CADDi Secures $114M Series D Funding at $1.2B Valuation to Scale Manufacturing AI Platform

CADDi Doubles Valuation with $114 Million Funding Round

Manufacturing AI startup CADDi has closed a $114 million funding round at a $1.2 billion valuation, more than doubling its March 2025 valuation of $470 million1

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. The Series D funding brings the Tokyo and Chicago-based company's total capital raised to $234 million since its 2017 founding by Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple respectively3

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. Eight investors participated in the round, including new backers Moore Strategic Ventures, Coreline Ventures, Salesforce Ventures, and Toyota's Woven Capital, alongside returning investors Atomico, Globis Capital Partners, and JPS Growth Investment Limited Partnership1

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Expansion into North America Drives Growth Strategy

The AI-driven manufacturing platform is prioritizing expansion into North America, which CADDi identifies as the world's second-largest manufacturing market2

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. The company's workforce has grown by approximately 50% since March 2025, reaching 900 employees from 600, while sales have been more than doubling year over year1

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. More than half of Japan's 100 largest manufacturers now use CADDi, and the company serves customers across 22 countries1

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. The funding will support product expansion, proprietary AI model development, global growth centered on North America, and talent acquisition3

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Manufacturing AI Data Platform Addresses Fragmented Data Challenge

CADDi has evolved from its initial product, CADDi Drawer, into a comprehensive manufacturing AI data platform that integrates fragmented manufacturing data across multiple systems1

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. The platform consolidates information from CAD files, enterprise resource planning software, HR systems, engineering drawings, and purchase orders into a unified semantic structure2

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. According to Yushiro Kato, more than 80% of manufacturing knowledge about work processes and supplier decisions exists only in the heads of experienced employees and is never recorded1

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. The company's next-generation platform, announced in Japan in August 2026, is now launching globally as a manufacturing operating system that enables both human and AI workflows to operate on shared data without manual handoffs3

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CADDi Explorer and CADDi Agent Lead Product Suite

The company has renamed CADDi Drawer to CADDi Explorer, positioning it as a manufacturing discovery engine that reads blueprints and allows engineers to search archived part designs by shape or specification2

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. CADDi Agent, a newly introduced AI agent, analyzes data and makes decisions about standardizing parts and performing quality impact assessments within each customer's operating context1

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. The platform now includes six workflow products designed to capture tacit knowledge from experienced engineers, including CADDi Design Review, which flags potential errors in new drawings and CAD models based on past problems with similar parts1

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. Purchasing teams use the search functionality to identify duplicate parts and compare supplier pricing across the organization2

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Proprietary AI Models Target Manufacturing-Specific Data

CADDi uses proprietary AI models to analyze product data like drawings and CAD files, while employing general-purpose large language models for documents and spreadsheets1

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. Kato emphasized the limitations of standard LLMs for manufacturing applications, stating that general AI cannot handle design reviews because it doesn't understand drawings or CAD2

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. Ken Hara, a partner at Coreline Ventures, noted that AI only delivers value where a data layer and semantic layer already exist, and in manufacturing, that layer is buried in information fragmented across departments and in records never digitized2

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. The company plans to invest heavily in building AI models that understand manufacturing-specific data such as 3D CAD files and 2D drawings1

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Addressing the Physical Bottleneck in Manufacturing

Kato frames CADDi's mission around solving what he calls the physical bottleneck—the gap between rapidly advancing AI capabilities and the unchanged pace of physical manufacturing1

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. While AI can build an e-commerce marketplace website in hours, developing a new car still takes approximately four years from planning to delivery. Kato argues that even if AI makes thinking ten thousand times faster, the upside gets diluted in the physical world if mass production timelines remain unchanged1

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. Over three decades, the world of bits has become more than a hundred times faster while the world of atoms has maintained roughly the same pace in most industries2

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. CADDi markets its products based on measurable returns such as lower direct material costs and shorter engineering lead times, which Kato identifies as critical for automakers and other manufacturing firms competing with Chinese rivals1

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. The company employs more than 100 customer success staff, outnumbering its salespeople, to help organizations manage the change required to adopt new manufacturing workflows1

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