Infrastructure for CAD Automation and Prediction Tools

How data is organized into queryable, relational formats

• CAD modeling workflows must be decomposed into discrete, named operation sequences so the ML model can observe and predict intent at the step level

Whether operational knowledge is systematically recorded

• User interaction telemetry (command sequences, undo events, rework patterns) must be captured per session in a structured log format for model training

How explicitly business rules and processes are documented

• Feature taxonomy covering the organization's common parametric modeling patterns must exist and be versioned before intent prediction can be scoped

Whether systems share data bidirectionally

• CAD API surface must expose programmatic access to feature history and parameter state so automation scripts can read and write model state reliably

Whether systems expose data through programmatic interfaces

• Prediction confidence thresholds and override mechanisms must be defined so engineers can accept or reject suggestions without disrupting model history

How frequently and reliably information is kept current

• Automation script versions must be tracked against CAD platform versions to prevent breakage when CAD software is updated