Find causal interventions to optimize raw material, energy, productivity and quality — even across competing objectives.
Causian connects industrial data sources into a high-speed time-series foundation, predicts hard-to-measure lab values, and carries that intelligence through causal reasoning to adaptive automatic control.
A prediction error tells you the model is wrong. It does not tell you which mechanism changed — or which intervention still works now.
The model detects that observed behavior diverges from the forecast — but the error remains a scalar symptom.
The causal structure localizes what changed, updates the relevant mechanism, and recomputes the action under current constraints.
Causian operates the full feedback loop: observing the process, understanding causal state, evaluating interventions, acting, measuring the result and adapting online.
Live and historical process signals enter the Causian data foundation.
The causal world model represents current mechanisms, state and constraints.
Candidate actions are simulated and translated into operator or automatic control.
Outcome feedback identifies mechanism change and updates the plan online.
Provide historical process data, the controllable variables and their operating ranges, and the optimization objective. Within 5 working days, we will provide a quantified estimate of the value Causian's control approach could create.
Start with a short scoping call. We align on the available historical data, controllable variables and their ranges, and the objective you want to improve. If the data fit is sufficient, the 5-day value assessment starts.
The scientific foundation behind commercially deployed causal AI, industrial sales and deep-tech company building.
