New playbook for building AI systems of intelligence that scale.
Read the playbook Break down silos and bring clarity to complex industrial environments with digital twins designed for human understanding. By unifying diverse data—from time series and P&ID diagrams to 3D models, images, and beyond—you create a connected, contextual view of operations. Simplify the creation, deployment, and scaling of digital twins across assets and locations to accelerate transformation and boost efficiency.
Data remains siloed and inaccessible: Accessibility of industrial data poses a significant hurdle. Often confined within disparate systems, accessing the right data is laborious for data scientists and application builders when building, deploying, and scaling industrial solutions.
Volume of data is rapidly increasing: With an exponential increase in data generation, managing industrial data at scale is becoming increasingly difficult. Current estimates suggest data generation will increase by 50% over the next 2 years.
Industrial data lacks context: Current strategies to consolidate industrial data into a data lake or lakehouse result in data swamps unusable by onsite personnel or data science teams. Without context, finding and verifying trusted data becomes a near-impossible task.
Digital initiatives are moving too slowly: Many digital initiatives remain stuck, unable to scale beyond pilots or one-time use case deployments. Challenges include inconsistent naming conventions, vendor lock-in, and heterogeneous asset protocols.
Industrial AI is missing production scale: Predictive AI models can’t be deployed at scale without tedious manual data work. AI/ML platforms often lack industrial domain expertise.
Disconnected from operational context: 3D models are often outdated and not aligned with real-time sensor data or asset conditions.
A digital twin should be seen not as a single all-encompassing model, but a modular ecosystem of micro twins, each built for a specific function (e.g., condition monitoring, optimization, maintenance). These twins evolve independently, scale as needed, and avoid rigid centralized systems.
IRIS Foundry enables this with:
Productized AI (Generative + Predictive)
Industrial LLMs
Role-based copilots
Prebuilt domain models
Low-code UX
Flexible deployment: SaaS or private cloud
Industrial DataOps powers high-quality data orchestration across dynamic systems.
IRIS Foundry offers:
Prebuilt connectors from IT, OT, and engineering sources
Polyglot data stores
Structured asset hierarchies via AI-powered P&ID ingestion
Unified namespace & industrial knowledge graph
Support for audit, governance, and security standards
A digital twin is a real-time, dynamic representation of a physical asset, system, or process. It unifies and contextualizes data—sensor readings, logs, models, specs—into an interactive, searchable environment.
Benefits include:
Break down silos: Prebuilt connectors to SAP, MQTT, OPC, Azure, and more.
Contextualize & visualize: Unified namespace, AI-powered P&ID, 3D visualization.
Scale AI insights: MLOps Studio, synthetic data, role-based copilots in IRIS Foundry.
Up to 80% faster incident response using real-time contextualized 3D/2D data.
Role-based copilots: Interact with predictive insights via prebuilt applications.
Industrial workflow apps: Tailored apps for asset performance and connected workers.
Live sensor overlays: Real-time 3D views of plant data.
Real-time fault visualization: Highlight issues in 3D.
Unified namespace: API-accessible single digital view.
Industrial knowledge graph: Simplifies navigation and analysis.
Multi-perspective views: Tailored digital twins for Ops, Maintenance, Engineering.
Industrial Copilot (Generative AI): Natural language queries for insights.
Linked 3D navigation: Clickable access to P&IDs, documents, and time series.
2D/3D diagram integration: Seamless navigation across visual formats.