Automation in CAD Modeling

Today’s a big day for the built environment. For as long as I can remember, dating back to architecture school, I recall conversations about automating scan-to-BIM—𝘵𝘩𝘦 𝘩𝘰𝘭𝘺 𝘨𝘳𝘢𝘪𝘭. The holy grail meant this: walk a space with a laser scanner, press a button, and get accurate as-builts in structured formats like BIM and CAD. It's been a decade of noise: inflated promises around BIM, digital twins, and AI in design technology. Disillusionment followed. Most modeling pipelines were either manual, half-baked, or both. 𝗔𝗳𝘁𝗲𝗿 𝘀𝗲𝘃𝗲𝗻 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗺𝗮𝗻𝘂𝗮𝗹 𝗟𝗢𝗗𝟮𝟬𝟬 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴—𝗲𝗹𝗲𝗺𝗲𝗻𝘁 𝗯𝘆 𝗲𝗹𝗲𝗺𝗲𝗻𝘁, 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗯𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁—𝘁𝗼𝗱𝗮𝘆, Integrated Projects 𝗶𝘀 𝗿𝗼𝗹𝗹𝗶𝗻𝗴 𝗼𝘂𝘁 𝗕𝗜𝗠𝗜𝗧 𝗙𝗶𝗿𝘀𝘁 𝗗𝗿𝗮𝗳𝘁 𝘁𝗼 𝗼𝘂𝗿 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗻𝗲𝘁𝘄𝗼𝗿𝗸. It’s v1 of our automated architectural modeling pipeline, modeling in seconds what would normally take us days. All BIMIT projects will now be powered by First Draft. Shifting our focus from BIM producers to product managers of our own engine. To start, we’re using it internally to prove to ourselves that we can deliver on what we already promise customers: • 95% on-time delivery • Fast-tracked as-builts • LOD200+ BIM, CAD, and IFC files But this internal rollout has broader implications for AECO and laser scanning professionals: Soon, you’ll be able to... • Upload a registered point cloud (up to 50GB) • In under 1 hour • Get a multi-level LOD200 Revit model • Including existing architectural elements • And derivative CAD & IFC files • All delivered securely in a SOC2 compliant platform • Enabling accurate floor area verification and material takeoffs Why does this matter? 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝟴𝟬% 𝗼𝗳 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝘄𝗻𝗲𝗿𝘀 𝗹𝗮𝗰𝗸 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗼𝗿 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗮𝘀-𝗯𝘂𝗶𝗹𝘁𝘀. Without them, it’s hard—if not impossible—to improve buildings proactively and at scale. And we don’t have time. Improving the built environment requires better data, delivered faster and more affordably than ever before. That’s why we exist. And it’s still very much day one.

⚡ Free n8n Workflow for CAD-BIM 𝗔𝗨𝗧𝗢𝗠𝗔𝗧𝗘𝗗 𝗘𝗟𝗘𝗠𝗘𝗡𝗧 𝗖𝗟𝗔𝗦𝗦𝗜𝗙𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗪𝗜𝗧𝗛 𝗟𝗟𝗠 & 𝗥𝗔𝗚 (works with Revit/IFC/DWG/DGN) Today, BIM and CAD specialists still spend significant time manually classifying elements, checking attributes, and aligning models with internal standards. This process is becoming more and more like what we did in the past: ▪️ photos were sorted into albums by hand ▪️ forum posts required careful selection of the “right category” ▪️ finding a video meant endless navigation through folders ML and AI have already freed us from that routine — and the same shift is now happening with CAD-BIM projects. LLMs and RAG will gradually take over the tasks of classification and data validation, while specialists evolve from “operators” to process architects. When trained with RAG on your BIM Execution Plan (BEP) or internal classification rules in XLSX or PDF format, the workflow acts as an intelligent auditor that can: ▪️ detect classification errors ▪️ highlight deviations from corporate naming standards ▪️ suggest corrections based on accumulated knowledge 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗻𝟴𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘄𝗼𝗿𝗸𝘀 1️⃣ Conversion — Revit / IFC / DWG / DGN → Open Database 2️⃣ Extraction — clean headers & select grouping parameter 3️⃣ Grouping — count elements & volumes 4️⃣ LLM + RAG — automatic classification by codes & standards 5️⃣ Reporting — Excel & HTML with summaries and charts Processing 1,000 elements with GPT-4.1-mini costs only a few cents. For maximum accuracy, I recommend the most effective models - Opus 4, Grok 4, Gemini 2.5 and ChatGPT5. 𝗣𝗮𝗿𝗮𝗱𝗶𝗴𝗺 𝘀𝗵𝗶𝗳𝘁 We’re moving from manual quality control to probability management. BIM professionals will increasingly act as conductors: defining rules, training models on corporate data, and making decisions in edge cases. This isn’t about replacing expertise - it’s about amplifying it: less routine, more analysis and decision-making. Explore the workflow: 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/eJyaySSR 📄 File: n8n_5_CAD_BIM_Automatic_Classification_with_LLM_and_RAG.json Examples of project classifications according to different classifiers will be posted later in our telegram group (links in messages). Distributed small solutions, simple 𝗻𝟴𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗰𝗿𝗲𝗮𝘁𝗲 "𝗮𝗻𝘁𝗶𝗳𝗿𝗮𝗴𝗶𝗹𝗶𝘁𝘆" for business - which is of course extremely boring in calm times, but extremely vital during crises. 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝗮𝗻𝘁𝗶𝗳𝗿𝗮𝗴𝗶𝗹𝗲 𝗱𝘂𝗿𝗶𝗻𝗴 𝗽𝗲𝗿𝗶𝗼𝗱𝘀 𝗼𝗳 𝘁𝘂𝗿𝗯𝘂𝗹𝗲𝗻𝗰𝗲 - 𝗹𝗲𝗮𝘃𝗲 𝘁𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁. 👉 If you need help testing n8n solutions with RAG and LLM on your data or adapting the workflow to real project tasks, contact us. ♻️ Share this with colleagues who still believe that manual checking and classification is “normal work.” In reality, these tasks can already be automated with LLMs and n8n workflows.

GenCAD - Turning Images into Editable 3D Designs Creating CAD models is still slow, manual, and often frustrating - especially when dealing with complex geometries. That’s why a team at MIT developed GenCAD, a new AI-powered system that generates parametric, editable CAD models directly from images. 👉 Instead of working with meshes or point clouds (which are hard to edit), GenCAD focuses on real-world engineering needs: - Modifiability - Manufacturability - Cross-modal generation (image → CAD) 🔍 How it works: GenCAD combines: - Autoregressive transformers (to model CAD command sequences) - Contrastive learning (to align images with CAD representations) - Latent diffusion (for high-quality generation) 📄 Paper: https://lnkd.in/eahBwEfC 🔗 Website: https://gencad.github.io/ 💻 Code: https://lnkd.in/eJgrNBqs

Today, Backflip AI is unveiling a new Foundation model that can build precise, engineering parts in existing 3D design packages. This breakthrough will dramatically accelerate the pace of hardware development and drive down the cost of manufacturing. Our new AI model solves the long-standing pain of converting a 3D scan into a parametric CAD model. In one click. We finally did it. 3D scanners map the surface of an object with incredible precision, quickly generating millions of data points, but they produce micro surface textures that can’t be manufactured with traditional tools. Our technology automatically converts these intricate surfaces into clean geometries designed for existing 3D CAD and manufacturing software. The first model will be available to early users in a month. You can access it online, or through a SOLIDWORKS plugin. After the AI generates a 3D model, it will drive Solidworks to create a native file with a full feature tree you can edit. Here's a cool article from Michael Alba at engineering.com (link in the comments). There are two target users for this new AI model. The obvious one is existing CAD designers who want to save hours of their life by automatically converting a scan to CAD. We're so excited to be done doing that by hand. The second set of users is much bigger. For a given automotive factory, there may be 1-2 CAD engineers, and 2,000-5,000 brilliant, mechanically savvy technicians assembling the cars / keeping the lines running. But many don't know CAD. Our new AI model will flatten the learning curve and help them get all the parts around them into parametric CAD. In the near future, everyone will be able to create the world around them.

🔍 Automating Section Definition in ETABS with Python & API Tool Defining reinforced concrete sections in ETABS can be a tedious and error-prone process, especially when dealing with complex reinforcement configurations. To streamline this workflow, I developed a Python-based ETABS API tool that automates section creation, reinforcement placement, and material assignment—saving time while improving accuracy. In my latest Medium blog, I cover: ✅ Section Designer in ETABS – Understanding its role in interaction surfaces, moment-curvature analysis, and structural optimization. ✅ Automating Section Creation – Seamlessly defining section geometry, materials, and reinforcement details using Python. ✅ Material & Reinforcement Assignment – Fetching concrete and rebar materials directly from ETABS to ensure consistency. ✅ Handling ETABS Database Tables – Efficiently updating section properties, rebar placement, and tie configurations through scripting. ✅ Time-Saving & Accuracy Benefits – Eliminating manual input errors while optimizing structural workflows. 🔗 Read the full blog here: https://lnkd.in/dmTM-Exb 🔹 Key Insights & Next Steps: 📌 Automating section creation in ETABS significantly reduces design time and ensures precision in reinforcement detailing. 📌 Next step: Extending ETABS API automation for nonlinear analysis, parametric section properties, and large-scale model integration. 📌 Python scripting enables engineers to integrate material, reinforcement, and load data, ensuring compliance with design codes while improving efficiency. #StructuralEngineering #Python #ETABS #Automation #ReinforcedConcrete #Programming #EngineeringInnovation

Blueprints in Revit with AI 🚀 Editing a blueprint no longer means searching through layers, selecting elements, and moving them manually. Now I just write: “move the door 1 meter to the right”... and the AI does it. Thanks to the metadata behind the model, each object has identity, context, and rules. The AI interprets it and makes changes directly in Revit. Behind this ability is a combination of Dynamo and Python that translates human instructions into actions inside the model. Every blueprint in Revit contains metadata: what each element is, where it’s located, how it relates to others. We use that structure so the AI understands the context and modifies the design using just text. It’s no longer just automation—it’s interpretation. When I say “move this wall 2 meters,” the AI analyzes constraints, collisions, and design criteria before executing. It doesn’t just know what to change, but how and why. It’s like talking to the blueprint—and having it understand you. Learn more here: 👉🏻 https://lnkd.in/efaMtp-h --------------------------- Planos en Revit con IA 🚀 Editar un plano ya no significa buscar capas, seleccionar elementos y moverlos a mano. Ahora solo escribo: “mueve la puerta 1 metro a la derecha”... y la IA lo hace. Gracias a la metadata detrás del modelo, cada objeto tiene identidad, contexto y reglas. La IA lo interpreta y ejecuta los cambios directamente en Revit. Detrás de esta capacidad hay una combinación de Dynamo y Python que traduce instrucciones humanas en acciones dentro del modelo. Cada plano en Revit contiene metadata: qué es cada elemento, dónde está, cómo se relaciona con el resto. Aprovechamos esa estructura para que la IA entienda el contexto y pueda modificar el diseño solo con texto. Ya no es solo automatización: es interpretación. Cuando digo “mueve este muro 2 metros”, la IA analiza restricciones, colisiones y criterios del proyecto antes de ejecutar. No solo entiende qué cambiar, sino cómo y por qué. Es como hablarle al plano, y que te entienda. Aprende más aquí: 👉🏻 https://lnkd.in/efaMtp-h #Arquitectura #Architecture #IA #Revit #InteligenciArtificial