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The Domain-AI Operating System for Industry

AI PlantOps

Unlock interconnected industrial intelligence with AI PlantOps

Why AI PlantOps?

Industries run on complex processes, legacy optimizers, and siloed AI models. Yet, decision-making remains fragmented — plant sections optimize in isolation, operators drown in dashboards, and models often stay unused. 

 

AI PlantOps was built to bridge this gap. It fuses domain intelligence, machine learning, symbolic reasoning, and physics-based validation into one interconnected operating system. The result: a new paradigm of AI PlantOps — where every section, cluster, and workflow is digitally traceable, intelligently optimized, and seamlessly connected. 

What is AI PlantOps?

AI PlantOps is the operating fabric of industrial plants. It delivers: 

Process Traceability

End-to-end visibility of streams, assets, and quality through a process-aware knowledge graph.

Unified Intelligence

ML models, optimizers, and existing APC/RTO systems connected into a higher-order orchestration layer.

Cognitive Workflows

Context-aware AI and agentic reasoning for dynamic decision support. 

Validated Decisions

Backed by a control twin (physics-augmented advisory agent) to ensure feasibility and reliability.

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AI PlantOps is not just predictive. It is prescriptive, interconnected, and plant-wide.  

How AI PlantOps Works?

Explore the Layers in 3D
 

Interact with the 3D pyramid to explore how AI PlantOps works from the ground up.Each layer of the pyramid is designed to give you a deeper understanding of the system's structure:

 

🔹 Rotate, zoom, and click on each layer to view detailed descriptions.
🔹 Visualize how the Foundation, Enablement, and Application layers connect and build on one another.
🔹 Discover how knowledge graphs, AI models, and real-world applications come together in a unified OS.

 

 Dive in and see the architecture come to life in 3D!

 

Features

Components of AI PlantOps

Process-Aware Knowledge Graph

Process-Aware Knowledge Graph

Digital DNA of the plant for end-to-end traceability, causality, and reasoning.

Machine Learning

Machine Learning

Predictive insights on quality, reliability, energy, and production performance.

Context-Aware AI

Context-Aware AI

Cognitive agents that understand plant situations, workflows, and operator intent.

Physics-Augmented Agentic AI

Physics-Augmented Agentic AI

Multi-agent systems that blend first-principles physics with AI reasoning for validated decision-making.

Control Twin (Validation Layer)

Control Twin (Validation Layer)

A first-principle advisory agent acting as a back-end guardian, validating every AI recommendation before action.

AI PlantOps Philosophy

Plants are not isolated units — they are networks of streams, utilities, and workflows. Optimizing one section without considering its impact on others leads to trade-offs, not transformation. 

 

98%
AI PlantOps
Interconnectedness across clusters (crude-to-products, hydrogen, utilities, emissions, maintenance)
Interconnectedness across clusters (crude-to-products, hydrogen, utilities, emissions, maintenance)
Fusion of intelligence <br> across symbolic, ML, optimizers, and physics models
Fusion of intelligence
across symbolic, ML, optimizers, and physics models
Total Plant Intelligence for operational excellence, reliability, and sustainability
Total Plant Intelligence for operational excellence, reliability, and sustainability
Interconnectedness

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Workflow Use Case

Industry  Use Case

1. Maintenance & Planning +

1. Maintenance & Planning

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Challenge: Reliability systems, planning tools, and ERP often run separately, creating reactive maintenance and unoptimized schedules.
With Xyve OS: Predictive reliability models connect with planning agents and asset knowledge graphs. The result: proactive maintenance, synchronized scheduling, and higher equipment uptime.
Value: Reduced downtime, optimized resource allocation, longer asset life.
2. Energy & Utilities +

2. Energy & Utilities

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Challenge: Boilers, steam, hydrogen, and power networks are often optimized in isolation without considering process demand. 
With Xyve OS: Utilities are connected to process clusters, enabling cross-optimization between energy supply and process demand. AI agents balance economics, emissions, and efficiency. 
Value: Lower energy cost, reduced emissions, improved sustainability scores. 
3. Process Optimization +

3. Process Optimization

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Challenge: APC, RTO, and ML models work well locally but remain siloed, limiting plant-wide optimization. 
With Xyve OS: Process-aware knowledge graphs and agentic AI orchestrate across crude units, conversion, and blending. Optimizations ripple across sections instead of being trapped in silos. 
Value: Higher throughput, improved yield, better product quality. 
4. Emissions & Compliance +

4. Emissions & Compliance

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Challenge: Emissions monitoring is often reactive, fragmented, and disconnected from real-time optimization. 
With Xyve OS: Emissions agents link with process and energy models, ensuring compliance is embedded in every decision — not an afterthought. 
Value: Continuous compliance, reduced penalties, sustainable operations. 

Industries

Our Related Industries
 

Oil & Gas – Upstream

Upstream operations are marked by subsurface uncertainty, equipment reliability challenges, and rising cost pressures. Xyve OS enables reservoir-to-facility optimization, ESP/PCP pump failure prediction, and production planning that adapts in real time. The result is higher recovery, lower lifting costs, and fewer unplanned shutdowns. 

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Refining – Upstream 

Refineries are complex networks of crude streams, conversion units, hydrogen, and utilities — often optimized in silos. Xyve OS interconnects these clusters, enabling crude-to-product optimization, hydrogen network balancing, and emissions compliance in a single decision fabric. This unlocks margin uplift, energy savings, and reliable compliance with sustainability targets. 

Petrochemicals & Chemicals – Upstream 
Petchem and chemical plants run highly integrated processes with narrow economic margins. Xyve OS brings yield optimization (ethylene, propylene), fouling prediction for exchangers, and plant-wide planning into a unified intelligence layer. Plants benefit from higher throughput, lower energy intensity, and reduced unplanned outages. 
Steel – Upstream 
Steelmaking faces high energy intensity and equipment wear across EAFs, ladles, and rolling mills. Xyve OS provides energy optimization, refractory life prediction, and production coordination across upstream and downstream. The outcome is reduced cost per ton, extended asset life, and improved throughput — enabling both profitability and decarbonization. 
Cement – Upstream 
Kiln stability, fuel variability, and CO₂ emissions are persistent challenges in cement. Xyve OS combines kiln feed quality prediction, alternative fuel optimization, and clinker yield intelligence with emissions monitoring. This leads to lower fuel costs, stable quality, and measurable CO₂ reduction. 
Aluminium – Upstream 
Aluminium smelting and calcination consume immense energy and suffer from process variability. Xyve OS enables smelter potline optimization, anode effect prediction, and alumina calcination efficiency, supported by utilities optimization. The value is in improved yield, reduced energy cost, and strengthened sustainability compliance. 
Mining – Upstream 
Mining operations juggle fleet reliability, ore recovery, and plant bottlenecks. Xyve OS connects predictive maintenance for haul trucks, grinding mill optimization, and fleet scheduling into one operational intelligence layer. This maximizes ore recovery, reduces downtime, and cuts operating costs. 
Life Sciences – Upstream 
In life sciences, batch consistency, cleaning cycles, and regulatory compliance drive competitiveness. Xyve OS optimizes clean-in-place operations, reduces batch cycle times, and balances utilities across facilities. Companies benefit from shorter cycles, reduced failures, and cost-efficient compliance. 
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CPG – Upstream 
CPG manufacturers must manage dynamic demand, supply chain variability, and cost pressures. Xyve OS unifies production scheduling, demand alignment, and utilities optimization. This ensures on-time delivery, reduced waste, and stronger margins in volatile markets. 
Food & Beverages – Upstream 
F&B plants rely on consistency, safety, and energy efficiency. Xyve OS integrates quality prediction, process optimization, and energy balancing across processing lines. The result: reduced wastage, lower energy cost, consistent product quality, and compliance with safety and sustainability mandates. 
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