Extract operational data directly from source systems and contextually integrate it with data from data warehouses and data lakes. Break down data silos and consolidate all data into a single cloud-based storage and access location.
Automatically calculate key statistical features of incoming data at various granularities. Interactive data exploration in milliseconds, not minutes or hours.
Explore all industrial data fully contextualized. See connections between data, physical 3D models, and P&ID process flows.
Data management and configuration reinvented for the digital use case-centric era. A use case-centric approach to accessing, managing, and monitoring collections of rich live industrial data.
Contextualized data as a service through a combination of machine learning, rules engine, and subject matter expert enablement. Set up contextualization pipelines to automatically relate time series to assets, 3D nodes to assets, and more.
Ingest, transform, and contextualize 3D models, P&IDs, and other visual data into your data backbone. Visually explore complex data dependencies. Design better models faster.
Supercharge your production optimization and predictive maintenance programs with physics-guided machine learning. Synthesize more data using domain insights from process simulations to unlock missing training data.
A tightly integrated environment for building, training, testing, deploying, and managing models. Tailored model deployment without the hassle of engineering or managing infrastructure. Easily schedule and run notebooks, ML models, calculations, simulators, and transformations.
Identify, profile, and resolve data quality issues at different steps of the data pipeline. From multiple data ingestion points, to data management, data contextualization, and ongoing monitoring ensuring data consumers continuously rely on high quality data in safety critical environments.
Enabling business citizen developers to build line-of-business web-based data-oriented applications using no-code or low-code development. Resulting in faster operational scalability and less reliance on tech experts to develop apps and test hypotheses.
Data sharing granularity puts data in the hands of everyone who needs it – including partners and suppliers – while keeping it safe from those who don’t.
Leverage AI to build optimal combination of data inputs, models, visualization, and notification configurations. Automate key steps of the data science workflow with little-to-no human interaction. Enabling citizen and expert data scientists to efficiently solve problems and create value at scale.