The ABB Genix Industrial IoT and AI Platform Suite is a comprehensive enterprise grade, yet modular platform designed to accelerate digital transformation. By leveraging contextualized data, AI, and do-main expertise, ABB Genix empowers asset-intensive enterprises to make data-driven decisions while ensuring scalability, security, and efficiency. It offers deep cross-functional and AI-powered advanced analytics to improve operational performance, asset integrity, energy efficiency, sustainability, and safety. Genix provides end-to-end capabilities of Industrial IoT and AI including the entire spectrum of enable-integrate-contextualize-model-analyze-deliver-optimize. It can seamlessly scale from asset to plant to an enterprise. The platform can be deployed across cloud, hybrid, edge or on premises. It also forms as the foundation for many Digital Value pillar applications of ABB.

At the core of the Genix architecture is a smart, pre-built, and extensible Industry Cognitive Model that structures and contextualizes diverse data sources. This model enhances cross-functional insights by linking operational, business, and engineering data, enabling intelligent decision-making.

Genix applies advanced AI and machine learning technologies within its architecture to solve complex industrial analytics and optimization challenges. By leveraging its AI-driven analytics engine, it provides predictive maintenance, anomaly detection, and process optimization capabilities, enhancing operational efficiency and sustainability.

By leveraging these architectural components, Genix enables enterprises to transition from reactive to predictive and prescriptive analytics, ensuring smarter, data-driven industrial operations.

The Edge Broker acts as a messaging hub, converting industrial data for local analytics and cloud integration. The Information Model Viewer simplifies data mapping, while the Edge Proxy (Data Publisher) ensures seamless protocol translation and cloud connectivity. A write-back feedback loop enables real-time control actions based on local analytics.

By integrating AI-driven insights, predictive analytics, and secure edge computing, Genix Edge AI enhances industrial efficiency, cybersecurity, and decision-making, making it a key enabler for modern smart industries.

Genix Omni Source Integration Hub is a modular component that allows low code tooling to develop and plugin any new adopter rapidly. Genix platform acquires and integrates data from IIoT sensors using a hybrid edge-cloud architecture. Leveraging <Field Instrument Management (FIM)> and support for industrial protocols (Modbus, OPC-UA, OPC Classic, MQTT, UDP etc..), the platform enables seamless communication with field devices and sensors. It can integrate equipment attached monitors such as smart sensors (ABB and Non-ABB), HMIs, environmental sensors such as ABB CEMS sensors, VR/ Maintenance inspection verification devices etc.

The platform provides deeper integration with IT source systems, including ERP platforms like SAP ECC, S/4 HANA, Oracle InfoR, Planning and execution systems such as ABB MES/MOM and EAM systems including IBM Maximo, SAP EAM and PM. This integration is accomplished using the native APIs of these respective systems, such as RFC for SAP, OData for S/4 HANA, and REST APIs for IBM Maximo.

Genix IIoT Hub (Gateway) is a cloud gateway and broker that facilitates secure, real-time data exchange between edge devices and cloud services. It supports device-to-cloud (D2C) and cloud-to-device (C2D) communication, ensuring seamless command and control operations in industrial IoT environments.

Key capabilities include secure data routing with encryption (AMQPS, MQTTS, HTTPS), adaptive data processing to filter and optimize bandwidth usage, and dynamic network resilience to handle unreliable connections. It ensures policy-based authentication and access control, integrating with enterprise security frameworks.

The IIoT Hub supports scalable device orchestration, enabling seamless onboarding and management of edge nodes. It provides an API and protocol abstraction layer, unifying industrial protocols (MQTT, AMQP, HTTP) for simplified integration. Edge-to-cloud synchronization ensures data consistency, versioning, and conflict resolution. By enhancing reliability, security, and scalability, the Genix IIoT Hub plays a vital role in industrial automation, predictive analytics, and cloud-based operations.

Genix Edge Management Portal is part of the ABB Genix™ Enterprise Layer, enables seamless configuration and management of Edge devices and source adaptors across cloud or on-premise environments. It provides self-service features for device configuration, monitoring, diagnostics, and control, ensuring efficient edge operations.

The portal facilitates real-time data streaming for instant insights and batch data processing for AI model training and predictive analytics. It ensures secure data flow from source systems through configurable adaptors.

With an intuitive interface, strong security, and remote management capabilities, the Genix Edge Management Portal enhances operational resilience, efficiency, and scalability, making it a critical tool for managing distributed industrial edge environments capabilities. By improving operational resilience and efficiency, it enables seamless edge deployment across distributed industrial environments.

The platform includes features for metadata extraction, data quality checks, and governance, ensuring secure, high-quality data ingestion aligned with business rules. Integration with the System Digital Twin Integrity Hub and Industry Cognitive Model (ICM) enhances data contextualization and accelerates value generation through digital twins, predictive analytics, and operational dashboards.

Security is built-in, with encryption, role-based access control, and data governance ensuring trust across all touchpoints. The Contextual Fusion Hub ultimately provides the data backbone for AI-driven operations, enabling organizations to unlock insights, improve efficiency, and drive digital transformation at scale.

Genix incorporates a Data Lake to store vast amounts of structured and unstructured industrial data, allowing flexible data processing and advanced analytics. This scalable and cost-efficient architecture ensures that raw and processed data are readily available for AI/ML-driven insights, historical analysis, and real-time operational intelligence. Alongside, AI-Augmented Data Processing & Optimization enables automated data enrichment, contextualization, and anomaly detection using built-in ML-assisted data cleansing, tagging, and metadata management, ensuring high-quality, analysis-ready data.

The Integration Hub also includes Asset Master Data Management through the System Twin Data Integrity Hub, ensuring standardization, consistency, data integrity, and interoperability of asset-related information. It defines common data models, naming conventions, and data formats, creating a unified structure for industrial assets. The System Twin Data Integrity Hub further supports the generation of multiple reporting hierarchies based on a master hierarchy, allowing organizations to create flexible, structured views of their assets for improved analytics, compliance, and decision-making.

Additionally, DataOps Message Sync enables bi-directional synchronization between edge and cloud, ensuring a seamless feedback loop where analytics-driven commands optimize industrial processes in real time. The Cognitive Data Model, powered by Industry Cognitive Model (ICM), structures AI-driven data models using relational databases (RDBMS), ensuring scalability, reliability, and high availability while supporting advanced machine learning applications and predictive maintenance.

Genix also features a Real-Time Data Pipeline & Low-Latency Orchestration, enabling event-driven processing, in-memory computing, and batch/stream ingestion with a visual pipeline builder. Its Enterprise-Grade Security & API-First Architecture incorporates RBAC (Role-Based Access Control), encryption, audit trails, and API connectors (REST, GraphQL) for seamless integration. Furthermore, Industrial Data Models & Schema Management provides pre-configured and customizable schemas for industrial datasets, ensuring adaptability and interoperability. By integrating these capabilities, Genix Industrial DataOps breaks data silos, ensures data consistency, security, and integrity, and empowers organizations with AI-driven insights for enhanced performance, operational excellence, and digital transformation.

Genix supports five types of digital twins:

• Status Twin provides real-time visibility into asset condition using IoT data
• Asset Information Model Twin creates a structured, unified view of an asset by integrating data across systems
• AI Twin (Life Twin) predicts failures and maintenance needs using machine learning
• Operational Twin offers insights into reliability, failure modes, and decision-making support
• Process Simulation Twin enables full system simulation using a blend of physics-based & AI model for performance improvement  

At the core of the platform is the Genix Digital Twin Modeler — a low-code environment for building and managing digital twins. Users can visually design workflows, simulate what-if scenarios, and connect twins to live and historical data. The modeler supports rapid configuration with drag-and-drop components, enabling experts to build complex models without coding.

The platform also includes a library of prebuilt digital twin templates for over 100+ asset types, allowing faster deployment and reuse across equipment classes.

With contextualized intelligence, Genix goes beyond basic monitoring. It links every data point to its role in the larger system—enabling more accurate predictions, better diagnostics, and smarter operations. Whether modeling a single asset or an entire process, Genix Digital Twin empowers users to understand, optimize, and evolve their operations with clarity and confidence.

At its core, Genix Copilot integrates AI-driven data processing and visualization across diverse data sources, including shop floor systems, enterprise platforms, operator inputs, unstructured content, and metadata.  The platform leverages a multi-agent architecture, featuring a Master Agent coordinating specialized sub-agents, each optimized for specific tasks. This modular design supports on-demand customization, allowing the development of tailored agents to suit unique industrial needs.

Key technical capabilities include dynamic data retrieval, dependency injection to swap default functions with custom logic, and flexible deployment across cloud, on-premises, or hybrid environments—leveraging Azure services or open-source frameworks and language models. Genix Copilot ensures robust data security and governance, making it a trustworthy solution for industrial-scale AI adoption.

By combining the power of generative AI with ABB’s industrial expertise, Genix Copilot empowers organizations to uncover hidden insights, streamline operations, and promote collaborative decision-making — transforming raw data into strategic intelligence with precision, speed, and scalability.