LIGHTz Product Lineup

"Blooplinter" is a knowledge database that enhances the recyclability of information generated in development projects and provides speed through a combination of form and knowledge. It allows you to directly teach know-how and issues related to your company's product 3D models. The AI provides push notifications during the development process, connecting the past and future, seniors and juniors, and various departments. Insights and information generated at each stage of the development project are collected and linked to 3D shapes, making them easier to reuse. 【Features】 ■ A unique 3D shape recognition AI takes on a vast amount of work on behalf of humans. ■ The AI automatically assesses the feasibility of uploaded 3D models based on their shapes. ■ Easily extract points of change between two models generated by design changes, etc. ■ Visualize the overall status of ongoing issues, including prioritization. ■ Visualize the progress and focus areas of issues by parts (model units). *For more details, please download the PDF or feel free to contact us.

The "LIGHT Solver" is a cloud-based troubleshooting system that converts the know-how of veterans and past trouble response data into a brain model, allowing anyone to quickly identify suitable measures. It alleviates concerns in the field such as "It takes time because of lack of experience" and "Responses vary by person," achieving both early recovery and personnel development simultaneously. It liberates troubleshooting from being dependent on individuals. 【Features】 ■ Early recovery from equipment troubles that does not rely on skills ■ Skill enhancement with each use ■ Reduced burden on experts ■ Utilized as educational material for personnel development ■ Accumulation and utilization of field knowledge *For more details, please download the PDF or feel free to contact us.

"Blooplinter" is a knowledge utilization tool that links design know-how and past examples to 3D CAD data, allowing users to quickly search for necessary information. By managing past drawings, design decisions, trouble cases, and check items alongside 3D shapes, it makes it easier for anyone to reuse the verification tasks and judgments that relied on the experience of skilled professionals. It supports the prevention of design rework, reduction of verification workload, and knowledge sharing through similar shape searches, requirement checks, and difference confirmations during design changes. 【Expected Effects】 ■ Sharing of Skilled Professionals' Know-How By visualizing the judgment criteria and design know-how of skilled professionals, it makes it easier to utilize across departments and among personnel. ■ Prevention of Recurrence of Troubles and Defects By linking past defects and points of caution to 3D shapes, it helps prevent the recurrence of similar mistakes and troubles. ■ Reduction of Design and Verification Time Since past similar shapes and related information can be searched immediately, it reduces the time spent on drawing verification and information exploration. ■ Reduction of Variability in Design Quality By sharing the verification perspectives and judgment criteria that varied among personnel, it supports the standardization of design quality.

The "LIGHT Solver" is a cloud system that draws effective measures against issues. It incorporates personalized know-how and past trouble data into a tree-shaped brain model. Due to its question-and-answer selection method, anyone can intuitively operate it from anywhere. By inputting past trouble response reports that are dormant within the company into the generative AI, it can analyze them from a unique perspective and generate a troubleshooting tree. 【Implementation Effects】 ■ Early recovery from equipment troubles that does not depend on skills ■ Skill enhancement with each use ■ Reduced burden on experts ■ Accumulation and utilization of on-site knowledge ■ Use as educational material for talent development *For more details, please download the PDF or feel free to contact us.

In the automotive industry, there is a demand for standardization of design quality and improvement of development efficiency in increasingly complex vehicle development. However, challenges such as reliance on the personal knowledge of skilled workers and the difficulty of accessing necessary information due to its dispersion can lead to rework in design and an increase in verification workload. 'blooplinter' is a knowledge utilization tool that links design know-how and past cases to 3D CAD data, allowing for quick access to necessary information. By managing past drawings, design decisions, trouble cases, and check items alongside 3D shapes, it makes it easier for anyone to reuse the verification tasks and judgments that were previously dependent on the experience of skilled workers. 【Usage Scenarios】 - Rapid search for past similar design cases - Automatic checking of requirements based on design standards - Visualization of the impact scope during design changes - Sharing of trouble cases and thorough implementation of recurrence prevention measures - Efficient transmission of design know-how to new personnel 【Benefits of Implementation】 - Reduction of variation in design quality and promotion of standardization - Shortening of development periods through reduction of design and verification workload - Organizational sharing and promotion of the utilization of skilled workers' know-how - Improvement of reliability through prevention of design errors and recurrence of troubles

For automobiles: General-purpose AI naturally accumulates the tacit knowledge of experts from daily operations and conversations, serving as a specialized organizational knowledge AI platform for the manufacturing industry that can be utilized as the company's "knowledge infrastructure." It structures and accumulates the experience and judgment criteria of veterans from internal documents and conversation records, allowing users to search and reference not only "what was judged" but also "why it was judged that way." It features AI agent functions that address various aspects of operations, such as risk detection in project documents, integrated searches of 3D drawings and knowledge, and presenting similar cases during trouble response. [Usage Scenarios] - Preserving judgment criteria and experiential knowledge as knowledge before the retirement or transfer of veterans. - Allowing younger or other department members to independently search and reference past judgment bases. - Early identification of risks from project documents during the design and production preparation stages. - Rapid response to troubles based on similar cases and countermeasures. [Implementation Effects] - Visualization and inheritance of judgment criteria that had become personalized. - Utilization of knowledge that not only allows for searches but also understanding of "why." - Reduction of rework through early detection of risks. - Shortening of lead time for trouble response.