Data Collection captures and validates every event. Active collection pulls data from source systems. Passive collection receives event streams pushed from client systems. Data validation enforces a strict data interface contract, ensuring that structure and content are consistent before further processing.

Causal Mapping and Measurement transforms events into a unified and reliable system of relationships. A semantic Rosetta translates all inputs into a common business language. Intelligent Data Quality reconciles overlapping observations of the same event across systems and aligns timing differences between them. Causal maps then define the cause-and-effect relationships between events, including constraints and measurable causal force.

Causal Results operationalize those relationships as an accessible database. Causal surveillance records each link activation as it actually occurred, which provides transparent auditability. Statistical denoising adjusts for outlier events, producing a stable view of causality for decisions.