TRAIN is a governed causal-inference platform that connects enterprise knowledge, operating data, models, and decision workflows. It learns normal behavior, explains deviations, evaluates interventions, and updates as new evidence arrives, all within your environment.
Scroll down · outcomes, the model, and the capability lifecycleParabole's platform addresses decisions across upstream, midstream, downstream and adjacent process industries, run in self-service mode within each customer's own infrastructure. See the oil & gas portfolio →
Traditional knowledge graphs and machine-learning models recognize patterns from history. Industrial decisions turn on a harder question: why did this outcome occur, and which factors are truly driving performance?
Many monitoring systems focus on anomalies or known failure patterns. TRAIN begins with a causal model of normal operation and evaluates new conditions against it.
Tests candidate causal drivers against explicit assumptions, engineering constraints, refutation checks, and uncertainty estimates before presenting an intervention effect.
The causal model is re-estimated as feedstocks, equipment and operating regimes move, surfacing structural, directional and magnitude drift before it becomes a costly incident.
Multimodal enterprise evidence feeds a living causal model. The seven-stage lifecycle runs on it, every skill level works from it, and the whole system sits inside your environment and your governance.
Every facility applies the same physics differently. TRAIN represents that reality in three complementary layers and fuses them into one Combined Causal Model.
The first principles that govern the process: physics, chemistry, engineering relationships, operating constraints. Implementation-agnostic; rarely changes.
The reasoning, assumptions and practice developed by your engineers: procedures, maintenance practice, decades of judgment. Captures even minute operating changes.
Learned directly from temporal process data, continuously updating how the operation behaves at this point in time.
TRAIN is not a monolithic stack. Use a single capability, connect selected stages, or run the whole lifecycle, depending on the problem, the data, and your existing analytical environment.
Turns engineering documents, P&IDs, SOPs, FMEA records, ontologies and guided 30–60-minute SME interviews into structured causal models. Automates much of model generation from documents, data, and structured SME input, while retaining source-level lineage for supported inputs.
Learns the baseline of normal system behavior from operational data and combines it with the Principle and Rationale models into one integrated, governed causal model.
Documents the assumptions behind each analysis, selects valid adjustment sets, and makes validity requirements and failure conditions explicit. Interoperates with open frameworks such as DoWhy and PyWhy rather than locking you into a proprietary method.
Selects estimators suited to the question, data, and causal structure; compares methods; quantifies uncertainty and practical effect size; and applies placebo, sensitivity, and data-robustness tests.
Evaluates counterfactual scenarios from the governed causal model, with assumptions, validity conditions, and uncertainty made explicit. Integrates with simulators, digital twins and physics models when scenarios run beyond the historical operating range.
The same governed model, three ways in: no-code for operators and SMEs, low-code for process engineers, high-code for data scientists, working in a shared environment without every user becoming a causal-inference expert.
Causal agents for root-cause analysis, continuous monitoring and optimization. Supported recommendations include a traceable rationale and are evaluated against configured operating, safety, and business constraints, human-in-the-loop or automated.
A different operating point is not necessarily a deviation because its constraints move with it. A meaningful signal occurs when observed relationships depart materially from the validated operating envelope.
Statistical significance alone is not treated as proof. TRAIN applies a layered validation regime before any estimate reaches a user, and reports weak or unidentifiable evidence rather than a number the data cannot support.
Confirms the quantity asked for is defined and identifiable, and checks the structure against first principles, process sequence and engineering constraints. Conflicts are surfaced for expert resolution.
Covariate overlap, sample size, missingness, temporal alignment and regime segmentation diagnostics, then comparison across estimator families, bootstrap and subset re-estimation.
Placebo and random-common-cause tests, unobserved-confounder sensitivity, and re-estimation across time windows and regimes to expose drift.
Review by subject-matter experts against engineering expectation (disagreements recorded as evidence, not suppressed) and outcome tracking that compares realized results against the expected effect.
Cross-checking against open causal frameworks (DoWhy, PyWhy, EconML) and reconciliation with simulation or first-principles baselines.
Models are versioned and assumptions documented. Supported events are logged with attribution and retained according to the configured policy.
Engineering-grade causal modeling, designed for self-service. The same platform, running one governed model, supports four roles: executive decision-makers, operators and SMEs, process engineers, and data scientists. Role-based enablement on your own use case builds an internal, repeatable capability, not a services dependency; the commercial journey (start with one decision, expand from there) is on Get started.
Outcome, intervention, value and governance: see what drives performance, compare interventions and their expected value, and track realized operational and financial impact.
Knowledge contribution, deviation investigation and action: contribute operating knowledge, investigate deviations without code, and act on explained causes with the evidence and constraints attached.
Configure analyses, compare scenarios and generate reports from persona templates, with assumptions rendered visible at every tier.
Full flexibility through Jupyter notebooks, Python and R integrations, APIs and custom estimation methods, inside the same governed model.
TRAIN strengthens the historians, platforms, models, and solvers you already use. It adds a causal layer without replacing your existing data foundation.
Historians, data lakes, lab / MES / CMMS systems, engineering documents and P&IDs, ontologies and knowledge graphs, and the outputs of your analytical models.
APIs, Jupyter, dashboards, simulators and physics models, ML models, MILP solvers, APC / MPC stacks, and agentic workflows.
Can supply causally informed inputs, objectives, and constraints to ML and optimization workflows; supported configurations can screen infeasible regions before solver execution.
TRAIN is not an LLM. It can run a local model inside your perimeter; in one documented diagnostic test, evidence selection reduced input tokens from approximately 15,000 to fewer than 8,000 while preserving the reviewed recommendation.
Once the causal model is validated against your modeling assets, it becomes the foundation for a broad range of industrial applications.
Trace an operational outcome to supported causal drivers, with assumptions, evidence, validation status, and uncertainty visible to the user.
Evaluate “what-if” operating strategies, process adjustments and disturbances that may never have occurred, before implementing them.
Monitor shifts in causal behavior and emerging risk indicators so teams can investigate developing conditions earlier.
Feed validated causal effects and constraints into simulation, control and solver workflows for decision support and policy optimization.
Can run entirely within customer-controlled infrastructure, including configurations that do not call external LLM APIs; control evidence and certification are provided through your architecture and cybersecurity review.
On premises or in your private-cloud tenancy, including restricted and low-egress environments. Topology is agreed at architecture review.
In approved customer-controlled configurations, operational data and models remain within the agreed perimeter; telemetry, support access, and training use are governed by contract and architecture review.
Single sign-on with your identity provider, and role-based permissions aligned to the no-code, low-code and high-code personas.
Supported model, evidence, recommendation, and user events are logged with attribution and retained according to the configured policy.
A large U.S. refiner managed semibatch lubricant-grade transitions using engineer-dependent judgment and delayed lab results, with no repeatable method. A Lube Blending Optimization agent built on TRAIN is ready to implement. It captures the expert knowledge through structured interviews, ingests process documentation and historical data, identifies the conditions that drove off-spec outcomes, and issues a daily engineering plan of optimal setpoints.
20% lower grade-transition losses · 22% throughput increase · 15% yield increase.
You don’t model the enterprise at once. Begin with one process, decision or deviation; because the framework is process-based rather than asset-specific, a working model extends across similar operations: a consistent causal layer that grows where it creates the most value. See the thirteen worked oil & gas decisions →
Bring the engineer who knows the process. We will walk through the causal model behind it and, if you want, scope a bounded paid pilot on one of your own problems.