What is Process Grounding?

Process Grounding means anchoring a language model in an organization’s real process and event data, so that its statements rest on verifiable facts rather than on linguistic probability. Instead of supplying documents as text snippets, the model queries a structured process model – such as an Event Knowledge Graph – containing cases, objects, timestamps, and relationships. Every answer can therefore be traced back to concrete process instances.

Why does this matter?

A language model without process context produces plausible-sounding but unsubstantiated claims about throughput times, causes, or responsibilities. Grounding is the difference between an assistant you have to believe and one whose statements you can verify – the precondition for involving AI in decisions at all.

What does this look like in practice?

Asked "why does invoice checking take so long?", an ungrounded model answers with generalities from its training corpus. A grounded one reaches into the process graph and names the 1,203 cases in which approval waited on a single reviewer, with the average wait attached.

Not to be confused with

Not to be confused with RAG on its own, which retrieves passages from documents. Process grounding anchors the model in structured event data, so it can count, compare and trace causes rather than quote.