|Industrial operations run on heavy machinery, and they need to run with maximum uptime. Heavy machinery, despite all the maintenance needed for it, is prone to downtimes. |
These lead to productivity losses and can get expensive. To get around these downtimes, these industries invest in backups and redundancies of critical heavy machinery. The problem here is that these backups increase the carbon footprint and GHG emissions of assets.
Eugenie uses AI to improve the reliability and environmental sustainability of heavy machinery. We do this with two patented products - Ray-Finn and Papillon.
Ray-Finn ingests petabytes of high-velocity, multi-variate data from machine-connected sensors and SCADA systems. Papillon processes this data with AI algorithms to predict machine performance anomalies, failures, and remaining useful life. Because of its ability to predict these failures and performance anomalies ahead of time with unparalleled accuracy, Eugenie reduces dependencies on backups and redundancies.
This reduction in reliance on backup systems lowers maintenance costs and reduces the carbon footprint and GHG emissions. This saves our customers' operating expenses.
We've proven our utility for several important assets:
1. Saved 15% OpEx for USA's largest oil major, while reducing annual GHG emissions by ~4 million metric tons of CO2 equivalent.
2. Unlocked $384M savings for India's 2nd-largest gas major.
3. Secured a water distribution network in Singapore with 10% higher accuracy than the status quo.
4. Predicted missile system failures up to six months in advance for a unit of Indian defense forces.
Eugenie’s work has been awarded by Microsoft and NASSCOM, and we’ve been featured by Forrester, CIO Insider, and Silicon Angle, among others.