Transform production and dynamic planning by turning complex datasets into actionable foresight — helping you cut costs, boost output, and increase resilience.
Our proprietary suite of Tabular Foundation Models powers dynamic production and capacity planning with state-of-the-art accuracy, surpassing traditional ML models like XGBoost and CatBoost. It continuously adapts to changing inputs without the heavy overhead of manual model development, retraining, or monitoring—enabling more resilient, real-time forecasting across complex production cycles.
Production plants and large organizations provide countless inputs for instant predictive maintenance, quality control, and operational optimization; instead of building a new model for each use case, you can leverage a single Predictive Foundational model for all of them, and focus your data resources elsewhere.Better accuracy, lower maintenance costs, and State-of-the-Art improvements straight from our research From volatile supply chains to fluctuating demand and asset performance variability, there is no lack of Predictive AI use cases in the Energy and Industrial sector. Leveraging a single tabular foundation model transforms the viability of addressing and maintaining them all.
Pretrained on millions of synthetic datasets, our predictive models have learned the complex patterns and correlations hidden within your real-life plant, IoT, and maintenance data.
By training our models on synthetic data that simulates real-world situations, our Predictive Foundation models outperform both traditional ML models trained on your data, and LLMs.
Unlocking Predictive AI for your structured data