Separating hype from reality — the specific AI/LLM use cases that are delivering value in engineering workflows right now.
The AI hype in engineering is real — but so are the practical wins, if you know where to look.
What Works Now:
Part Classification — LLMs can read part descriptions, drawing title blocks, and BOM notes to auto-classify parts into categories. We've used this to clean up 10,000+ part libraries in days instead of months.
Drawing Review — AI-assisted checks for missing dimensions, incorrect tolerances, and title block errors. Not a replacement for a human reviewer, but a solid first pass that catches 60–70% of common issues.
Report Generation — Feeding inspection data or simulation results into an LLM to generate formatted engineering reports. Saves hours of copy-paste work per week.
What Doesn't Work (Yet):
Generative CAD Design — Despite the marketing, no AI can reliably generate production-ready 3D models. The geometry understanding isn't there yet.
Autonomous FEA Setup — AI can suggest boundary conditions, but trusting it to set up a critical stress analysis without human oversight is irresponsible.
The key is treating AI as a productivity multiplier for repetitive cognitive tasks, not as a replacement for engineering judgment. We help teams identify the highest-value automation targets and build reliable prompt-based workflows around them.
