High-consequence integrity events seldom repeat in identical patterns. When AI relies only on cataloged past failures, it remains blind to novel risks. Parabole takes a causal approach: by modeling the fundamental principles that govern system equilibrium, the platform detects when those core dynamics begin to break down.
Throughput, emissions, energy demand, and equipment integrity constantly pull in opposing directions. The agent identifies the likely binding constraint under current operating conditions and estimates the operational and economic effects of changing operating limits.
Major process-safety incidents are high-consequence, low-frequency events that rarely present in the same pattern twice. Predictive models reliant on historical incident logs remain blind to novel, uncataloged operating conditions.
TRAIN evaluates process flow, subsystem interactions, hydraulics, and thermodynamics against current operating conditions.
It detects the gap between expected and observed behavior, including failure modes not previously recorded in the available fleet history.
TRAIN states the likely cause, estimates the production and integrity consequences of inaction, and ranks feasible interventions.
A different operating point is not necessarily a deviation because its constraints move with it. Flag conditions when observed relationships depart materially from the validated operating envelope.
Each begins with one decision owned by one team. None requires a catalog of past incidents to start.
Bring the engineer who knows the process. We will walk through the causal model behind it.