We are pleased to announce that HACARUS has received the TWENTY2X Award at TechBIZKON IV held at CIC Tokyo on December 2nd, 2020.
Adrian Sossna, VP of Global Sales for HACARUS, has participated in the pitch contest among other startups in the field of connected industries solutions. HACARUS’ introduced the firms vision for human-AI collaboration, and the role of its flagship inspection solutions SPECTRO in enabling digital transformation in an industry 4.0 environment. The TWENTY2X Award was presented to HACARUS as the “Most Creative and Innovative Startup”.
HACARUS is actively looking for European partners to collaborate in the field of AI and connected industries. If you’d like to learn more about HACARUS and the AI solutions we provide, please contact us here or send an email to firstname.lastname@example.org.
About TechBIZKON IV Event
TechBIZKON IV aims to bring together the industry key players and startups in Austria, Finland, Germany and Japan in the field of connected industries solutions. It offers panel discussions, start-up pitches and match-making opportunities to participants both online and offline.
The organizers of the event were Advantage Austria Tokyo, German Chamber of Commerce and Industry in Japan and Business Finland Tokyo. The event was also sponsored by Austrian Business Agency, German Accelerator, Global Incubator Network Austria, BMW Startup Garage. Nokia Corporation, Deutsche Messe AG / TWENTY2X, EWG – Essen Economic Development Agency, Red Bull, and supported by Embassy of the Federal Republic of Germany.
About HACARUS INC.
HACARUS INC., founded 2014 in Kyoto, Japan is the leading provider of Explainable Lightweight AI Tools, backed by Miyako Capital (Kyoto University) among others. Its solutions are used in the Medical and Manufacturing fields to enable humans to make better, faster and more reliable decisions, based on AI driven insights. Hacarus’ proprietary AI engine is built using Sparse Modeling, an AI method that understands data like a human would – by its unique key features and is far more resource, time and energy efficient when compared to Deep Learning.