The work repeats one move: preserve the object, expose the relation, validate what can be checked, and learn the remainder.
Applied Medical
Constraint: tooling had to survive material variation, operator habits, machine timing, and production pressure.
Work: CAD, prototypes, fixtures, pneumatic tooling, robotic EOAT, test, qualification, and shop-floor debugging.
Outcome: a MIM insert-molding process fix cut roughly $100k/month in operating variance.
T-UEBA
Constraint: tactical behavior shifts fast. Labels stay sparse. Analysts need evidence, not alert volume.
Work: constrained heterogeneous temporal graphs, calibration, active learning, synthetic-data triggers, and evidence drilldown.
Outcome: $250k SBIR PI work for Army tactical Zero Trust behavior analytics.
DXF/CAD representation
Constraint: geometry, relations, editability, and auditability have to survive conversion.
Work: exact artifact, object state, typed relation graph, deterministic validators, learned residual repair.
Signal: HATCH-IoU, provenance, and reviewable state make CAD-native learning less vague.
AIxCC / DL-Patcher
Constraint: code repair needs checker pressure, runtime feedback, and human review. Prompting alone cannot carry the patch loop.
Work: code-model harnesses, static analysis, fuzzing, verifier feedback, and patent-filed repair architecture.
Production systems
Constraint: small teams need systems that stay legible under cost, latency, and operational limits.
Work: AWS workflows, thermal/GIS processing, customer-facing tools, and production handoff.