Manufacturing Happy Hour · 21 Nov 2025 · 24m
Aaron Lober, VP of Marketing at CADDi, discusses how manufacturers can realistically prepare for AI implementation, emphasizing that companies should start by identifying specific use cases rather than chasing trends. He explains that CADDi’s platform uses AI to extract data from legacy 2D drawings and microfiche, allowing for pattern recognition in procurement, engineering, and supply chain visibility. Lober highlights that manufacturers no longer need to manually structure their data before adopting AI, as the technology can now prep the data for them, though engaging internal stakeholders to alleviate job security fears remains critical.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
DesignCADDi
CADDi uses AI to help engineers standardize design processes and consolidate fasteners to reduce design time and avoid duplication.
“companies like Subaru are using us in their design engineering teams to help them standardize design processes. … find anywhere where we can sort of design, uh, more efficiently, reduce design time if possible.”
Speaker 1 · Hear it at 5:20
Sourcing & supply chainCADDi
CADDi uses AI to analyze supplier history, price, and quality to recommend the optimal supplier for procurement, optimizing the ordering process.
“It going to be much easier for an algorithm or an autonomous system that's supporting the person doing the work to say, this is the right company for us to work with now based on their price, based on the history of quality, the lead time.”
Speaker 1 · Hear it at 5:20
Sourcing & supply chainCADDi
CADDi uses AI to identify product exposure to specific materials like aluminum from Southeast Asia across a complex supply chain for regulatory reporting.
“may be part of the name. Who knows? Yeah. You know, they're using us to help identify which of their products has exposure to aluminum from Southeast Asia across their supply chain, right? And so that's a really difficult thing for them to see at the surface level.”
Speaker 1 · Hear it at 5:20
Engineering & simulationCADDidone in less than 24 hours
CADDi uses trained AI and machine learning algorithms to reliably extract shape, text, and geometry from 2D drawings and microfiche to digitize and interlink data.
“we have trained AI and machine learning algorithms on 100s of 1000000s of data points to reliably analyze and extract every piece of shape, text, and geometry from every drawing in your database, whether that's on microfiche, or it's a nicely produced PDF.”
Speaker 1 · Hear it at 14:53
companies scaling up
Other shows on the same ground, closest first.
Yushiro Kato, Founder & CEO of CADDi, explains how his company addresses the loss of institutional knowledge as baby boomers retire from manufacturing. He details CADDi's two core services: a platform for matching buyers with suppliers for custom parts, and CADDiDraw, an AI-powered drawing management platform that digitizes analog engineering data. Kato argues that digitizing drawings and procurement data improves efficiency, security, and cost negotiation by making tribal knowledge accessible to new employees. He advises manufacturers to start small by digitizing their most critical asset—their drawings—to preserve decision-making records and avoid reinventing the wheel.
Peter Gutschi, a consultant supporting companies like ESCATEC and startups, explains how AI is moving beyond hype to drive real efficiency in manufacturing, from shop floor quality management to supply chain planning. He highlights specific technologies such as Luminovo's platform for calculating CO2 footprints and Cybord's component inspection tools, which handle data volumes impossible for humans to manage in real time. Gutschi argues that this enhanced visibility allows EMS providers to accurately commit to delivery dates and prevent the inventory overhangs seen in past crises like the 2000 tech wreck.
Timon Ruban, Co-Founder of Luminovo, discusses the evolution of AI in electronics manufacturing, contrasting early hype with current practical applications enabled by large language models. He explains that successful implementation requires balancing comprehensive data context with specialized tools, such as part search APIs, to verify AI proposals. Ruban highlights Luminovo's focus on extracting structured data from unstructured sources like PDFs and emails, and developing 'agentic workflows' where AI assists human decision-making in supply chain management rather than replacing it.
Alex Sandoval, CEO and co-founder of Allie Systems, explains the distinctions between machine learning, deep learning, and generative AI, and how these technologies apply to manufacturing operations. He details Allie Systems' approach of connecting PLCs and sensors to predict downtime and optimize processes, while also addressing the cultural resistance to AI adoption among traditional manufacturing leaders. Sandoval advises companies to start by forming a transformation squad, securing historical data from machines, and partnering with technology providers rather than building solutions in-house.
Oscar Lovera, COO at Xometry, explains how the AI-powered manufacturing marketplace is digitizing the sourcing of custom parts by integrating design, quoting, and manufacturability feedback into a single workflow. He discusses the shift from traditional, disconnected RFQ processes to instant, scalable matching between engineers and a global network of manufacturers, including CNC, 3D printing, and injection molding partners. Lovera emphasizes that while AI automates repetitive tasks, human expertise remains critical for complex problem-solving, trust-building, and ensuring operational excellence, arguing that strong processes matter more than flashy equipment. He also shares his perspective on supply chain resilience, advocating for global flexibility over local sourcing and highlighting the importance of intentional career choices for future engineers.
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