Prevent unplanned downtime, extend asset lifespan, and optimize maintenance schedules with a foundation model purpose-built for tabular data.
Our in-house suite of Tabular Foundation Models delivers predictive maintenance forecasts at state-of-the-art accuracy that outperforms traditional ML models like XGBoost and CatBoost, all while skipping the overhead of traditional model development and continuous retraining and monitoring.
Sensor data provides 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 team, guaranteed.
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