aivis® builds custom causal world models directly from raw process data — with minimal expert input. It enables deep understanding and precise control of complex, ever-changing processes by learning true cause-and-effect.
Why is it so hard to control complex systems in the real world?
Because they’re never fully predictable. Whether it's a production line, an energy system, or even a biological process — you're dealing with a mix of deterministic laws and random fluctuations.
Some parts follow strict physical or logical rules. Others react to changing inputs, environments, or noise. The art — and science — lies in separating the two, and learning how to influence outcomes reliably.
Take industrial production: thousands of signals, hundreds of setpoints, shifting products, and tight constraints. Keeping everything stable, efficient, and in spec is more than a monitoring problem — it’s a control problem.
And solving it means going beyond surface-level correlations. It means understanding cause and effect.
Plenty of AI promises control — but most fall short.
Many try to apply today's trending tools to real-world processes. But these approaches often miss the core challenge: understanding cause and effect in complex, ever-changing systems.
The result? Tools that might look smart in theory — but fail to deliver stable, actionable control in practice.
Discovering the causal structure through invariants
All begins with interventional data, because causality cannot be inferred from observations alone. This data records how the system responded to past setpoint changes, revealing how specific adjustments influence outcomes. From these histories, aivis® identifies invariants—relationships that remain stable across operating regimes—which form the system’s structural backbone and reflect causal structure that holds under interventions and changing conditions.
For each key objective (e.g., quality, energy, throughput), aivis® then builds a dedicated response model(using uncertainty-aware Gaussian Process (GP) predictors that capture non-linear behavior and provide confidence ranges) and finally fuses them into a single, consistent world model that encodes structure, response behavior, and uncertainty—robust to feedback loops, noise, and evolving process conditions.
In effect, the resulting world model offers a deep, uncertainty-aware representation of the process—able to simulate counterfactual what-if scenarios (through constrained optimization) and compute the smallest, constraint-safe setpoint adjustments that best satisfy competing objectives under changing conditions.
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With aivis®, a process-aware world model is built directly from raw data — with minimal expert input and no manual modeling. The result is not just prediction, but a system that truly understands how the process behaves and can actively support control decisions.
Accurate, real-time estimates for lab-grade KPIs between actual lab measurements — grounded in learned process structure.
Quality, energy, material use, and throughput are balanced together, not traded off blindly — always within real-world constraints.
Concrete setpoint adjustments, sized to be safe, minimal, and effective, delivered with expected impact and confidence bounds.
Structural invariants combined with incremental, local updates ensure stable recommendations as the process evolves — without catastrophic forgetting.
Quick setup, minimal data requirements, and no hand-crafted graphs — a model tailored to each line, learned directly from its history.
Model inferences are very lightweight, running on edge devices much more effectively vs neural networks.
Already in use across challenging industrial applications — including paper and nonwovens — this engine powers PBX in both Copilot (decision support) and Autopilot (closed-loop control) modes. It is the causal intelligence at the heart of PBX.