Every asset carries its own memory and its own Agent. Insmart combines device memory, predictive maintenance, digital twins, and per-asset self-diagnosis into one Physical AI decision layer.
Four capabilities make up the Physical AI decision layer. They share one asset model and one knowledge base, so each one makes the other three sharper.
Every asset keeps an evidence-linked, continuously accumulating memory — what was read, what failed, what was serviced, and the reasoning behind every conclusion. Memory is what makes decisions auditable and trustworthy.
Not "fix it when it breaks" but "know before it breaks." The engine returns a forward-looking failure probability and remaining useful life from the asset's own condition, history, and live events — so teams act before downtime.
Critical assets get a full digital-twin Agent running an Observe → Analyze → Decide → Act → Learn lifecycle, continuously mirroring the real asset's state and behaviour.
Every asset is assigned an Agent that continuously observes itself, judges its own health, locates root cause, and triggers action when needed — no manual asset-by-asset inspection.
All asset Agents share one lifecycle state machine — industry differences are configuration, not a rewrite of the system.
Load device memory and the industry template
Ingest documents, events and sensor data
Assess health and risk
Produce root cause and recommended action
Trigger dispatch or a recommendation
Write outcomes back to the knowledge base
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