CADDi Raises $114 Million to Break Manufacturing’s Physical Bottleneck

CHICAGO and TOKYO — CADDi has raised $114 million in a Series D funding round, lifting the manufacturing data platform’s valuation to $1.2 billion. The dual-headquartered startup was valued at $470 million in March 2025, meaning its valuation has more than doubled. Total funding now stands at $234 million.

The company’s workforce has grown to approximately 900 employees, up from 600 in early 2025. CADDi’s platform is active in 22 countries, and more than half of Japan’s 100 largest manufacturing corporations use its software. Yushiro Kato, CADDi’s co-founder and CEO, declined to disclose specific revenue figures but said sales continue to more than double year over year.

The new capital will support global expansion, particularly in North America. The U.S. manufacturing sector is undergoing a significant transformation because of federal initiatives to reshore critical supply chains and clean-energy manufacturing. That shift has increased demand for software that can quickly establish new factory operations and manage domestic supplier networks.

Woven Capital, the growth-stage venture utility of Toyota Motor Corp., was among the prominent backers in the round. Toyota, like many global automakers, has been investing aggressively in software, automation, and advanced manufacturing platforms to streamline its massive global supply chain. Moore Strategic Ventures, Coreline Ventures, and the HR Tech Fund, the corporate venture capital arm of Japanese conglomerate Recruit Holdings, also participated.

Eight new and existing investors supported the Series D. One new investor was not publicly disclosed, while Atomico, Globis Capital Partners, and the JPS Growth funds, managed by a subsidiary of Japan Post Bank, reinvested.

CADDi was founded in 2017 and initially addressed manufacturing inefficiencies through CADDi Drawer. The product ingested technical drawings, searched historical databases for duplicate parts, and analyzed supplier defect rates. Procurement teams could use that information to decide whether to reuse existing inventory or negotiate better rates with current suppliers.

The product has since been rebranded as CADDi Explorer as the company has expanded into an integrated “AI data platform for manufacturing.” It is paired with CADDi Agent, an AI-enabled assistant that helps engineering teams standardize parts and conduct automated quality impact assessments. Those assessments simulate how a change in one part’s design might affect the safety and performance of a complex machine.

CADDi has also rolled out six specialized “workflow” products. CADDi Design Review automatically flags potential manufacturing errors in new blueprints by comparing them with historical data on part failures and manufacturing defects. Kato said the goal is to pool the collective knowledge of veteran engineers into a single digital “superhuman” veteran that guides younger staff.

The manufacturing industry is confronting intense global competition, particularly from highly agile Chinese manufacturers, as well as a severe demographic cliff. In industrial hubs such as Japan and the United States, a generation of experienced manufacturing engineers is retiring with decades of unwritten, “tacit” expertise.

“More than 80% of the knowledge about manufacturing work processes, and often why a company chose a particular supplier or designed a part in a particular way, is never recorded anywhere,” Kato said. “Instead, it exists in the heads of experienced employees.”

CADDi’s platform is built to capture, structure, and codify that institutional knowledge, turning unstructured physical data into digital assets that humans and AI agents can navigate. The company’s executives say the chief obstacle to modernizing global factories is not modern AI’s capabilities, but the deeply ingrained culture of the industrial workforce.

Veteran factory workers and engineers must receive intensive, localized training to move away from legacy paper processes or Excel spreadsheets and adopt AI-driven platforms. CADDi therefore prioritizes customer integration over pure sales in its organizational headcount. More than 100 customer success specialists work at the company, outnumbering its direct sales force.

CADDi has also begun hiring “forward deployed engineers,” a role popularized by defense-tech and enterprise software firms. These employees work directly at customer factories and corporate offices to integrate the AI platform into daily operations. “The goal is to change the organization and create a business impact,” Kato said, adding that CADDi pitches its software through concrete, measurable metrics such as compressed engineering lead times and direct reductions in material costs.

The company is targeting what it calls the “physical bottleneck” of global manufacturing. Generative AI can write code or generate marketing copy in seconds, but the physical timeline for heavy industry has remained largely unchanged for decades. Developing a new commercial vehicle still routinely takes approximately four years from the drawing board to the factory floor.

Automakers and heavy equipment manufacturers typically conduct about 20 separate design reviews for a single product. Component errors or supply chain conflicts often become visible only during physical prototyping. CADDi wants to run those engineering steps in parallel and compress the physical innovation cycle tenfold by 2035, cutting a vehicle’s development timeline to four or five months.

Part duplication is one of the costly inefficiencies the company is addressing. Engineering data is often siloed across departments, factories, and legacy software systems, so large companies frequently design or purchase nearly identical parts from multiple suppliers at vastly different price points.

Manufacturing companies have traditionally relied on rigid Product Lifecycle Management (PLM) and Enterprise Resource Planning (ERP) databases. Those systems are notoriously manual and struggle to index unstructured data, including legacy 2D blueprints and complex 3D CAD models.

CADDi’s technology functions as an intelligent overlay. Its proprietary AI models parse complex engineering drawings and 3D CAD files, while general-purpose large language models (LLMs) scan accompanying spreadsheets, procurement documents, and supply chain records. “I’ve never seen anybody who uses LLMs to do design reviews because it doesn’t understand drawings or CAD,” Kato said, explaining why CADDi has developed in-house, manufacturing-specific AI models.