Cognite announced the integration of NVIDIA?s NV-Tesseract family of models into the Cognite AI and Data Platform to operationalize foundational forecasting models for heavy industry. This integration leverages the context-rich data within Cognite?s platform to power NVIDIA?s advanced time-series AI, delivering unprecedented predictive accuracy for critical manufacturing processes. Unlocking the full potential of industrial AI requires more than just powerful models; it requires unified data that understands the physical world.

This integration bridges that gap by connecting the Industrial Knowledge Graph within Cognite Data Fusion?the platform's core data foundation that makes data AI-ready?to NVIDIA?s NV-Tesseract models packaged as NVIDIA NIM microservices. Furthermore, the solution transcends raw predictions by leveraging Cognite Atlas AI?the platform?s low-code industrial agent workbench?and other no-code analytics and collaboration tools to visualize and engage with forecasting results, turning model outputs into actionable insights directly within daily operational workflows. Celanese, a global chemical and specialty materials leader, has deployed this advanced predictive modeling solution at its Clear Lake, Texas, facility.

Together with Cognite, Celanese identified a high-value opportunity to improve the prediction of reaction water levels in certain production units?a metric critical to stable and efficient plant operation. Currently, Celanese relies on manual samples to adjust existing prediction models. However, operators frequently observe "bias jumps" when new sample data is entered, indicating that current legacy models could more accurately predict real-time conditions.

By feeding historical time series data from Cognite into NVIDIA?s NV-Tesseract forecasting model, Celanese aims to eliminate these inconsistencies.