Capabilities
Each capability runs on the same causal digital twin, built from your own data. Ask why something happened, what will happen next, or what to change, and see the causal path behind the answer.
The platform
Real-time sensor data and environmental data flow into the causal digital twin, along with your documents and, if you have one, your existing digital twin. The twin returns recommended actions, root cause analysis, intervention analysis, outlier analysis, and causal strengths.
Scroll sideways to see the whole diagram.
Engineers review the outputs and act on the system.
The twin sends interventions directly to the system. This is what enables real-time fault-tolerant control.
Why causal
Rhizant adds the cause-and-effect layer on top of what your existing tools already collect.
Normal machine learning finds patterns (correlations). Causal AI understands why things happen (cause and effect).
It can answer “what if” questions, pinpoint root causes, and keep working when conditions change.
It learns rules such as worn bearings leads to more friction leads to more heat and vibration.
What you can ask
01 Root Cause Analysis
Find the original reason something went wrong, not the symptoms. A car overheats because coolant is low, because there is a leak, because a hose cracked. The cracked hose is the root cause.
Because the twin knows the cause-and-effect links between components, it traces backwards automatically, instead of engineers spending days or weeks untangling thousands of linked sensors.
02 Alarm Flood Suppression
One fault triggers a chain reaction and hundreds of alarms fire in the control room, like fire, smoke, and car alarms all blaring when one pan is burning. The twin says: ignore these 200 alarms, they are consequences; this one thing caused everything.
03 Prescriptive Maintenance
Normal AI: “This machine will break soon.” Causal AI: “This machine will break soon, and here is why, so here is exactly what to fix.” For example, this pump will fail in two weeks because it is running hot, because lubricant is low. Maintenance moves from predictive to prescriptive: fix the root cause before the failure happens.
04 What-If Simulation
Engineers test changes on the causal twin first, safely and cheaply. Because it understands real causes, predictions stay reliable in situations the machine has never seen (counterfactual reasoning).
05 Fault Propagation Forecasting
The forward version of root cause analysis: once a fault starts, predict which subsystems it will hit next and how fast, as in cascading failures in the power grid, where one tripped line overloads the next. Operators get time to act before the fault spreads, instead of reacting after.
06 Sensor Fault vs Machine Fault
The twin checks each reading against what its causes say it should be. If one sensor disagrees with everything around it, the sensor is likely faulty, not the machine. This cuts false alarms and needless shutdowns. The same idea gives virtual sensors: estimates of values that are not measured, or that come from a failed sensor, from their known causes.
07 Process Optimization
Factories have many settings (temperature, speed, pressure). Normal AI can be fooled by things that just happen together. The twin identifies the true cause, so you change the right setting. Strong fit for semiconductor and chemical manufacturing.
08 Robustness to Change
Correlations break when conditions change: new equipment, new season, new site. A causal model learns physics-like rules (worn bearings leads to more friction leads to more heat and vibration) that hold everywhere. Build one model and use it across sites, instead of retraining per site.
09 Fault-Tolerant Control
Detect a failing component in real time and automatically adjust the system to keep it running safely. This is the closed-loop mode of the twin.
On the same twin
Thousands of units in the field. Which design and usage factors cause the failures?
For manufacturers of engines and equipment: find which design or usage factors cause failures across thousands of machines in the field.
How much life is left in this asset, the way it has actually been used?
Predict wear and remaining lifespan under different operating conditions, based on how the asset has actually been used.
Which conditions push the system toward an unsafe state?
Understand which factors push a system toward unsafe states, so they can be avoided before they happen.
Which operating changes cut emissions without hurting performance?
Test interventions on the twin to find operating changes that cut emissions and improve fuel efficiency, for example in diesel engines.
Where should we add, move, or remove sensors to tell faults apart?
Many systems miss key measurements or record far more data than anyone uses. Causal models show which measurements are needed to pinpoint each fault, cutting hardware cost and data overload.
Did the new oil-change interval reduce failures, or did the weather?
Estimate the real effect of past operator and maintenance decisions from historical logs. Find what works without running new, expensive, or risky experiments, using data companies already have.
Outcomes
A 30-minute conversation with the founder about your system, your data, and whether a causal twin fits.