We engineer the intelligence layer for modern energy markets.
Our work bridges data science, physical-market research, and financial analytics to decode how real-world grid dynamics influence price formation.

We develop proprietary machine-learning and neural-network models that forecast electricity and fuel-market behavior across ERCOT, PJM, NYISO, and beyond.
By combining large-scale generation data, load patterns, and fundamentals with advanced statistical and AI architectures, our systems identify stress regimes, forecast price trajectories, and quantify risk in real time.

From renewable-integration modeling to cross-commodity predictive frameworks