Data-driven Solutions for Large Energy Enterprise

This case study is an example of our solution for

Data Engineering and Intelligent Platform


In partnership with Integral Engineering, we set out to solve a billion dollar problem in the energy industry. The world’s largest energy companies are facing an asset integrity problem—their infrastructure is aging with more than half of the pipelines in the U.S. being older than 50 years old. In the past 5 years alone, there have been 3,300 leaks reported, including the largest in U.S. history with eighty deaths and close to 400 injuries. Asset integrity teams at these massive enterprises use antiquated methodologies including reliance on manual observation, in order to solve a very complex problem. The teams are severely limited by legacy technologies and computing limitations.


The answer is modernized computing infrastructure and data-driven solutions to asset integrity management. Bitstrapped in partnership with Integral Engineering designed a cloud solution where using terabytes of IoT data, energy enterprises could apply predictive analytics, simulations, and machine learning to operationalize proactive asset integrity management. To achieve this cloud and software systems were developed to accomplish large scale data ingestion and batch and stream processing which made use of technologies like Apache Beam. The solution made use of massively scalable compute and data storage infrastructure including Kubernetes.


Through the development of a modern approach to asset integrity management, energy companies and their employees were able to access a variety of tools in the cloud that previously were not possible due to computing limitations. Analyses that would take in excess of 24 hours to run on a personal computer, run in less than 10 seconds and plug into an app that presents instant data visualization. Aggregation and centralization of data allows these companies to unlock insights and identify innovation opportunities.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.