Understand
Identify the likely root cause of observed effects in enterprise data.
Causal AI
Eradicating data misinterpretation
Conventional analytics is limited to correlations. Causal AI goes beyond correlation by learning the cause-effect structure in your data to support informed business decisions.
What causal models enable
Use one causal model to understand, predict, simulate, and optimize decisions.
Predict
Create robust predictions grounded in causal structures rather than unstable correlations.
Simulate
Evaluate hypothetical scenarios like: what happens to X if we change Y?
Optimize
Find action combinations that maximize objective outcomes under business constraints.
Trust
Understand why the model recommends actions instead of relying on black-box outputs.
Intervene
Prioritize high-leverage actions by quantifying expected impact before execution.
Featured challenges
High-impact decision contexts where cause-effect reasoning matters.
Discrimination-free insurance pricing
Ensure pricing models do not indirectly discriminate against protected characteristics such as ethnicity.
insurancefairnesscompliance
Root cause analysis
Identify the true causes of process faults and inefficiencies instead of reacting to noisy proxy metrics.
operationsqualityprocess
Customer churn treatment
Find the right intervention for the right customer at the right time to reduce churn risk.
retentioncustomeroptimization
Improved loan decision making
Support lenders with causal signals for accounts nearing default to improve intervention quality.
lendingriskdecisioning
Drivers of demand
Build causal demand models to identify which factors truly move product demand.
demandforecastingstrategy
Treatment effect estimation
Estimate how different interventions change outcomes for specific customer or process segments.
upliftsegmentationintervention
Our approach
From enterprise data to causal decisions in three practical steps.
1
Data refinement
We use AI to accelerate cleaning, integration, quantification, and enrichment of raw enterprise data.
2
Causal model
We create a DAG with algorithmic methods and expert input, then train a Causal AI model on your data.
3
Insights
We query the model to understand, predict, simulate, optimize, and explain recommended actions.
FAQ
Common questions before a first pilot.
Predictive models estimate what may happen. Causal AI estimates what changes when you intervene.
Ready to understand what truly causes your outcomes?
Tell us about your challenge. We'll show you what's possible.