The Platform
AI models learn from data. But production data doesn't capture cascade effects, blocking, or system-level interactions. We give your AI a validated digital twin to test against — independently verified within 1% of actual production. Test. Train. Validate. Trust.
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 56% more throughput than fixing the Labeler. The AI doesn't know this because it was never trained on the cascade effects. It learned from data that doesn't capture system-level behavior.
Your AI recommends a change — new shift pattern, equipment purchase, failure fix priority. Before you act on it, run it through a validated simulation. See what actually happens to throughput, blocking, and cascade effects — not a confident guess from an LLM with no grounded model of your line behind it.
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 matter enormously. Train your AI on data grounded in how your line actually behaves — throughput, blocking, and cascade dynamics included.
When the system changes — new equipment, new failure modes, seasonal shifts — the distributions drift. Your AI doesn't know. The validated model catches the divergence before it costs you. Continuous monitoring, not one-time validation.
Your data, your failure modes, independently validated against your historian within 1% OEE.
500 scenarios in seconds. See cascade effects, blocking, and system-level outcomes the AI can't predict from data alone.
Edge cases, failure scenarios, demand surges — unlimited validated synthetic data to make your AI smarter.
When reality diverges from the model, you know before your AI does.
Validate model outputs against physical reality. Generate synthetic training data with known ground truth. Close the sim-to-real gap.
Test AI-recommended changes before implementing them. Monitor for drift when the system evolves. Trust but verify.
Most digital twins mirror the current state. Ours simulates the future — what happens when you change something? Validated, not estimated.
Generate unlimited validated scenarios for operations research. Publish with confidence — the data respects physics.
Whether you want to validate recommendations, generate training data, or monitor for drift — let's talk about what fits.
