Engineering Knowledge Capture

Why This Matters

RapidDraft's longer-term moat is not just parsing geometry or speeding up CAD/CAE steps. It is turning tacit engineering knowledge, archived reports, design-review heuristics, and senior-expert judgment into reusable, traceable software behavior.

RapidDraft Relevance

• supports explainable design-review logic
• supports reusable institutional memory
• creates a path from engineering archives into structured product logic
• gives academic partners a serious knowledge-engineering question, not just a generic AI task

Main Technical Questions

• How should tacit expert heuristics become executable review rules?
• What is the right mix of ontology, retrieval, knowledge graph, and LLM support?
• How should archived CAD, drawings, reports, and review notes be linked?
• How should provenance and trust be represented so the result is auditable?

Best Academic Fit

• KBE and product-development methods groups
• knowledge-engineering and ontology work
• engineering knowledge graph and retrieval researchers
• explainable AI and decision-support groups

Linked Methods and Capabilities

• Knowledge Graphs and Ontologies
• Trustworthy and Explainable Engineering AI

Starter Work Packages

• WP-05 Engineering Knowledge Graph for Product Families
• WP-07 LLM-Assisted Engineering Review Comments

Recommended Next Moves

1. Use FAU and TU Darmstadt as the main framing lanes.
2. Keep the first implementation wedge tied to one document or review family, not all historical knowledge at once.
3. Separate retrieval from rule formalization when scoping opportunities.

Open Questions

• Should tacit-knowledge capture remain merged with review comments, or later split into separate work packages?
• What minimum archive or document set would be enough to prove value?

Sources

• TextCAD/04_Marketing and Outreach/13_Universities/deep-research-report Balanced.md
• TextCAD/04_Marketing and Outreach/13_Universities/deep-research-report Monster.md