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XMPro AI
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Experience the transformative power of XMPro’s Intelligent Business Operations Suite (iBOS) – Featuring comprehensive AI capabilities, XMPro iBOS helps to significantly increase product yield, drastically reduce downtime, and ultimately eliminate unexpected business events.
XMPro iBOS consistently empowers operations to achieve their full potential by delivering > 10X ROI.
5-10% Reduced
Maintenance Costs
10-20% Increased
Equipment Uptime
20-50% Decreased
Maintenance Planning Time
40-60% Reduced
MTTR (mean-time-to-repair)
10-30% Increased
Asset Service Life
45% Increased
Amortization in 1 year
10-25% Reduced
Carbon Emissions
1.5 – 2 X ROI
On ESG Investment
25-40% Reduction
In Waste
35-45% Reduced
Production Downtime
20-30% Increase
In Production
10-15% Increase
In Bottom Line
50-70% Reduced
Product Defects
15-25% Increase
In First-Pass-Yield
20-40% Reduction
In SPC Alerts
An Intelligent Operations Platform Infused with AI
Imagine a world where your digital twin is not just a passive repository of data but an active, always-on system that continuously monitors and interacts with the environment and assets.
Embedded AI >> Seamlessly integrate AI models into your digital twins
Embedding XMPro AI Agents in XMPro Data Streams enables Executable AI and Machine Learning for Algorithmic Business Processes, which in turn, enhances the capabilities of operational digital twins, such as integration of AI into core business processes, MLOps and real-time analytics.
Empower your MLOps teams and transform your AI Models into powerful assets that drive business growth and efficiency and bridge the divide between data flow and operational AI.
Innovation AI >> Collaborate faster and smarter with integrated real-time data
XMPro Intelligent Digital Twins offer a powerful platform for facilitating scalable and cost effective innovation and experimentation in AI by incorporating XMPro Notebooks. XMPro Notebooks provide an interactive environment that allows continuous innovation and experimentation, such as front running simulations and visualisations with data, algorithms, and models in real time.
Augmented AI >> Automate decisions and make AI more accessible to your business
XMPro Augmented AI for Self-learning Digital Twins harnesses the power of artificial intelligence and machine learning to enhance the decision support and automation capabilities of Digital Twins built on the XMPro platform.
Integrate real-time data to augment your event data with generative open AI.
How It Works
XMPro AI is integrated into our Intelligent Digital Twin Suite, from the agents, reading and contextualising your data sensors, to rapid development and management of AI Models, to our Generative AI to help make sense of the data.
Watch this demo to see how XMPro AI can leverage organisational knowledge to automate and streamline machinery maintenance.
Use Case Examples
In the power utility sector, the use of an AI-based ‘heat-rate optimizer,’ which analyzes numerous data points in real-time and suggests performance enhancements, can overcome optimization challenges, potentially increasing efficiency by 1%, leading to substantial annual savings, reduced greenhouse gas emissions, and more efficient use of workers’ time
AI-powered Digital Twins in manufacturing can streamline cycle times, automate defect detection, and enhance inspection success, while also lowering CO2 emissions by over 10%, reducing product testing time by 45% for cost savings, and nearly eliminating defects and false alarms, a significant improvement over the previous error rate from human inspections.
AI-powered Digital Twins used for predictive maintenance of underground conveyors have facilitated real-time failure anticipation and efficient scheduling of maintenance, leading to significantly reduced downtime and an additional 44,000 tons of ore mined in a potash mine over five months which equates to millions of dollars of additional revenue.
AI-powered Digital Twins are effectively used for real-time detection of pipe bursts in water distribution systems, where the system processes pressure and flow sensor data, predicts future values using artificial neural networks (ANN), and compares these with actual observations to gauge the probability of a failure event, thereby raising relevant alarms.