Open Studio Turn industrial data into decisions your team can review.
Illustrative concept imagery
CRG brings industrial engineering and digital workflows together to help teams assess AI opportunities, prepare operational data and evaluate analytical models. A useful first engagement defines one decision, the available evidence and the conditions a pilot must meet.
Choose a specific decision: which asset needs inspection, which reading deserves review, or which production variable needs investigation. Record the current process and the cost of an incorrect decision before choosing a model.
Identify equipment records, timestamped telemetry, maintenance history and inspection evidence. Check missing values, units, permissions and representativeness. Compare any proposed model with a simple baseline before expanding its scope.
Studio supports asset records, maintenance workflows, documents, reviews and release decisions. Its numerical modelling workflow provides a regression baseline; any predictive maintenance or production model needs suitable data and separate validation.
Agree the process boundary, data owner, evaluation method and review responsibility. Success criteria should include operational usefulness and error rates, with a decision to continue, revise or stop based on evidence.
Industrial AI applies data analysis and machine learning to operational problems in manufacturing, maintenance and infrastructure. Its value depends on relevant data, an appropriate baseline and validation in the intended operating context.
No. Predictive maintenance depends on the available equipment history, failure examples and operating conditions. CRG starts with data readiness and a measurable pilot scope rather than a guaranteed outcome.
Tell us your industry, the process involved, the data or equipment available and your intended timeline.