Symbol

US / Header

CaddiMenuBar

Core:Section

US / Footer

Stay updated with the latest CADDi Drawer news and innovations. Explore our news section now for insights and updates that matter to you!

Newsroom

US / News

<a href="https://www.assemblymag.com/articles/100585-video-ai-on-the-factory-floor-episode-6-ai-helps-engineers-find-parts-they-already-have" rel="noopener noreferrer" target="_blank" class="">Watch on ASSEMBLY</a> Written by: JJ MooreBIRMINGHAM, Mich. — One of the simplest opportunities for AI in engineering may be helping engineers find components their companies already have.Manufacturers can accumulate thousands of part numbers over years of product development. When engineers cannot easily search that history, they may specify a new component even though an equivalent part is already in the system.Tim Dorn, vice president of engineering at Amerequip, says the problem has affected the company's design engineering process.“In the past, it was very difficult for us to check and see what we had in our system,” Dorn says.That can create unnecessary complexity. Dorn says even something as common as a fastener could be difficult to locate, leading an engineer to specify the same type of component again under another part number.“Lo and behold, we would just go and spec out the same fastener, and we'd have multiple part numbers of the same type of part in our system unbeknownst to us,” Dorn says.Each additional SKU can create consequences beyond engineering. More part numbers can mean more components for purchasing and inventory personnel to manage and more opportunities for unnecessary complexity as products move toward production.AI-powered search offers another approach by making existing engineering information easier to find using ordinary search terms.Amerequip, for example, uses CADDi to search its existing part information. Dorn says engineers can enter simple terms and receive possible existing components within seconds.For a fastener, an engineer can review the available options and select an existing component that meets the requirements rather than immediately creating another part number.The engineering benefit is straightforward: Before designing or specifying something new, engineers can more quickly determine whether the company has already solved the problem.For manufacturers managing large numbers of components across multiple products, that can help control SKU proliferation, simplify inventory and give engineers more time to focus on decisions that actually require a new design. About CADDiCADDi 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. To learn more, visit <a href="https://caddi.com" rel="noopener noreferrer" target="_blank">caddi.com</a>.

AI on the Factory Floor Episode 6: AI Helps Engineers Find Parts They Already Have

<a href="https://www.imts.com/read/article-details/How-CADDi-Uses-AI-To-Improve-Manufacturing/2586/type/Read/1/tab/all-articles" rel="noopener noreferrer" target="_blank" class="">Read the article in IMTS</a> Written by: Kathy KeyesManufacturers have invested heavily in automation, robotics, and connected technologies to improve shop floor productivity. But what about the processes that happen before production begins?Engineering, quoting, and procurement still often rely on spreadsheets, ERP systems, and time-consuming searches through historical records.At <a href="https://www.imts.com/" rel="noopener noreferrer" target="_blank" class="">IMTS 2026</a> – The International Manufacturing Technology Show, <a href="https://directory.imts.com/8_0/exhibitor/1000679/CADDi-Co" rel="noopener noreferrer" target="_blank" class="">CADDi</a> demonstrated how Industrial AI can improve these processes by connecting engineering drawings, pricing, production records, and other manufacturing data.“How do we apply these same lean concepts and drive productivity upstream?” asks <a href="https://www.linkedin.com/in/paharrigan/" rel="noopener noreferrer" target="_blank" class="">Patrick Harrigan</a>, CADDi's vice president of sales.Originally founded as a procurement platform for custom-manufactured parts in Japan, CADDi developed software to preserve and reuse knowledge manufacturers accumulated from years of operation. Today, its Manufacturing AI Data Platform helps manufacturers put that experience to work in three key areas. <h3 class="">1. Engineering: Build on Proven Designs</h3>CADDi allows engineers to centralize reviews directly on drawings and CAD. They can leave comments directly on product data so design, procurement, manufacturing engineering, and quality teams can review in one shared workspace.Built as a manufacturing operating system, CADDi analyzes drawings, identifies similar parts, and retrieves historical design and quality information.“Have we ever designed something like this? And was it a good design or a bad design?” Harrigan asks. “Without a way to instantly identify which variants already exist, engineering teams design new components, and procurement teams develop new supply chains.”A modern fastener, seal, or pump manufacturer often runs tens of thousands of SKUs with small dimensional differences.One Wisconsin-based manufacturer of custom packaging equipment uploaded several hundred thousand engineering drawings into CADDi, enabling engineers to quickly locate similar designs rather than recreating them. <h3 class="">2. Quoting: Respond Faster to RFQs</h3>By centralizing RFQ distribution, collection, and comparison, CADDi allows teams to manage requests and responses across multiple suppliers in one place, with price, lead time, and terms normalized for side-by-side comparison.CADDi connects historical quotations with costs, pricing, margins, and job outcomes, helping teams prepare estimates more efficiently.Harrigan says, “Beyond process efficiency, external factors like trade volatility raise the stakes for U.S. manufacturers. It translates directly into sourcing decisions that have to move faster and rest on better data than they did three years ago.”The Wisconsin manufacturer expanded its use of CADDi into quoting and aftermarket sales, allowing employees to identify replacement parts and complementary components. <h3 class="">3. Production Planning: Streamline Flow</h3>CADDi detects inconsistencies across quality and production documents. Its proprietary AI compares the completed PFMEA with control plans, quality control process sheets, work instructions, and related documents, flagging conflicts and missed updates.CADDi helps manufacturers determine potential production challenges by examining lessons learned from similar projects.“Have we ever built a similar line to this in the past, and what were the challenges and issues that we ran into?” Harrigan asks. “What we’re seeing with our customers today is that the conversation has shifted from ‘what value does AI bring?’ to ‘how do we make the data we already have usable and accessible across teams?’ Our system is a manufacturing operating system built for engineering, procurement, and other teams across the supply chain. American manufacturers are thinking about ways to maximize the AI technology they are already investing in, and this is a significant mindset shift from earlier conversations.” <h3 class="">Investing in Industrial AI</h3>At IMTS 2026, CADDi announced $114 million in Series D funding, bringing its total funding to $234 million and valuation to $1.2 billion. The investment will support AI development, platform expansion, talent, and growth in North America. CADDi serves manufacturers in more than 20 countries and calls the United States, the world’s second-largest manufacturing market, its priority. Its vision is to accelerate physical innovation tenfold.For manufacturers, CADDi puts decades of accumulated knowledge to work across the business. See how manufacturers use the CADDi Manufacturing AI Data Platform at <a href="http://caddi.com/en-us/case-studies?ref=US-PR-2027-Q1-AMT-Online_IMTS2026-Patrick-Harrigan-MM" rel="noopener noreferrer" target="_blank" class="">caddi.com/en-us/case-studies</a>. About CADDiCADDi 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. To learn more, visit <a href="https://caddi.com" rel="noopener noreferrer" target="_blank" class="">caddi.com</a>.

How CADDi Uses AI To Improve Manufacturing

<a href="https://omny.fm/shows/ceos-you-should-know-chicago/yushiro-kato-ceo-and-co-founder-of-caddi" rel="noopener noreferrer" target="_blank" class="">Listen on CEOs You Should Know Chicago</a> <h2 class="">Summary</h2>On CEOs You Should Know Chicago, an iHeartMedia Chicago podcast, the host talks with CADDi co-founder and CEO Yushiro Kato about the CADDi AI Data Platform and why the company put its US headquarters in Chicago. Kato explains how the platform turns scattered manufacturing data into an asset, so engineering and procurement teams can use what a company already knows instead of starting from scratch. He discusses part proliferation, why quality problems often begin in design, and his essay, Materialization: Making Things in the Age of AI, including CADDi's 10-year vision to accelerate physical innovation tenfold. He also covers why general-purpose AI is not enough for drawings and 3D CAD, how the platform helps with procurement and aftermarket work, and how putting historical knowledge in one place can change the relationship between veteran and younger engineers. <h2 class="">Key takeaways</h2><ol class=""><li class="">Knowledge is the real asset. Kato says the most important asset in a manufacturing company is not machinery or factories, but knowledge in people's heads. The CADDi AI Data Platform turns past data into an asset so engineers can use it as if they had been at the company for 30 years, and so that knowledge stays when people move on.</li><li class="">Reuse beats reinventing the wheel. Many manufacturers, including in Chicago, keep creating parts that are similar or nearly identical to ones they already have. Kato calls this part proliferation, a waste of time and cost. The platform helps engineering and procurement teams reuse what they have already created.</li><li class="">Chicago was a deliberate choice. CADDi opened its US headquarters in Chicago in early 2023. Kato says many people told him to go to Silicon Valley, but he wanted the company closest to its customers. He calls Chicago the heart of manufacturing and notes that the Midwest has the largest manufacturing GDP in the United States.</li><li class="">The 10x goal is literal. AI is moving fast, but a car still takes about four years to build, the same as 30 years ago. CADDi's 10-year vision is to accelerate physical innovation tenfold, bringing that kind of cycle down to about four months, because the bottleneck is now in making physical products, not in generating ideas.</li><li class="">Manufacturing needs its own AI. Kato says general language models are strong with text, but they do not read 2D drawings or 3D CAD models. CADDi's proprietary AI model understands the shape and geometry of parts, so teams can reuse the closest past designs, reduce procurement cost, and respond to aftermarket requests in seconds.</li></ol><h2 class="">Episode chapters</h2><ul class=""><li class="">00:00 Welcome and what the CADDi AI Data Platform does</li><li class="">01:29 Knowledge as an asset, and ending reinvented wheels</li><li class="">03:30 Why the US headquarters is in Chicago</li><li class="">05:38 Why quality problems often start in design</li><li class="">07:22 The essay, the four-year car, and the 10x goal</li><li class="">12:06 Fourteen physical limits</li><li class="">15:42 How the platform helps engineering, procurement, and aftermarket</li><li class="">17:47 Why manufacturing needs its own AI</li><li class="">18:39 A culture change between veterans and younger engineers</li><li class="">20:28 Lessons from McKinsey, and why change is a risk</li><li class="">24:59 Why being an established company is now an advantage</li></ul><h2 class="">Speakers</h2><ul class=""><li class="">Yushiro Kato, CEO and Co-Founder of CADDi</li><li class="">Host, CEOs You Should Know Chicago (iHeartMedia Chicago)</li></ul><h2 class="">FAQ</h2>What is the CADDi AI Data Platform? Kato describes it as an AI data platform for manufacturing. It brings together data that is scattered across systems, value chains, and businesses, and turns that history into an asset for engineering and procurement teams.Why is CADDi's US headquarters in Chicago? CADDi established the office in early 2023. Kato says many people told him to go to Silicon Valley, but he wanted to be closest to customers. He calls Chicago the heart of manufacturing and notes that the Midwest has the largest manufacturing GDP in the United States.What is CADDi's 10-year vision? To accelerate physical innovation tenfold. Kato says a car still takes about four years to build, as it did 30 years ago, and the aim is to bring that kind of cycle down to about four months.Why isn't general-purpose AI enough for manufacturing? Kato says general language models are strong at reading text and summarizing meetings, but they do not read 3D CAD models or 2D drawings. CADDi built a proprietary AI model that understands the shape and geometry of parts so teams can reuse the closest past designs and learn from earlier work. <h2 class="">Related links</h2><ul class=""><li class=""><a href="https://caddi.com/en-us/platform/" rel="noopener noreferrer" target="_blank" class="">CADDi AI Data Platform</a></li><li class=""><a href="https://caddi.com/en-us/essay/2609/" rel="noopener noreferrer" target="_blank" class="">Materialization: Making Things in the Age of AI (essay)</a></li><li class=""><a href="https://caddi.com/en-us/news/articles/a-car-still-takes-4-years-to-build:-caddi-ceo-yushiro-kato-on-physical-limits/" rel="noopener noreferrer" target="_blank" class="">A Car Still Takes 4 Years to Build: CADDi CEO Yushiro Kato on Physical Limits</a></li><li class=""><a href="https://caddi.com/en-us/news/articles/yushiro-kato-thecube-nyse-wired-mixture-of-experts/" rel="noopener noreferrer" target="_blank" class="">Yushiro Kato, CADDi, Inc | theCUBE + NYSE Wired: Mixture of Experts</a></li><li class=""><a href="https://caddi.com/en-us/news/articles/mapping-the-future-of-ai-in-manufacturing-with-caddi-ceo-yushiro-kato-sept-26" rel="noopener noreferrer" target="_blank" class="">Mapping the Future of AI in Manufacturing with CADDi CEO Yushiro Kato</a></li><li class=""><a href="https://caddi.com/" rel="noopener noreferrer" target="_blank" class="">caddi.com</a></li><li class=""><a href="https://caddi.com/en-us/news/" rel="noopener noreferrer" target="_blank" class="">More CADDi news</a></li></ul>About CADDiCADDi 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. To learn more, visit <a href="https://caddi.com" rel="noopener noreferrer" target="_blank" class="">caddi.com</a>.

Yushiro Kato, CEO and Co-Founder of CADDi, on CEOs You Should Know Chicago

<a href="https://www.advancedmanufacturing.org/industries/aerospace-defense/the-speed-of-custom-manufacturing-is-ai-s-new-bottleneck/article_68c069e4-789f-404e-851b-6b0cb0647dec.html" rel="noopener noreferrer" target="_blank" class="">Read the article in SME / Advanced Manufacturing</a> Written by: Chris Cope, CADDi VP of Engineering In April, the White House invoked Section 303 of the Defense Production Act and designated transformers, substations, high-voltage circuit breakers and electrical core steel as industrial resources essential to the national defense, on the finding that domestic capacity to produce them is "dangerously limited."Industry tracker Wood Mackenzie put average U.S. lead times for large power transformers near 128 weeks, with the biggest units quoted at three to five years and medium-voltage switchgear sold out through much of 2028. Currence (formerly known as Sightline Climate) estimates that 30% to 50% of the U.S. data center capacity announced for this year will slip because operators cannot get the gear in time. An industry that spent three years worrying about chip allocation is now waiting on a coil-winding shop.That inversion is worth taking seriously, because of what kind of manufacturing sits at the bottom of it. A large power transformer is configured to a utility's specification, built in small runs and qualified over long cycles. It is high-mix, low-volume work, which is the least automatable category we have. You cannot buy your way through a high-mix backlog the way a high-volume plant can, by purchasing faster machines and spreading tooling cost across a million units, because the dominant cost of the next job is the engineering that goes into it.Throughput in that environment is measured in engineering decisions per week. Which means the expansion of artificial intelligence is currently rate-limited by how fast a few thousand engineers can make good calls about custom equipment. <h3 class="">The Constraint Is Not the Floor</h3>Capital is not the shortage. The Federal Reserve Bank of New York's Manufacturing Survey, released in August, showed factory activity at its highest level since December 2021, with unfilled orders climbing and supply availability worsening.Two days later, the Federal Reserve Bank of Philadelphia's Manufacturing Business Outlook Survey posted a five-year high alongside a future capital expenditures index at its highest reading since 1973. Orders are there and the money to expand is approved.Deloitte and The Manufacturing Institute project a net need for 3.8 million manufacturing workers by 2033—roughly 2.8 million of that from retirements—with about half of those at risk of going unfilled. Engineers are the hardest part of that pipeline to rebuild because the education timeline cannot be compressed.The realistic plan for the next three years is more output from roughly the engineering staff on hand today. That puts a lot of weight on where those hours currently go. Qualifying an alternate source for bushings because the incumbent is booked into 2029 means working out what the company built last time, what changed since and why, whether a clearance spec tightened in 2014 came from a customer requirement or a field failure, and what went wrong the last time they qualified a second source for the same part.Those are engineering judgments, and the right people are making them. But most of the elapsed time is not judgment, it is retrieval. The engineer guesses part numbers, opens three or four systems that were never designed to talk to each other and goes looking for the one person who remembers the program. That person is retiring. The drawing he leaves behind records what was specified and says almost nothing about why, so the next engineer cannot tell which requirements are load-bearing and which are the residue of a problem solved 20 years ago.In high-mix shops this compounds, because part counts are enormous, repeats are rare and the useful precedent is filed under a number nobody currently employed has a reason to know. This is why manufacturers in this position eventually point AI at the drawing archive—and it is the right instinct. The archive is the largest recoverable pool of engineering hours most of them have. It is also, for a growing number of them, about to stop being ordinary business data. <h3 class="">The Archive Is Changing Legal Status</h3>The defense industrial base is widening deliberately. Roughly 10,000 new firms entered the defense market over the past two years and contract obligations to nontraditional companies more than doubled over the decade to $122.6 billion in fiscal 2025. The fiscal 2026 National Defense Authorization Act stripped out compliance requirements that used to keep commercial firms away and the Pentagon built a simplified entry platform and a reserve manufacturing network to recruit commercial capacity directly.The April determination on grid equipment points the same way. The manufacturers being courted are exactly the high-mix, low-volume shops that the AI buildout is already waiting on. Most of them are evaluating that work as a customer question. The bigger change is what it does to their files. Under the International Traffic in Arms Regulations, technical data means the information required to design, produce, test, maintain or modify a defense article, and the regulation names the formats: blueprints, drawings, plans and documentation. The controlled thing is not a part in a crate. It is the drawing.Releasing it to a external person is an export even when that person is an employee sitting in your own building in Ohio. Manufacturers who have been in defense for decades run a technology control plan for this but companies arriving through the new on-ramps generally do not, and they arrive carrying 30 years of drawings that were never segregated—because, until this year, there was no reason to segregate them. <h3 class="">Maintaining Data Boundaries</h3>This is where AI evaluations go wrong, and it is a narrow technical point with a large consequence. Cloud storage of controlled drawings is only workable because of a specific carve-out in the export rules: Storing and transmitting technical data is not an export if it stays end-to-end encrypted and the vendor is never given the means to decrypt it.That is the basis for most of the reassurance manufacturers get from software vendors and for storage it holds up. An AI system cannot read an encrypted drawing. To pull text off a title block, extract geometry or compare a part against prior designs, the tool has to hold that drawing in the clear, in its own environment, using keys it controls. The carve-out covers storage. It does not cover processing, which is the only thing you are actually buying.So, a vendor who answers your compliance question with "encrypted at rest and in transit" has answered a question that was never the risk. The questions that matter are where the drawing is decrypted, who at the vendor can see it while it is readable and whether what the tool extracts from it—which is usually dimensions, materials and tolerances—is treated as controlled data, too. It generally should be, along with the index and the logs built from it.None of that makes AI unusable on controlled drawings. It makes the data path something to settle during evaluation rather than after a pilot has been running on live files for six months. A vendor's export registration, mine included, is not the control that matters. Where the data is processed and who can touch it in the clear is.Apply the same skepticism to the capability claims. Geometric similarity search on two-dimensional drawings is genuinely hard and has only recently become practical, and it changes the starting point by letting an engineer work from the drawing instead of a part number they would have to guess. What comes back is a search hit. It is not a finding that two parts are interchangeable and it is not a qualification. Any vendor blurring that line is describing something they have not built. <h3 class="">Where to Start</h3>Find out what a decision costs you now. Pick one your team already finished, ideally where a requirement changed or a source moved, work backward from the final configuration, and write down what an engineer would need to explain why the part ended up that way. Count the systems, the searches and the people.If the answer depends on an exact identifier or on someone within a few years of retiring, you have engineering capacity tied up in retrieval and you are about to need it back. Then find out where controlled data would actually travel in any tool you are considering. Not the storage diagram.The processing path, the derived data, the access list. If you are heading into the defense industrial base, that boundary gets audited eventually, and drawing it early is far cheaper than reconstructing it after your archive is spread across an AI pipeline.The chain here is worth saying plainly, because it is not the one the industry has been describing. The next gigawatt of AI capacity waits on a transformer. The transformer waits on engineering hours at a high-mix shop. A growing share of those hours are moving behind an export boundary that most of those shops have never had to manage. Export control is now on the critical path of the AI buildout, which is not where anyone expected to find it. About CADDiCADDi 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. To learn more, visit <a href="https://caddi.com" rel="noopener noreferrer" target="_blank" class="">caddi.com</a>.

The Speed of Custom Manufacturing is AI’s New Bottleneck

Originally published on <a href="https://www.youtube.com/watch?v=WIsBOYaG5Aw" rel="noopener noreferrer" target="_blank" class="">The MFG Connector</a> <a href="https://www.youtube.com/watch?v=WIsBOYaG5Aw" rel="noopener noreferrer" target="_blank" class="">Watch on YouTube</a> <h2 class="">Summary</h2>In the first episode of Leadership Decisions in Manufacturing, a six-part series from The MFG Connector produced in partnership with CADDi, host Jim Mayer sits down with CADDi co-founder and CEO Yushiro Kato at IMTS 2026 in Chicago. Kato discusses his essay, Materialization: Making Things in the Age of AI, and CADDi's 10-year vision to accelerate physical innovation tenfold. He explains why a car still takes about four years to build, why roughly 118 manufacturing processes run in series instead of in parallel, and how the CADDi AI Data Platform gives engineering and procurement teams one shared source of truth, so people and AI agents work from the same context. He also covers what leaders still own as AI agents take on more of the work, why general AI needs manufacturing-specific models for drawings and 3D CAD, how CADDi keeps each customer's data separate, and what the $114 million Series D will fund. <h2 class="">Key takeaways</h2><ol class=""><li class="">Intelligence is not the bottleneck. AI getting smarter and the physical world changing are separate events. To change the physical world, someone has to build something, and Kato sees physical limits as the next wall for humanity.</li><li class="">The 10x goal is literal. A car takes about four years to build, the same as 30 years ago. CADDi's vision is to cut that to one tenth within 10 years, or about four to five months for a car.</li><li class="">Three causes of delay. Kato groups manufacturing into about 118 processes. Each one takes time, they run in series so teams wait on each other, and rework sends work backward. A shared, company-specific operating system lets people and AI agents work in parallel from one source of truth.</li><li class="">People still own the outcome. Humans set the target and requirements, own the last mile of quality, and lead the change. Kato says change management is about half of the value CADDi delivers to customers.</li><li class="">Manufacturing needs its own AI. General LLMs are weak at spatial recognition, so CADDi built a proprietary AI model for drawings and 3D CAD, connected by a semantic layer that helps engineering and procurement teams stop reinventing the wheel.</li></ol><h2 class="">Chapters</h2><ul class=""><li class="">00:00 Welcome and the Series D news</li><li class="">02:02 The essay and who Yushiro Kato is</li><li class="">03:16 Why smarter AI doesn’t change the physical world</li><li class="">05:26 Fourteen physical limits and why now</li><li class="">07:59 What 10x means and the four-year car</li><li class="">10:36 Built from the shop floor</li><li class="">14:32 Why 118 processes run in a line</li><li class="">18:58 What leaders still own</li><li class="">23:11 Why general AI needs manufacturing-specific AI</li><li class="">26:00 Ending the reinvention of the wheel</li><li class="">28:29 Keeping customer data separate</li><li class="">29:25 What the Series D funds</li><li class="">30:58 Closing thoughts</li></ul><h2 class="">FAQ</h2>What is CADDi's 10-year vision? To accelerate physical innovation tenfold. CADDi CEO Yushiro Kato says the 10x goal is literal: a car that takes about four years to build today would take four to five months, and an aircraft about one year.Why does it still take about four years to build a car? Kato names three causes. Each of about 118 processes takes time, the processes run in series so teams wait on one another, and rework sends work backward when problems surface late, for example in quality assurance.What do leaders still own when AI agents do more of the work? Setting the target and requirements, giving the company its own direction, owning the last mile of quality, and leading change management. Kato estimates change management is about half of the total value CADDi delivers to customers.Why can't general-purpose AI handle manufacturing data on its own? Kato explains that LLMs are language models, while physical products call for spatial recognition. CADDi built a proprietary AI model that reads drawings and 3D CAD, plus a semantic layer that links data by meaning, so AI agents can connect past incidents to current designs.What will CADDi's $114 million Series D fund? Product development, including CADDi's proprietary AI model, go-to-market, and global expansion. Kato notes that CADDi has customers in 22 countries, no European offices yet, and plans continued investment in the US. <h2 class="">Related links</h2><ul class=""><li class=""><a href="https://caddi.com/en-us/platform/" rel="noopener noreferrer" target="_blank" class="">CADDi AI Data Platform</a></li><li class=""><a href="https://caddi.com/en-us/platform/caddi-design-review/" rel="noopener noreferrer" target="_blank" class="">CADDi Design Review</a></li><li class=""><a href="https://caddi.com/en-us/essay/2609/" rel="noopener noreferrer" target="_blank" class="">Materialization: Making Things in the Age of AI (essay)</a></li><li class=""><a href="https://caddi.com/en-us/news/announcements/caddi-seriesd-unicorn-status/" rel="noopener noreferrer" target="_blank" class="">CADDi Series D announcement</a></li><li class=""><a href="https://caddi.com/en-us/news/articles/mapping-the-future-of-ai-in-manufacturing-with-caddi-ceo-yushiro-kato-sept-26" rel="noopener noreferrer" target="_blank" class="">Mapping the Future of AI in Manufacturing with CADDi CEO Yushiro Kato</a></li><li class=""><a href="https://caddi.com/en-us/news/" rel="noopener noreferrer" target="_blank" class="">More CADDi news</a></li></ul>About CADDiCADDi 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. To learn more, visit <a href="https://caddi.com" rel="noopener noreferrer" target="_blank" class="">caddi.com</a>.

A Car Still Takes 4 Years to Build: CADDi CEO Yushiro Kato on Physical Limits

Originally published on <a href="https://www.youtube.com/watch?v=qhYhW5sSrrw" rel="noopener noreferrer" target="_blank" class="">SiliconANGLE theCUBE</a> <a href="https://www.youtube.com/watch?v=qhYhW5sSrrw" rel="noopener noreferrer" target="_blank" class="">Watch on YouTube</a> <h2 class="">Summary</h2>On theCUBE + NYSE Wired: Mixture of Experts, theCUBE host John Furrier talks with CADDi co-founder and CEO Yushiro Kato about how specialized AI is changing the work of engineering and procurement teams in manufacturing. Kato explains how the CADDi AI Data Platform ingests decades of scattered design, quality, and procurement data and puts AI agents to work: finding reusable designs, flagging risks before production, and selecting suppliers with cost benchmarks. He describes how CADDi's roots as a manufacturer shaped its products, why more than half of the quality defects CADDi tracked traced back to drawings and 3D CAD models, and how a data flywheel from downstream to upstream helps teams cut rework. He closes with CADDi's 10-year vision to accelerate physical innovation tenfold by building reusable design libraries, much like software libraries. <h2 class="">Key takeaways</h2><ol class=""><li class="">AI agents as team members. The CADDi AI Data Platform puts AI agents to work on real engineering and procurement tasks. The agents complete the work and report back, and people make the decisions.</li><li class="">Reuse over reinvention. In manufacturing, change is a risk. Instead of generating designs from scratch, CADDi's AI finds the closest existing design, identifies the required changes, and flags risks. Kato says this shortens product development lead time from several weeks to several hours.</li><li class="">Catch rework early. A car has about 30,000 parts, and each part typically goes through 10 to 20 reworks. Kato says more than 50% of the quality defects CADDi tracked came from drawings and 3D CAD models, so feeding quality data back to engineers early saves cost and time.</li><li class="">Built by a manufacturer. CADDi ran its own manufacturing business with more than 10 inspection centers and built 26 internal applications before productizing its technology. It now has about 900 people and customers in 22 countries.</li><li class="">Libraries for the physical world. Software developers write less than 10% of their code, while more than 80% of manufacturing designs are created from scratch. CADDi's 10-year vision is to accelerate physical innovation tenfold with industry-wide and company-wide design libraries.</li></ol><h2 class="">Chapters</h2><ul class=""><li class="">00:00 CADDi's AI Agents for Manufacturing</li><li class="">03:16 Reusing Engineering Knowledge and Automating Procurement</li><li class="">06:19 Building Trust and Reducing Automotive Rework</li><li class="">09:19 CADDi's Software Connecting Design and Global Manufacturing</li><li class="">13:11 Accelerating Physical Innovation With Design Libraries</li><li class="">15:31 Overcoming Physical Limits Beyond Manufacturing</li></ul><h2 class="">FAQ</h2>What does CADDi do? CADDi CEO Yushiro Kato describes CADDi as a vertically integrated AI platform for manufacturing. The CADDi AI Data Platform ingests past data scattered across systems, analyzes it with CADDi's proprietary AI, and puts AI agents to work on engineering and procurement tasks.How does CADDi help engineering teams? When an engineer receives a customer requirement, CADDi's AI reads it, finds the closest designs that can be reused, identifies the changes required, and flags the associated risks. After detail design, it checks drawings and 3D CAD models for potential manufacturing and quality risks before production.How does CADDi help procurement teams? It automatically selects suppliers for procurement officers and gives cost benchmarks based on past parts, reducing both cost and the time it takes to select suppliers.Why are manufacturers skeptical of software, and how does CADDi address that? Kato says hardware engineers are skeptical of software by nature, and earlier SaaS tools only scratched the surface. CADDi started as a manufacturer, liable for quality, cost, and delivery, and built its own internal applications before customers asked it to productize them.What is CADDi's 10-year vision? To accelerate physical innovation by 10x. Kato points out that a car took four years to build in 1995 and still does today, and that reusable design libraries, like the libraries software developers rely on, can change that. <h2 class="">Related links</h2><ul class=""><li class=""><a href="https://caddi.com/en-us/platform/" rel="noopener noreferrer" target="_blank" class="">CADDi AI Data Platform</a></li><li class=""><a href="https://caddi.com/en-us/platform/caddi-design-review/" rel="noopener noreferrer" target="_blank" class="">CADDi Design Review</a></li><li class=""><a href="https://caddi.com/en-us/platform/caddi-quote/" rel="noopener noreferrer" target="_blank" class="">CADDi Quote</a></li><li class=""><a href="https://caddi.com/en-us/essay/2609/" rel="noopener noreferrer" target="_blank" class="">Materialization: Making Things in the Age of AI (essay)</a></li><li class=""><a href="https://caddi.com/en-us/news/announcements/caddi-seriesd-unicorn-status/" rel="noopener noreferrer" target="_blank" class="">CADDi Series D announcement</a></li><li class=""><a href="https://caddi.com/en-us/news/" rel="noopener noreferrer" target="_blank" class="">More CADDi news</a></li></ul>About CADDiCADDi 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. To learn more, visit <a href="https://caddi.com" rel="noopener noreferrer" target="_blank" class="">caddi.com</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

<h3 class="">Originally published in <a href="https://www.assemblymag.com/articles/100477-new-software-error-proofs-assembly-monitors-production" rel="noopener noreferrer" target="_blank" class="">ASSEMBLY</a></h3> <h2 class="">AI Analyzes Manufacturing Data</h2> CADDi analyzes and connects manufacturing data fragmented across departments, locations, and systems, turning it into an enterprise asset. The software ingests diverse manufacturing data regardless of system or format, interprets it using proprietary analysis technology, and transforms it into AI-ready data. The software gives meaning to and connects data distributed across the value chain within each company's unique business context. The result is a "data map" that enables AI to understand relationships and trace both the information it needs and the evidence behind it. The software consists of several modules. CADDi Explorer is a data discovery platform that searches across information distributed throughout the enterprise. It connects related data across time, functions, and locations so users can quickly reach the knowledge and evidence they need and uncover insights. Granular access controls enable governed data use, giving everyone, from executives to frontline teams, secure access to the right information. CADDi Agent is an autonomous AI agent that analyzes manufacturing data and supports decisions using internal information and your company's own experience and decision criteria. It autonomously handles real-world tasks, such as part standardization, cost reduction, and quality-impact analysis, then supports the actions required, all while enforcing permissions by location, subsidiary, and contract. CADDi Composer is a design reuse simulator that identifies the best baseline product and configuration from similar past designs. It then assesses the quality, cost, and delivery risks introduced by each change. The rationale behind selections and changes is automatically captured as design intent and reused in future designs. Faster, repeatable decisions reduce rework and lost opportunities. CADDi ALM turns equipment specifications, inspection records, repair histories, and operational knowledge into reusable assets. When equipment fails, AI uses similar past cases to suggest likely causes and corrective actions, accelerating recovery. Accumulated data can also inform equipment modifications and generate checklists for future equipment designs. Production, maintenance, and manufacturing engineering teams share the same knowledge to minimize downtime and continuously improve equipment. CADDi Process Review is a production readiness control tower that generates an initial PFMEA draft from product drawings, process data, and similar past products. Once engineers review and complete the PFMEA, AI identifies inconsistencies across control plans, quality control process sheets, work instructions, and other related documents. After revisions, consistency is automatically rechecked, while review, revision, and approval history improves future PFMEAs and decisions. CADDi Design Review centralizes cross-functional reviews directly on drawings, CAD models, and schematics. AI-assisted checks and complete tracking of comments, revisions, and approvals reduce missed issues and rework. Review insights are standardized into criteria and reused in future reviews. CADDi Cost Review brings together historical purchases, quotes, similar items, and cost breakdowns to provide the evidence needed to assess price reasonableness. It adjusts for factors such as exchange rates and material costs and structures cost details and process data from different formats for comparison. Buyers can identify the drivers behind price differences and cost premiums, while the evidence and decision history are captured for future reviews and negotiations. CADDi Quote centralizes the RFQ process, from supplier selection and request distribution to response collection, comparison, and award. AI recommends suppliers based on purchase history, similar drawings, and quality and delivery performance. Quotes, lead times, and terms are normalized for side-by-side comparison, while communications and award rationale are captured to improve future sourcing. Source: ASSEMBLY, "New Software Error-Proofs Assembly, Monitors Production" by John Sprovieri, October 2, 2026. <a href="https://www.assemblymag.com/articles/100477-new-software-error-proofs-assembly-monitors-production" rel="noopener noreferrer" target="_blank" class="">https://www.assemblymag.com/articles/100477-new-software-error-proofs-assembly-monitors-production</a>

New Software Error-Proofs Assembly, Monitors Production

<h3 class="">Originally published on <a href="https://www.youtube.com/watch?v=AJ5S1UBtI6U" rel="noopener noreferrer" target="_blank" class="">SME Media</a></h3> <a href="https://www.youtube.com/watch?v=AJ5S1UBtI6U" rel="noopener noreferrer" target="_blank">Watch on YouTube</a> <h2 class="">Summary</h2>At IMTS 2026 in Chicago, SME Media Editor-in-Chief Steve Plumb sits down with CADDi CEO Yushiro Kato to talk about how AI is changing manufacturing. CADDi CEO Yushiro Kato explains how the CADDi AI Data Platform connects disconnected data across engineering, procurement, quality, and maintenance, and why its semantic layer works as a data map that helps AI agents deliver the accuracy, traceability, and repeatability manufacturers need. He walks through products including CADDi Design Review and CADDi Quote, describes how the platform captures tacit knowledge that has never been digitized, and shares CADDi's target of a first business impact within 45 days. He also outlines the company's 10-year vision to accelerate physical innovation by 10x, its $114 million Series D round, its proprietary geometry AI model, and its growing commitment to the US market.<h2 class="">Key takeaways</h2><ol class=""><li class="">Connected data, one platform. The CADDi AI Data Platform is a vertically integrated AI platform with a data layer, a semantic layer, and an application layer, connecting data from engineering through procurement, quality, and maintenance.</li><li class="">AI agents need a map. The semantic layer works as a data map so AI agents can be accurate, traceable, and repeatable, while access controls decide what each person sees, from a contractor to an engineering director.</li><li class="">Capturing tacit knowledge. CADDi CEO Yushiro Kato says less than 20% of manufacturing knowledge has been digitized. CADDi Design Review lets teams comment directly on 3D CAD, 2D drawings, and diagrams, capturing why a design was made, not just the result.</li><li class="">Speed to value. CADDi aims for a first business impact within 45 days by configuring its off-the-shelf products instead of building systems from scratch.</li><li class="">Growth and vision. CADDi's $114 million Series D at a $1.2 billion valuation supports product development, its proprietary geometry AI model, and US expansion, toward a 10-year vision of 10x faster physical innovation.</li></ol><h2 class="">Chapters</h2><ul class=""><li class="">00:00 Introduction</li><li class="">00:31 About CADDi: connecting disconnected data</li><li class="">01:51 The semantic layer and why AI agents need a data map</li><li class="">02:32 Product ecosystem: Explorer, Agents, Design Review</li><li class="">03:18 Access controls and data governance</li><li class="">03:50 Procurement and quoting with CADDi Quote</li><li class="">04:44 Capturing tacit and tribal knowledge</li><li class="">05:53 First business impact in 45 days</li><li class="">06:12 The 10-year vision: 10x physical innovation</li><li class="">07:26 Series D and the proprietary geometry AI model</li><li class="">08:12 US expansion and connecting data after M&amp;A</li><li class="">09:34 Off-the-shelf configuration versus custom builds</li><li class="">10:13 Closing</li></ul><h2 class="">FAQ</h2>What is the CADDi AI Data Platform? As CADDi CEO Yushiro Kato describes it, it is a vertically integrated AI platform for manufacturing. It connects disconnected data, from engineering to procurement to quality, and makes it usable. It has a data layer, a semantic layer, and an application layer of workflow products for design, procurement, quality, and maintenance.What is a semantic layer, and why does it matter for AI agents? The semantic layer is a data map. CADDi CEO Yushiro Kato explains that without a map, AI agents can only summarize relevant data, which is not accurate enough for manufacturers who need accuracy, traceability, and repeatability. The semantic layer also controls access for each person, so global teams can use data safely.How does CADDi capture tacit knowledge? CADDi CEO Yushiro Kato says less than 20% of manufacturing knowledge has been digitized. CADDi Design Review lets teams comment directly on 3D CAD, 2D drawings, and diagrams as part of their workflow, which captures context data, such as why a design was made a certain way and what mistakes were found.How quickly can manufacturers see results? CADDi targets the very first business impact within 45 days, starting with a smaller scope and then building additional impacts. Because the products are off-the-shelf and highly configurable, CADDi configures them rather than building systems from scratch.What is CADDi's 10-year vision? CADDi's vision is to accelerate physical innovation by 10x, cutting the time it takes to make physical things to one tenth. To get there, CADDi announced a new generation operating system for manufacturing where customers can leverage and generate data.<h2 class="">Related links</h2><ul class=""><li class=""><a href="https://caddi.com/en-us/platform/" rel="noopener noreferrer" target="_blank" class="">CADDi AI Data Platform</a></li><li class=""><a href="https://caddi.com/en-us/platform/caddi-design-review/" rel="noopener noreferrer" target="_blank" class="">CADDi Design Review</a></li><li class=""><a href="https://caddi.com/en-us/platform/caddi-quote/" rel="noopener noreferrer" target="_blank" class="">CADDi Quote</a></li><li class=""><a href="https://caddi.com/en-us/platform/caddi-explorer/" rel="noopener noreferrer" target="_blank" class="">CADDi Explorer</a></li><li class=""><a href="https://caddi.com/en-us/news/" rel="noopener noreferrer" target="_blank" class="">More CADDi news</a></li></ul><h2 class="">Full transcript</h2>Source: SME Media, YouTube, published September 25, 2026 (https://www.youtube.com/watch?v=AJ5S1UBtI6U) Recorded at: IMTS 2026, Chicago Speakers: Steve Plumb, Editor-in-Chief, SME Media (interviewer); Yushiro Kato, Co-Founder and CEO, CADDi (guest)Editor's note: Cleaned from YouTube's auto-generated captions. Filler words (uh, um) and repeated words are removed. Speech-recognition errors for company and product names are corrected (for example "Katana," "Caddy," and "Catia" to "CADDi"). Passages the captions could not resolve are marked [inaudible]. Square brackets mark editorial insertions. Timestamps are MM:SS from the captions.[00:00] Yushiro Kato (opening clip): Data from different companies should be connected, but it's not connected. So we help our customers to connect disconnected systems and help them create synergies across companies.[00:18] Steve Plumb: I am Steve Plumb, editor-in-chief of SME Media. Here today with Yushiro Kato, co-founder and CEO of CADDi.Yushiro Kato: Yes.[00:26] Steve Plumb: Thanks for joining us. We're at IMTS 2026 in Chicago.Yushiro Kato: Yeah, thank you for having me.[00:31] Steve Plumb: And you had some big news this week and last month, but before we get into that, tell us a little bit about the company. It's changed quite a bit since its founding in 2017, and your background: you were on the consulting side and then jumped into the manufacturing side.[00:49] Yushiro Kato: Yeah, so long story short, we are CADDi. We provide an AI data platform for manufacturing. It's a vertically integrated AI platform for manufacturing. So we basically enable our customers to connect the disconnected data, from engineering to procurement to quality defects, and then make it usable. Our application layer can also generate and capture tacit knowledge that has never been captured, and then we create a data flywheel for manufacturing so that they can leverage data, basically, like [inaudible] knowledge.[01:23] Steve Plumb: So you have a number of different products, and you keep expanding, and the digital platform kind of encompasses everything.[01:31] Yushiro Kato: Yep. Our platform is vertically integrated. So we have a data layer and a semantic layer, all the way to an application layer with six vertical products, from products for conceptual design and detail design all the way to products for procurement, quality, and maintenance.[01:51] Steve Plumb: So, you noted the data layer as well. What is the semantic layer?[01:55] Yushiro Kato: Yeah, so the semantic layer is a data map. Of course, in this era, you need to be able to leverage AI agents. And AI agents need a map, like human beings. Without a map, it can only summarize relevant data, but it's not enough, or it's not accurate enough, for manufacturing people, because manufacturing companies need accuracy, traceability, repeatability. So our semantic layer enables our customers and the AI agents to basically work on the data more correctly, and repeatedly, basically.[02:32] Steve Plumb: So how many different tools are there that feed into the digital platform area? You had the [CADDi AI Data Platform] that was introduced 3 or 4 years ago, and now Explorer. What all is there?[02:43] Yushiro Kato: Yeah, so we have two products on what we call the discovery and agent layer: CADDi Explorer and CADDi Agents. They are basically the products that enable our customers to search and discover data and leverage data. On top of that, we have the workflow product layer. We have six products, like CADDi Design Review, which is a design review platform. AI automatically checks the design drawings and 3D CADs and then identifies mistakes and errors on drawings so that they can mitigate the risk [inaudible]. There are six products.[03:18] Steve Plumb: Yeah. And basically anybody in the organization can access this information?[03:23] Yushiro Kato: Yes. Another point about the semantic layer is that it has access controls. So we have a lot of global enterprise companies, and of course, contractors in Mexico and, I don't know, engineering directors in the US should have different authorities, right? So our semantic layer enables our customers to control the access for each and every person, so that they can safely use and leverage the data.[03:50] Steve Plumb: So a lot of the AI applications are starting off on the quoting side, something like the CAD/CAM drawings. Is that where you're starting off as well?[04:01] Yushiro Kato: Yeah. So we actually started our company with a business called CADDi Manufacturing, which is an Amazon for custom-made parts.Steve Plumb: Yeah.[04:10] Yushiro Kato: And so we work with a lot of procurement officers in the US. So we started with a business called CADDi Quote on top of the data platform. CADDi Quote is basically an automatic quotation platform. It can automatically select suppliers based on similar parts in the past, and it can also give you a benchmark on the cost of the products.Steve Plumb: Mhm.[04:34] Yushiro Kato: So it streamlines the procurement processes and then reduces the cost. So that's one of the core products on the workflow product layer.[04:44] Steve Plumb: And you mentioned tacit information. How does that differ from existing tribal knowledge? What exactly is tacit information?[04:53] Yushiro Kato: So, unfortunately, less than 20% of the knowledge in manufacturing companies has been digitized.Steve Plumb: Mhm.[05:02] Yushiro Kato: So more than 80% is tacit knowledge or tribal knowledge in human brains, right? So you need to capture it in order to fully leverage your assets as an organization. So what we do, for example: our CADDi Design Review platform enables our customers to put comments on the design data itself, like 3D CADs, 2D drawings, diagrams, etc. And it's built into the workflow, and it automatically captures new data, like the context of the [design]. Manufacturing companies have result data, like drawings, 3D CADs, designs, but they do not have context data. Why is this design designed this way? What are the mistakes found in the workflow? That's missing. So we automatically capture that with our [inaudible].[05:49] Steve Plumb: [inaudible] that type of information and build that out.[05:53] Yushiro Kato: The quickness is super important. So we basically target 45 days to create the very first business impact, and then build another impact, and another impact, and another impact. So we should focus on a smaller scope, but the timeline is there.[06:12] Steve Plumb: I caught a little bit of your presentation yesterday. You were talking about a 10-year plan, 2035. Is it possible to think that far out? And what are some of the things you're looking at in that time frame?[06:27] Yushiro Kato: Yeah, so we announced a 10-year vision with a visual demo. In 10 years, our vision is to accelerate physical innovation by 10x. Everybody's talking about AI, and AI is creating new theories and new ideas exponentially. But when we look at the physical world we're living in, how much has it changed, right? There are not a lot of changes in commutes, hospitals, offices, houses, whatever. We live in the physical world, so someone needs to change the physical things, and someone needs to make physical things. But the time frame to make something is very long. So our vision is to shorten it, to cut it to one tenth. And in order to do that, we announced the new generation operating system for manufacturing where, again, our customers can leverage data and generate data.[07:26] Steve Plumb: Yeah. And just last week you announced the latest investment round, a Series D, I believe. Tell us a little bit about that, 100-plus million, and what that means to the company in terms of future investment.[07:41] Yushiro Kato: Yeah, absolutely. So we announced the Series D round with $114 million at a valuation of $1.2 billion. With this funding, we want to boost, of course, product development. And also, we actually own our proprietary AI model to analyze the shape of parts, the geometry of parts, that is super specific to the manufacturing industry. So we want to deepen the technology of our AI model. And also, we want to expand in the US a lot more.[08:12] Steve Plumb: Right. So this is about my next point, the expansion in the US. Obviously, we're here at IMTS, an important manufacturing show. What does that process look like as far as the expansion?[08:25] Yushiro Kato: Yeah, so we came to the US 4 years ago. I personally live in Chicago now. We are fully committed to the US market. Actually, the issues that US companies have and Japanese companies have are almost the same. They do have, of course, [inaudible], like systems of record. They run factories with similar operations. And now, after 4 years, our product is used by a lot of large enterprise companies in the US, which is great. But from now on, I don't know if we want to localize the products, because the issues are the same globally. But in the US, there are a lot of M&amp;As, right? Companies buy companies. So the data structure is relatively messy, because data from different companies should be connected, but it's not connected. So we help our customers to connect disconnected systems and help them create synergies across companies.[09:34] Steve Plumb: How much is customized versus, you know, can be off-the-shelf?[09:39] Yushiro Kato: So we have, again, the data layer and semantic layer, and also eight products on top of that.Steve Plumb: Mhm.[09:45] Yushiro Kato: All of them are configurable, highly configurable, but we do not create systems from scratch for each customer, because speed of implementation is critical. And we know manufacturing. We have, of course, a lot of manufacturing customers.Steve Plumb: Yeah.[10:01] Yushiro Kato: So we know how their semantic layer should be designed, for example. So we basically leverage our off-the-shelf products and customize and configure the top 20% of the products, basically.[10:13] Steve Plumb: All right. Well, congratulations again. Look forward to seeing what you do next.Yushiro Kato: Thank you.

Mapping the Future of AI in Manufacturing with CADDi CEO Yushiro Kato

<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

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