Engineering’s Spatial AI Moment - Campfire & Gravity Sketch

When AI Meets Sales, Support & Supply Chain: Omnae & Bardin AI

AI in manufacturing does not fail because the demo is bad. It fails when the answer cannot be trusted. In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Fay Goldstein, Co-Founder and CEO of Bardin AI, and Scott Lionello, Co-Founder and CPO of Omnae Technologies, about where industrial AI is really going: beyond chatbots, beyond copilots, and into the operational workflows that actually run manufacturing businesses. Bardin AI is building an application engineer for industrial automation sales and support teams, helping them answer complex technical questions without escalating everything to senior engineers. Omnae is building supply chain collaboration software that allows AI agents to operate safely across real suppliers, buyers, orders, invoices, and messy enterprise data.   The conversation goes straight into the hard parts of industrial AI: trust, auditability, determinism, human-in-the-loop workflows, knowledge graphs, API costs, token burn, procurement risk, sales engineering bottlenecks, and why “just add a chatbot” is not enough when mistakes touch contracts, general ledgers, supply commitments, or customer trust. Fay and Scott also discuss how AI is changing startup operations and software development, why young professionals need to show AI fluency rather than fear AI replacement, and why mid-market manufacturers may adopt practical AI faster than large enterprises waiting for top-down transformation programs. The big takeaway: the next wave of industrial AI will not be about flashy demos. It will be about operational relief. Fewer escalations. Faster quoting. Cleaner supplier collaboration. Better support workflows. Safer automation. More trust in the decisions AI helps make. This is a grounded, founder-level conversation about how AI is moving into the less glamorous but highly valuable parts of the product lifecycle: sales, support, procurement, supply chain, and the industrial back office. Topics covered: industrial AI, agentic AI, supply chain AI, procurement, pre-sales engineering, industrial automation, knowledge graphs, AI trust, human-in-the-loop workflows, manufacturing software, digital transformation, enterprise AI, startup innovation, and the future of AI across the product lifecycle.

CAD, BIM, and the AI Leap: Qonic & Raven!

What happens when AI moves beyond chatbots and starts reshaping the actual tools engineers, architects, and designers use every day? In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Chloë Guidi of Qonic and Moritz Rietschel of Raven about the AI-native future of CAD, BIM, and AEC workflows. Qonic is building a modern, cloud-based BIM platform from scratch, including its own solid modeling kernel, with a mission to make BIM lighter, faster, more accessible, and more data-rich. Raven is building AI-first workflows for complex design environments like Rhino, Grasshopper, Revit, Tekla, and Archicad, helping users navigate fragmented toolchains with less friction. The conversation cuts through the hype and focuses on what is actually changing: AI-assisted software development. AI-native design workflows. Smarter BIM quality checks. More accessible CAD and AEC tools. The economics of LLM-powered software. The difference between “software built with AI” and “software that only makes sense because AI exists.” Chloë and Moritz also discuss whether engineering and BIM are heading toward their own “OpenAI moment,” why open standards and data quality matter, and what young engineers should do as AI changes the skills required to stay relevant. This is a practical, founder-level look at how AI is moving into the real workflows of design, modeling, validation, and engineering decision-making. Topics covered: AI in CAD, AI in BIM, AEC software, digital twins, Rhino, Grasshopper, Revit, engineering workflows, AI coding, MCP, open standards, startup innovation, and the future of AI-native engineering tools.

Engineering’s Spatial AI Moment - Campfire & Gravity Sketch

What happens when AI, virtual reality, and spatial computing move beyond demos and start reshaping real engineering work? In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Jay Wright, Co-Founder and CEO of Campfire, and Oluwaseyi “Shay” Sosanya, Co-Founder and CEO of Gravity Sketch, about the future of immersive engineering workflows. This is not a “metaverse” conversation. It is about what spatial tools can actually do for product development, design reviews, manufacturing validation, training, collaboration, and digital transformation. Jay explains why AI is becoming a first-class user inside Campfire, acting almost like another participant in a 3D workspace. Shay breaks down why Gravity Sketch keeps humans at the center of the design process while using AI to remove friction, speed iteration, and help teams communicate better. The conversation covers the hard parts too: why LLMs still struggle with geometry, why industrial companies remain cautious about cloud and AI adoption, why employees are already using AI tools outside official policy, and why the next breakthrough in engineering may not be AI replacing CAD, but AI controlling and accelerating the tools engineers already use. For anyone working in CAD, PLM, industrial AI, digital thread, manufacturing, design, or engineering software, this is a sharp look at where spatial computing is actually useful and where the hype still needs to become workflow value. Featuring: Jay Wright, Co-Founder & CEO, Campfire Oluwaseyi “Shay” Sosanya, Co-Founder & CEO, Gravity Sketch Host: Michael Finocchiaro, AI Across the Product Lifecycle Transcript source:   TIMELINE 00:00 Welcome and guest introductions 03:05 Jay Wright on being bullish about AI after ChatGPT 04:33 Shay Sosanya on cautious optimism and the speed of AI progress 07:06 Why 3D geometry is harder for AI than language 08:42 AI capabilities are moving faster than expected 10:07 How Gravity Sketch adopted AI in software development 12:27 Campfire’s AI-assisted development workflow 13:32 AI agents in meetings, code, and product workflows 16:11 Using AI with existing 3D assets, BOMs, documents, and legacy data 18:26 Campfire’s spatial workflows for engineering, training, and sales 20:02 Where AI sits in the software stack 20:28 Campfire’s spatial agent as a first-class user 21:46 Gravity Sketch’s human-first approach to AI in spatial design 23:36 Foundation models, 3D generation, and geometry engines 25:29 AI cost, IP protection, customer data, and bring-your-own-LLM models 28:00 Has engineering had its ChatGPT moment yet? 29:05 Why physical product development will see staged AI adoption 31:17 The engineering-to-manufacturing gap 32:13 Simulating manufacturing workflows before production 34:12 AI connectors, Blender, Fusion 360, and tool control 35:18 Advice for young engineers worried about AI 39:41 Making real products, not just AI-generated concepts 40:00 Digital maturity in industrial companies 41:21 Why many manufacturers remain at low digital maturity 42:31 Headsets, cloud, InfoSec, and adoption barriers 43:39 Employees are already using AI and immersive tools informally 46:57 Can agile startups move industrial customers faster than incumbents? 48:17 Campfire on solving workflows rather than selling AI novelty 50:29 Gravity Sketch on value, workflow depth, and avoiding AI hype 53:09 Where to see Campfire and Gravity Sketch next 56:12 Closing thoughts

Physics has a ChatGPT Moment - Vinci 4D Special Edition!

Riverside Event Title Physics Has a ChatGPT Moment: AI, Simulation, and the Future of Engineering What happens when AI stops guessing and starts solving physics? In this episode of AI Across The Product Lifecycle, I’m joined by Hardik Kabaria, co-founder and CFO of Vinci, and Andy Fine of the Fine Physics Consortium, for a sharp discussion on one of the biggest shifts in engineering software: AI-native physics simulation. Vinci is building a physics intelligence layer: a foundation model for physics designed to answer real engineering questions around heat transfer, thermo-mechanical deformation, high-fidelity simulation, and manufacturing-resolution analysis. Hardik says Vinci is already deployed with tier-one hardware companies and can run simulations from hundreds of millions to over a trillion degrees of freedom.   This is not vague AI hype. We dig into what makes AI simulation credible, why deterministic physics matters, how engineers can validate results, and why thermal problems are becoming mission-critical across semiconductors, electronics, batteries, EVs, data centers, robotics, and advanced manufacturing. If your product generates heat, deforms under load, consumes power, or depends on simulation to avoid expensive failures, this conversation matters. Timeline 00:00 — Introduction: Vinci, Fine Physics Consortium, and the “OpenAI moment” for simulation 01:11 — What is physics intelligence? 02:18 — Why physics is universal and governed by differential equations 03:08 — Physics-based AI vs. surrogate models 04:01 — What makes a physics foundation model credible? 06:51 — Why business value beats white papers 08:33 — Where Vinci fits in the engineering workflow 10:16 — Heat transfer, fluid dynamics, and choosing the right wedge use case 11:14 — Vinci’s focus: semiconductor and electronics thermal problems 13:23 — Thermo-mechanical deformation and why materials warp 14:49 — Multi-physics simulation as a long-standing engineering holy grail 16:06 — Yield, reliability, and manufacturing risk in electronics 17:04 — ROI: faster design loops and thousands of analyses per day 19:23 — Uncertainty, validation, and trust in AI simulation 20:08 — Training on 45TB of physics simulation data 21:47 — Residual norms and transparency at inference time 24:42 — 300 million to 1.2 trillion degrees of freedom 25:51 — GPU requirements and why Vinci is built for modern hardware 27:09 — Quantum computing, GPUs, and future scalability 30:22 — Wedge use cases: chips, boards, servers, batteries, defense, robotics, steel plants 31:45 — Who buys AI-native simulation software? 33:50 — Why thermal engineers are Vinci’s first target users 35:06 — Power, cooling, throttling, and data center energy constraints 36:25 — What throttling means in chips, EVs, and thermal runaway scenarios 39:58 — Deployment, IP protection, Docker containers, cloud, and on-prem 41:27 — How to convince skeptical engineers 43:00 — Path to adoption: start with the customer’s real benchmark 44:16 — What engineering leaders should do next 45:47 — The physics brick in the AI factory of the future 46:03 — Final debate: can there ever be one general foundation model for all physics? Join us for a practical, skeptical, deeply technical conversation about what AI can actually do for simulation, hardware design, and the next generation of engineering software. #AI #Simulation #EngineeringSoftware #PhysicsAI #DigitalThread #Semiconductors #ThermalEngineering #CAE #ProductDevelopment #AIAcrossTheProductLifecycle #TheFutureOfPLM #BetterCallFino

FoPLM: Introducing Product Memory! w/Special Guests!

Riverside Event Title Product Memory: The Missing Layer Between PLM, Digital Thread, and AI Agents Riverside Event Description Everyone talks about the single source of truth. Then the real product decision happens in a meeting, spreadsheet, email, Teams chat, supplier exchange, or inside someone’s head. In this episode of The Future of PLM, I’m joined by Oleg Shilovitsky of OpenBOM, Rob McAveney CTO of Aras, Brion Carroll of Digital Solution Group, David Segal of TCS, and Jonathan Scott of Razorleaf for a sharp discussion on one of the most important emerging ideas in PLM and enterprise AI: Product Memory. The core question: If digital thread connects the data, what captures the reasoning? PLM manages parts, BOMs, changes, documents, requirements, and workflows. But it often misses the “why” behind decisions: assumptions, rejected options, supplier constraints, manufacturing context, cost tradeoffs, effectivity logic, and informal reasoning. This discussion explores whether Product Memory becomes the next layer above PLM, ERP, MES, QMS, ALM, supplier systems, documents, and collaboration tools: a contextual, semantic, AI-ready memory of how product decisions are made across the enterprise. We cover: Can Product Memory avoid becoming another inconsistent data layer? What should be captured, and what should be filtered out? Why does eBOM-to-mBOM still break so many digital threads? How do semantics and ontology determine whether AI can trust product context? Can AI agents safely recommend or execute PLM changes? How do we capture human decision-making without scaring the humans? Timeline 00:16 — Introduction: single source of truth, broken digital threads, and Product Memory 03:02 — Oleg defines Product Memory beyond single source of truth and digital thread 06:28 — Rob on dependency graphs and hidden context in unstructured documents 08:36 — Brion on Product Memory as an “orb” fed by siloed enterprise systems 11:39 — Jonathan on semantics: why “part” means different things across functions 13:46 — David on Product Memory from an enterprise architecture perspective 18:21 — Avoiding inconsistent data across PLM, ERP, PIM, e-commerce, and supply chain 22:09 — Why engineering-to-manufacturing translation is so hard 25:00 — Why engineering release is not the finish line 30:05 — Missing memory: decisions in people’s heads, spreadsheets, and informal actions 33:57 — Why skipping change steps can slow the enterprise down 35:57 — AI agents, requirements ingestion, and asking “why” like a three-year-old 39:48 — Why AI agents must document their own reasoning 42:49 — Product Memory flywheel: capture, review, flow, and distribution 45:35 — Industrial AI, physical AI, agentic AI, and real-time product memory 48:21 — Semantic consistency, meta layers, and vetting data before Product Memory 52:15 — Dependency graphs, imperfect data, and improving ontology over time 55:12 — Human maturity: is the organization ready? 56:56 — Where companies should start looking for missing Product Memory 1:03:58 — Rob’s call to action: start capturing decision traces now 1:05:03 — Closing: eBOM, mBOM, ISA-95, and semantic translation This is not a theoretical PLM buzzword session. It is a practical debate about architecture, governance, trust, and human maturity before AI agents can operate safely inside the product lifecycle. #PLM #ProductMemory #DigitalThread #AI #AgenticAI #EngineeringSoftware #EnterpriseArchitecture #BOM #MBOM #EBOM #Manufacturing #TheFutureOfPLM #BetterCallFino