CDFAM Computational Design Symposium — Washington DC 2026
Chris Helmerich · Celedon Solutions
Most ‘AI for engineering’ tools today sit beside the design process with a chat window next to a CAD viewer, a copilot that summarizes documents someone still has to act on. The harder problem is putting AI inside the loop, where it can read and write the same structured representation of the system that engineers, simulations, and downstream manufacturing all depend on.
This talk covers how we approached that problem at Celedon Solutions while building Davinci, an engineering platform where agentic AI operates directly on a live knowledge graph of the system under design. Requirements, components, interfaces, behaviors, and their relationships all live in one connected structure, and the agents that work on it can pull structured model content out of reference documents, generate and compare architectural alternatives against performance and cost constraints, trace requirements through simulation results, and reach into external tools such as parts databases, Python simulations, PLM systems through APIs and Model Context Protocol.
I’ll walk through the design decisions behind a graph-native rather than document-native foundation, show where generative exploration has meaningfully compressed early-phase trade studies in aerospace and defense pilots, and talk honestly about the failure modes where agents confidently produce plausible-looking nonsense, and what guardrails and iteration strategies actually work. The goal is a practical view of what it takes to move AI from the margins of the engineering workflow into the part of the process where design decisions actually get made.
Links
Talk page with full transcript
