Peloton Collaborates with Sfile Technology


HOUSTON, Nov. 19, 2020 /PRNewswire/ — Peloton is pleased to announce that our Well Data Lifecycle Team is now collaborating with Sfile, a cognitive computing company.

The opportunity to align Sfile’s technology with Peloton’s Platform solutions, will enhance our clients’ ability to access and analyze their operational data, improve decision making and optimize opportunities.

Sfile uses cognitive computing and machine learning to train bots and AI agents that autonomously mine unstructured data to accelerate and exchange advanced analytics. With Sfile, Peloton’s Well Data Lifecycle Team realizes the opportunity to apply new approaches to two old challenges.  First, how to best transform disparate unstructured data sets into structured and consistently coded information.  Second, the application of data science techniques to the analysis of the data. The solution is to leverage Sfile’s tools to mine the data, load it into our data platforms and apply advanced analytics to the data.

Fiona Hamilton, Vice President – Well Data Lifecycle Solution for Peloton, says, “Here is an opportunity to apply ML/AI to data loading and analytics for our products. A big win for our clients!  A strategy to find, import and in some cases create the data to support their desired analysis.” 

About Peloton

The Peloton Platform energizes the oil and gas digital transformation through mobility, automation and data integration by providing fully integrated well data lifecycle, production data lifecycle and land data management solutions. Today, over 600 oil and gas clients worldwide rely on Peloton technology to equip their stakeholders with the tools and information necessary to manage, simplify and optimize their operations. For more information about Peloton, visit

About Sfile

Sfile is a Cognitive Computing company and provider of Artificial Intelligence and Machine Learning solutions to synthesize vast amounts of raw unstructured, unnormalized, “dark data” into continuous flows of qualitative normalized understanding and features. Sfile’s AI and ML driven solutions transform fragmented information into qualitative datasets by data-mining all your well records (internal and external) using its machine learned clustering engines and artificial intelligence-driven bots to extract, normalize, and validate information from any form of disparate data.

SOURCE Peloton

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