SPONSORED EPISODE

Shiva 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 estimating
• How AI interprets drawings, symbols, and schedules from PDFs
• The challenges of inconsistent symbols and design standards
• Why human-in-the-loop QA is critical for reliable AI takeoffs
• Scaling from 50/50 human–AI effort toward a 90/10 model
• Why automation is essential as estimating labor remains scarce
• What’s next for AI in preconstruction, including estimates and bid filtering

This 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