Hybrid AI for Industrial Processes
AI Engine
Hybrid AI combining physics-based models with machine learning. Anomaly detection, predictive maintenance, process optimization — trained on your data.
Key Features
What AI Engine Does
Hybrid Physics + ML
Combine first-principles process models with machine learning. AI that understands your process, not just your data.
Anomaly Detection
Unsupervised learning detects abnormal patterns without labeled data. Works from day one.
Predictive Maintenance
Remaining useful life (RUL) models trained on your equipment's failure history. Predict breakdowns weeks in advance.
Process Optimization
Multi-variable optimization suggests setpoint changes to reduce energy, improve yield, or minimize waste.
Real industrial questions
Different Pipelines for Different Use Cases
Every industrial question maps to a proven ML pipeline. Train once in AI Studio, deploy as a FlowMaker node.
“What will the hot metal temperature be in 3 hours?”
Regression Pipeline
Continuous numeric forecasting from current process data
Regression“Will this pump fail within the next 2 weeks?”
Classification Pipeline
Binary or multi-class prediction of failure windows
Classification“Does this heat match our golden batch profile?”
Pattern Detection Pipeline
Similarity scoring against known reference trajectories
Pattern“What's normal behavior for this sensor set?”
Auto-Encoder Pipeline
Unsupervised learning of baseline — alert on deviation
Anomaly detectionHow It Works
Getting Started with AI Engine
Four steps from zero to production.
Connect Data
AI Engine pulls historical and live data from DataLake and DataCatalog automatically.
Select Model Type
Choose from anomaly detection, predictive maintenance, or process optimization templates.
Train
Auto-ML trains and validates models on your data. No data science expertise required.
Act
Model outputs flow into FlowMaker as triggers, or appear directly in dashboards as predictions.
Integrations