Predictive AI for Performance & Yield Optimization

Our proprietary suite of Tabular Foundation Models powers plant performance and yield optimization forecasts 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.

Industrial operations generate massive volumes of sensor and process data — valuable signals hidden in daily production runs. Instead of training separate models for each optimization challenge, our Predictive Foundation Model drives continuous yield, throughput, and performance improvements across the board.By unifying all your tabular forecasting needs into one predictive layer, you achieve system-wide efficiency gains with lower model upkeep. As research advances, the model seamlessly inherits State‑of‑the‑Art improvements — ensuring your optimization capabilities evolve as fast as your production targets.

Pretrained on millions of synthetic datasets, our predictive models have learned the complex patterns and correlations hidden within market microstructure, fundamental ratios, alternative data, and time-series features — out-of-the-box.
This deep understanding of tabular data enables our Predictive Foundational models to outperform both traditional ML models and LLMs.