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<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F2ecd4e9d029a4bf1bc4c57f8373b0ae0"><a href="https://www.nvenia.com/services/" rel="noopener noreferrer" target="_blank">nVenia</a> builds highly engineered-to-order equipment across five brands, where closely similar parts had proliferated across internal brands faster than manual searches could reconcile them. Ben Snyder, Vice President of Lifecycle Services, evaluated engineering AI early, paused the investment when the technology was not yet ready, and re-engaged CADDi once it had matured. In this VOICES article, Design World spoke with Snyder about controlling SKU proliferation, reducing inventory risk, and proving out engineering AI in a production environment.Design World: Tell us a bit about yourself and your role at nVenia.Ben Snyder: I am Vice President of Lifecycle Services, which is our aftermarket department. I’m relatively new in that role, having started in February of this year. Prior to that, I was in the operations role at nVenia for two and a half years. Thanks to my time with operations, I already had a good understanding of the execution and operational excellence side of the business, and now I also understand this from the commercial side dealing with questions like how we’re using new tools, such as CADDi, to really drive revenue increases at the same time as we streamline efficiencies inside our factory.nVenia builds highly engineered-to-order equipment across five distinct brands. How does the engineering team fulfill the core business mission for your customers when managing complex product variations across internal brands?We build a combination of what we call engineered-to-order (ETO) style machines, as well as configured-to-order machines.Configured-to-order is easier to describe. It’s like ordering a car. You have a set number of options, you select the combination you want, and then we will go and build your unit. The difference is that we have 30,000 to 45,000 different options, so there is a lot to choose from.On the ETO side, these are unique opportunities that come in. For example, a ramen company or a bottling company. We have machines that have a common core, but every product is slightly different in size. This requires significant customization, for how that specific product comes to the machine, how it gets bundled and packed, how we wrap the product and place it onto a pallet with a robotic palletizer, how that gets sent to a semi-truck or storage area.All of these unique customer opportunities require special components. Prior to CADDi, the idea was that we must build a new part, make a new SKU, release it to the factory and go from there.What we learned doing manual searches is that we have many components that are very similar. A part can have the same shape but the hole is in a different location, or we might find two parts with an identical shape but one is not used because someone in the engineering department designed it 15 years ago and newer engineers don’t know about it. We use CADDi to help us locate those similar parts so that we don’t have SKU proliferation.Engineers spend roughly an hour a day searching for fragmented parts data. What specific operational bottleneck made eliminating SKU proliferation the primary catalyst for evaluating a new software solution?That goes back to what I said earlier, and the idea that every time you produce a new part, there’s a lot of work that goes into that. It’s not just creating a drawing. It’s needing stock and a stocking strategy. You have to set it up in your enterprise resource planning (ERP) system. There are quality inspection steps. If we’re buying this from outside, I need a procurement plan. I need a ton of different things that come into play just to create a nut, for example.We wanted to stop that confusion. We have hundreds of thousands of drawings inside of our facility. How do we optimize using them over anything else before we create something new?nVenia currently holds sizable inventory to allow for decreased lead time with the many combinations of product. How will centralized part data help reduce excess working capital while generating new project revenue through component association?The first thing we wanted to do is stop the bleeding. In order to do that, don’t make new parts, to avoid SKU proliferation. We’re using CADDi on the design side, and we’re only producing new parts when we absolutely must.The second thing is dealing with what we call excess and obsolete (E&amp;O) inventory. What we want to do is be able to utilize those parts in a new design. When we get these ETO machines coming down the pipe, we want to design in those existing parts. Instead of using a 1.2 horsepower motor, we can use 1.3 horsepower because that’s in inventory, so we can save some money there.The last thing is what happens when a customer calls. We have this inventory because it’s been used somewhere; we’re not just buying parts that never get used. Typically, a customer has a custom machine and the part might only break every five or 10 or 15 years. So it becomes about us reminding the customer. What CADDi helps us do now is call customers to say, this is an at-risk inventory part. We can give you a discount if you buy two of these, or 10 of these. It flags us as a customer service representative, to make sure we’re letting customers know that we don’t plan to stock this and there’s a great opportunity to put some safety stock of this part on their shelf.So we’re stopping the bleeding, and not producing more SKUs. We’re using CADDi internally, so I have a list of parts that engineers can search where they can say, I need something that looks like this or I need a motor that does this. CADDi will suggest ways to use up existing internal inventory.Finally, we want to sell it. When a customer calls in for a spare part order, we’re able to identify the part quickly and say, ‘We have that and we have more in stock. Would you like some?’You previously paused engineering AI software investment because the early technology lacked a direct link to your engineering vault. What specific real-time synchronization capabilities convinced your team to reconsider the CADDi platform as a mature system ready for nVenia’s needs today?We engaged in CADDi about two years ago, and did a year-long trial. During that trial, we figured out that due to the engineered-to-order nature of our business, we were syncing about once every week, maybe once every two weeks, and that was too slow for us. We did this as a manual sync. Data would come in, but in real time when we’re designing something I need to find something new now, not two weeks from now.The game changer came when CADDi developed an API that allowed us to transfer this data almost in real time. That also allowed us to use another tool called CADDi Quote. Before, if drawings would come on Monday at noon, and I needed to quote them by three or four that afternoon, I couldn’t. Now we can.The core of CADDi is that I put in all the drawings and then I can look at those drawings through the AI filter. I can ask, ‘find me a pipe with this diameter’ and it’ll go search, or shape match, and find me something that looks the same. On top of that is the CADDi Quote add-on module. When we look at going out to our vendors and saying, ‘I need to manufacture this outside of our four walls,’ it’s to ensure we’re getting good pricing. We wanted to use this tool to help our procurement team get the best possible cost.These days, we get those new parts in real time. The procurement team gets a notification to go bid these out. We bid them out, usually to three manufacturers, and see which ones get the best price. We can also pull in quality data from that manufacturing group. We can say, ‘I got a good price from a good quality manufacturer—let’s move forward, let’s issue a PO to them.’ CADDi Quote really helped us on that front more than anything.Evaluating a returning software vendor requires clear evidence that their system can handle high-volume production data. What specific results from uploading 3,000 parts at once confirmed the software had evolved enough to process your real drawings accurately?They have approximately 750,000 drawings of ours right now. 3,000 were from doing the initial demo, which was really interesting.We grabbed 3,000 random parts, gave them the files, and they were able to find some neat information about commonalities. The biggest one was a right-angle bracket that we use to hold up sensors. Randomly in those 3,000 parts, we had three or four matches. The only difference was that the holes were in different locations.So the idea became, can we put all four holes in this at the same time so I only have one part? Or is this a modification where I can take the three parts down to two, or can I slot it instead? It raises a lot of questions about why we have so many similar parts. Just in that quick demo of 3,000 drawings, we found amazing opportunities.<a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices5-SC" rel="noopener noreferrer" target="_blank">CADDi</a> is a global technology company that develops a vertical data intelligence platform for engineering, design and manufacturing. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering knowledge. Rather than replacing PLM or ERP systems, CADDi operates as a shared intelligence layer across manufacturing teams.CADDi’s <a href="https://us.caddi.com/resources/whitepaper/decision-velocity-unlocking-faster-smarter-decisions-in-manufacturing?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices5-SC" rel="noopener noreferrer" target="_blank">new white paper</a> explains why improving decision velocity can drive significant productivity gains without large capital investments, while also improving the quality of decisions. CADDi will continue expanding its platform to help manufacturers strengthen operational performance, reduce friction in engineering and sourcing processes, and build the intelligence foundation needed for long-term competitiveness. More case studies and insights are available at <a href="http://us.caddi.com/case-studies?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices5-SC" rel="noopener noreferrer" target="_blank">us.caddi.com/case-studies</a>.

Manufacturing Voices: Ben Snyder, Vice President, Lifecycle Services, nVenia

Peer-to-peer forum on June 23 in Chicago gathers operations, quality and engineering executives on the data conditions automation depends onCHICAGO, Ill. — <a href="https://us.caddi.com/?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">CADDi</a>, the global technology company behind the AI data platform for manufacturers, today announced an invitation-only executive forum during AUTOMATE 2026 focused on a question shaping every plant operations agenda: what separates automation programs that deliver measurable results from those that stall after deployment.CADDi Connect AUTOMATE 2026 takes place Tuesday, June 23, from 4:00 to 7:00 p.m. at the Hyatt Regency McCormick Place in Chicago. The session is designed for up to 30 operations, quality, and engineering executives from manufacturers across automotive, aerospace, food, logistics, and pharmaceuticals. Seats are limited and require advance registration.The session opens with a keynote from Sara Zimmerman, vice president of customer experience and product at Sumitomo Machinery Corporation of America, the U.S. company behind the Sumitomo Drive Technologies brand and an existing CADDi customer. Zimmerman's keynote, "<a href="https://us.caddi.com/owned-events/caddi-connect-at-automate-2026?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">From AI Curiosity to Operational Impact: How We Applied Automation to Solve Real Manufacturing Challenges</a>," will examine how automation strategy succeeds or fails based on how clearly on-site problems are defined before equipment is selected. Group discussion among attendees follows, structured around the same question: what changes when the operational problem is defined first, and the equipment second.The forum draws on signals from CADDi's recent <a href="https://us.caddi.com/resources/whitepaper/2026-manufacturing-outlook-study?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">2026 Market Research Report</a>. The study found that 79 percent of manufacturers identify the skilled labor shortage as their most significant external challenge, and 56 percent point to poor cross-departmental collaboration as the primary obstacle to using parts data effectively. The average employee spends roughly an hour each day searching for engineering information, and substantial institutional knowledge sits with senior engineers approaching retirement. Tariff and trade volatility add further pressure, with 66 percent reporting a significant impact on their business.These conditions shape what automation investment can and cannot deliver. Robots, vision systems, and motion control equipment operate on the engineering data, supplier history, and prior decisions feeding them. When that information sits in disconnected systems, teams rebuild context every time a quality, yield, or changeover problem surfaces, and automation outcomes plateau regardless of the equipment installed.Zimmerman's organization is already operating against these constraints with measurable results. <a href="https://us.caddi.com/resources/news/sumitomo-drive-technologies-news-mar-31-2026?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">Sumitomo Drive Technologies partnered with CADDi to transform six decades of engineering data into an AI-powered intelligence hub, reducing research time by up to 90 percent.</a> In one documented case, the engineering team used CADDi to identify a past design that closely matched a new complex order, saving weeks of design work and shortening lead time for the customer. The company plans to expand the centralized knowledge base beyond North America into a global engineering asset. <a href="https://us.caddi.com/resources/news/amerequip-transforms-100-years-of-engineering-data?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">Amerequip, a 100-year-old equipment manufacturer, has applied the same approach,</a> using CADDi Drawer to unify drawings, assemblies, specifications, and cross-referenced part numbers into a single search workflow accessible across its engineering and quality teams."Operations leaders are well past debating whether to automate. The harder problem is making sure the data, supplier context, and prior decisions an automation strategy depends on are accessible when a team has to act," said Patrick Harrigan, vice president of sales at CADDi U.S. "This forum brings executive peers into one room to define the operational problem first, then evaluate the solution."The CADDi Connect session is structured to address the operational layer adjacent to that agenda: the engineering, supplier, and decision data that automation programs depend on. CADDi has hosted similar executive convenings adjacent to major industry events, including <a href="https://us.caddi.com/resources/insights/ces-2026?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">CES</a> and the <a href="https://us.caddi.com/resources/insights/caddi-connect-whats-next?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">Construction Expo</a>.Registration closes Saturday, June 20, 2026. Executives attending AUTOMATE 2026 can save a seat at: <a href="https://us.caddi.com/owned-events/caddi-connect-at-automate-2026?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">us.caddi.com/owned-events/caddi-connect-at-automate-2026</a>. Seats are limited.AUTOMATE 2026 takes place June 22 to 25 at McCormick Place and is expected to draw more than 50,000 attendees across robotics, vision systems, motion control, and industrial AI. In addition to CADDi Connect, live CADDi AI data platform demonstrations will be available upon request at Booth 1669.‍About CADDiCADDi is an AI-powered data platform that makes design and supply chain data accessible and actionable for manufacturing teams. Headquartered in Tokyo and Chicago, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. Its flagship product, CADDi Drawer, uses advanced AI to centralize and analyze unstructured design and production data, helping manufacturers improve efficiency, reduce redundancies, and unlock innovation. Recognized globally for innovation, CADDi was listed in Fast Company’s Most Innovative Companies and won the SaaS Award for Best Business Intelligence and Engineering Management Software. To learn more, visit <a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-CADD-Connect-Automate-Chicago-BW" rel="noopener noreferrer" target="_blank">us.caddi.com</a>.Media ContactMilbien MedinaSenior Manager, Brand MarketingCADDi USA<a href="mailto:milbien_medina@caddi.com" rel="noopener noreferrer">milbien_medina@caddi.com</a>

CADDi to Convene Manufacturing Leaders at AUTOMATE 2026 on What Positions an Enterprise for AI and Automation Success

As retirement rises, manufacturers risk losing more than workers. Institutional knowledge is disappearing – technology can help preserve it.By Aaron Lober, VP of Marketing and Manufacturing Intelligence Lead at <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">CADDi</a>Hiring issues take the heat for many of manufacturing’s greatest problems. <a href="https://us.caddi.com/resources/whitepaper/2026-manufacturing-outlook-study" rel="noopener noreferrer" target="_blank">Eighty percent of manufacturing executives</a> say the skilled labor shortage is their biggest challenge today. But once leaders take a closer look under the hood, a more critical challenge than low headcounts begins to emerge. Institutional knowledge is disappearing right before our eyes.Seasoned manufacturing professionals are retiring. The practical experience that keeps production running is becoming less and less available as shrinking teams need it most. Leaders can try to fill the roughly <a href="https://www.bls.gov/news.release/jolts.t01.htm" rel="noopener noreferrer" target="_blank">433,000 open manufacturing jobs</a> recorded at the end of 2025, but increasing headcount alone won’t fill this growing gap. We need transferable know-how – and we need it fast.To overcome the knowledge crisis, businesses must go beyond hiring. It’s time to digitize the storage and transfer of tribal information for better resilience.<h2>Manufacturing’s Complex Labor Landscape</h2>Manufacturing today is under rising pressure. Tariffs are unpredictable, cost volatility remains high, and many teams are running lean as production expectations increase.In January, the <a href="https://economics.td.com/us-ism-manufacturing-index" rel="noopener noreferrer" target="_blank">ISM Manufacturing Index</a> returned to expansion at <a href="https://economics.td.com/us-ism-manufacturing-index" rel="noopener noreferrer" target="_blank">52.6</a>, signaling that the sector had moved back into growth after more than a year of contraction. However, the employment component remained in contraction at <a href="https://economics.td.com/us-ism-manufacturing-index" rel="noopener noreferrer" target="_blank">48.1</a>, suggesting manufacturers are still cautious about rebuilding their workforce. On the other hand, those that’ve decided to grow their teams are struggling to do so with <a href="https://nam.org/mfgdata/facts-about-manufacturing-expanded/" rel="noopener noreferrer" target="_blank">1.9 million manufacturing jobs expected to go unfilled</a> in the next few years. As a result, organizations are operating with smaller headcounts and asking existing employees, who are often less experienced than those who left, to carry more responsibility.Another complexity is that the nature of the work itself is changing. Manufacturers now need workers with both traditional trade knowledge and digital fluency with new hires expected to navigate automation systems, production data, and increasingly complex workflows from day one. Yet most organizations haven’t built the training infrastructure to match that reality. Only <a href="https://us.caddi.com/resources/whitepaper/manufacturing-outlook-study-2025" rel="noopener noreferrer" target="_blank">20% of manufacturers</a> report making significant investments in upskilling. Just <a href="https://us.caddi.com/resources/whitepaper/manufacturing-outlook-study-2025" rel="noopener noreferrer" target="_blank">8% of employees</a> say their company actively supports the reskilling needed to keep up with new technologies.To add fuel to the fire, the industry is losing many of its veterans with no way to store what they’ve learned over the years. <a href="https://us.caddi.com/resources/whitepaper/manufacturing-outlook-study-2025" rel="noopener noreferrer" target="_blank">Seventy-three percent of senior manufacturing leaders expect to retire within the next decade</a>, taking with them the practical knowledge that was never fully documented to outlive them.Without their institutional knowledge answers become inaccessible. Every urgent decision gets harder, slower, and more expensive. As a result, people start to default to “good enough” assumptions and the impacts are felt immediately.<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F60fae2a064314c99bff2f524f5287a30">Connected manufacturing data gives teams faster access to drawings, decisions, and production insights.<h2>What the Knowledge Crisis Looks Like on the Floor</h2>For many manufacturers, the knowledge crisis creates wasted time on administrative execution. Teams spend <a href="https://www.assemblymag.com/articles/99806-skilled-labor-gap-reshapes-how-manufacturers-invest-for-2026-according-to-new-study" rel="noopener noreferrer" target="_blank">an hour a day</a> searching for drawings, part histories, supplier records, and past decisions that should be easy to find. The information exists, but it is scattered across disconnected systems, spreadsheets, file folders, and the memories of a few experienced employees.The impact builds quickly. Work bottlenecks around a handful of “go-to” people. New hires take longer to ramp. Teams repeat work that has already been done because finding the answer takes longer than starting over.In practice, that can be as simple as not knowing which revision is current, why a design change was made, or how a similar issue was handled last time. On the floor, it means defects take longer to troubleshoot, rework builds up faster, and small problems turn into bigger delays because the context behind past decisions is hard to access.Most manufacturers try to manage this with the tools they already have: shared drives, PLM folders, ERP notes, internal wikis, and stricter naming conventions. The intent is understandable. But those systems still depend on people knowing what to search for, where to look, and how the company has documented information over time.This is where the real gap exists. Manufacturers need a way to connect drawings, revisions, and downstream outcomes into a usable record of what happened, what changed, and why. That starts with making past work easier to find and use in the moment it matters.<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F67f2787976d74ec082267299feb8d648">Manufacturers are using AI and automation to reduce knowledge gaps and improve operational efficiency.<h2>﻿Overcoming the Knowledge Crisis with AI</h2>For most organizations, the answers are already there in drawings, revisions, quotes, supplier records, inspection results, and scattered notes. The problem is that this knowledge is fragmented across systems and formats, which forces teams to rely on memory, workarounds, and a small number of tenured folks to fill in the gaps.Modern manufacturing platforms are helping close that gap by unifying these sources and making them easier to search in plain language with Artificial Intelligence (AI). Instead of digging through ERP fields or scanned PDFs, AI enables teams to search across drawings and related records in one place, even when they do not know the part number or where the file should live. In practice, that gives newer employees faster access to the context behind prior decisions and makes it easier for experienced teams to build on work that has already been done.That matters because in a complex labor market, manufacturers can no longer depend on processes that only work when the most senior person is nearby. They need systems that make design intent, sourcing history, and past learnings easier to carry into the next decision. When engineers can quickly find similar designs instead of recreating them, and when teams can trace what changed and why, productivity stops depending so heavily on individual memory.<h2>The Next Generation of Manufacturing</h2>The labor market will stay volatile, but manufacturers can remain resilient. The teams that implement AI-powered systems that democratize knowledge will move faster with smaller, less experienced teams. The next generation of leaders will be the ones to digitize their career’s most important learnings and end the knowledge crisis.About the Author<a href="https://www.linkedin.com/in/aaron-lober-67349287/" rel="noopener noreferrer" target="_blank">Aaron Lober</a> is Vice President of Marketing at CADDi, where he leads U.S. marketing strategy focused on accelerating digital transformation in manufacturing. He brings 15+ years of experience launching and scaling products across multiple industries, including building and managing portfolios exceeding $100 million. Before joining CADDi, Lober served as Head of Marketing at Blameless. Prior to that, he spent several years at Procore, where he helped guide the company through its path to IPO and led major product launches, including Procore’s first data and analytics offering, Procore Analytics, and Procore Estimating.<a href="https://industrytoday.com/manufacturings-growing-knowledge-crisis/" rel="noopener noreferrer" target="_blank">Read the article on IndustryToday.com</a>

Manufacturing's Growing Knowledge Crisis

CADDi Inc. announced that Amerequip has implemented CADDi Drawer to centralize engineering data and change how teams search and reuse historical design information.Centralizing a century of engineering knowledgeSince 1920, Amerequip has designed and manufactured custom equipment and solutions for companies such as John Deere, Caterpillar and Oshkosh Corporation. The company supports OEM customers with equipment manufacturing, hydraulic systems, product design and component production. Over time, it has accumulated a large library of design data. However, the size and complexity of this library made it difficult for engineers to locate and use relevant information.<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F26ca61c7e4bd40d794a3593d262d6e9a">Shown standing (from left) are CADDi sales director Marcin Zmarzly, Amerequip’s head of quality Bryon Nolan and mechanical designer Stella Rezabek, and CADDi VP customer success Yongli Deng. Taken during a recent site visit at Amerequip. (CADDi photo)One issue involved mismatched part numbers between customers and Amerequip’s internal system. When a customer referenced a different part number, the process required searching the ERP system for a cross-reference, accessing a drawing server, opening multiple files and manually verifying matches. This workflow relied on familiarity with internal systems and took multiple steps.With CADDi Drawer, Amerequip moved to a single searchable environment. Engineers can search drawings, part numbers and keywords together, with results showing related drawings and cross-referenced internal numbers. The system also allows teams to identify similar components listed under different internal descriptions, which can reduce duplication and additional orders.Access to information across engineering and quality teamsThe system also provides visibility into how components are used in assemblies. Instead of generating reports in the ERP system and reviewing drawings separately, teams can search a part number and view related assemblies and documents in one place.This approach reduces the need to switch between systems and allows teams to review design relationships during quality checks, change evaluations and customer requests.Additional details are available in the case study titled “<a href="https://us.caddi.com/case-studies/amerequip" rel="noopener noreferrer" target="_blank">Amerequip: From Hunting for Drawings to a True “One-Search” Engineering and Quality Workflow</a>” on CADDi’s website.CADDi<a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">us.caddi.com</a><a href="https://www.designworldonline.com/amerequip-adopts-caddi-drawer-to-centralize-design-data/" rel="noopener noreferrer" target="_blank">Read the article in Design World</a>

Amerequip Adopts CADDi Drawer to Centralize Design Data

Single-search workflow unifies Amerequip engineering and quality teams with CADDi Drawer – connecting drawings, assemblies, specifications, and cross-referenced part numbers into one accessible systemCHICAGO, Ill.--CADDi Inc., a global technology company developing an AI data platform for manufacturers, today announced that Amerequip has implemented CADDi Drawer to centralize engineering data and improve how teams search and reuse historical design information.Centralizing a century of engineering knowledgeSince 1920, Amerequip has designed and manufactured custom equipment and solutions for brands such as John Deere, Caterpillar and Oshkosh Corporation. The company supports OEM customers with complex equipment manufacturing, hydraulics systems, new product design and component production. Through over a century of innovation and design, Amerequip has built up a substantial library of past designs. This library should be a useful reference point to improve new work, but its size and complexity created issues when engineers tried to search through it.One recurring issue involved mismatched part numbers between customers and Amerequip's internal system. When a customer referenced a part number that differed from Amerequip's internal numbering convention, the traditional workflow required searching the ERP for a cross-reference, navigating to a drawing server, opening multiple prints, and confirming the match manually. It was a multi-step process that depended heavily on experience and familiarity with the systems.By implementing the CADDi Drawer platform, Amerequip replaced a multi-system workflow with a single searchable environment. Engineers can now search drawings, part numbers and keywords simultaneously, surfacing relevant drawings alongside cross-referenced internal numbers embedded within the documents. The system also enables teams to identify existing components that appear under different internal descriptions, helping avoid duplicate parts and unnecessary purchase orders.According to Tim Dorn, Vice President of Engineering at Amerequip, "The breadth of our operations drives a high volume of data across the organization. Ultimately, our effectiveness depends on how quickly we turn data into decisions, accelerating speed to market in support of our partner customers."Faster answers across engineering and quality teamsCADDi Drawer also improved visibility into how components are used across assemblies. Instead of running "where-used" reports in ERP and manually reviewing drawings, engineers and quality teams can search a part number and immediately see related assemblies and documentation in a single view.This consolidated workflow reduces system switching and helps teams confirm design relationships faster during quality reviews, change assessments and customer inquiries. Amerequip Head of Quality Bryon Nolan shared, "Many times, we're working on a part and we want to understand what it goes into. With CADDi, we type in the part number we are reviewing, and in addition to seeing that part, we are also seeing drawings for assemblies that use it. One search. All the results we want."To learn more about how CADDi is helping manufacturers unlock the value of historical engineering data, read the full case study, "Amerequip: From Hunting for Drawings to a True "One-Search" Engineering and Quality Workflow" on CADDi's website.About CADDiCADDi is an AI-powered data platform that makes design and supply chain data accessible and actionable for manufacturing teams. Headquartered in Tokyo and Chicago, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey, Apple, and Lockheed Martin. Its flagship product, CADDi Drawer, uses advanced AI to centralize and analyze unstructured design and production data, helping manufacturers improve efficiency, reduce redundancies, and unlock innovation. Recognized globally for innovation, CADDi was listed in Fast Company's Most Innovative Companies and won the SaaS Award for Best Business Intelligence and Engineering Management Software. To learn more, visit us.caddi.com.<a href="https://www.manufacturingtomorrow.com/news/2026/03/20/amerequip-transforms-100-years-of-engineering-data-into-actionable-intelligence-with-caddi-ai-data-platform-/27234/" rel="noopener noreferrer" target="_blank">Read the article in Manufacturing Tomorrow</a>

Amerequip Transforms 100 Years of Engineering Data into Actionable Intelligence with CADDi AI Data Platform

The investment thesis here is not about a single product, but about positioning at the inflection point of an exponential shift. The global AI in manufacturing market is projected to grow from <a href="https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-manufacturing-market-72679105.html" rel="noopener noreferrer" target="_blank">$34.18 billion in 2025 to $155.04 billion by 2030</a>, a compound annual growth rate of 35.3%. This isn't linear expansion; it's the steepening of an S-curve where adoption is accelerating from niche experimentation toward strategic integration. The catalysts are powerful and structural.Two fundamental problems are forcing this acceleration. First, there's a severe <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">engineering resource gap</a> as senior talent retires, taking critical knowledge out of the system. Second, manufacturing data is fragmented across PLM, ERP, spreadsheets, and tribal knowledge, creating a costly bottleneck for innovation. These are not minor inefficiencies; they are the friction points that make AI adoption a necessity, not a luxury. The market is moving from pilots to production because the cost of not acting is rising faster than the cost of implementation.This shift is now a top-down priority. According to recent analysis, <a href="https://iot-analytics.com/industrial-ai-market-insights-how-ai-is-transforming-manufacturing/" rel="noopener noreferrer" target="_blank">most large manufacturers now have a formalized, CEO-driven AI strategy</a>. The focus areas emerging from these strategies are telling: industrial data management and architectures are paramount. This signals a move beyond point solutions to building the foundational infrastructure that can handle the data deluge required for true AI value. The paradigm is clear: the next wave of industrial productivity will be powered by AI, but only if the data plumbing is in place.Amerequip's CADDi implementation is a direct bet on this infrastructure layer. It's an attempt to solve the data fragmentation problem at the source-engineering design and manufacturing planning-thereby capturing value as the entire industrial AI S-curve steepens. The company is positioning itself not just as a software vendor, but as a builder of the rails for this new paradigm.<h4>CADDi as Foundational Infrastructure: Solving the Data Bottleneck</h4>The core problem Amerequip is solving with CADDi is a classic friction point in high-mix manufacturing: data is everywhere, but not where you need it. This fragmentation is a major source of rework and inefficiency. As one engineer put it, the traditional workflow for a simple part number match was a <a href="https://www.businesswire.com/news/home/20260319179061/en/Amerequip-Transforms-100-Years-of-Engineering-Data-into-Actionable-Intelligence-with-CADDi-AI-Data-Platform" rel="noopener noreferrer" target="_blank">multi-step process that depended heavily on experience</a>, requiring engineers to jump between ERP systems, drawing servers, and spreadsheets. This isn't just a minor annoyance; it's a fundamental bottleneck that slows down the entire product development cycle.CADDi Drawer is built as a first-principles solution to this. It creates a single searchable environment that unifies engineering and quality teams by connecting drawings, assemblies, specifications, and cross-referenced part numbers into one accessible system. The functionality is straightforward but powerful. Instead of hunting through multiple silos, a user can search a part number and instantly see the part itself, its related assemblies, and the cross-references embedded in the documents. As the Head of Quality noted, this replaces a fragmented hunt with one search. All the results we want.This centralization is strategic infrastructure. Amerequip has built up a substantial library of past designs over its 100-year history, a potential goldmine for reuse. Yet that library was a liability because of its complexity. CADDi turns that century of engineering knowledge into an active asset. By surfacing existing components that appear under different internal descriptions, the platform helps avoid duplicate parts and unnecessary purchases. More importantly, it accelerates the speed-to-market for OEM customers like John Deere and Caterpillar. The company's own statement is telling: "Ultimately, our effectiveness depends on how quickly we turn data into decisions, accelerating speed to market."This is the essence of building foundational rails. CADDi isn't just a tool for internal efficiency; it's a platform that solves the data bottleneck at the source-engineering design. By making historical knowledge instantly accessible and actionable, it removes a key constraint on innovation. For a company facing an <a href="https://www.amerequip.com/blog/our-complete-oem-engineering-services-from-cad-to-prototyping-to-production/" rel="noopener noreferrer" target="_blank">engineering resource gap</a>, this ability to exponentially reuse existing work is a critical lever. It positions Amerequip not just as a manufacturer, but as a partner that can rapidly scale its design capacity, directly supporting the industrial AI paradigm where data velocity is the new competitive moat.<h4>Financial Impact and Scalability: From Tactical Tool to Network Effect</h4>The financial payoff from CADDi hinges on converting data centralization into tangible operational leverage. Success is measured in reduced design cycle times and less engineering rework. Every hour saved hunting for a part number or verifying a cross-reference is an hour that can be spent on innovation or production planning. For a company operating in high-mix manufacturing, where <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">more SKUs, more complexity, fewer skilled workers</a> are killing efficiency, this directly impacts project margins and throughput. The earlier evidence noted that the traditional workflow was a <a href="https://www.businesswire.com/news/home/20260319179061/en/Amerequip-Transforms-100-Years-of-Engineering-Data-Into-Actionable-Intelligence-with-CADDi-AI-Data-Platform" rel="noopener noreferrer" target="_blank">multi-step process that depended heavily on experience</a>. By replacing that with a single search, CADDi attacks a known source of costly rework. The financial impact is a function of how much of that wasted effort is eliminated and how quickly new projects can be initiated.The path to exponential scaling, however, lies beyond internal efficiency. The real catalyst is expansion. If CADDi remains a tool for Amerequip's own engineers, its value is linear. But if it becomes the shared data layer for its OEM partners like John Deere and Caterpillar, a network effect kicks in. Imagine integrating CADDi with a partner's PLM or ERP system. This would create a unified data architecture where design knowledge flows seamlessly across the supply chain. The moat here isn't just in the software, but in the data itself-the historical library of past designs that becomes more valuable as more partners contribute and consume from it. This integration depth is what transforms a tactical tool into a defensible infrastructure layer.From a strategic investment perspective, this is a bet on data as the new competitive moat. The ROI, therefore, depends entirely on two factors: the depth of integration with key partners and the rate of user adoption across the entire organization. The company's own <a href="https://www.amerequip.com/blog/our-complete-oem-engineering-services-from-cad-to-prototyping-to-production/" rel="noopener noreferrer" target="_blank">engineering resource gap</a> makes internal adoption critical for immediate relief. But the exponential growth story requires that same platform to be adopted by external partners. The financial model shifts from cost savings to revenue acceleration and margin expansion as the platform enables faster, more innovative product development for the entire ecosystem. The risk is that integration proves complex or adoption stalls, leaving the platform as a valuable but contained internal asset. The opportunity is that it becomes the foundational data layer for a network of industrial AI, capturing value as the entire S-curve steepens.<h4>Catalysts and Risks: Navigating the Exponential Growth Path</h4>The path from a tactical data tool to an exponential growth engine is paved with specific catalysts and guarded by a key risk. The forward view hinges on measurable outcomes and the quality of the foundation being built.The primary catalyst is public validation. For investors, the critical watchpoint is Amerequip's own disclosure of measurable key performance indicators post-implementation. Success will be demonstrated not by internal satisfaction, but by concrete metrics like a <a href="https://www.businesswire.com/news/home/20260319179061/en/Amerequip-Transforms-100-Years-of-Engineering-Data-Into-Actionable-Intelligence-with-CADDi-AI-Data-Platform" rel="noopener noreferrer" target="_blank">reduction in design time</a> or a quantified drop in rework costs. Without these numbers, the platform risks being seen as a costly internal project rather than a scalable solution. The company's own statement that its effectiveness depends on how quickly we turn data into decisions sets the benchmark. If Amerequip can show that CADDi accelerates that cycle, it provides the proof-of-concept needed to push the platform beyond its own walls.The most significant risk is also foundational: the platform's value is only as good as the data it ingests. If the historical design library is poorly labeled, inconsistent, or contains errors, the system will amplify those issues. This is the classic "garbage in, garbage out" problem, but in a high-mix manufacturing context, it can lead to costly mistakes in procurement or production. The risk is that the very data centralization CADDi provides becomes a single point of failure if the underlying hygiene is poor. The solution requires disciplined data governance, but the burden falls on Amerequip to ensure the quality of its own century-old knowledge base before expecting partners to trust it.Yet the macro tailwind is powerful and persistent. The skilled labor shortage is not a temporary headwind but a structural, multi-year challenge. According to recent research, <a href="https://us.caddi.com/resources/news/79-of-manufacturing-executives-say-skilled-labor-shortage-is-greatest-challenge-according-to-new-caddi-research" rel="noopener noreferrer" target="_blank">79% of manufacturing leaders cite the skilled labor shortage as their greatest challenge</a>. This creates a relentless demand driver for any technology that can extract more value from existing assets and reduce reliance on scarce expertise. The platform's value proposition-accelerating speed to market by reusing historical design knowledge-directly addresses this pain point. In a market where <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">more SKUs, more complexity, fewer skilled workers</a> are killing efficiency, a tool that makes engineering knowledge instantly accessible is not a luxury; it's a necessity for survival.The bottom line is that Amerequip is navigating a classic S-curve inflection. The catalysts are clear: prove the internal ROI, then expand the platform. The risk is data quality. But the tailwind is structural and immense. The company's bet is on becoming the essential data layer for industrial AI, and its success will be measured by how well it converts its own century of engineering knowledge into the fuel for that exponential growth.<a href="https://www.ainvest.com/news/amerequip-caddi-seizes-industrial-ai-data-bottleneck-curve-steepens-2603/" rel="noopener noreferrer" target="_blank">Read the article in AI Invest</a>

Amerequip's CADDi Seizes Industrial AI's Data Bottleneck as S-Curve Steepens

Manufacturing skills and digital shift‍A survey by ECI Solutions (in Dutch) shows that Dutch manufacturing firms expect continued pressure in 2026 from labour shortages, cost volatility and supply chain disruptions. The report describes increased investment in ERP systems, automation and data integration to improve planning and productivity. You can download the report (in Dutch) after filling in your details.Research by CADDi finds that 79% of executives see the shortage of skilled labour as their main challenge. Companies are expanding training and redesigning workflows to compensate. You can download the report after filling in your details.AI moves to accountability‍A report (8.1 MB) by Deloitte indicates that AI adoption in enterprises is moving from pilot projects to scaled deployment. The report highlights stronger focus on governance, data quality and measurable business value. A Lewis Silkin report points to rising legal and ethical risks linked to AI use, including transparency and employee monitoring. You can download the report after filling in your details. Meanwhile, a report (6.43 MB) by Deloitte Digital describes how customer service is shifting to AI-first models, with automation handling routine queries and employees managing complex cases.Human skills in demand‍A study by Deloitte shows that team performance increasingly depends on collaboration, communication and adaptability alongside technical skills. The survey describes how human capabilities support productivity in technology-rich environments.Data in the Udemy Global Learning &amp; Skills Trends Report show rising demand for AI and data skills as well as leadership and problem-solving training. Organisations are updating learning strategies to combine technical and behavioural development. You can download the report after filling in your details. The conclusion is that continuous learning and balanced skillsets are essential for workforce effectiveness.Labour market rotation‍The LinkedIn labour market report (4.12 MB) suggests that the global labour market is rotating rather than shrinking. The report describes growing mobility between sectors and rising demand for digital and analytical skills. Employers are recruiting for transferable skills and reskilling existing staff, while workers are shifting towards technology-related roles. The impact is an increased need for ongoing training and flexible career paths.Hybrid work and safety‍A scenario study (in Dutch, 1.98 MB) by TNO (in Dutch) outlines four possible futures for healthy and safe work towards 2040, shaped by technology adoption, demographics and organisational choices. The report describes outcomes ranging from highly automated workplaces to more human-centred models.Research (in Dutch, 2.24 MB) from Instituut Gak (in Dutch) highlights legal and organisational challenges linked to hybrid work, including employer responsibility, working conditions and equal treatment. Organisations are updating policies and safety frameworks. The conclusion is that sustainable hybrid work requires clear governance, prevention measures and attention to employee wellbeing.<a href="https://www.en.nvc.nl/news/item/hrm-en-competentieontwikkeling---februari-2026/" rel="noopener noreferrer" target="_blank">Read the article in NVC Packaging Centre</a>

HRM and skills development, February 2026

<a href="https://lnkd.in/gvskyqHH" rel="noopener noreferrer" target="_blank">Read the article in Fastener Technology International</a>Fastener Technology International featured coverage of CADDi's AI data platform and its applications for the fastener and precision manufacturing industries. The article highlighted how CADDi helps manufacturers centralize engineering drawings and historical production data, enabling faster quoting and reduced reliance on tribal knowledge.

R&D teams reducing engineering and search time

CADDi could replace the manual, non-AI workflows that engineers use to search, compare, and reuse historical engineering data.<h4>How specifically will this AI tool help engineers?</h4><ul><li>Converts legacy drawings into usable engineering data. CADDi applies advanced OCR and AI models to extract text, dimensions, GD&amp;T symbols, annotations, and handwritten notes from 2D drawings and scanned documents. Information that was previously locked in PDFs, images, or paper archives becomes searchable and analyzable.</li><li>Enables geometry-based similarity search. Beyond keyword search, CADDi extracts shape and geometric features from drawings and builds an internal geometric representation. Engineers can search for parts based on visual and geometric similarity, even when part names, file structures, or naming conventions differ across systems or acquisitions. This allows teams to identify duplicate parts, create part families, and uncover prior designs that can be reused or adapted rather than recreated.</li><li>Automates drawing comparison and revision analysis. The platform highlights differences between revisions or similar drawings, allowing engineers to quickly see what changed and assess implications for manufacturability, cost, or quality without manual side-by-side review.</li><li>Preserves design intent and institutional knowledge. CADDi links drawings with design review notes, quality inspections, NCRs, and historical decisions. Engineers gain visibility into why a design was changed, what problems were encountered, and how those issues were resolved, even when the original engineers are no longer available. This prevents the loss of tribal knowledge as experienced engineers retire.</li><li>Connects engineering decisions to manufacturing outcomes. By linking engineering data with procurement history, supplier performance, lead times, margins, and quality outcomes, engineers can evaluate tradeoffs earlier in the design process using real operational evidence rather than assumptions.</li><li>Supports assembly and BOM-level navigation. For assemblies and subassemblies, CADDi automatically extracts BOMs and links components across documents. Engineers can navigate from a decades-old assembly drawing to individual components, identify alternatives, and evaluate reuse or replacement options for maintenance, repair, or aftermarket needs.</li><li>Enables AI-assisted exploration with human oversight. Engineers can use conversational AI to query historical documentation, such as identifying recurring quality issues or summarizing past defects. Every AI-generated response is linked back to source documents, keeping engineers in control of decisions and maintaining traceability.</li><li>Reduces non-design work. By eliminating time spent searching shared drives, PLM exports, PDFs, and paper archives, engineers spend less time hunting for information and more time designing, reviewing, and improving products.</li></ul><h4>Will this AI tool replace a non-AI tool or process?</h4>Yes. CADDi replaces the manual, non-AI workflows that engineers use to search, compare, and reuse historical engineering data.It eliminates activities such as:<ul><li>Navigating shared drives and legacy folder structures</li><li>Manually searching PDFs and scanned drawings</li><li>Relying on individual memory to locate past designs or suppliers</li><li>Hand-driven drawing comparisons</li><li>Recreating designs that already exist elsewhere in the organization</li></ul>CADDi does not replace CAD, PLM, or ERP systems. It replaces the fragmented processes engineers use to extract insight from those systems.The result is faster access to prior work, reduced duplicate design effort, improved consistency, and engineering decisions grounded in accumulated knowledge rather than individual recollection.<a href="https://www.designnews.com/artificial-intelligence/ai-tools-for-engineers-caddi-drawer-caddi-quote" rel="noopener noreferrer" target="_blank">Read the article in Design News</a>

AI Tools for Engineers: CADDi Drawer & CADDi Quote

<a href="https://www.assemblymag.com/articles/99806-skilled-labor-gap-reshapes-how-manufacturers-invest-for-2026-according-to-new-study" rel="noopener noreferrer" target="_blank">Read the article in Assembly Magazine</a>New research from CADDi, a company that builds a manufacturing AI data platform, signals three important takeaways for manufacturers as they head into 2026.Manufacturers report that the skilled labor shortage is no longer a looming threat; it is the defining constraint on manufacturing operations heading into 2026. Nearly 80% of respondents identified labor availability as their biggest external challenge, and 71% said it is already directly impacting their ability to meet production goals. What's changed is the scope of the problem. This is no longer confined to shop-floor operators. Shortages are spreading into maintenance, operations and even design and engineering. Retirements are accelerating faster than training pipelines can respond.As a result, manufacturers are being forced to rethink how work gets done. Sixty-two percent are prioritizing recruitment, training and retention, but the study suggests workforce strategy alone may not be enough.One of the clearest shifts in the study is how manufacturers are using data to blunt the impact of labor shortages. Access to accurate, real-time parts and production data is emerging as one of the most effective ways to preserve productivity with smaller teams.Manufacturers report that employees spend roughly an hour a day searching for parts data, an inefficiency that compounds across departments. More than half of respondents cited poor cross-department collaboration around data, while nearly 40% said fragmented or outdated systems are actively slowing decision-making.In response, companies are investing in centralized data platforms and data lakes that connect parts, supplier and production information. Predictive maintenance, automated inventory tracking and AI-driven procurement tools are allowing teams to shift from reactive troubleshooting to planned execution.The takeaway is that data infrastructure is no longer about visibility or reporting. It's about labor efficiency. Manufacturers are using data to make fewer people more effective.After years of volatility, manufacturers are entering 2026 with a markedly different capital mindset. Growth is no longer defined by adding capacity or new systems, but by extracting more value from what already exists. The study shows a clear pivot toward physical assets that deliver immediate, measurable returns on the shop floor. Sixty-nine percent of respondents plan to invest in robots and equipment in 2026, up from 60% the prior year. Planned investment in broad operational software systems like ERP and MES fell to 33%, from 60%.The findings are based on a survey conducted by CADDi in partnership with SME and Mercury Research LLC in fall 2025. The independent study collected responses from 195 manufacturing professionals holding management-level positions or higher.

Skilled Labor Gap Reshapes How Manufacturers Invest for 2026 According to New Study

<a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">CADDi</a> has released a new customer case study detailing how Mitsubishi Materials USA is using CADDi Drawing Intelligence to improve access to engineering data and streamline custom engineering and quoting processes. The case study highlights a broader shift within manufacturing organizations toward shared access to historical design and production data, reducing reliance on individual expertise as teams expand and experienced staff retire.Manufacturers supporting high-mix and custom production often maintain extensive archives of engineering drawings, pricing records, and quality documentation. However, these materials are frequently spread across multiple systems or managed by individuals, making them difficult to locate and reuse efficiently. This fragmentation can slow response times and lead to repeated work.Mitsubishi Materials USA, part of the Mitsubishi Materials Group headquartered in Japan, supplies cutting tools and engineered carbide products to manufacturers across North America. Founded in 1984, the company supports applications including turning, milling, drilling, and wear-resistant tooling.By using the CADDi platform, including the CADDi Drawer application, Mitsubishi Materials USA enabled its sales and engineering teams to search for information using customer names, tool numbers, or quote identifiers. This allowed teams to quickly retrieve drawings, inspection reports, and prior decisions without relying on manual coordination between departments.Improved access to historical data helped reduce duplicate engineering work and shortened response times for custom requests. Sales teams gained the ability to estimate and respond to customer needs independently, while engineering teams were able to reuse existing designs and reference prior outcomes more effectively.The case study also notes that centralized access to engineering records improved consistency across quoting and design workflows. By making past decisions and quality results visible across teams, the company reduced dependency on individual knowledge holders and supported continuity as projects scaled and personnel changed. According to a regional sales manager at Mitsubishi Materials USA, the ability to quickly search and retrieve information has significantly changed daily workflows and reduced time spent tracking down records.<a href="https://themachinemaker.com/news/dee-development-engineers-limited-expands-capacity-at-anjar-facility/" rel="noopener noreferrer" target="_blank">CADDi</a> is an AI-based data platform designed to make design and supply chain information more accessible to manufacturing teams. Founded in 2017 and headquartered in Tokyo and Chicago, the company develops tools that help manufacturers organize and analyze unstructured engineering and production data to improve efficiency and decision-making.<a href="https://themachinemaker.com/news/mitsubishi-materials-usa-improves-engineering-and-quoting-processes-using-caddi-drawing-intelligence/" rel="noopener noreferrer" target="_blank">Read the article in MachineMaker</a>

Mitsubishi Materials USA Improves Engineering and Quoting Processes Using CADDi Drawing Intelligence

<a href="https://www.designworldonline.com/voices/voices-yongli-deng-head-of-customer-success-caddi/" rel="noopener noreferrer" target="_blank" style="font-size: 16px;">Read the article in Design World</a><img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2Ff2d98c09a5724d718d45d25037542f24?format=webp">By Sana Kazilbash | May 11, 2026This article is sponsored by <a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices3-SC" rel="noopener noreferrer" target="_blank">CADDi</a>. In this Voices interview, Design World spoke with Yongli Deng, Head of Customer Success at CADDi, who shared a look at adoption of data intelligence tools, and the challenges and rewards of close partnerships and collaboration to drive a successful implementation.Design World: Tell us about yourself and your role at CADDi.Yongli Deng: I lead Customer Success for CADDi in the U.S. Since we’re a fast-growing startup, the work moves quickly and no two weeks look the same, which has been a fun challenge. I joined about eight months ago after spending eight years at Boston Consulting Group, and I made the move because I wanted to shift from advising companies to building alongside customers and driving real outcomes with them.Looking back at the early adopters of data intelligence technology, we often see a disconnect between the department head’s grand AI vision and the daily reality of the engineers expected to use the technology. As an adviser who steps in post-sale to manage expectations, what is the most critical step accountable leaders must take to bridge this gap and ensure the platform gets adopted?I think there are three main points.The most important element for success is to work together as one team in a partnership with our customer, rather than just the implementation of a software or a tool. CADDi brings our product, our expertise, our change management experience, our technical support. Our customers bring their data access, time, champions and leadership reinforcement to help us really drive success. It’s important, too, for the customer to name one single accountable project owner for the success of the CADDi implementation.Second, there’s often a gap between how users perceive and expect to use the technology, and the company’s vision. Executives talk about outcomes; what they care about is revenue or cost, and bottom-line improvement. But engineers are users that live in steps. The CADDi team helps translate that vision into day-to-day workflows the engineering team can understand and benefit from.&nbsp;We work together with the customer to define success by addressing both the management agenda and the ultimate benefit to the users — faster decision making, different use cases, standardized parts and reduced costs, for example.And lastly, it’s important for CADDi to be honest and transparent with our customers about what we can and what we cannot do. Adoption gets killed when the expectations are vague or magical, or because users get frustrated early on, and it’s very common because AI is a buzzword, people get excited and then they expect AI to do everything to solve their problem. So, during our onboarding process we educate our customers about what AI can do and cannot do.Some manufacturers’ teams initially adopt an AI platform simply to save on administrative and search time. However, high software login usage doesn’t automatically mean the business is seeing true value. When advising first-movers, how do you guide them past that ‘digital filing cabinet’ mindset to track specific, measurable outcomes — like increased decision velocity or procurement cost reductions?This is critical, specifically for our business. We do not want customers to just see us as a search platform. But search time is tangible, and users can feel the difference immediately when they start using CADDi Drawer. If we don’t intentionally educate our customers, they tend to think that search time is where CADDi’s value comes from. But our value is in how many problems we can help our customers to solve.For us to educate our customers about CADDi’s value, we spend time understanding their business, their team, and their processes. Then the CADDi team helps them come up with ideas and share inspiration, presenting real examples where CADDi can create value for them — not just reducing search time, but also contributing to their financial improvement or breaking silos across functions.Ultimately, all this education is necessary because CADDi Drawer and our platform is first to market. There wasn’t a comparable product existing in this market before, so customers may not know what to expect. It’s easy for them to think, CADDi is just a search platform. In reality, we’re not just about search. We’re here to be a partner who can create value.The manufacturing industry is currently facing a massive skilled labor shortage and the loss of ‘tribal knowledge’ as veteran engineers retire. You have mentioned that a key metric for CADDi’s success is ‘better onboarding and employee support.’ How do you help companies utilize their historical data to actually accelerate new-hire training and capture that expertise before it leaves the building?When we onboard our customers, it’s common for the new hires to become the power users the fastest because they don’t have 10 years of muscle memory to unlearn. But they also don’t have access to all that expertise because they are so new, and without CADDi, they would have to ask a more experienced member for help, either through training, hand-holding, or learning by doing. Our platform helps reduce the barrier of “I don’t know who to ask.”One way we help keep knowledge within the organization is by structuring historical data into onboarding workflows. CADDi is a platform that consolidates drawings and drawing-related documents, information and data into one place so it’s easy for people to gain access to all the information that they need. During the onboarding process, we help new members see us as the first stop when they need a reference, or have a question.At one recent manufacturer we worked with, the VP of Engineering was hiring multiple engineers, and shared that he used their CADDi partnership as a talking point to attract young engineers to his organization. Not all manufacturers are adopting AI as CADDi does, so he used this to signal his company is modern, serious about engineering excellence, and at the forefront of adopting AI in the engineering design world.When deploying complex intelligence platforms, a buyer’s initial expectations rarely match the immediate reality of the rollout. Looking back at the implementations your team has successfully turned around, why is a deeply consultative, “co-building” partnership more critical for ensuring long-term ROI than just relying on standard IT tech support?In this era of AI, everyone is excited about it, using it and building pilots. The barrier to build a tool yourself in-house is a lot lower. So increasingly often, the customer will not just want to pay for a tool, they want to pay for impact. For us to overcome the natural inclination to build in-house, we need to provide impact that transcends what their IT department would be able to implement.For us to deliver impact requires, first, that CADDi’s implementation be closely tied to customers’ strategic agenda. Otherwise, it won’t be prioritized, and the impact will be non-existent or minimal to their business.Second, the foundation of creating value is adoption, and driving that adoption requires collaboration between CADDi and our customers. A lot of manufacturers have used the same tools or same processes for years. It’s understandable, because there’s a very low tolerance for making mistakes in the manufacturing process. Imagine you send the wrong version of your drawing to your supplier or your customer. The cost of that mistake can be massive.To build the necessary momentum for change, our customers typically put a mandate in place to get through the harder part, which is going from zero to adoption. After that, our customers continue encouraging their users for adoption, while CADDi is here to help drive that change, to do training, share best practices, and come up with creative ideas like sending newsletters, creating champions, doing on-site training or office hours.Third, delivering value beyond adoption requires shaping our implementation to make sure it is aligned with our customers’ strategic goals, and ensuring we have aligned and defined the success criteria at the beginning of the project. It’s very much a partnership.For some real-life examples, walk us through how your team acted as a strategic adviser during a successful vs. challenging implementation, and explain why unifying multiple teams onto a CADDi-supported workflow drove tangible ROI from the POV of different key stakeholders.We recently published a <a href="https://us.caddi.com/case-studies/amerequip?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices3-SC" rel="noopener noreferrer" target="_blank">case study</a> on a successful integration with Amerequip. We are in the second quarter of their implementation, which is still early, but we have already provided strong value to the customer. They were skeptical of CADDi’s value proposition at first, and whether they could justify the ROI. I led this project myself with the CS team, and once we started the partnership with them we dedicated one resource to sit down with them to understand their needs, their pain points and their data, to co-design the environment and workflow with them, to educate them about our platform.During onboarding, we are not just implementing a tool, we are also helping our customers to prioritize. Customers will have lots of ideas. My role and my team’s role in this process is not just taking customers’ requests and implementing them. Instead, we actively help our customers to prioritize what’s most important and what’s less important, to build a roadmap for our implementation.Lastly, we’re building credibility through little actions, like recurring weekly check-ins and making sure every meeting has a clear agenda, clear outcome, clear action items. Even with something we needed to deprioritize, a few weeks later our team is proactively coming back to the customer saying, this was something we discussed a few weeks ago, now it’s time to bring that back to our agenda. We’re not letting anything fall through.We have been very transparent with Amerequip in terms of what we can and cannot do. Customers really appreciate that honesty and transparency. It helps them understand our capabilities, and also builds trust and credibility with them. Now Amerequip is connecting us to other companies in the area, and attending our workshops to help our other customers.A challenging implementation was with an American manufacturer who initially expected CADDi to deliver a function we’re not capable of. There was some miscommunication, and the customer expected us to do something different, which made the initial implementation very challenging because the customer was frustrated we couldn’t deliver what they initially asked for.But our CS team proactively helped guide the customer by sharing other relevant use cases with them, and redefining workflows that we later realized are even more connected to their goal of improving their quotation win rates and quotation speed. A year later, this customer became another champion of CADDi. They came to our customer event and shared their experience of this journey with other customers. It’s very inspiring to see that, when this customer was originally in a very challenging situation.This is the value of CADDi’s strategic partnership and the critical role our Customer Success team plays. The partnership starts from the first touchpoint and deepens as customers implement new technologies. Driven by our unique customer focus and the vision of our <a href="https://www.designworldonline.com/voices/voices-yushiro-kato-co-founder-and-ceo-caddi/" rel="noopener noreferrer" target="_blank">CEO Yushiro Kato</a>, CADDi operates more like a top-tier management consulting firm than a traditional SaaS development vendor.Through a high-intensity, white-glove partnership model and manufacturing-exclusive AI data platform technology, CADDi helps manufacturers strengthen operational performance, reduce interoperability friction and build the unified data intelligence foundation needed for sustainable growth in American manufacturing.To learn more about how CADDi AI platform first-movers are improving productivity by unlocking faster, smarter decisions, explore our collection of featured customer case studies at <a href="http://us.caddi.com/case-studies?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices3-SC" rel="noopener noreferrer" target="_blank">us.caddi.com/case-studies</a>. Or <a href="https://us.caddi.com/resources/whitepaper/decision-velocity-unlocking-faster-smarter-decisions-in-manufacturing?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices3-SC" rel="noopener noreferrer" target="_blank">read CADDi’s new white paper</a> which explains how improving decision velocity can drive significant productivity gains without large capital investments, while also improving the quality of decisions.

Manufacturing Voices: Yongli Deng, Head of Customer Success, CADDi

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.