Solution
Detect anomalies early with no-code Industrial AI.
Traditional alarms fire late and noisy. Intelecy models the joint behavior of dozens of tags, so subtle deviations surface early — and false alarms stay down.
- 14:22HIGHCMP_042 residual +3.2σ
- 11:08MEDDRY_B drift trend
- 07:41LOWBLR_01 efficiency dip
- 02:15HIGHPUMP_07 cavitation pattern
The problem
What makes this operationally expensive today.
Manual monitoring misses patterns
Engineers can't watch thousands of tags; subtle drift hides in plain sight.
Alarm flood / alert fatigue
Thresholds fire too often or too late — operators learn to ignore them.
No explanation per alert
Without a top-contributor view, alerts become tickets nobody resolves.
Signals we monitor
The process indicators behind every recommendation.
Intelecy reads these signals from your Historian, DCS, and SCADA — no new sensors required to get started.
- 14:22HIGHCMP_042 residual +3.2σ
- 11:08MEDDRY_B drift trend
- 07:41LOWBLR_01 efficiency dip
- 02:15HIGHPUMP_07 cavitation pattern
How Intelecy solves it
From signal to action, mapped to the modules.
STEP 01
Pick the asset and the envelope
Define a healthy period — Intelecy learns what 'normal' looks like across all selected tags.
STEP 02
Train a no-code anomaly model
One-click templates with built-in validation; engineers stay in control.
STEP 03
Make every alert explainable
Top contributors and a side-by-side with the last healthy period — designed for the control room.
STEP 04
Monitor the model itself
Drift, precision and false-positive rate tracked over time; retrain in a click when reality changes.
Expected KPIs
How customers measure success.
MTTD
▼min
Mean time to detect
Alert precision
▲%
Operator-confirmed
Recall
▲%
Real events caught
Model drift
▼σ
Tracked weekly
Proof
Specialty chemicals site
Chemicals
“We replaced four threshold alarms with one Intelecy model — same coverage, a fraction of the noise.”
Headline result
−71%
false alarms vs threshold-based
Implementation
What it takes to stand this up.
Data needed
20–60 tags per asset covering the operating envelope. 6–12 months of history recommended.
Timeline
First model live in days; full asset coverage in weeks.
Stakeholders
Process engineer, control room supervisor, plant IT/OT.
Integration
Read-only Historian access via Gateway. Alerts to email, Teams, ServiceNow, 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.