Use of RAG to quickly search for necessary information and supporting evidence in company’s technical documents

The Yokohama Rubber Co., Ltd., announces that it has developed and begun full-scale operation in August 2026 of a proprietary generative AI system utilizing RAG (Retrieval-Augmented Generation), which searches various technical documents accumulated internally and presents responses based on their contents. This new system contributes to further accelerating and enhancing tire development by providing rapid and accurate access to technical information necessary for decision-making during the tire development process, including material development.

Yokohama Rubber developed this new system to expand the practical environment of its proprietary HAICoLab*1 AI utilization framework, which was established in October 2020. Yokohama Rubber has previously developed AI systems that predict a rubber compound’s physical properties and tire characteristics, generate new rubber compounds, and support mold design, and the Company is increasingly using data to create material and tire designs. Meanwhile, the technical knowledge (domain knowledge)*2 that developers need to make decisions during the tire development process can be found in a vast array of technical documents that include regulatory documents, procedure manuals, technical reports, and case studies. Creating a system that can quickly search this vast collection of data and provide information appropriate to development objectives and circumstances has been a challenge.

*1) A framework for "collaboration between humans and AI" that drives a virtuous cycle of innovation in products, processes, and services alongside human growth through a process that starts with enhancing AI by using data and knowledge accumulated in the company, the formulation of hypotheses by humans using metacognition, and development staff interpret and judge the results using the enhanced AI.

*2) Expertise and knowledge in a specific field or industry

Yokohama Rubber development staff using this new system input questions tailored to the development’s objectives and situation, and the generative AI searches for the most relevant information from internal technical documents and provides responses based on that content. Yokohama Rubber development staff also created a mechanism in which an implemented AI agent grasps the intent of the staff’s questions and enhances the accuracy of its response by autonomously repeating the process of planning searches, retrieving information, and evaluating the results. Additionally, with the system displaying links to the original documents that serve as the basis for responses, development staff can verify the basis and appropriateness of those responses and use them in interpretation and decision-making. This has added a new mechanism that uses domain knowledge accumulated in technical documents with AI, thereby expanding the tire development environment based on HAICoLab.

Along with the development of these AI systems, Yokohama Rubber is training DX staff capable of using HAICoLab. Yokohama Rubber will continue its efforts to enhance the new system’s capabilities and use data and knowledge accumulated internally to develop innovative products, processes, and services.

Tagged: RAG

Yokohama Rubber Develops Proprietary Generative AI System to Support Decision-Making in Tire Development