Digital Twins

Leverage an innovative and modular digital twins platform to optimize operations, improve maintenance, increase products and services quality, and gain valuable insights through advanced simulation and analytics.

What we provide..

  • Plan

    Business Case Development - Defining the value proposition and ROI of digital twin initiatives.

    Roadmap & Visioning - Creating a strategic roadmap for digital twin implementation.

    Technology Selection & Evaluation - Assessing and recommending appropriate technologies and platforms.

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    Implement

    Data Acquisition & Integration - Collecting and integrating data from various sources.

    Model Development & Validation - Building and validating accurate digital twin models.

    Visualization & Interaction - Creating intuitive interfaces for users to interact with the twins.

    Deployment & Maintenance -Deploying and providing ongoing support for digital twin solutions.

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    Integrate

    AI/ML Integration - Leveraging AI and machine learning for advanced analytics.

    IoT Integration - Connecting digital twins with real-time sensor data.

    AR/VR Integration - Creating immersive experiences for interacting with twins.

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    Industry

    Manufacturing - Optimizing production processes, predictive maintenance.

    Healthcare - & Life Sciences: Personalizing patient care, accelerating drug discovery.

    Supply Chain - Enhancing logistics, optimizing inventory management.

    Financial Services - Improve risk management, enhance customer experiences, and drive innovation.

    Energy - Enhancing grid reliability, improving energy efficiency.

Digital Twin Studio

  • Modular Digital Twin Component Library

    Library of pre-built, configurable components (sensors, actuators, equipment models, etc.) that can be easily assembled into digital twins.

  • Dynamic Data Ingestion & Transformation Pipeline

    Flexible pipelines for ingesting data from diverse sources (IoT sensors, databases, APIs) and transforming it into a standardized format for the digital twin.

  • Agent-Driven Simulation & Optimization Engine

    Simulation engine that leverages Agent AI to simulate the behavior of digital twins and optimize their performance.

  • Graph-Based Relationship & Dependency Explorer

    Graph Database tools for visualizing and analyzing the relationships and dependencies within the digital twins.

  • AI-Powered Anomaly Detection & Predictive Maintenance Module

    Modules that use Agent AI and Machine Learning to detect anomalies in real-time data and predict future failures.

  • Interoperability and Collaboration

    Standardized data formats, robust APIs, and a plugin architecture for easy integration with existing and 3rd party tools.

Use Cases

Supply Chain Resilience (Logistics)

Delivered a digital twin of a national retailer's entire supply chain, encompassing a comprehensive network of suppliers, warehouses, transportation routes, and customer locations. This robust solution empowered the retailer to proactively identify and mitigate potential disruptions such as natural disasters, geopolitical instability, and unforeseen events. By simulating a wide range of scenarios, including demand surges, supplier delays, and unexpected disruptions, the company gained the ability to develop effective contingency and business continuity plans, ensuring operational resilience and minimizing the impact of unforeseen circumstances. Moreover, the digital twin facilitated data-driven optimization across key supply chain functions. This included optimizing inventory levels to minimize holding costs while simultaneously reducing the risk of stockouts, as well as optimizing transportation routes to minimize delivery times and fuel consumption, leading to significant cost savings and improved overall efficiency.

Technologies used - Google Vertex AI, Neo 4j Graph Database

Medical Device Manufacturing (Medtech)

Built a digital twin of organization’s manufacturing process which was used to identify and optimize production workflow. The solution helped to predict equipment failures and schedule maintenance proactively to minimize downtime. In addition, the company was able to use the digital twin platform to monitor its manufacturing processes in real-time and identify and address quality issues before they impact product performance. The solution was also employed to optimize the company’s supply chain by improving the timely delivery of critical components and reducing manufacturing costs.

Technologies used - Google Vertex AI, Neo4j Graph Database