Building AI-CAD: Technical Deep Dive

The Parallel: Your World vs. CAD World

The key insight: You don't need to learn Revit. BIM is already structured data—walls ARE entities with properties (height, material, fire rating). Rooms ARE objects with relationships (adjacency, access). You need to understand how to represent spatial/semantic data as structured information, then apply the same extraction-modeling-generation pipeline you already know.

Why BIM is better than CAD for AI: AutoCAD stores lines and circles. Revit stores "Wall: Type=8in CMU, Height=10ft, FireRating=2hr". That semantic structure is MUCH easier for AI to work with. Paul saying "AutoCAD is dead" is actually great news for this approach.

Data Sources: What Training Data Exists?

Just like you trained on SEC filings to understand financial events, you need training data for building designs. The good news: there's a lot of it, much of it public.

Just like you didn't need Bloomberg terminal data to disrupt SEC analysis—you used public EDGAR filings—you don't need Autodesk's proprietary Revit data. Public IFC models, open source BIM files, and synthetic data can get you far. IFC is an open standard specifically designed to break vendor lock-in.

The Technical Pipeline

Here's how you'd actually build this, step by step. Notice how similar it is to your SEC analysis pipeline.

Model Architecture: What's Different?

You'll Need Multiple Models (Just Like Your SEC Pipeline)

Your current system probably uses different models for different tasks: one for entity extraction, one for classification, one for summarization. Same here.

1. Requirements Extraction: Standard LLM

Use what you know: GPT-4, Claude, or fine-tuned Llama/Mistral. This is pure NLP—your wheelhouse.

2. Layout Generation: New Territory

This is the CAD-specific part. A few approaches:

3. Validation Models: Rule-Based + ML

Building codes: Encode as rules (like you'd encode GAAP rules). Use constraint solvers.
Quality scoring: Train classifier on "good" vs "bad" designs (like your alpha scoring).

Do You Need Special Models?

Short answer: Not entirely. You can start with:

• Standard LLMs for requirements extraction (you already do this)
• Existing image diffusion models fine-tuned on floor plans (similar to fine-tuning LLMs)
• Rule-based systems for validation (like parsing HTML or structured data)

The truly novel ML is in the layout generation. But even there, you can start with procedural generation + LLM-guided refinement. Get something working, then improve.

Think of it this way: You didn't build a custom LLM from scratch for SEC analysis. You used GPT-4/Claude and fine-tuned where needed. Same strategy here—use existing models, fine-tune on CAD data, add domain-specific post-processing.

The Tech Stack

Notice: Most of this is standard ML/web stack. The BIM-specific parts (IfcOpenShell, ezdxf, FreeCAD) are just Python libraries. You're not learning C++ or Revit's proprietary APIs. It's data processing with a spatial twist. IFC is an open standard—think of it as JSON/XML for buildings.

Unique Challenges vs. Text Processing

How to Address These

Spatial Reasoning

Start simple: Begin with rectangular rooms on a grid. Add complexity incrementally (non-rectangular, curved, multi-story).

Use graph representations: You understand graphs (entity relationships in text). Floor plans are just spatial graphs.

Hard Constraints

Validation layer: Like you validate SEC extractions against known entities, validate designs against building codes before showing to user.

Iterate until valid: Use LLM in a loop—generate, validate, regenerate if invalid. You probably already do this for data cleaning.

Multimodal Output

Pipeline approach: Internal representation → render to image → export to DXF → generate text specs. Each step is independent. You've built pipelines.

Physics/Engineering

Partner with domain expert: That's Paul. He validates structural soundness. You handle the ML pipeline. Exactly the division of labor that works.

Getting Started: 90-Day Roadmap

Key Insights for Someone from Text World

1. The pipeline is familiar. Extract → structure → model → generate → validate. You've done this before, just with different data types.
2. You don't need to be a Revit expert. BIM is already structured semantic data (walls, doors, rooms). You need to understand how to represent spatial objects and generate valid outputs. Paul provides domain validation. IFC is an open standard—much easier than proprietary Revit formats.
3. Start rule-based, add ML later. Procedural generation can get you far. Use ML to optimize and improve quality, not as the only solution.
4. Data is available. Unlike some ML problems, there's lots of public CAD data. You won't be data-starved.
5. Validation is critical. Building codes are like financial regulations—non-negotiable. Build validation from day one.
6. Niche down initially. Don't try to design skyscrapers on day one. Start with simple residential projects where constraints are well-understood.
7. This is achievable. You have 90% of the skills already. The CAD-specific parts are learnable in weeks, not years.

You disrupted SEC analysis without being a financial analyst. You built the tech that let analysts work faster. Same playbook here—build the tech that lets designers work faster (or skip Revit entirely). You're not replacing Paul's design judgment. You're amplifying it with AI. And Paul already knows "AutoCAD is dead"—he understands the modern BIM world you're disrupting.

Ready to Build?

The technical barriers are lower than you think. The market opportunity is massive. And you have the exact team composition that's needed. The question is: are you ready to start?