Equipcast is a Houston-based company dedicated to taking remote monitoring in the Oil & Gas industry to the next level. We were commissioned to build the application from scratch, which gave us the opportunity to leverage our full range of skills and capabilities. The result was a powerful industrial application that attracted the attention of major oil producers for its flexible structure, functionally rich and scalable software with state-of-the-art UX / UI design.
The requirement was to build a “holistic condition-based monitoring solution”. In technical terms, that meant integrating data from multiple data sources: ERPs, CMSs, sensors, weather and more. Tapping into multiple data providers and cross-referencing massive amounts of both structured and unstructured real-time data was a challenge in itself. In order to solve it, we built a complex environment of cloud-enabled microservices that interacted with both high-availability and high-reliability persistence layers.
Standing up against industry giants such as GE, Weatherford and Rolls-Royce, we knew that we needed to provide an application that was nimble and agile if we were to beat the competition. Therefore, we designed an advanced modelling language, that allowed users and integrators to easily create digital twins of real-world facilities. This feature enabled very fast integration and was easily customizable without the need to make modifications to the core of the application.
Because of the huge amount of data and the demand for high-performance, we developed low-level, time-series specific data analysis algorithms from scratch, in order to train an AI that could interpret streams of data from multiple sources and identify abnormal behavior patterns across different datasets.
Designed for the cloud, and featuring a fully-fledged DevOps workflow, we configured the Equipcast solution to require minimal IT support. Our use of self-replicating containers meant the application was ready to scale up or down in accordance with traffic volumes, cutting costs and optimizing efficient use of resources. We also introduced self-healing capabilities which allowed the app to be available at all times to handle critical loads.