Most AI demos in engineering end when a shape appears from a prompt. A single model is useful, but programs don’t lose time there. They lose it turning requirements into a design space, setting up analysis, running variants, and interpreting results well enough to make a decision.
In this webinar, we’ll walk through what it takes to put AI to work across the full engineering workflow, with requirements going in and a recommended design coming out. The principle is simple: agents reason, code computes, and humans decide. We’ll explain why that leads us to think of this as text to trade study rather than text to CAD. We’ll also cover what has to be in place for it to work: geometry an agent can build and change reliably, analysis tools your team already trusts, and compute that can evaluate many variants in parallel with nTop Orchestrate.
Then we’ll build the foundation live. Starting from a prompt, an LLM will author a highly parametric nTop model that captures the design logic as well as the shape. We’ll push its parameters through large changes to show it stays valid. Finally, we’ll show how that same model fed a complete trade study that ended in a short list of recommended designs.
Thursday, November 19th - 11am (EST)
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You’ll learn:
- Where AI adds value across the design loop, and where engineers stay in control
- Why AI should write parametric logic instead of authoring geometry directly
- What makes a model robust enough for an agent to explore thousands of variants
- How parallel execution turns one model into a full trade-space evaluation
Presenters:
- Brian Sather, Head of Marketing, nTop
- Tristan Antonsen, Technical Product Manager, nTop
