AI agents that learn how your assets behave and monitor them continuously, delivering diagnostics and performance recommendations you can trust.
Built from foundational asset knowledge, physics principles and your plant’s own data, then certified by experts.
Asset ontology, industry and OEM documents and SME input are ingested to create the first version of the Agentic Twin.
Plant documents and topology are layered in, adding FMEA++, subsystem boundaries and base physics models.
Historian data, O&M logs and expert review teach the twin real operating modes and known failure signatures.
With a behaviorally complete twin, specialized agents take over, each an autonomous solution on the platform.
Asset ontology, industry and OEM documents and SME input are ingested to create the first version of the Agentic Twin.
Agentic model development helps build accurate ML models fast; agentic diagnostics closes out alerts and names the failure mode with evidence.
Failure modes from FMEA, historian and O&M logs
Features, failure injection, model training
Backtested on real events, SME gate
Alert logic calibrated, model registered and served
Alert screened against operating context; false positives closed out
Agentic RCA names the failure mode with evidence attached
Verdict and SME feedback written back to the twin
Physics the agent cannot argue past
Signal, tags, rule applied, confidence
Knowledge enters only after SME sign-off
Every recommendation checked before it reaches an operator
From deepwater oil & gas and LNG to refineries, chlor-alkali plants and steel mills, the Agentic Twin™ adapts to your assets and your data.
“The diagnostics actually explain themselves. That is the difference between an alert we ignore and an alert we act on.”
“We went from a pilot conversation to monitored assets in weeks, without hiring a data science team.”
“It caught a compressor failure eleven days out. That single call paid for the deployment.”
See it on your own data, across your assets and processes.
