About
Rhizant builds causal digital twins of engineered systems, so engineers know not just what is happening, but why, what happens next, and what to do about it.
Mission
Causal intelligence for every engineering system, not just the few with a team of specialists.
Causal methods exist, but building a causal model of a real machine still takes manual expert work, does not scale, and is hard to trust. Rhizant exists to remove those three limits.
The problem
In large engineered systems, thousands of linked sensors go haywire at once when something fails. Engineers spend days or weeks untangling what started it.
One fault triggers a chain reaction, and hundreds of alarms go off in a control room. Operators are overwhelmed and can miss the one warning that matters. Alarm floods have contributed to real industrial accidents.
Standard predictive-maintenance AI says “this machine will break soon” but not why, or what to fix.
Models built on correlations break when conditions change: new equipment, new season, new site. Companies retrain per site.
Digital twins track thousands of variables and show what is happening. They do not explain why, and they cannot say what would happen under a change the system has never seen.
What Rhizant is
Rhizant is a causal intelligence platform for engineered systems: engines, plants, grids, fleets. It builds causal digital twins that explain faults, predict what happens next, and recommend what to do. It is not an IT incident-response tool, not a business-intelligence dashboard, and not a conventional digital twin.
Engineers and operations teams responsible for complex, sensor-instrumented systems: engines and powertrains, power plants and grids, process and semiconductor manufacturing, wind farms and other rotating equipment, vehicle fleets.
The first users are the people who untangle faults and alarms every day: reliability engineers, controls engineers, plant operators, maintenance managers, field-quality and warranty engineers.
It adds the cause-and-effect layer on top of what those tools already collect.
Why now
01
Modern large language models can read manuals, schematics, and maintenance logs and draft a first causal graph of a system. This replaces weeks of manual expert work and was not possible before.
02
Industrial systems are already instrumented. Historians, SCADA, telematics, and digital twins produce the time-series data that causal analysis needs.
03
Causal inference and causal discovery have matured as research fields but have barely reached day-to-day engineering practice.
Why Rhizant
Rhizant grows out of research on causal digital twins for real-time decision making and fault detection in engineered systems.
The approach is built around the open problems that stop causal digital twins from working in practice, not around a generic causal-inference library.
See the open problemsRhizant comes from rhizome, a root system that spreads underground. We trace faults to their roots.
Work with us
We will show you how a causal twin finds it.
A fault, alarm, or reliability problem your team needs explained.
Existing sensor data and documents. No new experiments needed to start.