The idea

Pair live telemetry with a model of the system. Simulate futures. Choose an action. Learn next time.

Pattern

• Ingest: clean events with time, identity, and quality flags.
• Model: physics + data models; versioned; testable.
• Policy: guardrails and approvals for when to act.
• Act: APIs back to the plant, fleet, or app—humans in the loop.
• Learn: capture outcome; update model; track drift.

First 30–60 days

• Pick one decision (dispatch, route, threshold). Define “good.”
• Wire one clean data feed; build a baseline sim; compare to reality.
• Run a human-approved action weekly; log results; tighten loops.

Signals / KPIs

• Decision delta ($/time/quality), approval latency, false alarm rate.
• Model drift; percent of actions auto-approved vs. manual.