The Platform
AI models learn from data, but production data doesn't capture cascade effects, blocking or how machines interact. We give your AI a validated simulation of your line to test against.
Your AI says "fix the Labeler — it has the most downtime." But 120 one-minute stops on the Filler cascade through the entire system, recovering 62% more throughput than fixing the Labeler. The AI doesn't know this because it learned from data that doesn't capture those effects.
Your AI recommends a change, such as a new shift pattern, an equipment purchase or which failure to fix first. Before you act on it, run it through a validated simulation and see what happens to throughput, blocking and cascade effects.
Real production data is scarce and slow to collect. A validated simulation generates 100 years of scenarios in minutes, including edge cases that rarely happen in production but still matter. Train your AI on data that reflects how your line behaves, including its throughput, blocking and cascade effects.
When the system changes (new equipment, new failure modes, seasonal shifts), the distributions drift and your AI won't notice. The validated model keeps checking and catches the drift before it costs you.
It's built from your data and failure modes and checked against your historian.
500 scenarios in seconds. See cascade effects, blocking, and system-level outcomes the AI can't predict from data alone.
Validated synthetic data for edge cases, failure scenarios and demand surges, as much as your AI needs.
When reality diverges from the model, you know before your AI does.
Check model outputs against how the line really runs, and generate synthetic training data with known ground truth.
Test AI-recommended changes before you make them, and watch for drift as the system changes.
Most digital twins mirror the current state. Our validated model is a playing field where you can test what happens when you change something.
Generate validated scenarios for operations research from a model that respects the physics of the line.
Whether you want to validate recommendations, generate training data or monitor for drift, let's talk about what fits.
