Your enterprise has far more examples of what works than what fails: its richest, least-used operational intelligence. Parabole learns what drives normal performance, then uses causality to explain deviations and identify what can change the outcome.
Illustrative view using simulated data. Refresh frequency is configurable.
Many AI systems learn primarily from historical patterns. TRAIN also models the relationships that define normal operation, reducing dependence on examples of every failure mode.
Parabole models the relationships that govern the process, then reads what breaks them. A deviation with no history in this plant or the fleet is still a signal, with a stated cause.
Recipe, first-pass yield, machine reliability, and material flow evaluated in a single causal model instead of systems that never talk to each other.
Customer data is not pooled across customers under the approved deployment and data-use terms. Existing control systems keep setpoint authority. Each supported platform-generated decision records its model version, inputs, causal path, and binding constraints.
Customers include global energy majors and sector leaders in industrials and manufacturing across Europe and the US, applying the causal-inference platform to complex production and operations problems in upstream, midstream and downstream.
TRAIN holds what the data says the process does and what your engineers say it should do as one reference, then reads live conditions against it: modeling business as usual, detecting the residual, and explaining cause and consequence. See how the platform works →
Each engagement begins with a single decision that is hard to get right. None requires a catalog of past incidents.
Upstream, midstream, and downstream, each published with the basis behind its numbers: from subsurface mapping to alarm rationalization to fired-heater energy and emissions.
Procurement and recipe, first-pass yield, touchless order flow, and operational safety: held in one causal model instead of four systems that do not talk.
Bring us the decision and we will walk through the causal model behind it.
Bring the engineer who knows the process. We will walk through the causal model behind it.