Decide on what the data prove, not on what they seem to show.
AllDecisionAI connects to your digital twins, checks whether the relations in the data are really causal, and only then recommends decisions, always with the level of evidence in plain sight.
Correlation is not a decision.
Data show well what goes together. But almost every decision asks something else: what happens if I change this?
"Faster triage, faster diagnosis."
Patients triaged quickly are also diagnosed quickly. The obvious conclusion would be to invest in speeding up triage.
Severity explains both.
Simple cases are fast at every step. Controlling for severity, the effect of triage disappears. What really shortens diagnosis is the time spent on tests.
How it works
Every recommendation goes through the same process, and every step is recorded.
Connect
Receives curated data from AllOntology, from one or more sources, and the state of the digital twins.
Discover
Proposes cause-and-effect hypotheses from the data. Digital twins can also ask for verifications.
Verify
Tests each hypothesis on held-out data, with refutation tests and correction for multiple comparisons.
Decide and learn
Simulates scenarios, recommends options, measures the actual outcome and reviews relations when the data change.
Every relation gets a verdict
The platform always says how solid the evidence behind a recommendation is.
The effect exists and has been measured. It can support decisions and be published to the digital twin.
They go together, but the cause is not proven. It only enters with an explicit warning.
There is not enough data. The platform says what is missing or proposes a test.
There is no relevant effect. The rejection is shared too, so it is not assumed again.
12 questions, from what happened to what to do
For each dataset, the platform first says which questions the data can answer, and why, then answers them with the right method.
Descriptive
- How much?
- How does it evolve?
- How does it compare?
- Who are they?
Diagnostic
- What goes together?
- Why?
- What is odd?
- Where is it lost?
Predictive
- What will happen?
Prescriptive
- What if I do X?
- How do I get there?
- What if it goes wrong?
Capabilities
Built for people who have to justify decisions, not just make them.
Causal engine
Causal structure discovery, adjusted estimation, refutation tests and verdicts with explicit criteria.
Dialogue with digital twins
Answers verification requests and publishes only relations that are validated and approved by experts.
Traps detected
Confounding variables, reverse causation, Simpson's paradox, effects that change over time, and open cases that flatter recent data.
Scenarios and recommendation
What-if simulation with uncertainty, waiting lists and queues, optimisation at the lowest cost and stress tests.
Full audit trail
Pre-registered plan, history of each relation and a record of who approved each decision.
Any data source
Tables from different systems, joined by key or by time, without depending on one format.
Where it applies
In any operation with a digital twin and decisions that cost a lot when they are wrong.
Healthcare
Emergency flows, rosters, waiting times and capacity.
Manufacturing
Defects, maintenance, line speed and energy use.
Energy and cities
Demand, grids, mobility and urban services.
Logistics
Lead times, routes, stock and service levels.
Join the pilot
We are looking for organisations with historical data and a digital twin, or plans to have one, to validate AllDecisionAI in a real setting.
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