About us

Singularity of future delivered today

Greater intelligence than humans is coming not future but today

Our Business

Optimization on digital twin

Automation with AI


3F/1F, Ebisu Bldg., 82 Shimotsutsumi-cho, Sakyo-ku, Kyoto-shi, Kyoto, Japan


3.5 M USD (including capital reserve)

Number of employees

Full time 24
Part time 60 

Press Release

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RUTILEA, Working to Solve labor Shortages Using AI, Launches Joint Research with Kyoto University
July 21, 2022

RUTILEA has been working to solve labor shortages in manufacturing, logistics, and retail through machine vision. In collaboration with Kyoto University, RUTILEA will examine the feasibility of a business model based on digital immigration, in which workers work across physical borders using information and communication technology and blockchain technology. Here, digital immigration means providing labor in remote areas beyond national borders.

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Featured in the Financial Times
February 2, 2022

The Financial Times (FT), a British business newspaper founded in 1888, published an article on Rutilea's efforts to solve Japan's labor shortage problem.

Article Summary:
It is difficult to discuss the use of robots and immigration.
Rutilea's solution to Japan's labor shortage is introduced as a concrete solution to the country's labor shortage, despite the lack of progress in the debate over the use of robots and immigration. The question is whether the outsourcing of quality control to other countries will progress in Japan, a country that values "monozukuri" (the art of making things). The evolution over the next few months and years, as well as trends in business dealings with Japanese companies, will be "a key indicator" as to whether Japan will be able to steer a more appropriate course in the replacement of human resources with more appropriate positions.

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Featured in Forbes Japan, January 2022 issue.
November 15, 2021

Rutilea was featured in the "200 SUPERSTAR ENTREPRENEURS" section of Forbes Japan (January 2022 issue).

No.089 January 2022 issue (Thursday, November 25, 2021)

▶︎Feature in the January 2022 issue①.
Japan's BEST 10 Entrepreneurs

The future is in sight!
The definitive "New Japan Startup Guide"

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Featured in "KANSAI - GLOBAL STARTUP CITY | Startup City Project Japan, STARTUP DB
April 1, 2022

KANSAI - GLOBAL STARTUP CITY | Startup City Project Japan, STARTUP DB," which aims to develop the startup ecosystem in Japan by bringing together the government, companies, and local cities in partnership with world-class accelerators.

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Development of an optimal AI camera for capturing "scratch resistance (scratch characteristics)" for automotive interior and exterior
July 28, 2021

KATO TECH CO. LTD., a manufacturer of texture inspection equipment that celebrates its 60th anniversary this year, has developed an AI camera that visualizes the "scratch resistance" of automobile interiors and exteriors in collaboration with RUTILEA Inc., a start-up company from Kyoto University that provides solutions based on advanced technologies in AI and robotics.
Full-scale sales will begin in August 2021.

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Development of an imaging method suitable for AI visual inspection and start of sales of hardware to realize it
June 19, 2020

RUTILEA Inc. is a venture company with an open business model. Our product, SDTest, is an integrated solution for the algorithms required for visual inspection, and we have been conducting research and development with the goal of introducing it to more production lines. We have newly developed an imaging method that is optimal for visual inspection AI, and have achieved high accuracy in AI visual inspection.

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Major update of open-source visual inspection SDTest: original development of visualization of anomaly detection methods that do not use autoencoders in addition to defect detection by segmentation
June 2, 2020

Our product, SDTest, is an integrated solution for the algorithms required for visual inspection, and we have been conducting research and development with the goal of introducing it to as many product lines as possible. This time, we succeeded in developing a segmentation method and an originally developed visualization method for anomaly detection (Figure 1). By using these technologies, we will be able to automate the visual inspection of more workpieces.

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Track record of open source software "SDTest" using AI and deep learning and development of hardware for automated visual inspection
April 2, 2020

RUTILEA Inc. released "SDTest", an open-source software using AI and deep learning, in September last year, and it has been downloaded by more than 500 companies so far. In addition, there is a wide range of work that has been done to automate visual inspection, and 22 projects are currently in progress. We are also developing hardware and inspection equipment that is more suitble to the inspection environment of each company, and have completed the delivery of five projects.

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Development of automated picking software using AI and deep learning
March 9, 2020

RUTILEA Inc. (Headquarters: Kyoto, Japan) developed and released open-source software for picking in bulk. The software, combined with SDTest, makes it possible to save the labor in the visual inspection process.

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Development and new release of automated visual inspection solution integrating collaborative robot arm and SDTest, an open-source software using AI and deep learning
September 17, 2009

RUTILEA Inc. (Head office: Kyoto, Japan) developed and released a new solution for automating visual inspection using the open-source software SDTest, which uses AI and deep learning, and a collaborative robot arm. By combining SDTest with a robot arm, the company succeeded in automating the visual inspection process and the area of accurately carrying and moving objects.

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Open-source release of "SDTest," inspection automation software using AI and deep learning
August 8, 2019

RUTILEA Inc. (Head office: Minato-ku, Tokyo; Development base: Kyoto City, Kyoto Prefecture) developed and released SDTest, an open-source visual inspection software that uses AI and deep learning. SDTest solves both technical and cost issues that have been difficult in automating visual inspection of manufacturing lines. In terms of technology, SDTest uses a deep learning approach to achieve 100% accuracy in detecting defects in shiny metal parts, black parts where scratches are difficult to see, and precision semiconductor parts. In terms of cost, by making it open-source, we reduced the cost of implementation and development, which had been barriers to its introduction.