Portfolio Capability
Physics-Aware AI Control
We combine model intelligence with hard physical limits so AI output remains useful without violating process constraints, operating envelopes, or known machine behavior.
What This CoversAction gating, operating-bound enforcement, plausibility checks, control constraints, and supervisory policies that keep intelligent systems grounded in physics.
Technical Scope
- Control logic that evaluates model output against machine limits, safety thresholds, and process constraints before execution.
- Physical plausibility screening for recommendations that may be statistically likely but operationally unsafe or invalid.
- Constraint-aware control envelopes that limit intensity, duration, amplitude, or speed of machine actions.
- Hybrid reasoning patterns that combine learned signals with deterministic rules and domain-specific control heuristics.
- Escalation pathways when the AI recommendation falls outside approved physical behavior.
Typical Deliverables
Constraint engine, supervisory policies, control boundary definitions, and integration hooks into runtime decision systems.
Why It Matters
It makes AI usable in physical systems where an unbounded prediction can create real operational risk.
Integration Pattern
AI output → plausibility check → control envelope validation → allowed, limited, or blocked action path.