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Watch <a href="https://www.youtube.com/watch?v=qhYhW5sSrrw" rel="noopener noreferrer" target="_blank">here</a>.

Yushiro Kato, CADDi, Inc | theCUBE + NYSE Wired: Mixture of Experts

<h5>Originally published in <a href="https://www.cio.com/article/4230237/5-metrics-to-prove-your-ai-strategys-business-value.html" rel="noopener noreferrer" target="_blank">CIO Online</a></h5> CIOs must have a continuous pulse on AI’s material impact on the business, not just to prove ROI but also to capitalize on opportunities and mitigate risks as they arise.<img src="https://www.cio.com/wp-content/uploads/2026/10/4230237-0-03295900-1791443875-AI-gets-practical-shutterstock_2716350391.jpg?quality=50&amp;strip=all&amp;w=1024" alt="photo illustration of an AI robot imparting its wisdom on IT execs" height="576" width="1024">Credit: Rob Schultz / Shutterstock Many organizations have licensed language models, tested AI agents, and asked DevOps teams to develop <a href="https://www.infoworld.com/article/4221724/how-to-keep-ai-generated-code-aligned-with-your-standards.html" rel="noopener noreferrer" target="_blank">code-generation standards</a>. But production deployments are still a work in progress for CIOs, with only 50% of organizations moving more than half of their AI projects from pilot to production, according to <a href="https://www.outsystems.com/1/state-ai-development" rel="noopener noreferrer" target="_blank">Outsystems’ State of AI Development 2026 report</a>.Increasing production deployments is a key step for many CIOs, followed by fostering more widespread employee adoption. But usage doesn’t always translate to business outcomes, leaving <a href="https://www.cio.com/article/4159326/does-it-have-a-value-problem.html" rel="noopener noreferrer" target="_blank">many IT organizations struggling to deliver business value</a> on their AI initiatives. Much of that has to do with how they’re measuring AI value. “Too many organizations are treating AI adoption as the metric when the real question is whether it’s improving business outcomes,” says Rob Scudiere, CTO at Verint. “The metrics that matter are the ones executives already track, and if those numbers aren’t improving, it’s hard to argue that AI is delivering meaningful value.”Measuring how AI impacts the business strategy and its relevant KPIs is a vital first step, especially when it comes to identifying AI’s business-specific opportunities and risks. After all, AI’s capabilities, practices, and security considerations are changing so quickly that it’s essential for C-level leaders to discuss AI’s strategic business value frequently — and to do so with the right data to back those discussions up. That, in turn, requires a more collaborative leadership approach. “Organizations should align with business stakeholders upfront on the ‘so what,’” says Keyur Ajmera, CIO at Boomi. “CIOs should partner with CMOs, CHROs, CFOs, and business practitioners to identify use cases, establish appropriate governance and standardization, prepare employees for new ways of working, and create financial guardrails as usage-based AI costs become more prevalent.”I heard from more than 30 leaders on their picks for the business value metrics that matter most when tracking your AI strategy’s impact. Below are my top five worth adopting. <h2>Growth driven by organizational expertise</h2>For AI to be tr<a href="https://drive.starcio.com/2026/04/ai-reshaping-business-not-digital-transformation-yet/" rel="noopener noreferrer" target="_blank">ansformational and not just reshaping business, </a>executives must show that building greater expertise through AI drives business growth.“Executives primarily talk about AI ROI in terms of cost savings and efficiency, and that doesn’t adequately measure long-term AI success, because it doesn’t capture whether the organization is actually getting smarter,” says Sarah Edwards, chief product and strategy officer at Kantata.“I’d point them to what I think of as an organization’s expertise compounding rate, which is its ability to capture, synthesize, scale, and continuously build on institutional and tribal knowledge,” she adds. “Firms also need to look beyond productivity and efficiency and consider how AI drives revenue growth, including new pipelines, better client retention, new pricing models, and the ability to take on more projects without adding headcount.”Ways to measure: Track how expertise developed via AI capabilities drives revenue. Examples include:<ul><li>DevOps teams, newly trained on <a href="https://www.infoworld.com/article/4166817/vibe-coding-or-spec-driven-development.html" rel="noopener noreferrer" target="_blank">vibe coding tools</a>, help increase revenue by adding AI capabilities such as shopping concierge agents and genAI search capabilities to customer-facing websites.</li><li>Benchmarking return on ad spend (ROAS) between two campaigns, one developed by a team using AI tools, the other without using AI.</li><li>Contact centers using AI to support service-to-sales or sales-through-service competencies can measure increases in revenue per agent hour or offer conversion rates. In one famous case, <a href="https://www.cio.com/article/4180896/how-ikea-turned-a-chatbot-with-13-million-users-into-a-business-worth-1-3-million.html" rel="noopener noreferrer" target="_blank">Ikea built a new €1.3 billion business line</a>, thanks to a contact center chatbot freeing up associates’ time.</li></ul>The key pattern is that when AI is introduced into a sales, marketing, or support function, it can be correlated with revenue increases. <h2>Improved customer retention</h2>Improving customer retention is a key metric for products or services, especially those with <a href="https://drive.starcio.com/2026/07/ai-subscriptions-repeat-saas-mistakes/" rel="noopener noreferrer" target="_blank">subscription business models</a>. Customer loyalty and the ability to influence repeat purchases are similar KPIs when the brand influences pricing and customer-perceived value.How CIOs implement <a href="https://drive.starcio.com/2026/09/ai-governance-lowers-the-ai-hallucination-tax/" rel="noopener noreferrer" target="_blank">data and AI governance</a> can positively or negatively affect customer retention. According to the <a href="https://usercentrics.com/resources/state-of-digital-trust-report-2026/" rel="noopener noreferrer" target="_blank">State of Digital Trust 2026</a> from Usercentrics, consumers will now pay 7% more for the brands that have earned their trust around AI. Meanwhile, 24% of consumers canceled a subscription or stopped buying from a brand because they were concerned about how their data was used in AI.Donna Dror, CEO at Usercentrics, says, “Most CIOs can measure what AI saves them, but the harder number, and the one that shows up in revenue, is what mishandling data in AI costs in lost customers.” Ways to measure: Measure consent withdrawals, Data Subject Access Requests (DSARs), or AI feature opt-out rates. Businesses with subscription products can track renewal rates in the 90 days following an AI feature launch and add a data-and-AI-use reason code to the cancellation flows. <h2>Employee capabilities beyond productivity</h2>I’ve warned CIOs that <a href="https://drive.starcio.com/2025/02/cios-genai-roi-productivity-trap/" rel="noopener noreferrer" target="_blank">focusing on AI’s productivity improvements is a trap</a>, because at some point the CFO will look for cost savings through headcount reductions. Other metrics beyond productivity may matter more, especially for organizations investing in <a href="https://drive.starcio.com/2026/01/ai-agents-transforming-what-cios-must-lead-now/" rel="noopener noreferrer" target="_blank">agentic AI workflows</a>.The first is time-to-decision: AI agents are either capable of taking action autonomously, or they are implemented to include human collaboration in decision-making. CIOs and the business owners of AI agents should determine how to measure the business impact of faster, smarter decision-making.“Most AI scorecards measure activity, including agents deployed, tasks automated, and prompts processed,” says Vikram Bhandari, chief technology and innovation officer at Riveron. “The metric I push clients toward is time to decision, such as how much faster finance or ops teams can act once AI is in the workflow, and what that speed translates to in revenue or risk avoided. Headcount metrics tell you AI has replaced work, while decision-speed metrics tell you AI changed the business.”Enterprises can scale beyond individual productivity improvements by <a href="https://www.cio.com/article/4157466/cios-reimagine-business-processes-to-reap-ai-benefits.html" rel="noopener noreferrer" target="_blank">re-engineering business processes</a> using <a href="https://www.cio.com/article/4138739/21-agent-orchestration-tools-for-managing-your-ai-fleet.html" rel="noopener noreferrer" target="_blank">AI orchestration platforms</a>. But CIOs have to get <a href="https://www.cio.com/article/4136302/how-to-get-ai-democratization-right.html" rel="noopener noreferrer" target="_blank">AI democratization</a> right by influencing experts to build the organization’s <a href="https://drive.starcio.com/2026/08/knowledge-management-ai-context-layer/" rel="noopener noreferrer" target="_blank">knowledge base</a> and <a href="https://drive.starcio.com/2026/08/ai-agents-knowledge-graphs-semantic-layers-context-layers/" rel="noopener noreferrer" target="_blank">the AI context layer utilized by AI agents</a>.“Data democratization’s real number is the percentage of institutional knowledge anyone can self-serve, without tracking down one specific person,” says Chris Cope, VP of engineering at CADDi. “Manufacturers map every flow on the factory floor and fixate on single-source supplier risk, yet miss the same risk when it’s a person holding the knowledge.” Ways to measure: In risk management functions, measure time-to-decision as the difference between mean time to remediate (MTTR) and mean time to detect (MTTD). Measure knowledge management impacts by tracking increased utilization of internal LLMs, while capturing the reduced time spent on answering requests for information (RFIs) by subject matter experts. <h2>Impacts to customer and employee experience</h2>CIOs should hunt for examples of value delivered to customers and for employees that go beyond doing today’s job better. This means exploring areas where AI provides a business capability that is not feasible, or is economically unscalable, if people were performing the function without AI capabilities.Venkat Ramakrishnan, president and COO at NeuBird, shares this example in IT and security incident management: “The focus has shifted from how fast you resolved an incident to how many users never felt it in the first place. When AI prevents an incident 30 minutes before failure, or resolves it in under 5 minutes instead of 175, it eliminates the experience impact rather than only reducing it,” Ramakrishnan says.Other examples require exploring game-changing operational transformations in other departments, or finding areas where AI impacts the connected experiences of customers and employees (CX and EX).“CIOs should measure genAI by whether it changes the economics of the business such as decision velocity, capacity created per employee, exceptions resolved without escalation, revenue leakage recovered, customer and employee effort avoided, and ultimately the rate at which AI benefits reach the P&amp;L,” says Arun Raghavapudi, chief customer officer at R Systems (RSI). “The strongest AI strategy is therefore one where CX, EX, engineering, and business KPIs form a single value chain rather than separate technology scorecards.” Ways to measure: CIOs should identify AI-native capabilities, those business and operational competencies that are net-new AI use cases. Once identified, business value metrics determine the value delivered, and the AI-native capabilities become <a href="https://www.cio.com/article/3250269/communicating-and-marketing-a-portfolio-of-initiatives-that-drive-digital-transformation.html" rel="noopener noreferrer" target="_blank">marketable innovations to communicate across the company</a>. <h2>Measure AI’s operational impacts</h2>Many CIOs used lift-and-shift cloud migration strategies or overly empowered DevOps teams to procure cloud infrastructure, only to find skyrocketing costs. CIOs applied <a href="https://www.infoworld.com/article/3699108/6-finops-best-practices-to-reduce-cloud-costs.html" rel="noopener noreferrer" target="_blank">FinOps best practices</a> and <a href="https://www.infoworld.com/article/4013485/how-to-shift-left-on-finops-and-why-you-need-to.html" rel="noopener noreferrer" target="_blank">shifted-left financial considerations</a> to reduce costs and instill fiscal discipline.Even as token costs decline, <a href="https://drive.starcio.com/2026/06/ai-cost-debt-how-finops-helps-cios/" rel="noopener noreferrer" target="_blank">AI cost debt</a> is very real as consumption of AI models and use of AI agents increases. <a href="https://www.finops.org/insights/token-economics-the-atomic-unit-of-ai-value/" rel="noopener noreferrer" target="_blank">Tokenomics</a> tracks and manages both costs and the business value delivered from spending on AI tokens. On the cost side, CIOs should factor in the human-effort costs, including the<a href="https://drive.starcio.com/2026/09/ai-governance-lowers-the-ai-hallucination-tax/" rel="noopener noreferrer" target="_blank"> AI hallucination tax</a> when employees have to recognize when AI models and agents are wrong.“Token spend is becoming a real line item, and if it grows faster than the outcomes it produces, that’s a unit economics problem, not a scaling milestone to be proud of,” says Yasmin Rajabi, COO of CloudBolt. “The metrics that matter include how often AI-generated work is accepted without rework, how many decisions or actions run without a human in the loop, and value per token you get for every token you spend.”Measuring the value of operational impacts needs to consider both tactical impacts based on individual contributions versus team, department, and business-level outcomes.“Indicators that AI is creating measurable value rather than simply generating activity include whether critical workflows are completed quicker, decisions are more consistent, and AI-generated recommendations are traceable back to authoritative information,” says Tony Grout, chief product and technology officer at M-Files. “The most successful AI programs will be judged by whether they deliver better business outcomes like faster project completion, higher-quality customer service, improved compliance, and more consistent decision-making.” Ways to measure: My research on <a href="https://drive.starcio.com/research/ai-agent-orchestration-platforms/" rel="noopener noreferrer" target="_blank">AI agent orchestration platforms</a> identifies several platforms that include tokenomics capabilities, including solutions from Boomi, Cisco, Databricks, Kamiwaze, LangChain, Microsoft, Nutanix, PagerDuty, Salesforce, Snowflake, Tray.ai, and Workato. <h2>Setting realistic expectations</h2>CIOs must lead a progression from AI experiments to production deployments, enterprise adoption, and delivered value. But the organization’s velocity to adopt and deliver value requires a strong <a href="https://www.cio.com/article/4082282/preparing-your-workforce-for-ai-agents-a-change-management-guide.html" rel="noopener noreferrer" target="_blank">change management program</a> that adjusts to business demand, especially if the <a href="https://www.cio.com/article/4113990/fearing-an-ai-bubble-cios-have-answers.html" rel="noopener noreferrer" target="_blank">AI bubble bursts</a>.“The reality is that enterprise AI follows a productivity J-curve, where companies invest in new skills, operating models, governance, and process redesign long before the full economic benefits become visible,” says Kris Lovejoy, global head of strategy at Kyndryl. “As a result, usage often rises months before measurable business outcomes appear, and this is one reason many executives feel uncertain about the return they’re getting from AI.”While AI agent deployment and employee adoption are not the end game, CIOs must excel at these before overpromising business value. Two places to start include <a href="https://www.cio.com/article/4218846/how-to-upskill-it-for-agentic-ai-7-pathways-to-success.html" rel="noopener noreferrer" target="_blank">upskilling IT for agentic AI</a> while <a href="https://www.cio.com/article/4230237/7%20mistakes%20IT%20leaders%20make%20when%20deploying%20AI%20agents" rel="noopener noreferrer" target="_blank">avoiding mistakes when deploying AI agents</a>.

5 metrics to prove your AI strategy’s business value

<h5 class="">Originally featured on <a href="https://www.designworldonline.com/voices/voices-jeremy-holt-strategic-advisor-at-caddi/" rel="noopener noreferrer" target="_blank">Design World</a></h5> Sponsored by <a href="http://us.caddi.com/company?ref=US-PR-2026-Q4-WTWH-DesignWorld-Voices7-SC" rel="noopener noreferrer" target="_blank" class="">CADDi</a>. In this VOICES interview, Design World spoke to Jeremy Holt, strategic advisor at CADDi, to learn how the right software helps companies save costs throughout design, development and production, by modernizing their systems and ensuring the right data is available at the right time. Design World: Tell us a bit about your background and your role at CADDi. Jeremy Holt: I’m a former automotive and vehicle industry supplier executive. I ran several different companies focused on engineering, transmission manufacturing and component manufacturing, in every form from startup to private to publicly owned. My entire career has focused on producing highly engineered products, some of which have complex assemblies or go into complex assemblies, such as powertrains in the automotive world.I have now moved into doing consulting work for clients, and CADDi is a key client for me in the AI software space. I previously ran a technical software business at Ricardo, the engineering consultancy, that did engineering simulation. I bring that manufacturing technology and industry experience into CADDi to help them deploy their software more effectively. What aspects of CADDi resonate with your experience designing, developing and manufacturing engineered products for the automotive space? The CADDi software is purpose-designed for highly engineered products. It applies upstream for designing, developing and producing new products or managing legacy products, including the aftermarket. Also compelling is the speed and insight that you can get from data that already exists within the organization which, in my experience, is often very time- and resource-intensive to access and leverage in a manual environment where you’re asking people to go and find data from different systems.What CADDi can do is connect the dots between those dissimilar systems, and legacy or longtail data, so that you can bring it to the surface, gain insights and make good decisions. Not slowly by adding work to people’s to-do list, but instantly by leveraging the data yourself without bothering engineers or other people to accumulate that information and insight for you. What opportunities do you see in aftermarket and legacy product line management? The relevance is greatest where data sits stagnant. It might be in a drawing format, or maybe it was never digitized but is lost somewhere, and to go find that data is very difficult. This means in the aftermarket, you may end up with duplication, or opportunities for consolidation are undiscovered. There might be information, parts and tooling out there that you don’t know already exists and identifying parts the company has already designed, tooled and qualified avoids paying for that work twice. Designing a new part with backward compatibility to earlier service parts is a real opportunity.For example, transmission manufacturers who have been making transmissions for 30 years have a lot of parts, with a lot of service parts and possibly several design changes along the way. With CADDi, you can dive into that data, and with the right questions can find actionable insights and save time, work, tooling investment and labor investment by leveraging that existing data. Cost is a constant focus in manufactured products. How can an AI data platform help achieve costing goals? On costing, you can bring all of that data to the surface and do more accurate costing faster. Connect the design, development and product data with commercial data from dissimilar systems: costing, pricing and actual production data. Together, that might tell you can make 100 parts an hour when you thought you could make 200, and perpetuate erroneous costing. In many companies, the costing system has a formula, a model. That model could be wrong, and you might be providing bad quotes by using that model and not realizing that it doesn’t work when you get to the manufacturing floor. The CADDi system, as it relates to cost, can bring that shop-floor production data into the data set with the costing data and allow you to adjust the models to meet the reality of making similar parts. More informed, more accurate and faster costing is one of the key benefits of the CADDi software. Some software platforms find certain capabilities hard to implement. How does CADDi approach that challenge, and how does it work with current PLM or CAD systems? That’s one of the other compelling value propositions for CADDi: it’s very easy to get started. The general approach is to start with a manageable data set that you can put into the CADDi environment, and use that to prove a use case in a manageable deployment. They run a pilot study very quickly, generally at no significant cost, and prove that you can find new insights even in a limited data set. That value can then be expanded by adding more information from different environments and systems.A larger data set surfaces more of these connections and points to the next problem worth solving. You can then continue expanding the systems that can access information through the CADDi software, so that you can have a progressive deployment with very early wins during the process, and compelling future opportunities.I think the Subaru case study example is a good one. They started with one department using the software, and after less than one year — only 50 weeks — 50 departments were using the CADDi software with expanded data and amplified value. CADDi assigns an engineer to help deploy the software, which is how companies get early wins. For engineering and enterprise leaders who know they need to modernize upstream operations but don’t know where to start, what’s the first step? What should they expect early versus later? The first step is easy, because you don’t have to restructure any data or system to work with CADDi. Simply provide access to your data. CADDi locates that data and maps the relationships between dissimilar systems: ERP, PLM, development spreadsheets, documents, production data and quality reports.The downstream manufacturing can then have access to all that data without restructuring anything. They can see into your existing system of record and provide opportunities to attain insights from all of that information by allowing the manufacturing operations to see across that data and use it to make better decisions about implementing their production. In fact, many people across the organization can start to leverage information they couldn’t see before.Early in the deployment, put questions to your own data. Ask what a part cost the last three times you bought it, which suppliers quoted it and what the quality records came back with. Cross-functional associates can explore the data and find things that they didn’t know, or discover insights that are useful for decision making, allowing deeper probing into company pain points or opportunities.One example might be that if you can look at maintenance records and downtime records, you can tie a specific part to the machine problem it causes, and fix the cause instead of the symptom. Downstream, you can expect to get higher productivity, lower scrap rates and simpler changeovers because you can choose the right parts to change over from. These benefits can be optimized and monetized for improved financial performance from the manufacturing operation, as well as putting pressure on upstream processes to improve design for manufacturing. For engineering and manufacturing, most of a part’s cost is determined in design, long before a supplier is chosen. The teams that manage cost best are the ones whose engineers can see what the company has already qualified and paid for while the design is still open. That is the window where value analysis and value engineering (VA/VE) can impact the bottom line. Once a part is released, the options narrow and most of the savings are already committed. Read the white paper <a href="https://us.caddi.com/resources/whitepaper/value-analysis-value-engineering-for-manufacturing?ref=US-PR-2026-Q4-WTWH-DesignWorld-Voices7-SC" rel="noopener noreferrer" target="_blank" class="">Value Analysis &amp; Value Engineering for Manufacturing: The Challenges and Solutions of Implementation</a>. <a href="https://www.designworldonline.com/voices/voices-yushiro-kato-co-founder-and-ceo-caddi/?ref=US-PR-2026-Q4-WTWH-DesignWorld-Voices7-SC" rel="noopener noreferrer" target="_blank" class="">Yushiro Kato</a>, co-founder and CEO at CADDi offers a related perspective in a recent Design World interview.CADDi is a global manufacturing technology company driven by the vision to accelerate physical innovation tenfold. Headquartered in Chicago and Tokyo, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi currently serves manufacturing businesses in more than 20 countries, including many of the largest publicly traded companies in Japan and the United States. To learn more, visit <a href="http://us.caddi.com/company?ref=US-PR-2026-Q4-WTWH-DesignWorld-Voices7-SC" rel="noopener noreferrer" target="_blank" class="">caddi.com</a> or <a href="https://www.linkedin.com/company/caddiinc/" rel="noopener noreferrer" target="_blank" class="">follow the conversation on LinkedIn</a>.

VOICES: Jeremy Holt, Strategic Advisor at CADDi - Design World

Originally featured in <a href="https://www.bizjournals.com/chicago/news/2026/09/16/caddi-manufacturing-funding-billion-valuation.html" rel="noopener noreferrer" target="_blank">Chicago Business Journal</a>

AI manufacturing startup Caddi hits $1.2B valuation with $114M funding round

<h5 class="">Originally featured on <a href="https://pulse2.com/caddi-raises-114-million-series-d-at-1-2-billion-valuation/" rel="noopener noreferrer" target="_blank" class="">Pulse 2.0</a></h5> CADDi has raised $114 million in Series D funding at a $1.2 billion valuation as the manufacturing technology company expands its AI-powered data platform globally. The financing increases CADDi’s total funding to $234 million.New investors in the round include Moore Strategic Ventures, Coreline Ventures, HR Tech Fund, Woven Capital and Salesforce Ventures. Existing backers Atomico, Globis Capital Partners and JPS Growth Investment also participated.CADDi plans to allocate the capital toward proprietary AI and technology development, expansion of its Manufacturing AI Data Platform, global business growth centered on North America and talent recruitment and development.The Chicago- and Tokyo-based company is building a data and intelligence layer for manufacturers whose information has historically been fragmented across engineering, procurement, production and quality systems.Its Manufacturing AI Data Platform analyzes, structures, contextualizes and connects manufacturing information, while domain-specific workflow applications use that shared data foundation to support decision-making across the value chain.CADDi argues that general-purpose AI has difficulty working directly with manufacturing information because the industry depends on complex relationships, technical semantics and constraints that often exist in separate systems or remain undocumented.The company is increasingly positioning its platform as the semantic foundation that AI agents need to reason over decades of engineering and production knowledge.CADDi now serves manufacturers in more than 20 countries, including major publicly traded companies in Japan and the U.S. Its official U.S. platform launch represents a broader push into the world’s second-largest manufacturing market. <h4 class="">KEY QUOTES:</h4>“AI only delivers where a data layer and a semantic layer already exist. In manufacturing that layer is buried in information fragmented across departments and in records that were never digitized at all, which is why horizontal tooling never reaches it.”<h5 class="">Kenichiro Hara, Co-Founder and Managing Partner at Coreline Ventures</h5> “CADDi is evolving from solving individual manufacturing workflows to building the intelligence layer for the entire manufacturing lifecycle. Its new AI-native data platform connects engineering, procurement, production and quality through a shared semantic data foundation, giving AI agents the context to reason across decades of fragmented manufacturing knowledge.”<h5 class="">Luca Eisenstecken, Partner at Atomico</h5>

CADDi Raises $114 Million Series D At $1.2 Billion Valuation

Funding will accelerate the global expansion of CADDi’s next-generation Manufacturing AI Data Platform CHICAGO, Sept. 16, 2026 — <a href="/en-us/?ref=US-PR-2026-Q4-CADDi-Series-D-Funding-NPI" rel="noopener noreferrer">CADDi</a>, the global technology company that develops the CADDi Manufacturing AI Data Platform, today announced that it has entered into definitive agreements for $114 million at a $1.2 billion valuation in Series D funding, with eight new and existing investors participating, including Moore Strategic Ventures and Coreline Ventures. This funding brings CADDi's total funding to $234 million. Funding overview:● Series D Round Size: $114 million● New Investors: Moore Strategic Ventures, Coreline Ventures, HR Tech Fund LLC*, Woven Capital, and Salesforce Ventures● Existing Investors: Atomico, Globis Capital Partners, JPS Growth Investment Limited Partnership** <h2>Fueling what’s next for manufacturing in the AI era</h2>CADDi is headquartered in Chicago and Tokyo, and is led by Yushiro Kato, co-founder and CEO. In an essay recently published on CADDi's corporate website, titled <a href="https://caddi.com/en-us/essay/2609" rel="noopener noreferrer" target="_blank">"Materialization — Making Things in the Age of AI,"</a> Kato wrote, "Over thirty years we made the world of bits more than a hundred times faster. The world of atoms moved at roughly the same speed in most industries. In the AI era, the gap widens further. Even if AI makes thinking ten thousand times faster and produces ten thousand times the theory, the upside from AI gets diluted in the physical world if it still takes four years to mass-produce cars. Solving physical limits means solving this asymmetry."Kato believes the key lies in transitioning manufacturing from sequential execution to parallel processing. In a world where processes that were once executed step-by-step now run simultaneously, a common platform that enables humans and AI across departments to draw on the same information and context becomes indispensable. However, manufacturing data contains highly complex semantics, relationships, and constraints that general-purpose AI cannot adequately address. Domain-specific vertical AI capable of mapping data semantics is essential. The funds raised will be allocated across four areas:● Proprietary AI and technology development● Expansion of the Manufacturing AI Data Platform● Enhancement of global business operations centered on North America● Investment in talent acquisition and development "AI only delivers where a data layer and a semantic layer already exist. In manufacturing that layer is buried in information fragmented across departments and in records that were never digitized at all, which is why horizontal tooling never reaches it,” said Ken Hara, partner at Coreline Ventures. “CADDi has spent four years building it, with some of the most demanding manufacturers in the world as its first customers. Knowledge that lived only in individual experience is becoming an asset the organization, and in time the industry, can use.”“Eighteen months ago, we backed CADDi because we believed they had the potential to build one of the defining AI companies in manufacturing. Since then, that conviction has only grown,” said Luca Eisenstecken, Partner at Atomico. “CADDi is evolving from solving individual manufacturing workflows to building the intelligence layer for the entire manufacturing lifecycle. Its new AI-native data platform connects engineering, procurement, production and quality through a shared semantic data foundation, giving AI agents the context to reason across decades of fragmented manufacturing knowledge. The ambition is enormous, the execution has been exceptional and we’re thrilled to deepen our partnership with Yushiro, Aki and the team for the decade ahead.” <h2>CADDi previews next-generation platform technology</h2>Kato emphasized, “As artificial intelligence pushes past the boundaries of intelligence, the gap between what is conceptualized and what is implemented in the physical world widens. We see this as a broader human challenge that goes well beyond manufacturing: ʻphysical limits.’"CADDi announced the next-generation Manufacturing AI Data Platform in Japan in August 2026 to serve as this foundational platform, initiating the global products release. “At our CADDi Unleash 2026 event, we presented a renewed vision: to overcome physical limits and accelerate physical innovation tenfold. That is our priority from here on, and this commitment is what guided both our recent product rebrand and the development of new products we are bringing to our U.S. customers,” said Kato.Designed as a manufacturing operating system, <a href="/en-us/platform/?ref=US-PR-2026-Q4-CADDi-Series-D-Funding-NPI" rel="noopener noreferrer">CADDi</a> provides a semantic structure to fragmented data, enabling human and AI workflows to run on shared data without manual handoffs. The new product, CADDi Explorer, is a manufacturing discovery engine and the next-generation version of CADDi Drawer. CADDi Agent is a new AI agent that analyzes manufacturing data, makes decisions, and takes action within each company's operating context. CADDi will also offer six domain-specific Workflow Products spanning the manufacturing value chain, enhancing decision-making and execution to drive business-critical impact.The platform's official U.S. launch signals CADDi's full-scale expansion into the United States, the world's second-largest manufacturing market. <h3>About CADDi</h3>CADDi is a global manufacturing technology company driven by the vision to accelerate physical innovation tenfold. Headquartered in Chicago and Tokyo, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple, respectively. CADDi currently serves manufacturing businesses in more than 20 countries, including many of the largest publicly traded companies in Japan and the United States. Total funding stands at $234 million, valuing the company at $1.2 billion.CADDi’s Manufacturing AI Data Platform analyzes, structures, contextualizes, and connects data that has historically been fragmented across the manufacturing value chain. With a proprietary AI model, CADDi offers domain-specific Workflow Products spanning the manufacturing value chain, enhancing decision-making and execution to drive business-critical impact. To learn more, visit <a href="/content/caddi.com/en-us" rel="noopener noreferrer">caddi.com</a>. <h3>Media Contact</h3>Milbien MedinaSenior Manager, Brand Marketing at CADDi<a href="mailto:milbien_medina@caddi.com" rel="noopener noreferrer">milbien_medina@caddi.com</a> View digital copy of this release, including a full list of investor comments on CADDi’s news page: <a href="/en-us/news/?ref=US-PR-2026-Q4-CADDi-Series-D-Funding-NPI" rel="noopener noreferrer">caddi.com/en-us/news</a> *HR Tech Fund LLC is a corporate venture capital entity under Recruit Holdings Co., Ltd.*JPS Growth Investment Limited Partnership and JPS Growth II Investment Limited Partnership, managed by a subsidiary of Japan Post Bank Asset Management Co., Ltd. <img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F7cde983fcf1d441e947366100e384172"> <h4>Michiko Kato, Chief Investment Officer &amp; Partner, Woven Capital</h4>Manufacturers hold decades of engineering knowledge in their specifications, past project records, and the day-to-day decisions of design, sourcing, and production. Much of it is hard to draw on at the moment it is needed, and as experienced engineers retire, that knowledge becomes harder to pass on.CADDi has built the platform to structure that knowledge and make it retrievable and usable at scale, with the accuracy required to inform real engineering decisions. As AI becomes more capable of handling the complexity and precision these workflows demand, it is this kind of domain-specific intelligence that we believe will define the next era of manufacturing. The company is already helping leading global manufacturers address some of the industry’s most enduring priorities, from reducing defects to shortening lead times and lowering cost, and we are excited to support CADDi on their journey. <h4>Kenichiro Hara, Co-Founder Managing Partner at Coreline Ventures</h4>Coreline is investing in CADDi again at scale. My history with the company goes back further than the firm does. I led CADDi's seed, Series A, and Series A extension, three consecutive rounds, and served on the board through the company's early years. I was in the boardroom when CADDi decided to make the data platform the whole company, setting aside the business it was founded on. Few companies make that call, and fewer make it work. AI only delivers where a data layer and a semantic layer already exist. In manufacturing that layer is buried in information fragmented across departments and in records that were never digitized at all, which is why horizontal tooling never reaches it. CADDi has spent four years building it, with some of the most demanding manufacturers in the world as its first customers. Knowledge that lived only in individual experience is becoming an asset the organization, and in time the industry, can use. What gets built on that foundation, in pursuit of accelerating physical innovation tenfold, is what will make CADDi a global company. <h4>Luca Eisenstecken, Partner at Atomico</h4>Eighteen months ago, we backed CADDi because we believed they had the potential to build one of the defining AI companies in manufacturing. Since then, that conviction has only grown. CADDi is now deeply embedded with many of the world’s most sophisticated manufacturers across Japan, the US and Europe, with customers expanding the platform across teams, workflows and geographies, while the company continues to grow at exceptional speed and efficiency.What excites us even more is what comes next. CADDi is evolving from solving individual manufacturing workflows to building the intelligence layer for the entire manufacturing lifecycle. Its new AI-native data platform connects engineering, procurement, production and quality through a shared semantic data foundation, giving AI agents the context to reason across decades of fragmented manufacturing knowledge. The ambition is enormous, the execution has been exceptional and we’re thrilled to deepen our partnership with Yushiro, Aki and the team for the decade ahead. <h4>Emre Hidekazu Yuasa, Partner, Globis Capital Partners</h4>Since our Series A investment in 2018, this Series D marks our fourth investment in CADDi. World-class growth, proven traction in the U.S. market, and the bold vision behind the AI data platform unveiled recently all give us confidence that CADDi's rapid growth is far from over. Having now become a unicorn, we see this as merely a waypoint toward decacorn and beyond, and we will continue to fully support CADDi's ambitious challenge in the global manufacturing industry.

CADDi Raises $114 Million in Series D at $1.2 Billion Valuation

<h5 class="">Originally featured on <a href="https://finance.yahoo.com/technology/ai/articles/caddi-raises-114-million-series-130000779.html" rel="noopener noreferrer" target="_blank" class="">Yahoo Finance</a></h5> CHICAGO, September 16, 2026--(<a href="https://www.businesswire.com/" rel="noopener noreferrer" target="_blank" style="font-size: 14px;" class="">BUSINESS WIRE</a>)--<a href="https://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fcaddi.com%2Fen-us%2F%3Fref%3DUS-PR-2026-Q4-CADDi-Series-D-Funding-NPI&amp;esheet=54605181&amp;newsitemid=20260916456307&amp;lan=en-US&amp;anchor=CADDi&amp;index=1&amp;md5=7efaf94a60b6e4e121979efd8d74617e" rel="noopener noreferrer" target="_blank" style="font-size: 14px;" class="">CADDi</a>, the global technology company that develops the CADDi Manufacturing AI Data Platform, today announced that it has entered into definitive agreements for $114 million at a $1.2 billion valuation in Series D funding, with eight new and existing investors participating, including Moore Strategic Ventures and Coreline Ventures. This funding brings CADDi's total funding to $234 million.Funding overview:<ul class=""><li class="">Series D Round Size: $114 million</li><li class="">New Investors: Moore Strategic Ventures, Coreline Ventures, HR Tech Fund LLC*, Woven Capital, and Salesforce Ventures</li><li class="">Existing Investors: Atomico, Globis Capital Partners, JPS Growth Investment Limited Partnership**</li></ul> <h2 class="">Fueling what's next for manufacturing in the AI era</h2>CADDi is headquartered in Chicago and Tokyo, and is led by Yushiro Kato, co-founder and CEO. In an essay recently published on CADDi's corporate website, titled <a href="https://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fcaddi.com%2Fen-us%2Fessay%2F2609&amp;esheet=54605181&amp;newsitemid=20260916456307&amp;lan=en-US&amp;anchor=%26quot%3BMaterialization+%26%238212%3B+Making+Things+in+the+Age+of+AI%2C%26quot%3B&amp;index=2&amp;md5=874def2b87a7110e73555a53903d68ac" rel="noopener noreferrer" target="_blank" style="font-size: 14px;" class="">"Materialization — Making Things in the Age of AI,"</a> Kato wrote, "Over thirty years we made the world of bits more than a hundred times faster. The world of atoms moved at roughly the same speed in most industries. In the AI era, the gap widens further. Even if AI makes thinking ten thousand times faster and produces ten thousand times the theory, the upside from AI gets diluted in the physical world if it still takes four years to mass-produce cars. Solving physical limits means solving this asymmetry."Kato believes the key lies in transitioning manufacturing from sequential execution to parallel processing. In a world where processes that were once executed step-by-step now run simultaneously, a common platform that enables humans and AI across departments to draw on the same information and context becomes indispensable. However, manufacturing data contains highly complex semantics, relationships, and constraints that general-purpose AI cannot adequately address. Domain-specific vertical AI capable of mapping data semantics is essential.The funds raised will be allocated across four areas:<ul class=""><li class="">Proprietary AI and technology development</li><li class="">Expansion of the Manufacturing AI Data Platform</li><li class="">Enhancement of global business operations centered on North America</li><li class="">Investment in talent acquisition and development</li></ul>"AI only delivers where a data layer and a semantic layer already exist. In manufacturing that layer is buried in information fragmented across departments and in records that were never digitized at all, which is why horizontal tooling never reaches it," said Ken Hara, partner at Coreline Ventures. "CADDi has spent four years building it, with some of the most demanding manufacturers in the world as its first customers. Knowledge that lived only in individual experience is becoming an asset the organization, and in time the industry, can use.""Eighteen months ago, we backed CADDi because we believed they had the potential to build one of the defining AI companies in manufacturing. Since then, that conviction has only grown," said Luca Eisenstecken, Partner at Atomico. "CADDi is evolving from solving individual manufacturing workflows to building the intelligence layer for the entire manufacturing lifecycle. Its new AI-native data platform connects engineering, procurement, production and quality through a shared semantic data foundation, giving AI agents the context to reason across decades of fragmented manufacturing knowledge. The ambition is enormous, the execution has been exceptional and we're thrilled to deepen our partnership with Yushiro, Aki and the team for the decade ahead." <h2 class="">CADDi previews next-generation platform technology</h2>Kato emphasized, "As artificial intelligence pushes past the boundaries of intelligence, the gap between what is conceptualized and what is implemented in the physical world widens. We see this as a broader human challenge that goes well beyond manufacturing: 'physical limits.'"CADDi announced the next-generation Manufacturing AI Data Platform in Japan in August 2026 to serve as this foundational platform, initiating the global products release. "At our CADDi Unleash 2026 event, we presented a renewed vision: to overcome physical limits and accelerate physical innovation tenfold. That is our priority from here on, and this commitment is what guided both our recent product rebrand and the development of new products we are bringing to our U.S. customers," said Kato.Designed as a manufacturing operating system, <a href="https://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fcaddi.com%2Fen-us%2Fplatform%2F%3Fref%3DUS-PR-2026-Q4-CADDi-Series-D-Funding-NPI&amp;esheet=54605181&amp;newsitemid=20260916456307&amp;lan=en-US&amp;anchor=CADDi&amp;index=3&amp;md5=12263102916e9c3d503c7dc29bf7aa25" rel="noopener noreferrer" target="_blank" style="font-size: 14px;" class="">CADDi</a> provides a semantic structure to fragmented data, enabling human and AI workflows to run on shared data without manual handoffs. The new product, CADDi Explorer, is a manufacturing discovery engine and the next-generation version of CADDi Drawer. CADDi Agent is a new AI agent that analyzes manufacturing data, makes decisions, and takes action within each company's operating context. CADDi will also offer six domain-specific Workflow Products spanning the manufacturing value chain, enhancing decision-making and execution to drive business-critical impact.The platform's official U.S. launch signals CADDi's full-scale expansion into the United States, the world's second-largest manufacturing market. <h2 class="">About CADDi</h2>CADDi is a global manufacturing technology company driven by the vision to accelerate physical innovation tenfold. Headquartered in Chicago and Tokyo, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple, respectively. CADDi currently serves manufacturing businesses in more than 20 countries, including many of the largest publicly traded companies in Japan and the United States. Total funding stands at $234 million, valuing the company at $1.2 billion.CADDi's Manufacturing AI Data Platform analyzes, structures, contextualizes, and connects data that has historically been fragmented across the manufacturing value chain. With a proprietary AI model, CADDi offers domain-specific Workflow Products spanning the manufacturing value chain, enhancing decision-making and execution to drive business-critical impact. To learn more, visit <a href="https://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fcaddi.com%2Fen-us%2Fcompany%2F%3Fref%3DUS-PR-2026-Q4-CADDi-Series-D-Funding-NPI&amp;esheet=54605181&amp;newsitemid=20260916456307&amp;lan=en-US&amp;anchor=caddi.com&amp;index=4&amp;md5=72a843845a17cc0cbae8d42660e45dfb" rel="noopener noreferrer" target="_blank" style="font-size: 14px;" class="">caddi.com</a>. View digital copy of this release, including a full list of investor comments on CADDi's news page: <a href="https://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fcaddi.com%2Fen-us%2Fnews%2Fannouncements%2Fcaddi-seriesd-unicorn-status%2F%3Fref%3DUS-PR-2026-Q4-CADDi-Series-D-Funding-NPI&amp;esheet=54605181&amp;newsitemid=20260916456307&amp;lan=en-US&amp;anchor=caddi.com%2Fen-us%2Fnews&amp;index=5&amp;md5=fb61256329744c56b0f7041552b25cdb" rel="noopener noreferrer" target="_blank" class="">caddi.com/en-us/news</a>*HR Tech Fund LLC is a corporate venture capital entity under Recruit Holdings Co., Ltd.**JPS Growth Investment Limited Partnership and JPS Growth II Investment Limited Partnership, managed by a subsidiary of Japan Post Bank Asset Management Co., Ltd.View source version on businesswire.com: <a href="https://www.businesswire.com/news/home/20260916456307/en/" rel="noopener noreferrer" target="_blank" style="font-size: 14px;" class="">https://www.businesswire.com/news/home/20260916456307/en/</a> <h3 class="">Contacts</h3><h4 class="">Media Contact</h4>Milbien MedinaSenior Manager, Brand Marketing at CADDi<a href="mailto:milbien_medina@caddi.com" rel="noopener noreferrer" style="font-size: 14px;" class="">milbien_medina@caddi.com</a>

Materialization — Making Things in the Age of AI by Yushiro Kato

<h5 class="">Originally featured in <a href="https://www.thesaasnews.com/news/caddi-raises-114m-series-d/" rel="noopener noreferrer" target="_blank" class="">The SaaS News</a></h5> CADDi Raises $114M Series D at $1.2B Valuation CADDi, a manufacturing AI startup with dual headquarters in Tokyo and Chicago, has raised $114 million in a Series D funding round, bringing the company to a $1.2 billion valuation. The latest capital infusion increases the company's total funding to $234 million. <h2 class="">Investors</h2>The funding round included eight new and existing investors. Participants include Moore Strategic Ventures, Coreline Ventures, Woven Capital, HR Tech Fund, Atomico, Globis Capital Partners, and JPS Growth funds. <h2 class="">CADDi Use of Funds</h2>CADDi plans to use the new funding to expand its product lineup, specifically by building AI models that can interpret manufacturing-specific data such as 3D CAD files and 2D drawings. The company also intends to use the capital to support its global expansion efforts, with a primary focus on the North American market, as well as to increase its headcount. <h2 class="">About CADDi</h2>Founded in 2017 by Yushiro Kato, CADDi provides an AI-powered data platform designed to help manufacturing companies organize and utilize engineering and production data. The company's platform integrates data from diverse systems, including CAD files and enterprise resource planning software, to assist manufacturers in standardizing parts, performing quality impact assessments, and capturing the institutional knowledge of experienced engineers. <h2 class="">Funding Details</h2>Company Website: <a href="https://corp.caddi.jp/?ref=thesaasnews.com" rel="noopener noreferrer" target="_blank" style="font-size: 18px;" class="">https://corp.caddi.jp</a>Company: CADDiRaised: $114MRound: Series DFunding Date: September 2026Additional Investors: Moore Strategic Ventures, Coreline Ventures, Woven Capital, HR Tech Fund, Atomico, Globis Capital Partners, JPS Growth fundsCompany Website: <a href="https://corp.caddi.jp/?ref=thesaasnews.com" rel="noopener noreferrer" target="_blank" style="font-size: 18px;" class="">https://corp.caddi.jp</a>Software Category: Manufacturing AISource: <a href="https://fortune.com/2026/09/15/caddi-manufacturing-startup-valuation-funding-round-series-d-exclusive/?ref=thesaasnews.com" rel="noopener noreferrer" target="_blank" style="font-size: 18px;" class="">https://fortune.com/2026/09/15/caddi-manufacturing-startup-valuation-funding-round-series-d-exclusive/</a>

CADDi Raises $114M Series D

<h5 class="">Originally featured on <a href="https://www.citybiz.co/article/904290/caddi-raises-114-million-series-d-at-1-2-billion-valuation/" rel="noopener noreferrer" target="_blank" class="">citybiz.co</a></h5> Manufacturing companies generate large volumes of engineering, procurement, production and quality data, but much of that information remains fragmented across departments, legacy systems and undigitized records. <a href="https://caddi.com/en-us/" rel="noopener noreferrer" target="_blank" class="">CADDi</a> has raised $114 million in Series D funding to expand an AI data platform designed to connect that information and make it usable across manufacturing operations.The financing values Chicago- and Tokyo-based CADDi at $1.2 billion and brings its total funding to $234 million. Eight new and existing investors participated in the round.New investors include Moore Strategic Ventures, Coreline Ventures, HR Tech Fund, Woven Capital and Salesforce Ventures. Existing backers Atomico, Globis Capital Partners and JPS Growth Investment Limited Partnership also participated.CADDi plans to invest the capital in proprietary AI and technology development, expansion of its Manufacturing AI Data Platform, global business operations centered on North America, and hiring and employee development.Founded in 2017 by CEO Yushiro Kato and Aki Kobashi, CADDi initially focused on individual manufacturing workflows but is expanding toward a shared data and intelligence layer spanning the manufacturing lifecycle.The company’s platform analyzes, structures and connects information that would otherwise sit separately across engineering, sourcing, production and quality functions. CADDi’s approach is based on creating a semantic layer that preserves the relationships and context within manufacturing data so both employees and AI applications can work from the same underlying information.That addresses a practical limitation in applying general-purpose AI to industrial operations. Manufacturing records can include engineering drawings, specifications, procurement history, production information and quality data with technical relationships that are difficult to interpret without industry-specific context.“AI only delivers where a data layer and a semantic layer already exist,” Coreline Ventures partner Ken Hara said. He said CADDi has spent four years developing that foundation with manufacturing customers, turning information previously concentrated in individual experience into data that can be used more broadly across an organization.CADDi introduced its next-generation Manufacturing AI Data Platform in Japan in August 2026 and is now moving into a global product rollout, including a full-scale expansion in the United States.The platform includes CADDi Explorer, a manufacturing discovery engine that succeeds the company’s CADDi Drawer product. Explorer is designed to help users find and work with manufacturing information distributed across an organization.CADDi is also introducing CADDi Agent, an AI agent that can analyze manufacturing data, make decisions and take actions using the operating context of an individual company. Six domain-specific Workflow Products are planned across different parts of the manufacturing value chain.The broader objective is to reduce manual handoffs between functions by allowing workflows to operate against a common data foundation. CADDi sees that infrastructure as increasingly important as manufacturers explore AI agents capable of performing work rather than simply retrieving or summarizing information.Atomico partner Luca Eisenstecken said CADDi is moving from addressing individual workflows toward providing an intelligence layer across the manufacturing lifecycle. Atomico first invested in the company approximately 18 months ago and returned for the Series D.CADDi’s U.S. rollout represents a significant part of the expansion strategy supported by the new financing. The company currently serves manufacturers in more than 20 countries, including large publicly traded companies in Japan and the United States.Kato has framed the company’s longer-term challenge around narrowing the gap between faster digital processes and the comparatively slower physical processes required to manufacture products. His strategy calls for moving more manufacturing activities from sequential workflows toward processes that can operate in parallel, supported by shared data and context.CADDi’s $114 million Series D gives the company additional capital to pursue that strategy as manufacturers increase their use of AI across engineering and factory operations. Its expansion will center on building the data infrastructure, domain-specific software and AI agents needed to connect decisions across the manufacturing lifecycle while accelerating its commercial presence in North America.

CADDi Raises $114 Million Series D at $1.2 Billion Valuation

CADDi Raises $114 Million To Fix Manufacturing’s Biggest Bottleneck With AI

Originally featured on <a href="https://www.advancedmanufacturing.org/news-desk/press-releases/caddi-raises-funds-in-series-d-at-1-2-billion-valuation/article_23f67d13-00c6-40c0-a97d-6148f8182314.html" rel="noopener noreferrer" target="_blank">advancedmanufacturing.org</a> <a href="https://caddi.com/en-us/" rel="noopener noreferrer" target="_blank" class="">CADDi</a>, the global technology company that develops the CADDi Manufacturing AI Data Platform, today announced that it has entered into definitive agreements for $114 million at a $1.2 billion valuation in Series D funding, with eight new and existing investors participating, including Moore Strategic Ventures and Coreline Ventures. This funding brings CADDi's total funding to $234 million.Funding overview:<ul class=""><li class="">Series D Round Size: $114 million</li><li class="">New Investors: Moore Strategic Ventures, Coreline Ventures, HR Tech Fund LLC, Woven Capital, and Salesforce Ventures</li><li class="">Existing Investors: Atomico, Globis Capital Partners, JPS Growth Investment Limited Partnership</li></ul> <h2 class="">Fueling What’s Next for Manufacturing in the AI Era</h2>CADDi is headquartered in Chicago and Tokyo, and is led by Yushiro Kato, co-founder and CEO. In an essay recently published on CADDi's corporate website, titled "<a href="https://caddi.com/en-us/essay/2609/" rel="noopener noreferrer" target="_blank" class="">Materialization — Making Things in the Age of AI</a>," Kato wrote, "Over thirty years we made the world of bits more than a hundred times faster. The world of atoms moved at roughly the same speed in most industries. In the AI era, the gap widens further. Even if AI makes thinking ten thousand times faster and produces ten thousand times the theory, the upside from AI gets diluted in the physical world if it still takes four years to mass-produce cars. Solving physical limits means solving this asymmetry." Kato believes the key lies in transitioning manufacturing from sequential execution to parallel processing. In a world where processes that were once executed step-by-step now run simultaneously, a common platform that enables humans and AI across departments to draw on the same information and context becomes indispensable. However, manufacturing data contains highly complex semantics, relationships, and constraints that general-purpose AI cannot adequately address. Domain-specific vertical AI capable of mapping data semantics is essential.The funds raised will be allocated across four areas:<ul class=""><li class="">Proprietary AI and technology development</li><li class="">Expansion of the Manufacturing AI Data Platform</li><li class="">Enhancement of global business operations centered on North America</li><li class="">Investment in talent acquisition and development</li></ul>"AI only delivers where a data layer and a semantic layer already exist. In manufacturing that layer is buried in information fragmented across departments and in records that were never digitized at all, which is why horizontal tooling never reaches it,” said Ken Hara, partner at Coreline Ventures. “CADDi has spent four years building it, with some of the most demanding manufacturers in the world as its first customers. Knowledge that lived only in individual experience is becoming an asset the organization, and in time the industry, can use.”“Eighteen months ago, we backed CADDi because we believed they had the potential to build one of the defining AI companies in manufacturing. Since then, that conviction has only grown,” said Luca Eisenstecken, Partner at Atomico. “CADDi is evolving from solving individual manufacturing workflows to building the intelligence layer for the entire manufacturing lifecycle. Its new AI-native data platform connects engineering, procurement, production and quality through a shared semantic data foundation, giving AI agents the context to reason across decades of fragmented manufacturing knowledge. The ambition is enormous, the execution has been exceptional and we’re thrilled to deepen our partnership with Yushiro, Aki and the team for the decade ahead.” <h2 class="">CADDi Previews Next-generation Platform Technology</h2>Kato emphasized, “As artificial intelligence pushes past the boundaries of intelligence, the gap between what is conceptualized and what is implemented in the physical world widens. We see this as a broader human challenge that goes well beyond manufacturing: ‘physical limits.’" CADDi announced the next-generation Manufacturing AI Data Platform in Japan in August 2026 to serve as this foundational platform, initiating the global products release.“At our CADDi Unleash 2026 event, we presented a renewed vision: to overcome physical limits and accelerate physical innovation tenfold. That is our priority from here on, and this commitment is what guided both our recent product rebrand and the development of new products we are bringing to our U.S. customers,” said Kato.Designed as a manufacturing operating system, <a href="https://caddi.com/en-us/platform/" rel="noopener noreferrer" target="_blank" class="">CADDi</a> provides a semantic structure to fragmented data, enabling human and AI workflows to run on shared data without manual handoffs. The new product, CADDi Explorer, is a manufacturing discovery engine and the next-generation version of CADDi Drawer.CADDi Agent is a new AI agent that analyzes manufacturing data, makes decisions, and takes action within each company's operating context. CADDi will also offer six domain-specific Workflow Products spanning the manufacturing value chain, enhancing decision-making and execution to drive business-critical impact.The platform's official U.S. launch signals CADDi's full-scale expansion into the United States, the world's second-largest manufacturing market.

CADDi Raises Funds in Series D at $1.2 Billion Valuation

CADDi leverages AI to digitize drawings and procurement data, making manufacturing insights searchable by shape, text, dimensions and part names.