Japan’s Neurospace raises $890K to help enterprises improve employees’ sleep health


See the original story in Japanese.

Tokyo-based Neurospace, the Japanese startup developing a program to improve sleep quality for large companies, revealed that it has raised 100 million yen (about $890,000 ) from Real Tech Fund, the investment fund by Euglena, SMBC Nikko, and Leave a Nest Capital. For Neurospace, it follows funds raised from Slogan Coent and individual investors in July 2015 and from Glocalink in December 2016 (both the funding rounds and amounts are undisclosed). In addition to this funding, Neurospace also revealed that it was adopted as a R&D venture support project of the New Energy and Industrial Technology Development Organization (NEDO).

Since its launch back in in December of 2013, the company has developed a sleep improvement program called Sommnie for corporate health management. Currently the company focuses on what is called a “Sleep Analysis Platform” utilizing AI (artificial intelligence) and IoT (Internet of Things) technology, and the beta version launched October 11th.

With this platform, the company will measure individual sleep data with high precision, provide individualized sleep analysis results, and then offer the optimum solution and improvement data derived from independent analytical technology using AI. The platform makes it possible for companies that intend to promote the health and productivity of employees and companies that are considering entering into the sleep business to incorporate into the improvement of management with the company services and IoT products through API (application programming interface).

As a PoC (proof of concept) test of the “Sleep Analysis Platform”, the company distributed a device to measure sleep and an app that presents a sleeping solution from the measured data to shift workers at Yoshinoya, a major Japanese beef bowl restaurant chain. It plans to demonstrate and brush up the effectiveness of the platform after conducting one month of sleep measurements and recommending measure for improvement and shift adjustment.

Translated by Amanda Imasaka
Edited by Masaru Ikeda