HIGHLY CUSTOMIZED OPTICAL SENSING SOLUTIONS
MEETING YOUR COMPANY'S NEEDS TODAY AND IN THE FUTURE
STRIVING TO BE A WORLD LEADER IN INTEGRATION OF HYPERSPECTRAL, MACHINE LEARNING, AND ROBOTICS
The saying – an image is worth a thousand words – is gaining ever deeper meaning, as hyperspectral camera technologies, customized lighting, robotics, and machine learning are integrated and used to produce frontier solutions in the 21st Century. Spectral Analytix Inc. was founded in 2019 as a company that develops and commercializes classification and sorting solutions based on integration of hyperspectral imaging, robotics, and machine learning.
Spectral Analytix Inc. focuses on classification and sorting of objects - "optical solutions" - such as, seeds, insects, food products, pharmaceutical products. Optical solutions are customized to specific client needs can be offered as service (objects sent to Spectral Analytix for analyses) or as installation of a system (purchase of hardware and software and training of personnel).
Spectral Analytix Inc is a small company, but we still believe that the decisions we make have an impact. The way we are structured and communicate affects our ability to attract and retain human talent, collaborate with partners, and it also affects the quality of our services to clients. Our use and consumption of resources affect our carbon footprint and therefore our relative contribution to the future - the Planet we are handing over to our children and the next generations.
Spectral Analytix Inc. strives to follow as many of the articles in the United Nations Universal Declaration of Human Rights as operationally possible.
Spectral Analytix Inc. strives to follow as many of the recommendations put forward in the United Nations report, Our Common Future, which was published in 1987. This report provides in-depth recommendations and guidelines to mitigate short- and long-term risks associated with non-sustainable use of fossil fuel and water, degradation and depletion of natural resources, socio-economic inequalities, and risks associated with climate change.