Japanese company CADDi announced the “CADDi” industrial data platform on August 6, 2026. The platform is an architecture that brings together fragmented manufacturing data and connects it with artificial intelligence to support the operations of industrial companies. It includes two main products, CADDi Explorer and CADDi Agent, in what the company describes as an attempt to build a shared operating layer for industrial data.
The announcement was made during an event held in Tokyo, and its details were published on August 18. CADDi believes that the rapid development of artificial intelligence technologies has not had an equivalent impact on industrial knowledge, as manufacturing sectors still rely heavily on undocumented accumulated expertise and on data distributed across different systems and departments.
The Problem of Disconnected Data and Tacit Knowledge
According to the company, engineering drawings, CAD data, ERP systems, and other manufacturing tools often operate separately within an organization. This makes sharing data across the entire company difficult and limits employees’ ability to access information outside the scope of their immediate work.
CADDi notes that more than 80% of the knowledge and expertise required for industrial work exists in employees’ experience, rather than in systems or organized documents. In environments involving more than 100 manufacturing processes, an error or lack of information at one stage can lead to rework or sending the process back to earlier stages, increasing operating costs and making it difficult to identify the source of the problem.
Two Products for Access and Analysis
CADDi Explorer functions as a data discovery engine. It analyzes manufacturing data distributed across systems such as CAD and ERP and enables users to search it through a single interface. The company says the tool connects disparate data and displays it according to the permissions granted to each user, helping employees in different departments find information relevant to their objectives.
CADDi Agent is an artificial intelligence agent that relies on each company’s business data and standards. CADDi says it can automatically carry out a series of analysis processes, including tasks related to standardizing products, reducing costs, analyzing defects, and forecasting risks. The aim is to reduce repetitive manual work and expand the use of accumulated knowledge within the organization.
What Changes in Practice?
CADDi does not present the platform as a replacement for all existing manufacturing systems, but rather as a layer that connects them and adds an understanding of their relationships and meanings. An engineering drawing alone may not convey complete operational meaning, as it may be associated with materials, products, bills of materials, or quality issues. The platform aims to build a semantic data map of these connections so that artificial intelligence can use them in the context of actual work.
According to the published presentation, the platform consists of a four-layer architecture that includes ingesting manufacturing data, organizing the meanings and relationships among that data, and then providing products aimed at practical use cases. CADDi also announced its intention to develop a range of specialized products covering six industrial fields, while continuing to expand the platform around companies’ data and internal knowledge.
Background of the Platform
In June 2022, CADDi began offering the cloud-based CADDi Drawer service, which focuses on searching and using engineering drawing data. The company says the new CADDi platform reorganizes this experience within a broader framework that includes different types of manufacturing data, rather than handling each data type or system separately.
The company links this direction to its vision of accelerating the shift from tacit knowledge to searchable and usable knowledge. However, the platform’s practical benefits will depend on the quality of the data companies enter and their ability to connect the different systems and processes within each organization.