Data Ontology
It learns your business, not just your tables.
Connect spreadsheets, databases, documents and data warehouses. RootCause.ai resolves entities, time, location and meaning without a bespoke pipeline.
Simulate the decision. See the outcome. Then commit.
Insights are easy to get. Knowing which decision moves the number isn't. RootCause.ai is a decision intelligence platform that unifies your systems into one data ontology, finds what drives your outcomes, and lets you simulate a decision before you commit to it.
Digital Twin Simulation
25%
Promotion length
- Net units
- −1.2%
- Gross profit
- −$79.6k
- Verdict
Daily units for one product line, with 5 weeks of history. Forecast for a 25% discount for 3 weeks with a 20% tariff on exports into the US, measured over weeks 1 to 10: net units down 1.2%, gross profit down $79.6k, gross margin down 3.6 points. Significant loss. The promotion on its own: gross profit up $1.4k against no promotion. Without the tariff: net units up 23.8%, gross profit up $1.8k. The tariff alone takes $81.0k of gross profit. Units rise during the promotion, then dip below the baseline as demand is pulled forward. The tariff lowers every day from week 1, the promotion's included.
Data ontologyCausal modelSimulation
Fragmented data in. A digital twin you can simulate decisions against out.
RootCause.ai compresses a months-long specialist workflow into one autonomous system.
Causal Model
Generate the model. Inspect every edge.
RootCause.ai searches the causal structure, quantifies confidence and exposes the evidence behind every relationship. Nothing is asserted that you cannot trace back to the data. The result is a temporal digital twin you can interrogate.
Simulation engine
Ask what happens before you act.
Run interventions, counterfactuals, forecasts and optimizations through the same underlying model. Use it directly, or expose it through API and MCP.
Conventional discovery
O(n²)
RootCause.ai
O(n log n)
volume of data (GB)→
time to result→
Causal AI Scaled.
What this looks like in production.
Four deployments, four different questions, and what the causal model found in each.
20% → 5%Forecast error
Market and operational levers mapped into forecasts that planning teams can use. Built in one week against an estimated 1.5 years to build internally.
- Demand Forecasting
- Churn Prevention
- Marketing ROI
95%Less analysis effort
KPI root cause that took two analysts three to four days per KPI per market, run automatically across 142 KPIs and 42 markets.
- KPI Root Cause
- Performance Drift
€30MAnnual savings potential
Demand, shrink and pricing connected to the decisions that move them, including the feedback loop where a stockout compounds losses that never get attributed back to it.
- Pricing
- Shrink
- Demand
$500KSavings per 1,000 vehicles
12,000 incidents and 10GB of telematics. The real driver was minutes since last rest break. Not speed, not weather, not location. Found in one hour.
- Safety
- Fleet Optimization
Use cases
20% → 5%Forecast error
Market and operational levers mapped into forecasts that planning teams can use. Built in one week against an estimated 1.5 years to build internally.
- Demand Forecasting
- Churn Prevention
- Marketing ROI
95%Less analysis effort
KPI root cause that took two analysts three to four days per KPI per market, run automatically across 142 KPIs and 42 markets.
- KPI Root Cause
- Performance Drift
€30MAnnual savings potential
Demand, shrink and pricing connected to the decisions that move them, including the feedback loop where a stockout compounds losses that never get attributed back to it.
- Pricing
- Shrink
- Demand
$500KSavings per 1,000 vehicles
12,000 incidents and 10GB of telematics. The real driver was minutes since last rest break. Not speed, not weather, not location. Found in one hour.
- Safety
- Fleet Optimization
Built for the questions procurement will ask.
A platform engineered for security, usability, and transparency. Built for regulated industries.
Your data never leaves your organization.
RootCause.ai runs inside your own environment.
Connect to how you already work.
API, SDK, and MCP support so the model plugs into your stack.
Every answer is traceable to its source.
No black box. Trace the answers back to the data.
Powered by causal inference.
Decades of causal science sat with big tech, in-house data teams, and long consulting engagements. RootCause.ai packages it into one repeatable platform. Thousands of causal models in parallel, built in hours. Specialists used to manage only a handful a year.
See exactly what matters. Control what happens next.
Bring a high-value question and the data around it. We will show you the causal model behind the answer.
Book a Demo
A 30-minute walkthrough with a member of our team.