Injecting formal process knowledge from guidelines, regulations and documented procedures into learning systems, as a prior that shapes what a model believes and as a guardrail that bounds what it predicts and recommends.
Process mining for processes in which orders, items, invoices, machines and patients interact, instead of one isolated case at a time.
From predicting process problems to recommending actions and testing them in simulation models and digital twins of organizations before they reach the real process.
Turning event data into process models and diagnoses that people can read and trust, from sound yet expressive discovery to performance patterns, deviations and resource effects.