AI agents that learn how your assets behave and monitor them continuously, delivering diagnostics and performance recommendations you can trust.
Assists data scientists in building asset and process models, and operators with prognostics and root cause analysis, and optimum set points.
Explore the Agentic Twin™Agents onboard the asset into a Digital Twin representation. Asset ontology, industry documents, asset type documents, OEM documents and SME information are ingested to create foundational information about the asset: the first version of the Agentic Twin.
Plant documents (P&IDs, PFDs), engineering drawings and plant-specific topology are layered in. The enriched Asset Model now includes FMEA++, subsystem boundaries, parameter relationships and base physics models.
Historical time-series, O&M logs and expert review teach the twin real operating modes, known failure signatures, and how the asset behaves across seasons and duty cycles.
With a behaviorally complete twin, specialized agents take over, each one an autonomous solution running on the Agentic Twin platform.
Agents onboard the asset into a Digital Twin representation. Asset ontology, industry documents, asset type documents, OEM documents and SME information are ingested to create foundational information about the asset: 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
Every recommendation passes five checks before it reaches an engineer or operator. An LLM pointed at plant data will always produce an answer; in a plant, a confident wrong answer is a lost shutdown window or a safety event.
Assembled from the certified plant model.
Physics the agent cannot argue past.
Signal, tags, rule applied, confidence.
Knowledge enters only after SME sign-off.
The agent proposes. Your team approves.
The output is not an opinion about your plant. It is an engineering decision you can defend.
From deepwater oil & gas and midstream pumping fleets to LNG, refineries, chlor-alkali plants and steel rolling mills, the Agentic Twin™ adapts to your assets, your processes 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 in action across your plant, assets, and processes, with your own data.
Spector.ai is live in production across the US, the Middle East and India, with deployments in oil and gas, chemicals, power and manufacturing. See the Agentic Twin™ on your own plant data.
