Imubit

Imubit is Closed-Loop Physical AI for the process industries. Most plants run on operating strategies spread across different teams, systems, KPIs and shift rhythms. Nobody owns the whole picture, so a gap opens between what the plan assumed and what the plant actually does. It shows up in safety, reliability, margin, compliance and workforce competency. We trace those gaps back to the recurring decisions behind them. Process engineers then model the causality involved using Deep Learning Process Control (DLPC®), a neural network trained on years of the plant's own operating data, which learns how the unit actually behaves rather than how it was assumed to behave. Correlation is not enough for a decision you intend to automate. Those decisions then run closed loop through the control infrastructure you already have, alongside your APC and DCS rather than replacing them. Closing the loop does not remove the operator, it changes where their attention goes. When the model handles routine adjustments, manual moves per shift fall and the operator supervises the strategy rather than correcting the unit. The same models connect the control room to the executive level, so the strategy leadership approved is the strategy actually running. Point solutions close the loop on one application. Most AI stops at recommendations. The gap between an identified opportunity and a decision that executes on its own is where industrial AI programs stall, and the barrier is trust more than software. Before a plant delegates a control decision, the model must prove it understands cause and effect on that unit. General-purpose AI trains neural networks on text and images. Imubit trains them on process data from operating plants, so the model learns the physical behavior of the unit itself. Today Imubit operates 100+ closed-loop controllers and 250+ deployed applications in production facilities, executing live control decisions every shift under operator supervision.