What We Do
Digitas Technologies provides value through the provision of a cost-effective and secure IT infrastructure that enables the usage of Machine Learning (ML) and Artificial Inteligence (AI) in industrial operations. Once implemented we support enhancing general business service processes in which large data sets are used.
We empower industrial companies of all sizes to enhance their competitiveness by enabling secure, cost-effective, ML/AI-supported data-driven decision-making across their production, maintenance and business processes.
Our History
Our journey began as PhD scientists in statistics at the Vienna University of Technology. In 2014, we founded our first company, “Applied Statistics”, in response to the growing demand for analytical services on large data sets. In 2022, we sold our data science company to TÜV Austria, playing a key role in the development of AI system audits and certifications. We also led the first successful AI audit in the EU.
Upon successful integration we decided to bundle our experience and focus our energy on enabling the wide use of Machine Learning (ML) and Artificial Intelligence (AI) in the process / manufacturing industry through the provision of a cost-effective and secure technical infrastructure. For 10 years all our clients including DAX, SME’s or public organizations, have been dealing with challenges related to the technical side of making effective use of the vast amount of their process & product data.
“Scaling AI remains a challenge: Only 28% of [corporate] respondents reported that their organizations have moved at least half of their AI projects from pilot to production.”
Deloitte’s 2023 report “State of AI in the Enterprise, 5th Edition”
In 2023, we founded Digitas GmbH and developed a practical, fast-deploying Industrial Cloud solution for industrial corporations, branded as Digitas Technologies.
At Digitas Technologies we aim to help industrial production companies of all sizes capitalize on the opportunities and gains of ML / AI for their products and their production processes.
To explore examples of how our solutions have been successfully implemented, visit our Use Case page.