Kausalyze is a University of Sheffield spinout building causal AI solutions for process manufacturing. We help manufacturers in chemicals, energy, pharmaceuticals, utilities and FMCG prevent unplanned downtime by delivering what predictive maintenance tools cannot: actual root cause analysis.
Predictive maintenance has promised much but delivered little. Today's tools can tell you something is wrong, but they cannot tell you why. They flag anomalies, generate alerts, and leave your engineers to figure out the rest. That model worked when experienced process engineers could interpret the signals. It no longer does.
We take a different approach. Our causal AI platform doesn't just correlate patterns in your process data. It maps the underlying causal relationships in your operations, identifying not just that a failure is coming, but precisely what is driving it and what action will prevent it. This is engineering-led root cause analysis, not statistical correlation dressed up as insight.
Our team combines deep process engineering expertise with cutting-edge causal machine learning. We've built our technology on years of research into how faults propagate through complex manufacturing systems, and we've validated it with some of the world's largest chemical and energy companies.
