SIGAInsight

Transforming Brownfields
into Industry 4.0 SIGA offers
a Paradigm Shift in Industrial
Analytics OT / ICS environments

The ONLY Autonomous Industrial Analytics solution
sourcing unique insights directly from LEVEL 0

SigaInsight is an Autonomous IIoT Solution for Operational ICS Environments, Offering Anomaly and Process Inefficiencies Detection, and Advanced Industrial Analytics on Mission-Critical Automated Equipment, Machinery & Processes

 

AUTONOMOUS

Detached from any network

Plug & Play, “as a Service”, remote monitoring, agnostic to ICS platform

RELIABLE

Monitoring “Electric Signals”

Gain visibility of deepest source of un-filtered, un-hackable and reliable data

SMART

Artificial Intelligence

Advanced machine learning to detect anomalies and deliver actionable insights

Safe Granular Remote process visibility

Unparalleled resolution

 

Situational awareness of critical operations

24/7 anywhere

Out of Band: unidirectional secure data export

0 exposure to cybersecurity threats

Detection of simple and complex correlations

Machines or i/o’s

Independent data archiving

Enabler for root cause analysis and fast recovery of operational processes

Enable Continuous operation 

Even when the ICS/SCADA system is compromised or shut down

Early Failure Detection

The SigaInsight allows constant monitoring and detection of operational anomalies and inefficiencies. Detection of simple and complex correlation anomalies between different machines or i/o’s in their initial evolvement stage will alert the operators for upcoming failures at an early stage.

An enabler to maximize uptime, optimize yield, improve asset health and eliminate bottlenecks.

 

 

Root-Cause-Analysis

Unparalleled, granular visibility into operational processes coupled with accurate measurements at high frequency, allows the SigaInsight to be an advanced root-cause-analysis enabler.

A novel & affordable IIoT solution offering asset-health leaders with a paradigm shift in operational optimization and situational awareness.

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