Making the value of technology clear
It is one thing to have formal processes for customer complaint handling, return materials authorization, and process non-conformance; that’s common. It is rare to be confident that the desired outcomes are complete. It is nearly unheard of to achieve that level of certainty in a rapid, automated fashion. This is where a causal graph and very precise process definitions come into play.
MetaFloor AI founders argue: “Standards-bound operational work is not fundamentally a document problem and not merely a generic workflow problem. It is a problem of preserving valid causal structure under uncertainty, coordination cost, and partial visibility. I don’t have the space to explain it all here, but the founders’ vision and fully documented formulas for typed causal governance are convincing. With this approach, it becomes clear when a case can safely be closed or when there are other dependencies to resolve.
While every manufacturer might face these issues, MetaFloor AI’s initial customers are in the electronics industry. The company is also focused on aerospace & defense (A&D), automotive, and medical devices. These industries have both industry regulations and standards, as well as company-specific standards for production processes. MetaFloor AI’s custom workflows are designed for each industry, and they anticipate an 80% fit once they have created the workflow for a few customers in that industry and the model is trained.
Users tend to be process engineers, quality engineers, operational excellence, or continuous improvement (CI) professionals. These are typically the employees tasked with day-to-day incident management. The AI can pull together knowledge and past experience from all these people and projects, identifying where a current situation might be similar to past events.
The MetaFloor AI platform is designed to be self-serve. A customer can sign up and without consulting or integration up front, start with an incident to record. The system will prompt about that event and also request previous RCA reports. Building the knowledge base happens incrementally with each problem the team brings to the system, providing an always value-adding, low-friction adoption path.
To encourage use, MetaFloor AI has bundled everything into a monthly price. For $499/month, a company can get a manager seat with all three system layers and 100 events per month. The second user is free to further encourage companies to learn and spur greater use and compliance easily and quickly.
The company was founded in the fall of 2025 and is just getting off the ground. However, the idea took hold earlier. The founding team spent months refining the thesis and validating the problem with operators in regulated manufacturing before MetaFloor AI was officially launched. The founders each bring particular strengths to this venture. This is the third startup CEO and commercial leader Anup Mehta has founded; previous ones include DeepEdge and Clarice Technologies. Sridhar Perepa is COO and has worked in engineering across the electronics, life sciences, transportation, and aeronautics sectors. Arun CS Kumar is Head of AI and Product, and also heads AI for DeepEdge; he has a PhD in AI/computer vision and a background in perception engineering for autonomous driving.
Closing the loop to ensure process improvements take place is not easy, but this AI- and industry-based approach holds great promise. The platform’s deep capabilities, combined with industry focus, bundled, cost-effective pricing, and NL interaction, bode well for growing adoption.
Thank you, Anup, Arun, and Sridhar, for briefing me on your breakthrough concept and sharing your white paper on Typed Causal Governance. I look forward to following MetaFloor AI’s progress in the market!
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Where are manufacturers focusing their AI initiatives? We surveyed over 250 manufacturers to find out, and we’re sharing a preview of the findings in an upcoming webinar on April 28th.
What is the current state of AI readiness from a data, organizational, and technical perspective? Are manufacutrers prioritizing the plant, R&D, engineering, the front office, or the backoffice? What are they trying to improve, and how well are they achieving the value they’re looking for?
Datacor asked Tech-Clarity to get down to the real truth about plans, approaches, and progress for AI in the process industries. Datacor Chief AI Officer, Sundar Kuppuswamy, will hold a fireside chat with the report author, Jim Brown, to understand the state of AI in process manufacturing. Sundar will also share insights from his customer experiences.
Join the webinar to learn more and get your questions answered. After the webinar we will be releasing three industry-specific reports based on this data, with details on:
Does PLM drive better outcomes in new product development?
Tech-Clarity invites you to participate in a research study on using PLM to support new product development across engineering and product development teams. Please take 10 minutes to fill out this short survey. As a thank you, we will send you a copy of the report summarizing the findings.
In addition, eligible respondents will be entered into a drawing for one of twenty $25 Amazon gift cards. See the survey for eligibility details.
Take the survey now to share your perspective!
Please feel free to forward this survey to others you feel have an opinion to share. Individual responses will be kept confidential.
Thank you for your support. Please check out our Active Research page for additional Tech-Clarity survey opportunities.
During a closed-door introduction with ReilAI Corporation, I began to believe agentic AI might actually be ready for operations. I am delighted I got a peek at a new approach to making it easier to understand what’s happening in complex, ever-changing manufacturing and supply chain operations. This young company was founded on the belief that agentic AI holds the key to understanding and fully leveraging diverse data to inform the actions of a system in motion.
In this call, they showed me (representing Tech-Clarity, Inc.) and a select few others their Governed Agentic Execution Layer (GAEL), intended to bridge between data infrastructure and execution systems. Most exciting, it does so without displacing people or systems already operating in the plant, company, and ecosystem.
One of the curious points that ReilAI founder Joanne Friedman points out is that manufacturers measure the return on investment or return on invested capital for a $50,000 piece of equipment, but not for the $5-6M in data infrastructure that keeps the entire facility running – or not.
Data infrastructure is crucial to execution success. They point out three areas where an execution time gap exists today; these are how long it takes to:
In previous research (A New Era of Continuous Improvement), I’ve pointed out that time matters; it is one thing we cannot replace or regain. As most of us know, AI can save quite a bit of time. It can also capture and leverage knowledge, which is crucial as much of our experienced workforce nears retirement. So, a platform that leverages a knowledge and context graphs as well as agentic AI could help close those gaps.
A manufacturer adopting GAEL can keep their current workflows or change them up front. Agentic AI choreography enables agents to run in parallel or in swarms. This goes beyond prescriptive orchestration to enable adaptation as conditions change, which is essential for ongoing execution. The design focuses on extensibility, observability, and explainability.
At the foundation of this new platform, and the first word in its name, is governed. Governance for swarms of AI agents is crucial. Key principles of this design is observability, traceability, and flexibility. The starting use cases are manufacturing and supply chain, which are inherently fast-moving, multi-disciplinary, and challenging.
The platform is also designed for collaboration. I mentioned a knowledge graph for context. This architecture is not unique to GAEL, but essential for many current systems to be execution-ready. Graphs can create meaning and context from otherwise fragmented data. GAEL also has context graphs and judgement layers. So context might include user intent, perspective, experience, expertise, authority, and security. With agents for nearly any role or discipline, the data appears with this full, deep context, ready for action.
The question is how to ensure the agents will generate great results. Friedman points out that no executive will just trust AI agents to work autonomously. Agents must earn trust. So, ReilAI has developed a training pathway for the agents.
Given that the founders are deep manufacturing and supply chain experts, they have built agents for specific roles with some starting knowledge:
The platform has built-in “governed trust paths” that make sense for manufacturers. These start with physical safety, move to compliance, and also include margin protection logic. Until all of these are satisfied, even a “Master” level agent cannot autonomously execute its commands.
If you know Joanne Friedman, you have likely seen her work on Return on Data (RoD). She and I were recently on a podcast together discussing this topic. Her equation for RoD is:
[Sunk Data Capital] X [Contextual Intelligence] = [EBITDA Expansion]
The returns can be in top-line or bottom-line; in cost or revenue enhancements. Most manufacturers store tremendous amounts of data and pay for infrastructure and cloud services. The question is, how much of that data is delivering value, and how much?
One aspect of that we’ve also written about is expanding beyond production to link multiple companies in an ecosystem. The ReilAI vision is that each industrial facility (plant, warehouse, distribution center) can leverage GAEL to become an intelligent smart node within a coherent system. They say, “As more partners integrate, predictive power and execution value compound exponentially.”
Agentic AI may be ready for industrial environments. To date, news about challenges in scaling and trusting AI in industrial settings abounds. We hear that investments may or may not pay off. The team at ReilAI has me thinking that might be news of the past, not the future or even the present. If GAEL can deliver everything in their vision, it will be a powerful platform to enhance operational execution. Governed agents ranked by expertise, leveraging existing people and data, seems like a good path forward.
Thank you, Joanne Friedman and Matthew Funderburg, for inviting us to this fascinating early look at your innovation at ReilAI Corporation. We look forward to following your progress in the market.
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We had the chance to spend some time with PTC leaders recently to discuss their strategy. We’ve followed PTC for over two decades and watched them transform numerous times. They’re currently in a new era under the leadership of CEO Neil Barua , who took over the CEO role several years ago after serving as the CEO of ServiceMax. Our impression from the conversation is that the PTC strategy feels refreshingly focused and leverages their core strengths. We think customers will respond very well to this approach.
Stepping back, what are PTC’s core strengths? PTC started with parametric CAD and has remained strong in the design world, currently with Creo, Onshape, and Creo Elements Direct. They were also one of the earliest PLM companies with their Windchill platform. They have deep roots in these spaces and have continued to innovate and invest over time.
But PTC has also been forward-thinking and pushed the boundaries of what a “PLM” suite should offer. They have consistently looked more broadly beyond engineering, supporting what today we’re all calling the digital thread. Examples of that include expanding to ALM to support product software, Arbortext for product documentation, and Servigistics for SLM. Product data is PTC’s core strength, and their current strategy has “shifted exclusively to product data value.” Given the increasing value of structured data to fuel AI initiatives, we believe this is a solid strategy.
The new vision PTC unveiled is IPL, or “Intelligent Product Lifecycle.” Following on their digital thread history, it covers the full product lifecycle from engineering, through manufacturing, operations, service, and sustainment. PTC says IPL is “Powered by product data, fueled by AI.” The strategy leverages both PTC’s broad product portfolio and a belief in openness to connect to product data in adjacent and even competitive systems following an OSLC and other standards-based approaches. An example of openness is Windchill, which has always put a high priority on supporting multiCAD environments. Their open, enterprise-level approach is highly valuable as manufacturers continue to streamline operations and remove product development friction.
PTC is also increasing investment in their design capabilities, what they’re calling Integrated Product Engineering, or IPE. The IPE approach consists of orchestration and collaboration across design disciplines. PTC supports this with a collection of the right pieces for today’s complex, software-defined products including the Codebeamer ALM solution to support software-defined products. This gives PTC both mechanical and software design, and they partner with leading ECAD vendors.
PTC’s strategy extends further down the digital thread and product lifecycle to manufacturing, where they support manufacturing process planning. From there it extends to the service lifecycle and end-of-life. These areas follow the PTC strategy to focus where product data drives value. For example, PTC can offer configuration-specific work instructions for field service or MRO based on the as-designed, as-manufactured, and as-maintained product structures.
PTC will continue to focus on five markets they feel they can best service because they have what they call “whole product” needs, including software-defined products, safety-critical / regulated industries, serviceable / circular products, and high rates of engineering change. The industries they focus on are:
Product data also supports PTC’s “Applied AI” strategy. It’s a practical strategy to help their customers gain new value through high value, achievable use cases. PTC has already delivered AI through existing capabilities like shape recognition and topology optimization in CAD. Now, they are using AI in ALM to validate and improve requirements and draft test requirements. In field service, they plan to leverage AI’s ability to gather data from disparate systems to streamline field service through generative data aggregation. PTC will surely extend these capabilities, and we look forward to learning more as they progress. From what I learned from a recent PTC AI in Focus webinar I joined, PTC has been making progress and delivering on a holistic, practical AI strategy.
We’re also looking forward to learning more about a new PTC product, Asset360. Asset360 is a product twin that serves as the data hub for physical assets in the field. The Product Twin Is PLM-connected and includes fielded asset configuration and activity data.
PTC also made a strategic decision to divest Kepware and ThingWorx. The capabilities were intended to further PTC’s “smart connected products” strategy, but their IoT capabilities gained traction on factory equipment around the product being manufactured, rather than on the products themselves. Divesting Kepware and ThingWorx closes that chapter and allows PTC to focus more squarely on product-centric capabilities versus manufacturing asset-centric functionality.
Lastly, it’s important to mention PTC’s SaaS strategy with "Plus" offerings for Creo, Windchill, and Codebeamer. This is in addition to cloud-native solutions Onshape and Arena. Today, PTC focuses Onshape on smaller companies and Arena on fast-moving products like electronics, what we would call supply-chain-centric versus engineering-centric manufacturers. They also have their FlexPLM solution for footwear and apparel. These solutions aren’t being force-fit into the IPL strategy, which allows PTC to focus on their core capabilities.
We expect a continued positive reaction to the focus from their customers and we’re excited to follow their progress. Thank you to PTC's Danaya Ostine, Dan Kerns, and Dave Duncan for your time sharing your vision with our analyst team. Thank you to Tech-Clarity's Michelle Boucher, Howie Markson, and Julie Fraser for joining the briefing and providing input to this post.
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How can food and other recipe-based manufacturers accelerate their innovation without wasting time and compounding their compliance risks? Startup Prodeen offers AI agents and playbooks designed to turn the tide, and early customers are finding benefits. These early customers are global food and beverage companies. Initial users are in regulatory affairs, quality, and food safety, with a view to R&D, innovation, supply chain, and procurement, as well as other recipe-based industries in the future.
The Prodeen team sees a SaaS paradox: more tools but less intelligence. A significant disconnect is between internal master data and external intelligence. They say 60% of regulatory time is wasted fighting this “compliance crisis.” So, they set out to develop agentic AI not for governance, but for operating across tasks and discipline siloes.
They set out to complement the existing systems of record and data governance, such as PLM and ERP, as well as the many point solutions for specific roles or tasks. Like Prodeen, we have also seen that existing systems often reinforce the siloes for each discipline in a company.
Our State of PLM in CPG research shows that the majority of CPG companies do not feel their PLM is prepared to meet their future needs. Replacing PLM is difficult, but getting more value from it by using AI may be a better approach. Prodeen’s solution aims to orchestrate across PLM, ERP, and point SaaS systems. Worth noting: this platform is not only for analyzing the mountains of regulatory, R&D, recipe, ingredients, and operational data, but also for executing on what they see in a governed yet rapid and agile manner.
The Prodeen agentic AI platform includes several capabilities, each focused on a common industry need.
Fascinating also is that they recognize the potential for rapidly escalating costs and tokens; they are focused on engineering a framework by taking a multi model approach or allowing companies to bring their own model to make this sustainable for a large company with its many ingredients, suppliers, and regions while complying with regulations.
Prodeen’s strategy is to leverage every customer engagement to create reusable compliance workflow automation. The vision is that each new customer and use case will compound value across all customers. Examples might include conformance documentation and label simulation. With this approach, Prodeen can quickly engineer specific workflow templates into the product. The user can not only enter chat, but also use a framework to execute a process, such as editing recipes or build dashboard for risk assessment. The flywheel does not use the customer’s data, but the needs and priorities across customers. In practice, the system that listens to what the customers say during sales, demos, and use, and enables Prodeem with a 2-person team to develop one or two new playbooks per week. As they prove this out, they can quickly scale into new functionality. This is also important since AI tools and regulations change regularly.
Though the company is young, the founders have deep experience in recipe-based product lifecycle management and solutions for batch process manufacturers. The founders, Nicola Colombo, Tye B., and Jakub Janoštík, worked together most recently at SGS DigiComply. Nicola was also a co-founder of Selerant, which became Trace One, and his father owned a flavor house.
They know how to run and scale a software company, and their entire careers have been focused on recipe-based industries and the challenges they pose. The AI agentic platform poses new challenges for them, but their core competencies have already been proven.
While Prodeen is young, we see great promise in a system that has such deep industry expertise at its core and aims to orchestrate workflows that leverage existing systems. The promise of greater clarity on regulatory needs as they change, coordinated across disciplines, can begin to alleviate some of the challenges of recipe-based companies.
They have recently won some big-name customers by getting up and running quickly to add value. One large company saw results and went from PoC to production with Prodeen in less than three months. We know how thorny recipe-based compliance and innovation can be, and look forward to watching Prodeen’s progress in the market.
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How can manufacturers meet customer traceability requirements faster and easier, with a higher level of reliability? Arcstone Advanced MES would argue that using the customer’s choice of LLM AI tool to access the real-time MES and supply chain data in their solution is the answer. Apparently, quite a few automotive components and food and beverage companies would agree, as Arcstone has been growing worldwide in these industries. Arcstone has also added an AI governance tool and a studio for building and governing apps. We recently caught up with founder Willson Deng to learn the latest.
The concept behind Arcstone’s offering is that MES at every level is the missing link to achieve real-time, end-to-end supply chain visibility. The company’s stated mission is to provide complete manufacturing transparency across the entire supply chain. CEO Willson Deng states: “By digitalizing and integrating manufacturing operations from the shop floor right to the hands of consumers, we aim to enable a more responsive, responsible, and sustainable manufacturing ecosystem for us all.”
This company offers both MES and supply chain software, aiming to enable even the smallest suppliers to deliver accurate manufacturing data into their ecosystem. We had our first briefing with Arcstone a few years ago; that insight goes into the concept in more detail, lists the product elements, and shows them in a graphic.
The company serves many industries, but two in particular have adopted this multi-tier supply chain via MES approach. Precision-engineered automotive parts and components, as well as food and beverage products, have driven excellent worldwide growth for Arcstone.
Both industries face significant risk from faulty materials in their products and are thus regulated accordingly. Automotive parts and food and beverage are highly competitive. They can both capture more or higher-margin revenue with better traceability.
Every conversation about software these days touches on AI. Arcstone’s focus is not on creating new tools, but on ensuring companies have protection when accessing actual manufacturing data from their software. Operations people spend plenty of time seeking data, and Arcstone created an MCP interface to enable any LLM or third-party system to read and interpret data across their own enterprise and their suppliers’, customers’, and partners’ systems. It has also created a manufacturing assistant agent that makes it easier to leverage data from the plant floor.
Exposing the Arcstone MES and supply chain systems’ data across the ecosystem has also made it easier to create integrations. An MES-to-ERP integration is crucial, and rather than the two or three people and a month it can often take, the new AI approach enables it to be handled by one person in a few days. MCP also cuts time by half or more when customizations are used to help ensure the MES matches operating best practices and is adopted by operators, Arcstone says. The Arcstone ecosystem of system integrators (SIs) is finding that this AI approach enables them to focus on customer success and satisfaction with fewer headaches, too.
In a pragmatic yet visionary way, Arcstone has also recently released two new capabilities to support governance in the age of AI and low-code apps.
Deng is a true visionary and continues to offer new insights. One is that the mindset for MES and supply chain must shift from what it can do to how customers can use it. Today, AI can build code to do specific functions. Yet, the complexity of managing manufacturing data and sharing it across a particular enterprise and its supply chain ecosystem is where the fundamental value now lies. This underlying MES capability enables significant efficiency gains, whereas functional improvements and the addition of AI for specific capabilities typically deliver only incremental improvements.
Thus, the Arcstone roadmap is less focused on functionality than on robustness, reliability, and ease of use for SIs to do what they need to do. Arcstone’s focus on architecture and ease of customization is to satisfy the SIs. The SIs are there to help manufacturers learn to use the system, then hand it over to end customers to manage and maintain, with no support beyond what they might need.
Arcstone’s vision of end-to-end multi-tier supply chain visibility to the factory floors is a strategic dream for most manufacturers. So is the level of sustainability it could enable. In many industries, being wasteful is more cost-efficient than addressing yield issues at the source. However, as regulations change, we expect to see more uptake of this unusual plant-first approach to supply chain resilience.
As commercial LLMs and AI tools mature, their integrate-what-you-choose approach may also serve them and their customers well. Adding arc.ai appears to lower the risk considerably. It can also help corporate IT as they gain AI expertise and new approaches continue to emerge. When combined with arc.studio for governed apps, the picture starts to make sense, particularly for larger enterprises and their ecosystems.
Arcstone’s partner-first philosophy further differentiates them. While many other MES and supply chain providers seek to perform services or ask customers to do it themselves with low-code or AI approaches, this can create competition with SI partners. Arcstone sees the SIs as the expert human touch that customers want and need for implementation, customization, and ongoing 24x7 support worldwide.
We have been impressed by Arcstone's vision and approach for years. We look forward to hearing about what comes next.
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Our research shows that 86% of companies consider environmental sustainability to be critical or important to their long-term business success. Further, our studies show that digital transformation is crucial to achieving it. But how can companies determine the sustainability impact their technology adoption makes to make a business case for new solutions? This eBook explores the relationship between digital transformation and sustainability, and more specifically, how sustainability impact can be credibly measured and used to make business decisions. The eBook uses and shares highlights from eleven credible, audited case studies from real companies that have improved sustainability through technology adoption.
Please enjoy the summary below. For the full research, please visit our sponsor, Dassault Systèmes (registration required).
Sustainability Demands Digital Transformation
Sustainability is good business, and ESG initiatives require supporting technology. More than three-quarters of companies, 82%, report that technology / digital transformation is important or critical to support environmental and social sustainability. Digital transformation and sustainability can go hand-in-hand.
Difficulty Determining Sustainability Value
While most digital transformation initiatives and technology investments are initiated and justified to achieve a financial return on investment (ROI), it’s important to recognize their ESG advantages in addition to their financial benefits. For example, manufacturers may reduce cost by improving production efficiency while also reducing carbon emissions. Similarly, engineers could optimize designs to enhance performance while simultaneously reducing energy usage and waste. These values should be calculated to complement the financial ROI and improve decision-making based on ESG impacts.
Driving Economic and Environmental Advantages
A joint Rockefeller Asset Management-NYU Stern Center for Sustainable Business analysis reports a growing consensus that good corporate management of ESG issues typically results in improved operational metrics such as return on equity (ROE), return on assets (ROA), or stock price. It’s increasingly valuable to determine the sustainability impact of digital transformation more directly associated with the initiative leading to the improvement. It’s even more valuable to estimate this impact in advance, complementing the financial ROI with a strategic sustainability ROI to help justify the investment.
While companies have significant experience deriving financial metrics, they often lack the ability to accurately and holistically measure the ESG impact of their digital transformation efforts on their value chain. This research explores how to properly determine the value, and reviews case studies that show tangible sustainability impact based on a credible, scientific methodology.
How can manufacturers develop a digital thread and unlock the business value necessary to stay competitive?
Today’s manufacturers operate in an environment defined by compressed timelines, increasing product complexity, and heightened customer expectations. Success depends on the ability to move quickly without sacrificing quality, compliance, or profitability. To achieve this, organizations must enable seamless collaboration and smarter decision-making across engineering, manufacturing, quality, and the supply chain.
True operational efficiency comes from connecting people and processes through a single, reliable source of product information. When every team works from accurate, up-to-date product information, organizations reduce errors, eliminate rework, and respond more effectively to change.
A product digital thread makes this possible. Enabled by product lifecycle management (PLM), the digital thread creates a continuous, end-to-end flow of product data across the organization and throughout the product lifecycle. This eBook explores what a PLM-enabled digital thread is, why it matters, and how manufacturers can build one to drive lasting business value.
Please enjoy the summary* below. For the full research, please visit our sponsor, Propel (registration required).
New Product Development is More Complex
For manufacturers, delivering profitable products to the market has become significantly harder. Products are more complex than ever, requiring additional resources with expertise in new disciplines, driving up development costs, and putting profit margins at risk.
The Heightened Impact of External Pressures
Some of this complexity arises from external factors outside a manufacturer’s control. Customers are increasingly demanding, expecting innovative products more quickly than ever before. Competition is coming from all directions. Not only from traditional competitors, but also from new entrants. Our State of Product Development survey found that 56% of manufacturers face competition from adjacent industries, while 52% compete with low-cost or offshore manufacturers.1 Today’s supply chains add to the challenge. In fact, 74% of manufacturers in the survey identified supply chain disruptions or market volatility as a top challenge in product development.2 Beyond that, government and industry regulations are widespread, especially in High Tech and medical technology, demanding strict engineering and quality processes with thorough data collection and management.
Multi-CAD Environment Complicates Design Collaboration
Some of the complexity stems from internal issues. Remember when products were primarily mechanical?
Those days are gone. Now, mechanical, electrical, and software teams all need to work together – and be productive doing it. They must ensure that form, fit, and function all work in harmony while delivering their designs on the same development and launch timeline.
However, each design discipline uses different tools, with product data stored and managed separately or, in the worst case, only on an individual engineer's drives. Managing and accessing product data across multiple design systems, let alone file folders and shared drives, negatively impacts collaboration and reduces productivity.
Where to Start
For some manufacturers, establishing a digital thread may be viewed as out of reach when facing budget, resources, and time constraints. However, manufacturers can establish a digital thread despite these challenges.
Use 80-20 Rule
Applying the 80-20 rule helps focus on the most important and common use cases and workflows first. These deliver the most significant business value without getting bogged down with less common and more complicated edge cases. In short, keep it simple.
Keep Established Workflows
Established workflows need to continue, especially those supporting regulatory requirements, but avoid excessive customization whenever possible. Using out-of-the-box functionality saves implementation time and money, and reduces the need for dedicated IT resources.
Connect Existing Systems
There is no need to start from scratch. A practical approach is to connect existing CAD, PDM, and PLM investments and applications that are working well to create the product digital thread.
Take a Phased Approach
The best path is to take it one step at a time. Since data across systems is probably not perfectly aligned, a phased approach to PDM-PLM integration is preferred. Start with a small project, or assembly, to avoid a massive data cleanup upfront. Then add new projects and products as data cleansing progresses.
*This summary is an abbreviated version of the eBook and does not contain the full content. For the full research, please visit our sponsor, Propel (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
[post_title] => Building the Digital Thread to Improve NPD Performance
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What is the role of AI in Pharma manufacturing? Can highly regulated pharmaceutical companies use AI effectively? What are companies doing to leverage AI without compromising their CGMP-validated processes? Please join this practical, real-world conversation on moving AI in pharma beyond pilots and into meaningful results.
Tech-Clarity’s Julie Fraser joined a panel discussion with Sam Laermans, Global AI Lead at NNIT, Senior IT Leader and Biotech Expert Joseph Ricci, and Kate Porter, Director of Product Management and Research at POMS. Roland Esquivel, POMS VP of Sales and Marketing will moderate the discussion and lend his experience also. This diverse panel will discuss what is already working and where regulatory, quality, IT, and other questions and challenges lie.
The lively discussion touches on these topics:
In November, I had the pleasure of returning to Rockwell Automation Fair at McCormick Place in Chicago, typically one of the last vendor conferences of the calendar year. At the event, Rockwell offered training classes, presentations, workshops, and a vendor expo. They highlighted, among other topics, digital twins, elastic MES, AI, and software-defined automation as key technologies for transforming the manufacturing industry.
From the opening keynote by Chairman and CEO Blake Moret, it was clear that Rockwell’s vision embraces cloud, digital twins, and AI as key elements of their technology strategy and future. It was interesting to see how Rockwell has focused significantly more on software since I first started attending Automation Fair a decade ago, and that change appears to be accelerating rapidly.
It was a program as large as McCormick Place. While I can’t cover everything discussed during the multi-day event, I will focus on a few areas that were particularly interesting.
As in previous years, Plex held its own conference-within-a-conference, the Plex Summit, at the start of Automation Fair, prior to the Automation Fair opening Keynote.
Following an initial session that covered Plex’s growth over the past year and customer successes, the Plex team introduced the concept of “Elastic MES”. As MES is “the operational backbone of the modern plant” (as demonstrated by the continued interest in a three-letter acronym that is nearly thirty-five years old), it has also undergone a number of evolutionary changes. MES has evolved from homegrown, hard-coded behemoths to so-called “COTS” (commercial off-the-shelf) solutions that required significant customization, to composable MES, which shifted the customization burden from the vendor to the end customer. With this history, I was intrigued by Plex’s concept of “Elastic MES” and what it might mean for manufacturers.
Elastic MES does not refer to ease of implementation; it is an MES that adapts and evolves as it operates. In the words of Mike Hart, Head of Product - Industry Strategy & Growth, “a platform that helps evolve, adapt, and optimize your business in real time.” This is not a single product; it is a systems approach. At the core are industry-specific workflows, data models, rules, and best practices. This is applied across a broad range of business processes, with end-to-end IT/OT integration, aiming to marry Plex’s IT experience with Rockwell Automation’s OT experience.
Elastic MES is extensible, resilient, and interoperable, supporting open integration with other enterprise systems. Elastic MES is delivered on a unified edge-to-cloud architecture. In effect, it is designed to deliver the agility of the cloud without compromising up time on the line. As analysts, we were initially puzzled by Rockwell’s decision to acquire Plex Systems back in 2021. Four years later, there is no question that this acquisition has benefited both Rockwell and Plex, as well as their customers. The promise of Elastic MES and the evolution toward intelligent autonomous operations are key examples.
A crucial component of achieving manufacturing autonomy is resilient edge-to-cloud connectivity. Rockwell has Plex, one of the first cloud-native MES systems, and the FactoryTalk suite, which includes MES on the edge. Their strategy is to bring these two products together and unify their portfolio. As presented by Hayden Foot, Rockwell is extending Plex with a lightweight, resilient edge component called FactoryTalk ResilientEdge. Available in Q1 2026, this will leverage the elasticity of the cloud with the resiliency of the edge. Rockwell claims this capability will enable 24/7 factory operation, as the resilient edge will react if the cloud connection is disrupted and keep IT and OT systems synchronized.
This capability is also valuable for implementing system upgrades. Upgraded components are released to a cloud repository and can be swapped into a Kubernetes cluster in standby mode without disrupting production. The final piece of the resilient edge capability is leveraging FactoryTalk Optics to display Plex MES, automation, and IIoT information to the operator on a single screen.
A decade ago, it was difficult to find a manufacturer that believed moving manufacturing software to the cloud was feasible. Some were concerned about IP protection, some about vendor lock-in, but ALL were concerned about the twin issues of availability and latency. Resilient edge-to-cloud technology, implemented appropriately, addresses both latency and availability once and for all. These new capabilities make cloud manufacturing a reality.
There were six press conferences held specifically for analysts and media: “Transforming Manufacturing from Within”, “AI and Autonomy”, “Cyber Security”, “Sustainability Forum”, “Robotics”, and “Software-Defined Automation”. Of these, Software-Defined Automation (SDA) drew significant attention from many media and analysts, as it represents a continuation of Rockwell Automation’s journey from hardware-centricity to software-centricity.
For anyone coming from a software background, the capabilities introduced in this presentation were not earth-shattering. Yet, in the context of accelerating the evolution of OT, the topic could be viewed as revolutionary. Coming from a background in programming and testing controllers individually, the SDA capabilities presented by Dan DeYoung, Julie Robinson, and Sherman Joshua are potential game-changers.
SDA allows engineers to store all their code in version control systems, so that they can track code seamlessly across tried-and-true IT elements of design, test, and deployment. Code that is documented and under version control can then enable features such as virtual commissioning.
Rockwell pointed out that the SDA is more than just a soft controller; it is changing the way automation is done. Development is done at the component level, tested virtually – including regression testing – and deployed as containerized workloads on the edge. Rockwell also stated that customers can continue to use all the OT tools they currently have. SDA does not require customers to relinquish their investment in Logix, Optics, and other installed OT infrastructure. They also stated that they expect to have Logix on a panel where customers can also run things like machine vision, robotics, and AI, all in one single package.
Rockwell’s new design environment is intentionally designed to enable concurrent development and collaborative design. Engineers across the plant can work on the same project simultaneously. It was designed to support object orientation, which makes sense to automation engineers. Concurrency enables automated regression testing. It also gives engineers the ability to decide where to deploy at the end of the process, since all development is done virtually. In practice, this creates DevOps for automation systems.
This is certainly not the Rockwell we knew from back in the day! The culture at Rockwell Automation today is very different from what we saw in the past. They are making huge strides in cloud, edge, robotics, and AI. At the same time, they are honoring their past by providing a path forward so their customers can leverage, rather than lose, their investments in Rockwell equipment and software. I can’t wait to see what 2026 brings.
Thanks to Michael Kane and Kristen Kubesh for the invitation and for coordinating to help me make the most of my time. I’d also like to thank Mike Kane and Michael Hart for taking the time for an in-depth conversation about Rockwell’s MES direction.
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How do manufacturers integrate design data (PLM) with manufacturing data (MES)?
This survey is now closed, please see our active survey page link for more survey opportunities.
[post_title] => How are Manufacturing Leaders Integrating PLM and MES?
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How can a strong auditing program, as practiced in major automotive suppliers, improve? By going digital. Ease.io as been doing that for years, with a SaaS software platform for Layered Process Audits (LPAs), 5S, Safety Inspections, Gemba walks, Root Cause Analysis (RCA), and problem-solving. They recently added on-the-job (OTJ) training support to strengthen customers’ outcomes.
Many lean and operational excellence programs include regular audits. Audits are designed to improve quality, productivity, and safety, and nearly always do. However, using paper, spreadsheets, tribal knowledge, and legacy or homegrown software can create inefficiencies and missed opportunities. For example, if standard processes are not executed consistently or when follow-ups to issues are slow, manufacturing issues can arise and cause problems.
For over 10 years, EASE has been selling software to support these process audit activities. Customers report vastly increased audit completion rates, with up to a 90% reduction in leadtime for major audits. Allowing data to flow smoothly with less administrative burden can help LPA and other audit processes deliver their value with minimal non-value-added overhead.
Manufacturers adopting a digital platform may encounter early resistance from end-users to the change in how audits have been conducted in the past. EASE encourages customers to explore how new technologies enable them to rethink how they do things. Using integrated technology can also help identify all trends and breakdowns. It also helps to trace the root causes of problems and track whether actions have improved the situation.
One of the most significant benefits of a digital approach is the speed to identify and notify about non-conformances. Another is the ability to make audits more effective, consistent, and visible. The digital record also makes it easier to detect when corrective actions have not had the expected impact, to re-address needed issues, and truly close the loop to optimize outcomes.
EASE is available as a SaaS subscription. The base audit & inspection version of EASE supports mobile audit and inspection checklist authoring through both pulling existing checklists and creating new ones. It also incorporates automated scheduling, findings management, and real-time data and dashboards for clear visuals of audit results. Naturally, EASE must connect to the systems of record, including QMS, MES, and CMMS. EASE Connect also enables bulk data access for BI tools and dashboarding, while Insights is their own dashboard solution that delivers custom-built dashboards specific to individual customers.
The next Level of EASE subscription includes creation and management of action plans. Action plans support collaborative RCA and analysis to document and facilitate problem investigation and understanding. Here, the EASE solution enables customers to create a library of guided problem-solving processes, milestones, and tasks. Then, the customer sets an action plan based on findings, assigning owners and approvers to each task along with due dates. Finally, this enables monitoring progress and scheduling validation tasks for sustained corrective actions.
A new release from summer 2025 includes OTJ capabilities. In performing corrective actions, EASE saw a way to facilitate training. As operator errors and poor training are shared drivers of non-conformances across the customer base, this became a clear need. Customers generate training from existing documents and publish it as contextual training that is triggered from findings. It can accommodate individual or group training, quiz users, and require sign-offs after training, also checking whether it addressed the issue. With the current “gray tsunami” of knowledgeable workers retiring, this need is only increasing.
EASE reports that customers have achieved excellent results. These include a 20% decrease in the cost of poor quality, a 2% improvement in OEE, and a 67% decrease in time to close out findings. Better audits and process improvements lead to lower cost of poor quality, higher productivity, and improved labor efficiency.
EASE claims to have over 350 customers using EASE in more than 3,500 plants across 60 countries. Customers are in the automotive, aerospace and defense, furniture, and a range of both process and discrete manufacturing industries. It appears that in these companies, use is also growing, as EASE reports that the platform now supports over four million audits each year.
We look forward to following EASE’s continued progress and growth in the manufacturing markets. Clearly this company Is helping manufacturers rethink and improve their audit processes. Ironically, Julie Fraser met Ease.io at the Manufacturing Leadership Council’s Rethink 2025 event. Thank you, Josh Santo, John Fredrickson, and Andrea Walter, for the briefing!
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How are manufacturers approaching the AI opportunity?
This survey is now closed, please see our active survey page link for more survey opportunities.
[post_title] => AI Maturity in Manufacturing and EPC
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If you are in the semiconductor industry, do you have the right development and manufacturing solution to scale the business to meet growing demand?
The semiconductor industry is entering a period of rapid growth, driven by AI, electric vehicles, autonomous systems, industrial connectivity, and rising data demands. To capitalize on this opportunity, semiconductor companies must scale to meet growing demand.
Yet, nearly all semiconductor companies report challenges with New Product Introduction (NPI), often caused by disconnected processes, limited visibility, and tools that don’t scale. Top Performers are addressing these issues by investing in digitalization and adopting PLM platforms tailored for semiconductor development. What should semiconductor companies consider to select the right solution?
Based on a survey of 207 semiconductor and high-tech professionals, the Buyer’s Guide for Semiconductor Development: Ideation through Manufacturing outlines key buying criteria across four critical areas: software functionality, service and implementation support, vendor capabilities, and company-specific needs. Based on expert interviews and survey research, it’s designed to help semiconductor leaders evaluate solutions to invest in the tools that will support scalable, profitable growth.
Please enjoy the summary* below. For the full research, please visit our sponsor, Siemens (registration required).
To learn more about the business value of investing in development and manufacturing processes, read our survey-based research report, Three Ways Semiconductor Companies Can Prepare for Profitable Growth.
By diversifying into different industries and broadening their portfolios, semiconductor companies can adapt their existing expertise and innovations for new applications and high-growth areas that require specialized chips like AI, electric vehicles, and autonomous driving. Not only does this open new revenue streams, but it also reduces development costs and improves margins. It also helps offset demand shifts, such as slowing mobile phone sales.
However, managing multiple product lines adds complexity, necessitating efficient processes to encourage reuse and streamline development. Improving how they manage and integrate data can help.
To uncover what drives leading performance, Tech-Clarity surveyed 207 semiconductor and high-tech professionals and identified “Top Performers” as the top 25% that outperform their competitors in metrics that indicate business success. These metrics were:
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit our sponsor, Siemens (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
[post_title] => Semiconductor Buyer’s Guide: Ideation through Manufacturing
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How can design engineers balance conflicting time, cost, and quality goals?
As businesses and products grow in complexity, design engineers have much to consider to produce optimal product designs. This is particularly true for smaller and medium-sized businesses (SMBs) that struggle with the same challenges as their larger counterparts, but have fewer resources to address them. What are the most successful SMBs doing to manage this? This research explores this question.
Based on a survey of 230 respondents, this research study examines engineering practices and simulation use. It identifies how executives at SMBs (companies with revenues less than a billion US dollars) can realize higher development returns through simulation-driven design, which should lead to increased profitability.
Please enjoy the summary* below. For the full research, please visit our sponsor, Siemens (registration required).
Increasing Complexity
Engineers have much to consider to design products with the best chance of market success. Products must be high quality, economical, and fast to market. However, as business environments and products become more complex, old ways of working may no longer be enough. Engineers need better methods to navigate the complexity of their engineering and design decisions to meet their goals. This can be especially challenging for a resourced-constrained smaller or medium size business (SMB).
What SMB Top Performers Do
Despite this complexity, Top Performers have implemented practices that allow them to be 2.1 times more likely to have highly effective processes to understand trade-offs. To achieve this, they increase their use of simulation and invest in more software capabilities. Unlike their less successful competitors, they leverage simulation throughout the entire product development lifecycle, supporting a simulation-driven design approach. In fact, SMB Top Performers are 75% more likely than Others to use simulation at the concept phase, and they continue to use it from this early stage through testing.
This is such a powerful approach that 99% of SMBs using simulation to explore design ideas report finding value. They report benefits such as better products, greater productivity, faster innovation, and a higher return on their development investments.
The Right Solution
Part of successfully adopting this approach requires using the right solution and technology. Design engineers report that CAD/CAE integration and embedding simulation inside CAD are the most important solution qualities to support their use of simulation.
This Research Report
This report shares research findings that provide an in-depth look at why today's engineers at SMBs have so much more to consider than they did even just ten years ago. Design decisions are even more complicated, and simply relying on experience is no longer sufficient. The research reveals what the most successful SMBs do to address this, helping them to release more successful products and improve their profitability.
Significant Product Complexity
While business complexity has created a challenging environment, the products have also become more complex, creating even more difficulties for engineers. The graph shows the top sources of product complexity. Much of this complexity comes from increased requirements.
More Requirements
With increased regulations, engineers have more safety requirements to deal with. As we saw earlier, quality is critical to product success, and this is driving more quality requirements. Customers also expect high performance, which is vital for competitive differentiation. Yet, innovation requirements have increased the number of components and systems, creating more factors to consider and making it even harder for engineers to understand the impact of their decisions. They need better ways to understand this to optimize designs and avoid inadvertently introducing errors. They also need to validate and verify requirements.
Getting this insight as the engineer works on it is the most efficient use time, especially compared to waiting weeks or months for physical test results when the design details are no longer fresh in the engineer's memory. Not to mention, the later it is in the design process, the more a design has solidified, meaning changes will impact far more components. Any error or overlooked impact will result in errors that can increase costs, cause delays, and hurt quality.
More Configurations
Finally, as companies need to appeal to various market and customer needs, engineers must manage multiple product configurations. Each variant must also meet safety, quality, and performance requirements, adding further complexity while increasing the risk of missing the mark on critical product success factors.
Integrated Design and Analysis
Regardless of performance, design engineers agree on what helps them use simulation the most. Integrated simulation and design tools and simulation embedded inside CAD are the commonly identified features. These features make simulation more accessible to design engineers and provide a way to access the functionality without disrupting their workflow. Plus, engineers stay in a familiar environment.
Integrated Test and Simulation
Beyond making simulation easier to access, most design engineers also appreciate when simulation and test are integrated. As discussed previously, this can help reduce test time. At the same time, engineering teams can benefit from access to test results to improve future simulation models to catch problems caught during physical testing.
How can semiconductor companies establish a foundation to scale and profitably grow?
The semiconductor industry is projected to experience substantial growth over the next five years. How can semiconductor companies position themselves to take advantage of this growth and increase their profits? What challenges should they overcome to scale and grow the business?
Based on a survey of 207 semiconductor and high-tech professionals, this research study examines semiconductor companies' growth strategies. It identifies challenges related to new product introduction (NPI) challenges that hinder their progress. The research reveals best practices for overcoming these challenges and shares recommendations for establishing a foundation for scalable and profitable growth.
Please enjoy the summary* below. For the full research, please visit our sponsor, Siemens (registration required).
Expand Offerings
By venturing into new industries and broadening their portfolios, semiconductor companies can leverage their existing expertise and innovation investments while tailoring offerings for different use cases. For example, AI, electronic vehicles, and autonomous driving all require specialized chips. By adapting their offerings for these various applications, semiconductor companies can unlock new revenue opportunities. Additionally, reworking existing offerings for specific applications reduces development costs for adjacent offerings, thereby boosting profit margins. Moreover, diversification can help mitigate risks associated with fluctuating demand in specific segments, as experienced with mobile phones in the past. However, overseeing the development of various offerings introduces complexity that must be managed, especially to encourage and support reuse.
Accelerate Time to Market
The cyclical nature of the semiconductor industry means timing is crucial. Being the first to market allows a company to seize emerging trends and technological advancements ahead of competitors, thus gaining a competitive edge by capturing market share before rivals respond. This strategy also maximizes the revenue potential of new offerings before the next generation emerges. To achieve this goal, companies must find ways to improve process efficiency.
Innovation
Semiconductor companies face many opportunities for innovation, especially to meet the demanding requirements of AI applications. Those that can improve performance and reduce power consumption better than competitors should capture a substantial share of that market segment. Capabilities that foster collaboration and leverage existing expertise should help to generate new ideas and solutions to accelerate innovation.
Sustainability Strategy
Once semiconductor companies establish a foundation for growth, another important consideration that can provide a competitive advantage is sustainability. While only 15% of companies reported that sustainability is part of their growth plans, an impressive 97% of Top Performing semiconductor companies have implemented a sustainability strategy.
A well-defined sustainability strategy can give a semiconductor company a competitive edge. Many customers increasingly focus on producing energy-efficient, sustainable products with a reduced carbon footprint. Consequently, these customers are more likely to engage with semiconductor companies that prioritize sustainability. The graph shows the top actions taken by Top Performers to become more sustainable
Leverage the PLM Foundation for Sustainability
With the integration of data through a semiconductor PLM platform, companies can also utilize this information to support their sustainability initiatives. By employing digital technologies such as digital twins, simulations, analytics, and BOM roll-ups, companies can evaluate sustainability factors like the carbon footprint from the early stages. This helps understand the impact of different scenarios or options, enabling businesses to identify the best strategies for achieving their sustainability goals. The collaboration tools, supplier management capabilities, and integrated data provided by PLM can assist in capturing the necessary information for these assessments and enabling more informed decision-making.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit our sponsor, Siemens (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
[post_title] => Three Ways Semiconductor Companies Can Prepare for Profitable Growth
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How can automotive manufacturers improve engineering productivity?
It's an inspiring time for the automotive industry. Innovations through electrification, automation, and more are revolutionizing the industry like never before. Vehicles continue to be more comfortable, safer, and fuel-efficient, while new service offerings present further opportunities for innovation. All of this relies on the engineering team’s ability to deliver. Unfortunately, engineers regularly lose productivity to non-value-added tasks that not only rob them of their ability to innovate, but also threaten their company’s ability to compete, differentiate, and grow. Imagine the potential of identifying and removing the most common non-value-add activities engineers face and empowering them to focus on better vehicles, components, and systems.
This research examines how engineers spend their time, where they lose productivity, and the impact on the business. It then identifies solutions and approaches to reduce time wasters. Based on a survey of 228 manufacturers across industries, this report focuses on automotive companies and looks at the challenges and opportunities from their perspective.
Please enjoy the summary* below. For the full research, please visit our sponsor, Siemens (registration required).
This report is based off the research published in The Business Value of Reducing Engineering Time Wasters which takes a look across all industries.
For other industry-specific related research, read our Reducing Engineering Time Wasters reports for:
To received personalized recommendations for how your company could improve engineering productivity, take our 5-minute online assessment.
Engineers Impact Business Success
Automotive companies’ ability to deliver exceptional offerings is critical to success. Likewise, their engineers are crucial to ensuring vehicles, components, and systems have what it takes to succeed in the market. Therefore, empowering engineers is key to the successful execution of business strategies.
Too Many Time Wasters
Unfortunately, engineers report spending too much time on non-value-added work with too many interruptions, taking them away from critical innovation work. Furthermore, 97% of surveyed automotive companies say this loss in engineering productivity comes at a significant business cost due to missed deadlines, higher costs, and less innovation. To overcome productivity losses, one approach is to manage product data and processes better and make it accessible to those who need it, when they need it.
Reclaiming Wasted Time
This report identifies major engineering time wasters in the automotive industry. It explores how companies of all sizes reclaim lost time by examining the use and value of PLM (Product Lifecycle Management) solutions to centralize data, manage processes, and collaborate better. PLM users reported fewer changes due to outdated information and errors, significantly reducing non-value-added work and shortening development times. This report also examines how companies select and use PLM solutions, including cloud-based implementations.
Based on industry experience and research for this report, Tech-Clarity offers the following recommendations to automotive companies:
Why do audits continue to find problems after appropriate root cause analysis (RCA) and corrective and preventative actions (CAPA) processes are complete? MetaFloor AI argues that there is a missing capability to capture and consistently reuse causal data for process intelligence. Their AI-based platform uses a causal graph with AI to not only capture process standards and details of actual events, improvement processes, and outcomes, but also store and leverage them as codified institutional knowledge.
The platform this young company has developed has many capabilities:
It is one thing to have formal processes for customer complaint handling, return materials authorization, and process non-conformance; that’s common. It is rare to be confident that the desired outcomes are complete. It is nearly unheard of to achieve that level of certainty in a rapid, automated fashion. This is where a causal graph and very precise process definitions come into play.
MetaFloor AI founders argue: “Standards-bound operational work is not fundamentally a document problem and not merely a generic workflow problem. It is a problem of preserving valid causal structure under uncertainty, coordination cost, and partial visibility. I don’t have the space to explain it all here, but the founders’ vision and fully documented formulas for typed causal governance are convincing. With this approach, it becomes clear when a case can safely be closed or when there are other dependencies to resolve.
While every manufacturer might face these issues, MetaFloor AI’s initial customers are in the electronics industry. The company is also focused on aerospace & defense (A&D), automotive, and medical devices. These industries have both industry regulations and standards, as well as company-specific standards for production processes. MetaFloor AI’s custom workflows are designed for each industry, and they anticipate an 80% fit once they have created the workflow for a few customers in that industry and the model is trained.
Users tend to be process engineers, quality engineers, operational excellence, or continuous improvement (CI) professionals. These are typically the employees tasked with day-to-day incident management. The AI can pull together knowledge and past experience from all these people and projects, identifying where a current situation might be similar to past events.
The MetaFloor AI platform is designed to be self-serve. A customer can sign up and without consulting or integration up front, start with an incident to record. The system will prompt about that event and also request previous RCA reports. Building the knowledge base happens incrementally with each problem the team brings to the system, providing an always value-adding, low-friction adoption path.
To encourage use, MetaFloor AI has bundled everything into a monthly price. For $499/month, a company can get a manager seat with all three system layers and 100 events per month. The second user is free to further encourage companies to learn and spur greater use and compliance easily and quickly.
The company was founded in the fall of 2025 and is just getting off the ground. However, the idea took hold earlier. The founding team spent months refining the thesis and validating the problem with operators in regulated manufacturing before MetaFloor AI was officially launched. The founders each bring particular strengths to this venture. This is the third startup CEO and commercial leader Anup Mehta has founded; previous ones include DeepEdge and Clarice Technologies. Sridhar Perepa is COO and has worked in engineering across the electronics, life sciences, transportation, and aeronautics sectors. Arun CS Kumar is Head of AI and Product, and also heads AI for DeepEdge; he has a PhD in AI/computer vision and a background in perception engineering for autonomous driving.
Closing the loop to ensure process improvements take place is not easy, but this AI- and industry-based approach holds great promise. The platform’s deep capabilities, combined with industry focus, bundled, cost-effective pricing, and NL interaction, bode well for growing adoption.
Thank you, Anup, Arun, and Sridhar, for briefing me on your breakthrough concept and sharing your white paper on Typed Causal Governance. I look forward to following MetaFloor AI’s progress in the market!
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