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.

Anomaly detectionAlertsExplanationsModel monitoring
ALERTS · last 24h4 events
  • 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.

multivariate residualtag contributiondrift scorealert severitymodel freshnessoperator acknowledgments
ALERTS · last 24h4 events
  • 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.

  1. Pick the asset and the envelope

    Define a healthy period — Intelecy learns what 'normal' looks like across all selected tags.

  2. Train a no-code anomaly model

    One-click templates with built-in validation; engineers stay in control.

  3. Make every alert explainable

    Top contributors and a side-by-side with the last healthy period — designed for the control room.

  4. 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."
Control room lead
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.

Get started

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.