Founded 2018
Industry
Manufacturing
Dr. Soudip Roy Chowdhury
Founder & CEO
Indraneel Tembhre
Head of Products
Sooraj Sreekumar
Head of Quality Assurance
Sangam Chaturvedi
Head of Customer Success
Ankit Bhardwaj
Head of Sales
Atharva Sabnis
Head of Growth
Shivam Bhardwaj
Senior Data Scientist
About the Company
Eugenie improves the efficiency and asset reliability of heavy machinery, thus reducing their carbon footprint, and making industrial operations more sustainable.
In addition to predicting machine failures and anomalies in advance with unparalleled accuracy, Eugenie goes a step further and prescribes solutions to avoid or delay those failures. This is done using Eugenie’s proprietary Explainable AI framework, which means that for every prediction and recommendation made by Eugenie, the root causes driving that prediction are transparently shown so that the human decision-maker is able to trust the recommendations and justify taking the recommended course of action.
Eugenie has completed deployments with the world’s largest organizations in industries such as oil & gas, metals & mining, and other process manufacturing applications as they’re able to support both legacy as well as new-age machinery.
About the Solution
Eugenie has two SaaS products: Ray-Finn and Papillon.
Ray-Finn is a data integrity assurance tool that ingests high-velocity data from machine-connected sensors, PLC, or SCADA systems to ensure that it is as expected. This is important because it helps to identify early on whether the data quality is compromised due to sensor degradation or other factors, and adjust its processing accordingly.
Eugenie’s second product Papillon analyzes the data processed by Ray-Finn to make predictions about any potential performance deviations, breakdowns, or failures. Papillon then suggests prescriptive measures to avoid or delay those failures, to ensure unparalleled reliability of operations.
Both products are available via the Azure Appsource, and are proven to deliver visible impact within days of deployment while being able to support rapid forward-deployment for fast-scaling of Eugenie across manufacturing facilities.