Data Model and contextualization
Create industrial models and contextualize your data to perform analyses, save events and timeseries, creating a true digital twin of your assets.
Hierarchy Asset
Model the structure of your industrial plant with a tree hierarchy: Plant → Area → Production Line → Equipment → Sensor. Each level can have custom attributes and relationships.
Model ISA-95
International standard for industrial asset modeling
Flexible Relationships
Parent-child, dependencies, alternative paths and custom rollups
Dynamic Attributes
Add custom properties to each level of the hierarchy
Full Versioning
Track every change to the structure with audit trails
Asset Hierarchy Tree
Milan factory
Asset Time History
Associate time series with your data model to track the evolution of quantities over time. Collaborate with your team through annotations and prepare data for advanced AI analytics.
Compressor
Production Line 1 → Press Area → North Department
Start of production shift - parameters normal
Abnormal peak in vibration - preventive maintenance required
Diagnostics completed - main bearing replaced
AI capabilities
Gen AI will use all time series history to provide advanced insights
Search for Anomalies
AI identifies anomalous patterns and automatically flags potential issues
Predictive Analysis
Forecast of the evolution of quantities based on history
Tips Series
The AI suggests related time series to add to the chart
Documentation link
Automatic link with relevant technical documentation
Statistics
Complete history
Each asset maintains a complete history of its quantities, creating photographs of the data over time
Team collaboration
Users can annotate comments and describe behaviors directly on the time series
AI-Ready Data
Structured and contextualized data, ready for advanced analysis with artificial intelligence
Automatic Events and Notifications
Intelligent rule engine to generate automatic alerts based on real-time conditions. Send notifications across multiple channels to keep your team informed.
Rule Engine
Define rules based on logical conditions applied to real-time data. Each rule can monitor thresholds, anomalies and temporal patterns.
Flexible conditions
Logical and temporal operators
Real-time validation
Continuous control over data
Automatic Severity
Alert classification
Examples of Rules
Temperature Threshold
temperature > 85°CVibration Anomaly
vibration > baseline * 1.5Maintenance Due
hours_since_maintenance > 1000Output Channels
Send automatic notifications across multiple channels to reach your team
Database
Persistent history
File
CSV, JSON, XML
Teams
Microsoft Teams
WhatsApp Business
Telegram
Telegram Bot
Slack
Slack Webhooks
Multi-Channel Routing
Send the same alert on different channels based on severity
Customizable Templates
Configure the message format for each channel with dynamic variables
Asset Metadata
Enrichment of contextual information
Enrich with Metadata
Add contextual information to each asset: technical specifications, maintenance, certifications, documentation. Make data searchable and analyzable.
Flexible Scheme
Define custom attributes for each asset type
Automatic Import
Integrate with ERP, CMMS and existing systems
Full-Text Search
Search all metadata with advanced queries
Validation Rules
Ensure data quality with validation rules
Language Common
Create a semantic layer that translates technical data into business terms. A single language for production, quality, maintenance and engineering.
Business Metrics
Transform technical tags into understandable KPIs: OEE, MTBF, MTTR
Unit Conversion
Automatically convert units of measurement between different systems
Contextual Rules
Apply business logic for complex calculations
Data Lineage
Trace the origin of each metric back to the sensor
Semantic Mapping
From raw data to business metrics
Start Today.
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