Which lever moves which outcome, and by how much?
Circonomit builds a causal model of your decision problem: every lever, every outcome, every dependency is explicit. Calculated, traceable, defensible.
Correlation is not a lever. Causation is.
Your decisions rest on cause-effect assumptions — which lever moves which outcome, and by how much. Most of those assumptions are never made explicit, never calculated, never tested.
Black-box models deliver results without showing the path. When the result is questioned, there is no trace — just a number the tool produced.
Correlations in dashboards look like causation but are not. Which driver really moves the outcome, which just moves alongside it, no report can tell you.
Circonomit makes the causal structure explicit.
Your dashboards show correlations. Circonomit builds a causal model — every lever, every outcome, every dependency explicit. No black box, every assumption traceable.
Model
First the causal structure is modelled: which levers exist? Which outcomes matter? Which cause-effect chains connect them? Circonomit connects your data and your experts’ knowledge into an explicit causal model.
Simulate
What happens if a lever moves? How far does the effect propagate? Which other outcomes shift with it? Circonomit simulates the causal chain, not just the direct effect.
Optimize
Circonomit calculates which lever produces the biggest effect on the target outcome, under your real conditions. With an explicit causal path — not a black-box number.
Decisions with a traceable causal path.
The result: a calculated causal model for your decision problem, with explicit levers, outcomes and cause-effect chains. You know which lever moves which outcome, and what every deviation in the assumptions concretely costs.
From correlation to calculated causation.
You see which lever really moves the target outcome — calculated, not correlated.
Every assumption in the model is explicit. No black box, every result traceable.
No new data project. Circonomit works with your existing data.
What you want to know.
Q:
How is this different from a BI dashboard?
A:
BI dashboards show correlations — what moves together. Circonomit models causation — which lever actually moves which outcome, and by how much. That is a different question. Correlation tells you what happened, causation tells you what happens if you act.
Q:
What does the pilot concretely deliver after 3 weeks?
A:
A calculated causal model for the agreed decision problem, based on your real data. Explicit levers, outcomes, cause-effect chains. Three to five quantified scenarios with sensitivities per assumption. The success criterion is agreed in writing before work starts. Fixed price: EUR 7,500 net.
Q:
How do you handle assumptions we cannot prove?
A:
Explicitly. Every assumption that is not backed by data is marked as an expert assumption in the model and tested with sensitivities. If the result holds across a wide range, the assumption matters less; if the result flips, the assumption is a priority for validation. That is how expert knowledge becomes defensible input, not a hidden bias.
Circonomit testen
Sprechen Sie in Ruhe mit dem Team.
Erstgespräch bei einem digitalen Kaffee
Wir verstehen Ihr Problem
Pilotmodell in 3 Wochen, Fixpreis 7.500 €
Auf Basis Ihrer realen Daten. Erfolgskriterium schriftlich vereinbart
Entscheidungen, die Sie vertreten können
Nachvollziehbar, quantifiziert, wiederholbar
