Overview
Digital Twin Blueprint to Power Smart AI Factories
Deploy scalable digital twins enriched with AI models to simulate workflows, monitor assets, and optimize operations in real time. NexaStack’s blueprint accelerates design, testing, and deployment of intelligent factory systems
Scalable Deployment Across Factory Lines
Data-Driven Operational Visibility
Real-Time AI-Powered Optimization
What help you get to reinvent
Digital twins allow you to create dynamic virtual models of factory systems—from layout to workflows and machinery. This enables design experimentation, process simulation, and performance tuning without physical disruption, accelerating innovation at scale
With sensor data and AI integration, digital twins provide live feedback on equipment health, process bottlenecks, and operational efficiency. Predictive analytics help preempt failures, reduce downtime, and ensure consistent quality across production lines
Digital twins connect easily with industrial IoT devices, SCADA systems, MES, and ERP platforms. This unified data layer enables synchronized operations and supports data-driven decision-making across the enterprise ecosystem
AI-powered digital twins go beyond monitoring—they support autonomous decision-making. By continuously analyzing performance data, they can recommend or even execute real-time actions to optimize throughput, resource allocation, and energy use
Architecture Overview
Operator Interface Layer
This is the visual gateway for engineers, operators, and decision-makers to interact with the digital twin platform. Built using secure UI frameworks (React, Angular), it provides live dashboards, 3D factory visualizations, control panels, and status reports. Enterprise SSO ensures secure access, while the intuitive design supports rapid insight and control
Factory Logic & Simulation Layer
This layer contains the core simulation and control logic for replicating and optimizing physical factory processes. It models machines, production flows, and operational constraints—allowing users to test changes, run "what-if" scenarios, and deploy optimized workflows without impacting live systems
Twin Coordination & Workflow Layer
This orchestration layer synchronizes the various digital twins (machinery, workflows, layouts) into a unified system. It manages real-time events, multi-agent coordination, and task sequencing, enabling closed-loop automation across lines and processes while ensuring system-wide consistency and collaboration
AI/ML Intelligence Layer
This layer powers intelligent decision-making through machine learning and AI models. It supports predictive maintenance, anomaly detection, energy optimization, and adaptive process control. Models continuously learn from operational data and improve over time to drive smarter outcomes
Sensor Data & Knowledge Integration Layer
At the foundation lies the data layer, ingesting real-time signals from IoT devices, PLCs, SCADA systems, and enterprise databases. It structures and stores this data for modeling, AI training, and visualization. It also integrates domain knowledge, historical logs, and digital manuals to enrich decision logic
Core Components
Orchestrator
Twin Orchestration Engine
Acts as the central coordinator of digital twin components. It synchronizes physical assets with their virtual counterparts, manages simulation flows, and triggers adaptive actions based on real-time events and process feedback
Simulation Router
Model Training and Inference
Optimizes the flow of contextual data into AI/ML models, enabling realistic simulation of factory scenarios. Ensures consistent inference for predictive maintenance, resource allocation, and process optimization
Monitoring
Performance Tracking and Anomaly Detection
Continuously monitors system metrics, equipment behavior, and twin accuracy. Uses AI to detect deviations and initiate corrective actions, helping maintain uptime and operational efficiency.
Tracks real-time system health, comparing live data with digital twin models to identify discrepancies early
Knowledge
Operational Knowledge Graph
Connects real-time sensor data with historical logs and domain knowledge. Enables retrieval-augmented simulations, root-cause analysis, and guided decision-making across the factory floor
API Development
Integration Gateway and Control Layer
Serves as the secure interface between digital twins and enterprise systems. Manages authentication, API rate limits, and validates incoming data to ensure smooth integration with SCADA, MES, ERP, and IoT layers
Featured Blogs
Compliance and Privacy – Digital Twin AI Blueprint
Multi-Platform Access
Access factory insights, twin simulations, and analytics dashboards from any device—desktop, tablet, or mobile. Ensure seamless operations and monitoring, no matter where your teams are working
Custom Simulation Templates
Create reusable templates for digital twin configurations, simulation models, and compliance checks. Standardize design across production units and ensure process consistency
Encrypted Cloud Storage
Confidently store simulation data, operational logs, and AI model outputs in a secure cloud environment. All data is encrypted at rest and in transit, compliant with industrial security standards
Endpoint Data Security
Your data remains protected across all endpoints. Role-based access controls and audit trails ensure only authorized users can view or modify sensitive factory data
Compliance-Ready Cloud
Support GDPR, ISO/IEC 27001, and other compliance mandates with secure cloud integration. Easily manage data residency and retention across regions and partners
Secure Twin Operations
Consistently protect digital twin pipelines, sensor data feeds, and AI predictions. Every operation is monitored and logged to maintain transparency and accountability