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When Big Data is Not An Option

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Small Data AI Solutions

Hacarus creates highly efficient AI solutions for areas where typical Big Data or Deep Learning approaches fail. Our algorithms can extract meaning from small data and are a cost-efficient, low-risk alternative to common machine learning solutions.

Manufacturing and Industrial

Manufacturing and Industrial

Hacarus’ Sparse Modeling technology enables higher performance of industrial equipment and production defect reduction.

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Image and Video Analytics

Image and Video Analytics

Hacarus provides solutions for automated image and video analytics to extract key information from a video stream and summarize the observed activity.

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Medical research

Medical Applications

Diagnosis and treatment support based on vital data or medical image analysis. Our algorithms are tailored for medical studies, treatment analysis, and drug research.

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Embedded and FPGA

Embedded and FPGA

Low energy integrated technology to extend industrial or medical equipment. Our solutions can run on low power and are easy to embed into devices.

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Small Data AI Benefits

No Big Data required

Our Sparse Modeling algorithm only requires a small data set for analysis.

This opens new opportunities to benefit from AI technology in areas where big data is not available or is too costly to collect.

Explainable AI

Unlike common Deep Learning, the results of our algorithm are understandable and the process is transparent.

This is particularly important in the medical fields, where it is critical to understand how the results where generated.

Low Computing Power

With small computing power needs, our algorithm can be embedded into existing hardware.

Our technology can be easily distributed at scale in remote devices – while using minimal power supply and network bandwidth.

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Our story

Hacarus was originally started as a healthcare AI venture. Our initial mission was to achieve health and a long life through a nutritious diet, using AI technology to identify the best food choices.

This technology was the result of a close collaboration with our Chief Science Advisor, Associate Professor Ohzeki of Tohoku University (formerly Kyoto University). When we found that the algorithms are relevant for other applications in manufacturing and medical fields, we started expanding our expertise into these areas, which are now our primary focus. The expansion was possible through an initial investment from Miyako Capital.

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