Find the relationships that matter.
Build causal models from historian data, process drawings and lab results. Account for time delays, connected equipment and the physical constraints that shape a process.
Industrial decisions need more than correlation. We are building causal AI that helps engineers find the cause of a recurring problem, test a change and understand its expected impact.
Join a team bringing chemical engineering, causal research and software into the same problem.
How do you separate a driver from a symptom? How do you make the evidence inspectable? How do you turn a finding into a change an engineer can evaluate?
Build causal models from historian data, process drawings and lab results. Account for time delays, connected equipment and the physical constraints that shape a process.
Give engineers a way to inspect causal paths, review evidence and test interventions. Turn research workflows into software they can use to evaluate an operational decision.
Work with process engineers to validate important relationships, investigate recurring failures and quantify the expected value of a prescribed change.
We are a small team working across research, product and industrial application. The work needs all three perspectives.
Meet the teamMake assumptions explicit and test the explanation. A finding is useful when someone else can inspect how you reached it.
Engineers are accountable for what happens in a unit. Their questions and process knowledge shape the models and the product.
Connect the research to a usable workflow and a measurable plant outcome. Work across disciplines to resolve what sits between them.
In a small team, you can shape the approach as well as the implementation. Document what you learn so others can build on it.
We welcome introductions from people working in causal AI, process engineering and software.
Tell us what you have worked on, where your expertise lies and why you want to build with Kausalyze. A link to a project, paper or portfolio is a useful starting point.
Use our contact form to introduce yourself and let us know where you are based.
Introduce yourselfSee how plant data, process physics and engineering validation come together in a causal model.
See how it works