Forecast network traffic, allocate resources ahead of peaks and increase uptime.
Our in-house suite of frontier 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.

From forecasting network capacity to predicting traffic and allocatig the requisite resources, data scientists can leverage a single model and endpoint for all their different use cases.
By unifying all your predictive needs into one predictive layer, you achieve system-wide efficiency gains with lower model upkeep.

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.



