Reduce unplanned downtime with no-code Industrial AI.
Unexpected stoppages cost a process plant more than any single quality defect. Intelecy turns Historian data into operator-grade early warnings — typically hours before a failure event.
What makes this operationally expensive today.
Unexpected stoppages
Critical assets trip without warning; reactive maintenance burns shifts and parts.
Process upsets
Cascading deviations across a unit are spotted too late to recover the batch.
Hidden degradation
Slow drift on bearings, heat exchangers, and pumps hides under normal alarms.
The process indicators behind every recommendation.
Intelecy reads these signals from your Historian, DCS, and SCADA — no new sensors required to get started.
From signal to action, mapped to the modules.
- STEP 01Data Explorer →
Build a normal envelope per asset
Use Data Explorer to define healthy operating windows from historical runs your engineers approve.
- STEP 02Anomaly detection →
Train a multivariate anomaly model
Templates handle compressors, pumps, dryers and heat exchangers — no Python required.
- STEP 03Forecasting →
Forecast time-to-event
Project residuals forward so maintenance can intervene during a planned window, not at 02:00.
- STEP 04Operator workflow →
Route alerts to the right team
Severity tiers, quiet hours and escalation routes keep alerts trusted on the shop floor.
How customers measure success.
Nordic process operator
"We get a residual alert with a clear top contributor list. Maintenance now intervenes the same shift — not after the trip."
What it takes to stand this up.
20–40 tags per critical asset (vibration, current, temp, pressure, flow) from your Historian.
First anomaly model live in 1 week; backtested against the last 12 months.
Reliability lead, process engineer, plant IT/OT, maintenance planner.
Read-only from PI / AVEVA / AspenTech via the Intelecy Gateway. Alerts to email, Teams, or your CMMS.
See it on your own plant data.
A 30-minute working session with our process AI team. Bring a unit, a goal, and a few tags — leave with a working model proposal.