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What is OPUS?

OPUS stands out as a premier no-code AI platform designed specifically for the industrial sector, enabling users to effectively model both equipment and processes. By utilizing OPUS, organizations can access a wide array of benefits including insights for process optimization, predictive maintenance, reduced energy consumption, enhanced productivity, precise forecasting, greater asset reliability, diminished maintenance expenditures, improved planning capabilities, and valuable ESG reporting with carbon reduction insights. Teams can leverage this platform without needing any programming skills, allowing them to extract insights from their data and anticipate future scenarios effortlessly. Dive deeper into your asset's data like never before, revealing unforeseen correlations that may not have been immediately apparent. Conducting root cause analysis on specific components can help direct your efforts where they matter most, ensuring efficient use of resources. The automated AI predictive insights provided by OPUS enable strategic planning for interventions and empower you to make well-informed business decisions confidently. With the capability for rapid deployment and receiving AI model results in mere minutes after creation, organizations can harness the full potential of their existing operational data, leading to tangible returns on investment with the expertise of their current team of asset engineers, operators, and maintenance managers. Transform your entire facility, plant, or work site by tapping into the remarkable potential of automated AI and witness the significant improvements in operational efficiency and effectiveness. Seize the opportunity to elevate your industrial processes to new heights with OPUS.

What is DC-E DigitalClone for Engineering?

DigitalClone® for Engineering stands out as the sole software that seamlessly combines various scales of analysis within a unified platform. Recognized globally as the premier tool for predicting gearbox reliability, DC-E excels not only in its modeling and analysis capabilities specific to gearboxes and gear/bearing interactions but also uniquely incorporates fatigue life modeling through advanced, physics-based methodologies (US Patent 10474772B2). By enabling the creation of a digital twin for gearboxes, DC-E encompasses every phase of an asset's lifecycle—from the optimization of design and manufacturing processes to the selection of suppliers, followed by thorough root cause analysis of failures and condition-based maintenance along with prognostics. This innovative computational environment significantly decreases both the time and costs associated with launching new designs and ensuring their long-term maintenance, ultimately enhancing operational efficiency. Moreover, it empowers engineers to make informed decisions at every stage, leading to improved performance and reliability.

Media

Media

Integrations Supported

AVEVA PI System
AWS App Mesh
Aspen HYSYS
Azure Industrial IoT
ControlST
FactoryTalk Historian
Microsoft Entra ID
PowerLogic ION EEM
Siemens APM

Integrations Supported

API Availability

API Availability

Pricing Information

Competitive pricing structure based on number of sensors. Included unlimited users and AI model development.

Pricing Information

Upon request
Depending on AWS based non-government and government operations
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

VROC

Date Founded

2016

Company Location

Australia

Company Website

vroc.ai/products/opus/

Company Facts

Organization Name

Sentient Science Corporation

Date Founded

2001

Company Location

United States

Company Website

sentientscience.com

Categories and Features

Artificial Intelligence

Machine Learning
Predictive Analytics
Rules-Based Automation

No-Code Development

AI-Assisted Development
Data Aggregation and Publishing
Deployment Management
Drag & Drop
Integrations Management
Iteration Management
Performance Monitoring
Templates
Visual Modeling

Root Cause Analysis

Not specified

Categories and Features

Digital Twin

Not specified

Engineering

2D Drawing
3D Modeling
Design Analysis
Mechanical Engineering

Root Cause Analysis

Not specified

Simulation

Not specified

Statistical Analysis

Statistical Process Control
Statistical Simulation

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