From manual CAE to computational autonomous engineering

Implicit modeling practice made explicit: documented procedures, machine-checkable quality criteria and repeatable recipes per model class. The foundation everything else builds on.

Scripted preprocessing steps, batch execution and tool integration that remove the repetitive share of engineering work and stabilize quality.

Quality gates as executable checks: mesh criteria, guideline conformity, plausibility rules — run automatically instead of reviewed manually.

Capturing decisions, modeling standards and workflow knowledge in structured, retrievable form — so it belongs to the organization, not only to individuals.

AI where it measurably helps: geometry classification, setup suggestions, anomaly detection in models — always proposing, with engineers deciding.

A tool-neutral abstraction layer over heterogeneous CAD and CAE systems: one consistent representation of geometry, mesh, properties and provenance that automation and agents can operate on.

Software agents that plan and execute preprocessing and model-preparation tasks end-to-end — selecting procedures, running checks, and escalating what they cannot verify.

Multi-step simulation workflows orchestrated by agents across tools — from CAD intake to solver-ready model — under continuous validation.

Not an afterthought but the operating principle at every stage: engineers define the criteria, supervise execution and own the result. Autonomy grows only as fast as verifiability does.