XMPro

We’re pleased to share our latest document, "XMPro Asset Operation Solutions: Transforming Asset Management through Advanced Predictive Maintenance." This document outlines how senior managers and engineers can reduce downtime and extend asset life by leveraging AI-driven predictive maintenance. XMPro integrates real-time data from existing systems, allowing for informed decisions based on real-time insights. Our platform enables organizations to move from reactive to proactive asset management, optimizing maintenance schedules and improving resource allocation. Download the document today and see how your organization can reduce operational inefficiencies and achieve more reliable asset performance. #AssetManagement #PredictiveMaintenance #AI #OperationsOptimization #XMPro #PDM #DigitalTwins #Industrial #Operations #IoT

AI is reshaping the mining industry, improving efficiency, sustainability, and decision-making. On March 18, XMPro CEO Pieter van Schalkwyk will present at the AI and Applications of Data Working Group Webinar, sharing insights on the Foundations of AI in Mining Project. This session will cover: ?? How AI and machine learning are driving real-time optimization and predictive maintenance. ?? Practical applications of AI in mining operations. ?? The role of data-driven strategies in improving productivity and safety. Pieter will speak alongside: ?? Mary A. (Lia) Walker, Freeport-McMoRan – Driving Step Change in Mining ?? Rob Johnston, CITIC Pacific Mining – Working Group Update AI is changing the way mining companies operate. Learn how industry leaders are putting it to work. ?? Register here: https://lnkd.in/ggjARunP

???????????? ?????? ?????????? ????????: ?????????? ????'?? ???????? ?????????? ???????? We've all experienced it: a new AI model launches, stocks swing wildly, social media floods with impressive demos... then reality sets in. Remember the market panic during the "DeepSeek moment" or the breathless coverage of Manus? Just the latest examples of our fixation on individual models. Working with XMPro has revealed a crucial truth: AI models themselves are becoming commodities. The real value lies in business operation intelligence systems that embed these models within end-to-end business processes. Why business operation intelligence systems matter more than any flashy new model: ? A single breakthrough model is impressive; a complete intelligence system that orchestrates traditional analytics, machine learning, and generative AI is transformative ? When a standalone model fails, work stops completely; comprehensive intelligence platforms maintain business continuity even when components fail ? Models age quickly with each release; well-designed business intelligence systems evolve continuously without disruption ? Generic models struggle with specific industry challenges; complete operation intelligence platforms adapt to your unique operational requirements ? Models generate ideas; intelligent operation systems turn those ideas into protected IP and measurable business outcomes The quiet revolution isn't happening in research labs releasing new models—it's happening on factory floors and in operations centers where complete business intelligence platforms are transforming disconnected AI capabilities into sustainable operational excellence. What about you? Is your organization still jumping from model to model, or are you building a comprehensive intelligence system that can evolve beyond today's AI hype cycle? Share your experience or challenges below! #ArtificialIntelligence #AIStrategy #BusinessTransformation #IndustrialAI

???????????? ?????? ?????????? ????????: ?????????? ????'?? ???????? ?????????? ???????? We've all experienced it: a new AI model launches, stocks swing wildly, social media floods with impressive demos... then reality sets in. Remember the market panic during the "DeepSeek moment" or the breathless coverage of Manus? Just the latest examples of our fixation on individual models. Working with XMPro has revealed a crucial truth: AI models themselves are becoming commodities. The real value lies in business operation intelligence systems that embed these models within end-to-end business processes. Why business operation intelligence systems matter more than any flashy new model: ? A single breakthrough model is impressive; a complete intelligence system that orchestrates traditional analytics, machine learning, and generative AI is transformative ? When a standalone model fails, work stops completely; comprehensive intelligence platforms maintain business continuity even when components fail ? Models age quickly with each release; well-designed business intelligence systems evolve continuously without disruption ? Generic models struggle with specific industry challenges; complete operation intelligence platforms adapt to your unique operational requirements ? Models generate ideas; intelligent operation systems turn those ideas into protected IP and measurable business outcomes The quiet revolution isn't happening in research labs releasing new models—it's happening on factory floors and in operations centers where complete business intelligence platforms are transforming disconnected AI capabilities into sustainable operational excellence. What about you? Is your organization still jumping from model to model, or are you building a comprehensive intelligence system that can evolve beyond today's AI hype cycle? Share your experience or challenges below! #ArtificialIntelligence #AIStrategy #BusinessTransformation #IndustrialAI

Nicolas Waern - Digital Twin Specialist posted an interesting Vitruvian principles comment on my article on "Beyond Code: How Parametric Agents Transform Industrial AI Implementation." (https://lnkd.in/grcknR-V). Here is a short reply ?? Nicolas' comment made me think about how our parametric agents follow these ancient but powerful design truths. Vitruvius showed us that success comes from working with reality rather than against it. His principles - durability, utility, and elegance - map perfectly to how MAGS operates in complex industrial environments. Let me share how these principles appear in our system: ?? Firmitas: Durability?means our agents operate within clear physical and operational boundaries. Parameters reflect actual equipment limitations and safety constraints rather than arbitrary rules in code. ?? Utilitas: Utility?lets subject matter experts shape agent behavior using concepts they already understand. Operators work with familiar equipment data and visual displays instead of learning to code. ?? Venustas: Elegance?comes from separating what agents can do from how they're controlled. This creates simpler solutions that operators can adjust without changing underlying systems. #MAGS #AgenticAI #MultiAgents

???? ????. ???? – ?? ?????????????????? ??????????????????????, ?? ?????????????????????? ???????????? The friction between IT (Information Technology) and OT (Operational Technology) is one of priorities and perspectives: ?? IT is focused on security, governance, and enterprise-wide standardization. ?? OT prioritizes real-time control, uptime, and operational agility. Yet, IT/OT convergence isn’t optional—it’s critical. Organizations must bridge these two worlds to unlock predictive insights, automation, and intelligent decision-making. But this convergence introduces major challenges: ?? ???????????????? ????. ?????????????????????????– IT enforces cybersecurity, while OT needs always-on systems. ?? ?????????????????????????????? ????. ???????????? ???????????????– IT prefers cloud and scalable solutions; OT relies on long-lifecycle industrial systems. ?? ???????? ???????????????????? ????. ????????-???????? ?????????????????? – IT structures data for analytics; OT must make split-second decisions at the edge. ?? ?????????????? ????. ?????????????? – IT wants controlled, standardized solutions, while OT needs flexibility to adapt quickly. ?? ?????????????? ?????????????????? & ?????????????? – Many organizations struggle with defining who leads IT/OT convergence and how it’s funded. Four Pillars of IT/OT Convergence ?? ???????????????? ?????????????????????? – Connecting industrial assets (PLCs, sensors, controllers) to IT networks ?? ???????????????? ?????????????????????? – Integrating enterprise IT (ERP, CRM) with OT systems (MES, SCADA) ?? ???????? ?????????????????????? – Unifying IT & OT data ????????????????? ?????????????????????? – Synchronizing business processes with shop-floor workflows. ??????????: ?????? ???????????? ?????????????? ???? & ????: This is where XMPro makes the difference. Our Intelligent Business Operations Suite (iBOS) is built to satisfy both IT and OT teams by: ? Seamless IT/OT Connectivity – XMPro integrates industrial assets (SCADA, sensors, machinery) with enterprise IT systems (AI, analytics, cloud)—enabling a unified operational strategy. ? Real-Time Data Utilization – XMPro provides enhanced visibility into operations, allowing teams to monitor performance, detect anomalies, and respond instantly to critical events. ? Predictive & Prescriptive Insights – Going beyond traditional data analysis, XMPro enables AI-driven recommendations to prevent failures, optimize workflows, and drive efficiency. ? Empowering SMEs with No-Code Tools – OT teams can rapidly compose and deploy solutions without needing IT for every change. XMPro’s no-code platform allows SMEs to configure workflows and automate processes—without programming. ? Scalability & Long-Term Adaptability – XMPro evolves with your business as it grows in its digital transformation journey. With XMPro, IT and OT don’t have to compete—they can collaborate seamlessly to create a secure, agile, and data-driven future.

?????????????? ????????????????: ?? ?????????????????? ???????????????? ???? ???? ???????????????????? Autonomous AI agents shouldn't be an all-or-nothing debate. Pieter van Schalkwyk breaks down the real challenge—balancing autonomy with governance—using XMPro’s Multi-Agent Generative Systems (MAGS). Instead of fearing unbounded autonomy, we need structured independence: ? Computational Policies – Enforcing fundamental constraints ? Deontic Principles – Encoding ethical guardrails ? Expert System Rules – Leveraging domain knowledge This approach ensures AI agents are powerful, safe, and scalable—without losing human oversight. A must-read for anyone working with industrial AI. ??

New on YouTube! "Multi-Agent Generative Systems in Industrial Applications" Presented by Pieter van Schalkwyk, XMPro This presentation introduces Multi-Agent Generative Systems (MAGS), an advanced AI-driven platform that integrates multiple software agents with large language models to optimize industrial processes. Unlike traditional multi-agent systems, MAGS agents can reason, collaborate, and adapt to complex environments. In this clip, Pieter provides a definition of MAGS and breaks it down into three primary categories. Pieter explores how MAGS agents observe their environment, reflect on past experiences, plan actions, and execute tasks. He highlights the crucial role of digital twins in providing a real-time proxy for physical assets, enabling continuous monitoring and simulation, including: 1. Advanced reasoning capabilities 2. Collaborative problem-solving among agents 3. Adaptive decision-making in dynamic environments 4. Integration with real-time data and business applications Pieter concludes with a live demonstration of MAGS managing an industrial business process using digital twins, showcasing its potential to transform operations through intelligent automation and continuous optimization. Watch the entire video on our YouTube channel and subscribe: https://bit.ly/40OXL75 #MultiAgentGenerativeSystems #MAGS #AI #ArtificialIntelligence #IndustrialSector #ManufacturingSector #IntelligentAutomation

New on YouTube! "Multi-Agent Generative Systems in Industrial Applications" Presented by Pieter van Schalkwyk, XMPro This presentation introduces Multi-Agent Generative Systems (MAGS), an advanced AI-driven platform that integrates multiple software agents with large language models to optimize industrial processes. Unlike traditional multi-agent systems, MAGS agents can reason, collaborate, and adapt to complex environments. In this clip, Pieter provides a definition of MAGS and breaks it down into three primary categories. Pieter explores how MAGS agents observe their environment, reflect on past experiences, plan actions, and execute tasks. He highlights the crucial role of digital twins in providing a real-time proxy for physical assets, enabling continuous monitoring and simulation, including: 1. Advanced reasoning capabilities 2. Collaborative problem-solving among agents 3. Adaptive decision-making in dynamic environments 4. Integration with real-time data and business applications Pieter concludes with a live demonstration of MAGS managing an industrial business process using digital twins, showcasing its potential to transform operations through intelligent automation and continuous optimization. Watch the entire video on our YouTube channel and subscribe: https://bit.ly/40OXL75 #MultiAgentGenerativeSystems #MAGS #AI #ArtificialIntelligence #IndustrialSector #ManufacturingSector #IntelligentAutomation

?? Why can't we just document everything our experienced operators know? Industrial companies are losing decades of operational expertise as experienced workers retire. Many think the solution lies in better documentation and content-based automation. But here's the reality: You can't capture human judgment in a procedure manual or workflow. In my latest article, I explore why cognitive agents represent the missing piece in industrial automation. Learn: ? Why content-based automation isn't enough ? How cognitive agents actually make operational decisions ? Why preserving expert knowledge requires more than documentation ? How XMPro helps companies maintain operational excellence even as their workforce changes #IndustrialAutomation #CognitiveAgents #OperationalExcellence #Manufacturing #ProcessIndustry #MAGS #DigitalTwins Thoughts? I'd love to hear your experiences with preserving operational expertise in your organization.