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

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" alt="Design World Voices interview banner featuring a portrait of Yongli Deng, Head of Customer Success at CADDi">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

<a href="https://www.manufacturingdive.com/spons/data-intelligence-drives-total-cost-of-ownership-in-industry-40/818640/" rel="noopener noreferrer" target="_blank">Read the article in Manufacturing Dive</a>By Philip Carpenito, Industry Advisor &amp; Former Chief Procurement Officer at L3Harris Commercial Aviation<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F3df874fa846d465dbd28fc02f46c0d21?format=webp" alt="Person working on a laptop showing a CADDi Quote quotation project with a bearing drawing, in front of windows overlooking an automated factory floor with robotic arms.">We are in an era of extended supply chain uncertainty. Is the Strait of Hormuz open as you read this? Will it be open tomorrow?&nbsp;What will the next geopolitical issue bring?Companies need to get comfortable with uncertainty and implement real-time software to help manage it. Preparing for a new technological era under these global constraints means extending digital transformation beyond the production environment to become more knowledgeable, agile and flexible in procurement, engineering and overall supply chain. This enables companies to truly understand the Total Cost of Ownership (TCO).Industry 4.0 is often defined by automation and connectivity on the manufacturing floor. In reality, it depends on how decisions are made across procurement, engineering, and supply chain. Bringing the level of data visibility required for Industry 4.0 procurement decisions and processes is even more critical than modernizing manufacturing operations. It provides the connected data foundation that AI needs to generate intelligence for faster, better sourcing decisions. From a procurement standpoint, the challenge is the inability to see supplier capability, cost history, and part requirements in one place when decisions have to be made. This data is also necessary when designing new products to ensure the best cost at launch and a robust long-term supply chain.Organizations that empower more functions with component data intelligence move away from reactive crisis management and into sustained operational efficiencies backed by data-driven decisions. That said, improving upstream tools and systems is only part of the answer. The leaders running procurement have to evolve, too.<h4>Why Procurement Can’t Afford to Be Left Behind</h4>Sixty percent of procurement professionals report sourcing parts at higher costs than necessary due to insufficient component and supplier data for effective negotiations. In practice, procurement teams operate under time pressure, often without a complete view of supplier performance, cost history, or prior part sourcing decisions.Similar to other upstream functions, procurement teams remain burdened by siloed systems, outdated tools, static reports and spreadsheets that don’t match future-forward Industry 4.0 changes in decision-making. Most procurement professionals often must search across multiple ERPs, PLMs and shared drives to answer basic questions about suppliers, pricing, and parts history. This is sometimes referred to as “search tax”; that time loss translates into slower quote cycles, delayed product launches and higher costs when people can’t see what’s been purchased, from whom, and for what.Bringing procurement into the digital transformation is beyond critical for two key reasons:<ul><li>Decision blind spots. What slows procurement down is the time it takes to find, validate, and trust the information needed to make a decision. For many manufacturers, decades’ worth of information about which suppliers can handle specific parts, where they are located, why quality issues tend to surface, and what trade-offs have been made often live only in the memory of key decision-makers within the organization. Without capturing this context, organizations will repeat past mistakes. Increase cost, lead times, and supplier qualifications</li><li>Knowledge loss. When seasoned employees retire, those insights leave with them unless they’re captured, digitized, and democratized. Digitizing data is no longer a nice-to-have but an absolute necessity, and must be accomplished before it is no longer available. Digitizing procurement intelligence preserves institutional knowledge and reduces the need to reinvent the wheel.</li></ul>Just like operations teams need real-time visibility into production, procurement teams need real-time visibility into factors that drive sourcing decisions. Visibility is now a foundational requirement for running a global manufacturing business. You have to know what’s going on with suppliers, costs, and risks if you want to run your organization. In the end, it comes down to communication. Procurement decisions require a clear view of engineering requirements, supplier capability, and historical outcomes. This is what Industry 4.0 looks like in procurement.Implementing AI for procurement enables it to operate strategically. Organizations that unify procurement intelligence can reclaim this lost time and redirect it toward supplier collaboration, cost optimization, quicker time to market for new products and overall innovation.<h4>A More Complex Role Demands Better Data</h4>In Industry 4.0, procurement is no longer a downstream function. It is part of how cost, risk, and supplier decisions are managed in real time.<ul><li>The chief procurement officer or CPO’s mandate has shifted drastically from placing POs and chasing cost reductions to managing a lack of manpower, tariffs, supply chain disruptions, supplier concentration, global trade shifts, logistics and risk exposure across regions. Every sourcing decision is a trade-off between cost, supply, lead time, and risk. The challenge is making that trade-off without full visibility into what has already been done before.</li><li>Modern procurement teams function as intelligence hubs informing executives on decision-making.</li></ul>This all plays out in a world where data can change every hour on the hour; where supplier conditions, costs, and availability shift constantly — often faster than teams can validate the data behind their decisions.&nbsp;It has never been more important for the CPO to understand the financial health of their suppliers and the immense logistics challenges that now define global sourcing. Without better tools and connectivity, these procurement leaders are being asked to manage modern risks with yesterday’s infrastructure.With this level of responsibility and exposure. The new CPO has become a strategic advisor to the executive team and is responsible for designing resilient global supply chains and networks rather than simply managing purchasing transactions. This is exactly why the technology and data environment must catch up and support these new responsibilities<h4>Closing the Digital Disconnect in Total Cost of Ownership</h4>If fragmented systems and workarounds prevent procurement from using AI effectively, the first step is to consolidate data into a single, unified view. For many businesses, that means building a data warehouse that connects the core systems procurement depends on, so teams can access part numbers, part specifications, pricing, supplier, and cost context in one place.&nbsp;This includes enterprise resource and demand planning software, quality systems, product lifecycle management tools, bills of materials, and engineering drawings.Cleaning up and consolidating data does more than simply break down silos; it also creates a foundation for AI-powered tools to turn that unified data into actionable data intelligence. Once data is connected, AI can be deployed to support faster, smarter decisions and total cost. Teams describe what they need, and the system surfaces the information in minutes. Industry 4.0 data is consolidated and built to support decisions at the speed required by supply chain operations.Connected data helps procurement, engineering, quality, and manufacturing to tap into real-time supplier intelligence instead of working from snapshots in time. For instance, when a labor strike hits one region or a natural disaster threatens production in another, you can quickly see which suppliers and parts are exposed, along with possible alternatives. The question is not just who can supply the part. It is who has supplied it successfully before, what location they are in, under what conditions, and at what cost. It also supports predictive cost and supply risk insights to provide a forward-looking view of where material prices or lead times will be heading in the future.The result of these insights is better communication and faster sourcing decisions. AI can connect historical sourcing data — such as pricing, supplier performance, delivery, and defect history — to drive data-driven outcomes. Teams know who their best or approved suppliers are, where costs are headed, and how to stay within budget before decisions become too expensive to reverse. This reduces the need to re-source known parts, revisit prior decisions, or absorb unnecessary cost from incomplete information. You have a system to share with your supplier; your engineer can share design tools and capabilities. This collaboration helps optimize sourcing and supplier collaboration.<h4>Crucial Steps to Effectively Embrace the Fourth Industrial Revolution</h4>Adopting a system of insight can accelerate sourcing decision velocity, improve supplier selection, collaboration, and enable early cost optimization. AI software partner selection is critical to ultimately ensure the technology adoption within procurement and the full supply chain workflow. You’re looking for a technology adviser who can provide not only implementation but also training and ongoing support. This will help manufacturers reduce sourcing cycles, achieve cost savings and improve quality.Verify that their platform will integrate with the systems you run without a disruptive overhaul. And finally, prioritize ease of use — a system that intimidates users won’t be adopted, and unused systems produce no value. The data you need to work smarter already exists within your organization. You just need to put it to work. The lack of integration makes it difficult for teams to get a unified view of operations and can delay decision-making processes. Yet, redefining operational strategies can drive substantial cost savings.To see what this might look like, <a href="https://us.caddi.com/resources/insights/the-procurement-playbook" rel="noopener noreferrer" target="_blank">CADDi offers a free procurement playbook</a> that defines 11 actionable strategies and a data-driven approach to start driving Total Cost of Ownership. Investing in a data intelligence platform isn’t like cutting a PO, putting it in a drawer, and calling it a day. These systems directly affect how data is stored, and how procurement decisions are made and a new system will be as effective as your strategic approach to gain buy-in at all levels of the organization.Finally, Industry 4.0 in procurement is not defined by systems. It is defined by how decisions are made. Cost control depends on how quickly and confidently teams can act with complete, connected information.What is it costing you to operate without the full picture?Author BioPhilip Carpenito is a Strategic Advisor and Consultant at CADDi and former Chief Procurement Officer at L3Harris Technologies Commercial Aviation. He brings more than three decades of global supply chain and procurement leadership across complex manufacturing environments and industries. His experience spans procurement, operations, NPI, supply chain, subcontracts, and program management, where he has led large-scale initiatives focused on cost control, supplier strategy, supplier relationship and operational performance. He currently advises organizations on how to strengthen decision-making across engineering, procurement, and supply chain functions. He has also held leadership roles at Nature’s Bounty, Motorola, Raytheon and Bose.About CADDiCADDi is an international technology company that develops AI-powered data intelligence platforms for manufacturing, engineering and design. Headquartered in Tokyo and Chicago, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering and design knowledge. Recognized globally for innovation, CADDi was listed in Fast Company’s Most Innovative Companies and received the SaaS Award for Best Business Intelligence and Engineering Management Software. To learn more, visit <a href="http://us.caddi.com/company?ref=US-PR-2026-Q3-TechTarget-ManufacturingDive-Interview2-SC" rel="noopener noreferrer" target="_blank">us.caddi.com/company</a>

Data intelligence drives total cost of ownership in Industry 4.0

<a href="https://themachinemaker.com/news/nichirin-tennessee-and-caddi-inc-transform-24-years-of-engineering-data-into-actionable-manufacturing-intelligence/" rel="noopener noreferrer" target="_blank" style="font-size: 16px;">Read the article in MachineMaker</a><img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F2f4300796a5349a88770d8a5e7d0bdde" alt="Long hose production line inside a factory, with rows of blue tubing on a conveyor, pressure gauges and a control panel in the foreground, and a stack light beside it">Image Courtesy: NICHIRIN Group<a href="https://www.nichirin.co.jp/" rel="noopener noreferrer" target="_blank">﻿Nichirin Tennessee Inc</a>, a US-based manufacturer of brake and power steering hoses for passenger vehicles and motorcycles, has successfully modernized its operations through the implementation of CADDi Drawer, an AI-driven data platform developed by <a href="https://us.caddi.com/company" rel="noopener noreferrer" target="_blank">CADDi Inc</a>. The initiative marks a significant step toward reducing reliance on individual expertise and creating a more transparent, efficient, and self-reliant organization.Founded in 2002 and operating as a subsidiary of Nichirin Co, Ltd, a global leader in automotive hose manufacturing, Nichirin Tennessee has long depended on accumulated experience and institutional knowledge to manage engineering data. Accessing critical information such as drawings, bills of materials (BOMs), and historical records traditionally required precise part numbers and guidance from experienced personnel. This approach created operational bottlenecks, slowed onboarding, and limited responsiveness to customer needs.To address these challenges, Nichirin Tennessee deployed CADDi Drawer as an integration layer across its systems. The platform centralizes engineering and commercial data, linking drawings, BOMs, quotations, and control documents into a unified, searchable environment. As a result, employees across departments now have immediate access to critical information without relying on specialized knowledge or manual retrieval processes.The transformation has significantly improved operational efficiency. Team members can now locate relevant data using keyword or similarity searches, eliminating the need for exact identifiers. This shift has reduced time spent on information retrieval, accelerated quoting processes, and enabled employees to focus on higher-value tasks.In addition to operational gains, the initiative has fostered a cultural shift within the organization. Employees are encouraged to independently access and analyze data, promoting accountability and cross-functional collaboration. New hires, in particular, benefit from faster onboarding, as the platform allows them to navigate complex datasets with minimal supervision.Company representatives emphasized that the success of the implementation extends beyond technology. By making knowledge widely accessible, Nichirin Tennessee has strengthened its ability to operate as a unified team while leveraging decades of accumulated expertise as a shared organizational asset.<a href="https://themachinemaker.com/technology/euroblech-trumpfs-machine-diagnostics-minimizes-production-downtime/" rel="noopener noreferrer" target="_blank">Building on</a> these results, the company plans to expand the use of CADDi Drawer to additional facilities, including operations in Texas and Mexico, further enhancing efficiency and knowledge sharing across its manufacturing network. CADDi Inc, founded in 2017 and headquartered in Tokyo and Chicago, continues to develop AI-powered solutions designed to unlock the value of design and supply chain data for manufacturers worldwide.

Nichirin Tennessee and CADDi Inc. Transform 24 Years of Engineering Data into Actionable Manufacturing Intelligence

<a href="https://www.thefabricator.com/thefabricator/news/cadcamsoftware/caddi-delivers-ai-platform-to-nichirin-tennessee" rel="noopener noreferrer" target="_blank">Read the article in The Fabricator</a><a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">CADDi Inc.</a> has announced that Nichirin Tennessee Inc. has implemented CADDi Drawer, a platform that uses AI to centralize and analyze unstructured design and production data, in its operations in Lewisburg, Tenn. The company manufactures brake and power steering hoses for use in passenger cars and motorcycles.By linking drawings, BOM data, quotations, and control cards, Nichirin is helping to ensure that every department can use the platform as a single source of truth across disconnected systems. In addition, the platform has turned individual expertise into scalable knowledge reuse shared across the organization.“Our goal wasn’t just to implement new software—it was to eliminate barriers and operate as one unified team,” said Takehiro Ishimoto, sales manager.

CADDi delivers AI platform to Nichirin Tennessee

<a href="https://www.manufacturingtomorrow.com/news/2026/04/21/nichirin-tennessee-and-caddi-inc-turn-24-years-of-engineering-data-into-manufacturing-intelligence-reducing-reliance-on-tribal-knowledge/27441/" rel="noopener noreferrer" target="_blank">Read the article in Manufacturing Tomorrow</a>New case study reveals how long-time manufacturer Nichirin Tennessee reduced reliance on individual knowledge to improve quoting speed and cross-functional decision makingCADDi Inc., the global developer of a manufacturing-exclusive AI data platform, announced today that Nichirin Tennessee Inc. has implemented CADDi Drawer to modernize its operations and reduce reliance on individual expertise. Established in April 2002, Nichirin Tennessee is the U.S.-based manufacturer of brake and power steering hoses for use in passenger cars and motorcycles and one of the U.S. subsidiaries of Nichirin Co., Ltd., a century-long leader in automotive hoses for global OEMs. Nichirin Tennessee strategically deployed the platform to build a more transparent, accessible, and self-reliant organization under real operating conditions.Manufacturers historically rely on individual experience to access drawings, BOMs, and historical data. This approach slows onboarding, delays decision-making, and limits how quickly teams can respond to customer demand. The new case study delves into how Nichirin Tennessee Inc. addressed this constraint by centralizing access to engineering and commercial data with CADDi, enabling faster execution across teams.Reducing dependency on individual knowledge and manual data retrievalHistorically, accessing drawings, BOMs, and historical data required exact part numbers and a heavy reliance on the "tribal knowledge" of senior Japanese expatriate staff. This concentration of expertise created significant information silos that slowed down onboarding and daily operations for the entire team. In the past, new employees frequently relied on managers to identify relevant data, an approach that was time-consuming for both trainers and trainees.Centralizing access to engineering and commercial data across systemsBy adopting CADDi as an integration layer, Nichirin has successfully democratized its organizational knowledge. By linking drawings, BOM data, quotations, and control cards, the company is ensuring that every department can use CADDi as their single source of truth across disconnected systems."Our goal wasn't just to implement new software—it was to eliminate barriers and operate as one unified team. CADDi is a tool, but the real success comes from our team's commitment to make knowledge accessible and use it to optimize our workflow," said Takehiro Ishimoto, Sales Manager at Nichirin Tennessee.Today, any team member can instantly retrieve drawings, BOM data, and necessary historical information using keywords or similarity search, without relying on drawing numbers or individual experience. Information that was once concentrated among a few veteran engineers is now broadly accessible across departments, supporting cross-functional execution across engineering, procurement, sales, and operations.Improving quoting speed and enabling independent execution across teamsThis structured engineering knowledge has significantly reduced the time required to search for information, directly improving quoting turnaround and allowing a focus on higher-value tasks. Furthermore, the project is fostering a cultural shift toward empowerment where employees are encouraged to first search within CADDi, think independently, and take ownership of their work before relying on supervisors.Nathan Yifrach, a member of the sales staff, experienced this firsthand: "I was able to start working smoothly from day one. Even without prior knowledge or detailed instructions, the system was intuitive enough to search and navigate on my own". By turning individual expertise into scalable knowledge reuse and a shared organizational strength, Nichirin is ensuring that every department can leverage expert insight.The case study, "Democratizing knowledge to empower independence with Nichirin Tennessee" explores how CADDi Inc. and Nichirin Tennessee democratized decades of information, enabling independent work and turning individual expertise into a shared organizational asset. Building on this success, Nichirin is now expanding the initiative to Nichirin Flex USA, spanning manufacturing facilities in El Paso, Texas, as well as Juarez, Chihuahua in Mexico.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 us.caddi.com.

Nichirin Tennessee and CADDi Inc. Turn 24 Years of Engineering Data into Manufacturing Intelligence, Reducing Reliance on Tribal Knowledge

<a href="https://www.techtarget.com/searchenterpriseai/feature/GenAI-in-product-manufacturing-cuts-costs-but-adds-risks" rel="noopener noreferrer" target="_blank">Read the article in Industry Today / TechTarget</a><h5>In product design and manufacturing, GenAI systems are consolidating supplier data, improving processes and cutting significant software costs -- yet they raise unique concerns.</h5>The third-party manufacturing worksheet scheduler didn't work to his satisfaction, so Moulydharan Vallal, manufacturing design and implementation manager at battery material maker Sila Nanotechnologies, built his own in a few months <a href="https://www.techtarget.com/searchenterpriseai/feature/Battle-of-the-bots-Best-GenAI-chatbots-for-business" rel="noopener noreferrer" target="_blank">using ChatGPT</a>.The <a href="https://www.techtarget.com/searchenterpriseai/definition/generative-AI" rel="noopener noreferrer" target="_blank">generative AI</a>-built scheduler replaced a spreadsheet and checks equipment availability, upcoming maintenance, material levels and storage capacity. The Sila team has been building other systems, including a scenario-planning model that tracks the cost of goods sold. Over the past two years, build-your-own systems has saved the manufacturer "hundreds of thousands of dollars" in software costs, according to Vallal, who credits GenAI for the savings. The cost of building systems, he said, "is almost collapsing to zero."GenAI-developed systems have the potential to replace third-party software applications in several industries, but <a href="https://www.techtarget.com/searcherp/tip/Use-cases-for-generative-AI-in-manufacturing" rel="noopener noreferrer" target="_blank">GenAI's use in manufacturing</a> poses different kinds of concerns. When integrating AI-related capabilities into manufacturing systems, any system failure can shut down production.AI explainability is critical, said Austin Locke, principal and global lead for data science and AI at Rockwell Automation. "If the AI system is designed as a black box, there's definitely resistance to that in manufacturing," Locke said. Still, manufacturers aren't resistant to advanced technology.Manufacturers have long used high-performance computing (HPC) systems, including leasing the U.S. government's most powerful supercomputers, to test and design products in virtual environments, such as the wings of airplanes. Before the advent of HPC, manufacturers had to build physical prototypes and test each one.HPC runs its <a href="https://www.techtarget.com/searchenterpriseai/news/366640672/How-simulations-and-digital-twins-are-advancing-robotics" rel="noopener noreferrer" target="_blank">computationally intensive simulations</a> based on physics to produce an output "closer to nature" and more realistic, said Steve Conway, independent consultant for AI and HPC. AI is "just one more step removed from nature -- from realism," he explained. It examines historical data to identify the best design options. As a result, HPC makers are integrating AI into their systems, Conway added.<h2>GenAI in product design: Who's the inventor?</h2>While GenAI can help design products, select materials and produce innovative components, it can also create legal risk because U.S. copyright law doesn't recognize machines as inventors. "To be able to get a patent, the inventor has to be a human being. And the question becomes, Who is the inventor?" conjectured Shabbi Khan, a partner at consultancy Foley &amp; Lardner and co‑chair of AI, automation and robotics group.Until recently, large language models (<a href="https://www.techtarget.com/whatis/definition/large-language-model-LLM" rel="noopener noreferrer" target="_blank">LLMs</a>) were viewed as <a href="https://www.techtarget.com/whatis/feature/12-of-the-best-large-language-models" rel="noopener noreferrer" target="_blank">tools for generating ideas and producing content</a>. But "we're pretty much at the point now where LLMs can give you an answer -- and that's the invention," Khan explained. "You have an obligation to the patent office. You have the duty of good faith and candor, which includes declaring that you are the true inventor of this technology."A business can claim that a human, not AI, produced the invention, but there could be <a href="https://www.techtarget.com/searchcontentmanagement/answer/Is-AI-generated-content-copyrighted" rel="noopener noreferrer" target="_blank">significant legal risk in doing so</a>. "You may get away with it, and nobody will notice until something gets litigated," Khan noted. The real inventor can surface in discovery, when a party is required to "provide all documentation relating to the conception of the invention," he added. "And if somebody comes across your LLM history, it could really jeopardize certain things."A practical guardrail is to treat AI's involvement as a starting point. "Don't use the output as is," Khan advised. "Think about tweaks, go experiment with it, modify it."<h2>GenAI in autonomous processes</h2>Geminus AI makes an intelligence layer that sits on top of existing industrial control systems, such as those from Schneider Electric and Rockwell, said Greg Fallon, CEO of Geminus.AI. The platform uses GenAI to ingest the operations data and return specific control settings to improve process performance. In manufacturing plants that rely on HPC and physics‑based simulations, Geminus AI's platform augments those systems by using what it calls "physics‑informed" AI models to drive continuous, real‑time control decisions.While the <a href="https://www.techtarget.com/searchenterpriseai/feature/GenAI-is-ready-for-more-autonomy-but-supply-chains-are-not" rel="noopener noreferrer" target="_blank">process can run autonomously</a>, Fallon noted, the decision depends on the customers. "We have some customers who prefer to have a human in the loop because they don't have the autonomous backups in place," he said. "We have other customers that are fully comfortable running autonomously."There are also <a href="https://www.techtarget.com/searchenterpriseai/tip/Top-generative-AI-tool-categories" rel="noopener noreferrer" target="_blank">GenAI-infused tools</a> that can improve manufacturing speed and efficiency. "Engineers spend more time searching for information than using it," said Chris Cope, vice president of engineering at CADDi, whose tools are designed to centralize data, such as drawings, spreadsheets and data, stored in manufacturing supplier systems.In addition, "generative AI is really good at extracting knowledge and intent from engineers," said Nathan Evans, co-founder and head of climate and AI at digital manufacturing platform provider Fictiv. That kind of context from engineers is vital. GenAI can build at the "speed of thought," Sila's Vallal said, but​ "without context [from people], it will break."Patrick Thibodeau has worked for several decades as an enterprise reporter, focusing on IT and workforce management, ERP, high-skills immigration, tech policy, and high-performance computing.

GenAI in product manufacturing cuts costs but adds risks

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.