
Coverage of full AI project cycle
AI Power through Crowd Sourcing
“Artificial intelligence will have more influence on mankind than the invention of computers.”
Sepp Hochreiter
Management Team
An amazing team is able to do amazing things

Dr. Franz Juen
CEO
Expert in software design and development, machine learning, data warehouses and business intelligence

Dr. David Striegl
CEO

Dr. Ulrich Bodenhofer
CHIEF AI OFFICER
Expert in deep learning, support vector machines, statistical learning, rule-based machine learning and computer vision
Advisory Board

Univ.-Prof. Dr. Sepp Hochreiter
Advisory BoardHead 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.













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Address
Industriezeile 35, 4020 Linz, Austria