FoDES - Future of Design & Engineering Software

We talk with John Harrington, co-founder of HighByte, about why factory-floor data stays invisible to the teams who need it most and how Industrial DataOps closes that gap. We explore contextualized data pipelines, the post-IoT architecture shift toward cloud data platforms, and why AI agents will force a new level of data quality and governance. • Moving beyond “throw it over the wall” design and giving engineers real manufacturing feedback loops • Defining Industrial DataOps and why context makes raw OT data usable • Handling messy realities across MES, ERP, historians, inspection systems, files, and streaming telemetry • Avoiding data swamps by standardizing, governing, and observing data pipelines at scale • Using no-code tooling to build and maintain pipelines without relying on programmers • Filtering and sampling data based on use case, frequency needs, and event triggers • Preparing for AI agents as massive new consumers of shop floor data • Realistic talk on AI and jobs, focusing on better work through better signal detection 

We talk with Juan Carlos Santamaria about how AI in engineering has evolved from rule-based robotics to modern systems that perceive job sites and help machines make better decisions. We dig into Trimble’s push from AI perception to operator assist and what it will take for engineers and operators to trust AI in the field and in design tools. • Juan Carlos’s PhD-era view of AI as a multidisciplinary field • Planning versus reactive robotics and why brittle plans fail • AI for perception on construction sites using point cloud images and video • Turning recognition into jobsite semantics like cycles and bucket loads • The shift toward decision making with operator assist in the cab • Autonomy in mining versus the realities of safety policy and adoption • Why trust builds faster when operators can experience the system • Zero tolerance expectations for machines compared with human error • Point cloud segmentation today and what engineers want next • Natural language interfaces that execute software commands from prompts • SketchUp and 3D Warehouse visual search and AI-assisted edits • Interoperability across tools and the “Tower of Babel” problem • Planning for unknown unknowns when digging into existing infrastructure • How AI work gets prioritized across product teams • Why kids and professionals should learn AI as a tool for thinking clearly 

We talk with OpenSpace CTO Michael Fleischman about turning job-site photos into spatial data that teams can actually act on, from 360 degree capture to progress tracking and AI agents. We dig into why construction software adoption is so hard, and what changes when your phone can be used to create higher-quality data and automation. • Michael’s path from philosophy and psychology to computational linguistics and AI • Meeting OpenSpace co-founders at MIT Media Lab and pivoting into construction • Reality capture as the foundation and why “agents need eyes” • Cameras vs LiDAR on phones and why photos solve most field needs • AI autolocation as indoor GPS without beacons • Lowering friction as the real key to construction tech adoption • Progress tracking against BIM models or 2D drawings and closing the loop with reality • Disperse acquisition and why construction-specific spatial understanding matters • Flagging potential issues with Spotlight and keeping humans in control • Letting customers build customized agents for safety, QA, and workflows 

Matt McElvogue, VP at Teague, talks about Teague's human-centered design. We explore how building early aligns design, engineering, and business, and why full-scale prototypes beat slide decks. From accessible aircraft cabins to friendlier autonomous shuttles, we show how human-centered design meets hard constraints while accounting for many factors, such as aesthetics and functionality, that design engineers may not consider.• Teague’s “thinking through making” philosophy across aerospace, automotive and defense• Tooling choices from Rhino and SolisWorks to CATIA for aerospace rigor• Full-scale cabin mockups and high-fidelity showpieces that survive travel• Aligning desirability with feasibility in regulated environments• Accessibility in aviation using sensors and smart wayfinding• Factory-floor innovation with a microphone-based wire seating tool• Where Teague complements in‑house prototype shops• Autonomy, safety, public trust and external vehicle communication• A friendly, bidirectional autonomous school shuttle concept• Faster visualization, humanoid robots and emergent AI-driven design

We talk with Makistry founder Parth Meta about turning plain English prompts into parametric CAD and why a structured AI “brainstorm” can speed design without losing engineering control. We dig into standards-aware reasoning, exports, limitations, and the roadmap for assemblies and 2D-to-3D.• Why CAD still slows real projects• Text to parametric models through a guided brainstorm• Using RAG to ground standards like M4 and hole specs• Open Cascade kernel, STEP exports, in-browser visualization• Measuring and parameter checks for trust and repeatability• Limits on complexity and plans for sketches to 3D• Assemblies, mates, and constraints on the roadmap• Moving beyond copilots to AI-native design and CAM• Balancing startup work with school and sport• Making geometry organic yet manufacturable

We trace how focused simulation wins over all-in-one platforms, then try to find out more about the design of an Olympic helmet  — with no luck. Rand Simulation experts use LS-DYNA and validate the results to cut risk and time. We close with a frank take on what AI can and cannot do for complex physics and where humans must stay in the loop.ANSYS ability to simulate crash behaviour, fragmentationGPU solvers move design feedback to secondsHow designers and analysts collaborate without overlapServices, training, and keeping skills sharpAerodynamic shell vs impact liner complexityLS-DYNA for explicit dynamics and complex foamsDOEs to find worst cases across speed and temperature3D-printed lattices promise and current limitsSafety trade-offs comfort, mass, and adoptionWhere AI assists setup vs where experts decide

Amit Shastri, CTO Americas at Digitate, explains how composite AI moves operations from reactive firefighting to predictive and autonomous action, without sidelining human judgment or ripping out trusted systems. Digitate’s approach blends logical reasoning, LLMs, and guardrails to deliver unified observability across IT, OT, and business processes.• Regional CTO role bridging customers and product• Autonomous and ticketless operations as the North Star• Predictive alerts that prevent downtime• Horizontal observability across procure to pay• Integrations with ITSM, monitoring, and CMDB• Composite AI with logic, LLMs, and human approvals• Action firewalls and role-based controls for safety• Job shifts from doers to exception handlers• Build vs buy tradeoffs and enterprise scale• Natural language interfaces over complex tools• Leveraging legacy systems for rich operational data• AI’s global landscape and India’s momentum

We talk with Lukas Fraser, VP of Product at Looq AI, about a camera-first platform that delivers survey-grade 3D models and automates utility workflows. We cover hardware design, accuracy claims, pole and cable analysis (for power lines), and why controlled capture makes photogrammetry competitive with LiDAR.• Controlled handheld capture with four synchronized lenses and GNSS• Calibrated hardware and encrypted storage with cloud processing• Point clouds, panos, and ground-view outputs for engineers• One-centimeter relative measurements for pole loading analysis• Image-based detection projected to 3D for faster extraction• Complementary roles of photogrammetry and LiDAR• Subscription pricing with hardware, processing, and updates• Integrations with existing survey and utility software• Change detection to update models without resurveying

We talk to Tudor Vasiliu, founder and director of Panoptikon, about how architects use AI to elevate visualization without losing control, unpacking “AI passes,” professional guardrails, and why speed still needs expertise. A live demo shows rapid mood and lighting iteration, while we call for better client tools and more usable AEC software.• AI passes that enhance materials, lighting, foliage, and people• Why expert workflows beat one‑prompt myths• Accuracy and ethics in public review visuals• Faster iteration with in‑house tools and cloud models• Video enhancements from stills and CG using diffusion• Client expectations for immersive, truthful previews• Bridging 2D habits to BIM and lightweight planning• Startups’ agility vs software giants’ slow pace• Natural interfaces as the next AEC breakthrough• Guardrails to keep framing, dimensions, and intent intact

We talk with CTO and co-founder Theopile Allard about Neural Concept’s AI copilot for engineering and how option-driven workflows change speed, creativity, and trust in simulation-heavy design. We explore physics prediction, geometry generation, LLM agents, and how legacy solvers stay central.• AI copilot that creates and evaluates many variants• Physics predictive engine for CFD, structures, EM• Geometry generation to expand design spaces• LLM agents linking rules, docs, and constraints• Solver integration and uncertainty triggers• Fluids as a high-impact domain for discovery• Real-world examples including quieter impellers• Iterative, interactive design with plain English• Training, boot camps, and community kit• Scaling through PLM and enterprise systems• Human-first approach to protect jobs and skills

We talk with Arjun and Kanal Jain, co-founders of Tandem about building an AI knowledge layer that captures design decisions, links requirements to CAD, and helps engineers spend more time designing. We compare text-to-CAD promises to enterprise reality, dig into traceability and DFM, and explore how integrations unlock better simulation.• Capturing design intent across CAD, PDM, PLM• Linking requirements, tests, and design changes• Closing the manufacturing feedback loop• Reducing rework and documentation overhead• Enabling simulation through shared context• Funding path, pilots, and early customers• Differentiation from text-to-CAD and new CAD software• Partner strategy with incumbents and AI tools

We explore how complex electrical systems can be designed faster and with more confidence by combining an intuitive canvas with deep electrical intelligence and pragmatic AI. Anthony Samuel shares lessons from aerospace startups, Y Combinator and competing with incumbents while staying focused on validation and usability.• Seed funding from YC, Floodgate, Boost VC, enabling hiring and product build• Why New York and other hubs matter for advanced hardware• Startups versus incumbents framed as validation of the market• Respect for safety: AI as assistant, not replacement• Common failure modes in harness design and integration• Artifact’s core: collaborative system schematics to manufacturing outputs• Visio-like ease with domain intelligence under the hood• Parts libraries, BOM accuracy, and rules checking roadmap• Concept-to-detail design flow and future integrations• De-risking: technical execution and market fit through real users

We explore how GIS connects BIM, CAD, and reality capture into usable context for design, construction, and operations. Gaussian splatting takes center stage as Patrick Wallace explains how it preserves fine detail and enables point clouds, meshes, and object detection at scale.• Indoors product ingesting DWG to power floor-aware campus maps• Difference between authoring tools and GIS as the system of context• Drones, LIDAR, photogrammetry workflows for operational models• Weekly drone flights for 4D construction review and issue forensics• Gaussian splatting fundamentals, benefits, and capture best practice• Filling gaps with open imagery and managing artifacts• Building point clouds and meshes from images and video• Cloud-first processing, streaming data, and single source of truth• AI deep learning models for solar panel and asset detection• Living Atlas curation, partner data, and scalable access

Tassos Hadjicocolis, CEO of Phenometry, and Stephanos Androutsellis-Theotokis, CTO and founder, give a detailed demo of Phi, a browser-based modeler that makes organic shapes quickly and precisely, then sends results to Onshape as clean NURBS for downstream CAD operations. • Running Phi inside or alongside Onshape• Direct push–pull of vertices, edges, faces• Curvature combs and smoothen for fairing• Precise move, rotate, and scale with inputs• Image‑based modeling and pop‑out extrusions• Exporting to STEP with tight NURBS tolerance• Snapping to Onshape faces and edges• Dissolve to replace face groups with a single patch• Mirror vs symmetry for flexible constraints• STL attachment and flatten for surface control• Limits of round‑trip import and current workarounds• Roadmap toward G2/G3 continuity and shape optimization

We dive into why CAD feels hard and how natural-language automation can remove friction without forcing a platform switch. Co-founder and CEO Owen Dourneau explains MecAgent’s approach to compiling plain English into SolidWorks actions, the limits of file translation, and where AI can truly help engineers.• Pain points with feature trees and steep learning curves• Why building on top of SolidWorks beats starting from scratch• LLMs as a CAD compiler for dependable automation• Examples like DXF export and sheet metal unfolding• Simplicity over code editors to reach non‑programmers• Risks from incumbents and startup speed advantage• Drawings automation tradeoffs and scope• Library, remixing, and sharing of automations• Roadmap toward more autonomous CAD agents• Auxiliary tools for engineering Q&A and part finding• Partner discussions and realistic adoption paths

We talk with Mastercam CEO Russ Bukowski about how AI, vertical integration are reshaping CAM. From voice‑enabled Copilot to reseller acquisitions under Sandvik, Russ lays out a roadmap for faster programming, safer code, and a tighter art‑to‑part thread.• Modernizing Mastercam’s UX and onboarding the next generation• Vertical integration of sales, service, and support to get closer to customers• Sandvik acquisition and the digital thread from design to inspection• Copilot as an action layer: voice commands, automation, and scripts• Leveraging tooling data for safer feeds and speeds across materials• Enabling one‑off and small‑batch work with generative programming• Partner tools for faster quoting and job breakdowns• Elevating experts while reducing time‑to‑competency for novices• Competitive stance on AI leadership in CAM

SPONSORED EPISODEShiva Dhawan, CEO & Co-Founder at Attentive.ai shares his journey from mechanical engineering to building an AI-based takeoff software for construction, and explains why manual takeoffs from PDFs remain one of the biggest bottlenecks in bidding workflows. The conversation dives into how AI can read construction drawings, the role of human verification in ensuring accuracy, and why PDFs, not BIM files, still dominate the bid phase in North America.The discussion also covers:What construction takeoffs are and why they’re foundational to estimatingHow AI interprets drawings, symbols, and schedules from PDFsThe challenges of inconsistent symbols and design standardsWhy human-in-the-loop QA is critical for reliable AI takeoffsScaling from 50/50 human–AI effort toward a 90/10 modelWhy automation is essential as estimating labor remains scarceWhat’s next for AI in preconstruction, including estimates and bid filteringThis episode is especially relevant for estimators, preconstruction managers, contractors, and engineers interested in how AI can increase bid capacity without sacrificing accuracy.For more about Sponsored Episodes, see our Sponsorship section. 

We explore how 3D utility-centric design on top of AutoCAD speeds grid and fiber projects by connecting CAD, GIS, and SAP  by enforcing standards that prevent costly errors with Al Eliasen of SBS. We also dig into pragmatic AI uses that shorten proposals and enhance UX without risking safety.• Origins of SBS and Autodesk utility heritage• Competition with Bentley; partnership with Esri• GIS as source of truth for underground design• 2D input with 3D models for true digital twins• Intelligent design rules for safety and standards• Training designers fast with drag-and-drop workflows• Pragmatic AI for planning, help, and materials selection• Substation design in 3D with embedded logic• Fiber design acceleration and strand peel-off handling• Security and reliability realities for modern grids

We sit down with Michael Bogomolny, Ph.D. of InfinitFORM, which has blasted through the hype of topology optimization with a deterministic, GPU-accelerated engine that creates parts that are both optimized and manufacturable. The result: shorter design cycles because prismatic parts are ready for machining. In this podcast, Michael talks about:• Funding update and market momentum• Why mesh-based generative design fails machining• Manufacturable, parametric outputs with feature trees• AI as assistant for setup, critique and reports• GPU solvers for fast, deterministic results• Cloud and on‑prem options for regulated teams• SolidWorks add‑in and CAD‑native export• Roadmap for injection molding and die casting• Beta learnings and January release timing• Trust, IP strategy and integration into workflows

We sit down with Uzair Sayid, founder of NextCad AI, to explore how automated drawing checks cut busywork, reduce errors, and capture expert standards without slowing design. The conversation tracks his journey from frustrated mechanical engineer to building an on-prem tool that blends rules with AI to expertly check engineering drawings.• Origin story rooted in wasted time on documentation• Local, secure checker for PDFs and native CAD• Standards and company rules embedded in a knowledge graph• Detection of hidden dimensions, missing depths, and BOM issues• CAD integrations with Inventor and SolidWorks for deeper checks• Early drawing creation features and auto-tolerance suggestions• Roadmap toward model-based definition and 3D-first checks• Pricing is for setup plus subscription• Bootstrapped traction, government support• Long-term vision of CAD-less, decision-first engineering with fast simulation

We explore how an AI copilot layers on top of HFSS to automate RF simulations without losing rigor or control. Gustavo Navarro shares a live demo, a practical roadmap from post‑processing to pre‑processing, and a vision for cross‑domain orchestration across trusted solvers.• Why HFSS expertise is hard but essential• Natural language to HFSS automation without hiding code• Generating S‑parameters, 3D patterns, and full reports• Interactive agents that ask for missing setup details• Orchestrating sweeps and long runs with monitoring• Using ML for fast screening, solvers for validation• Pre‑processing: geometry creation and defeaturing• Moving RF designs across tools for platform studies• Roadmap: a future with thermal and structural workflows?

We explore how DraftAId automates mechanical drawings from 3D models, why focus beats hype in CAD AI, and how human-in-the-loop design keeps engineers in control. A live demo shows associative drawings in Inventor, and we preview cost estimation and an upcoming open version.• Drafting automation for mechanical fabrication• Lessons from YC to 175k+ drawings generated• AI hype versus real manufacturing workflows• Human-in-the-loop interactivity and PMI implementation• Datum strategy, tolerances, and communication intent• Integrations with major CAD and vault systems• Competitive landscape and why focus matters• Preview of cost estimation for North America• Shift from enterprise-only to open access

We sit down with Brad Rothenberg of nTop to explore how implicit modeling and signed distance fields make computer models that are robust, physics-aware and ready for fast iteration. From aircraft wings to heat exchangers and turbine cooling, we show how fields, splines, and optimization unlock design spaces that B-reps can’t handle.• Why B‑rep models fail under topology changes • How signed distance fields encode geometry and space • Spline-driven aircraft surfaces and robust lofts • Custom blocks for reusable parametric assemblies • Duct and inlet optimization tied to flow targets • Integrated CFD and meshless solver connections • Heat exchangers for 3D printing and AI surrogates • Turbine blade cooling strategies and manufacturability limits • FEA, topology optimization, and nTop Connect SDK • Design sprints that compress vehicle-level development

Shape3D demonstrated how CAD can feel as natural as drawing with a pencil on paper, while still handling complex product design with an iPad and the Apple Pencil. Shapr3D’s founder, István Csanády, has taken the company from its Apple roots to Windows, and by doing so is starting to be recognized by the market. Join as István discusses:• Pain points with legacy CAD and steep learning curves• Origins of Shapr3D and mission to simplify serious design• GDP-scale impact from productivity gains in manufacturing• Building a global, multicultural team from Budapest• Apple partnership and a single app across iPad, Mac, Windows, Vision Pro• Offline-first performance, local compute, cloud sync and versioning• Comparison to cloud CAD and limits of browser-scale assemblies• Current strengths and acknowledged gaps in drawings and large assemblies• Beyond B-rep ambition for robust Booleans, fillets and shells• Practical AI for visualization and auto-generated drawings• Enterprise traction, shop-floor use, and secure environments

"Why do hardware teams still rely on brittle slides and spreadsheets?" asks Mai Bui, cofounder of Quarter20. "A CAD‑connected wiki can turn documentation into living, executable knowledge."Quarter20 shows auto-updating work instructions, technician analytics, and a cloud workflow that links design to the shop floor.• The boiling frog of legacy CAD tooling and manual documentation• Quarter20’s origin story from real manufacturing pain• A CAD-connected wiki as a single source of truth• Replacing PowerPoint and Word with live documentation• Linking hardware tools like software’s integrated stack• Customer use cases in robotics, agtech, and medical• Demo of auto-updating images, BOMs, and part tagging• Cloud access without CAD laptops and large model performance• Technician mode, analytics, and feedback loops• Pricing by team with editors and technicians"Mention 'Roopinder' and I’ll give you a discount because I’m excited to promote more conversations for innovation and design," says Mai. 

Maorr Farid, co-founder and CEO of Leo AI, shares how his company is revolutionizing mechanical engineering by creating the first AI that truly understands CAD. His mission is to transform engineers from "glorified secretaries" back into creative innovators by eliminating the tedious 85% of time they spend not actually designing.• Former Unit 8200 intelligence officer with a PhD from Technion at an unprecedented young age• Founded Leo AI after discovering engineers spend only 15% of their time moving the mouse in CAD software• Already reached 55,000+ users with zero marketing budget, including major clients like HP, Intel, and Scania• Leo AI focuses on augmenting engineers rather than replacing them• Customer testimonials include completing designs in 1.5 hours that previously took three engineers three weeks• Leo connects to PLMs, Windows directories, and CAD files to leverage organizational knowledge• Engineers maintain control of decision-making while AI handles information retrieval• Leo is the only AI system specifically built to understand CAD geometry, not just text• 80% of elite engineering students leave the profession due to the gap between expectations and reality• Join the Mechanical Intelligence community - the first global community for AI in mechanical engineering