In healthcare and life sciences, the rules that decide success are rarely written down.
Published regulations tell only part of the story. How a query is typically resolved, how long a review realistically takes, how a similar submission was treated before: this knowledge shapes outcomes, yet it lives in people's experience rather than in any system.
HERMS captures that local judgment and encodes it into machine-readable, auditable intelligence. Five capabilities carry this work: rule extraction, precedent matching, temporal prediction, contradiction detection, and bounded refusal.
Bounded refusal matters as much as the rest. When the evidence is insufficient, the system declines to answer rather than guessing. In regulated environments, an honest refusal is safer than a confident error.
The pattern across files, not the files themselves, is the product. Precedent, query behaviour, and cycle times become intelligence that every application can use.
Make the unwritten rules visible, structured, and accountable.