Digitizing physical settings like sites, plants or facilities starts with data sources that are rooted in reality, not design. We work with the most down-to-earth sources of brownfield data including equipment, sensors, process historians or even physical documentation.

Raw data alone is rarely clean or useful. Our scraping and cleaning processes streamline data preparation; making primary sources both accessible and readible. Moreover, we create context for your analytics. We digitize the relationships of all assets within a physical site, to each other and to production.

Contextualized data serves as a foundation for the promises of tomorrow. Once data is cleaned and assets are related, we build, train, and deploy models at scale. These models are rooted in cases for immediate ROI and are used to support use cases like sensor health, equipment maintenance, production optimization and end-to-end production simulation.

Physical environments change, and so should the models that are deployed to them. Our solutions are purposed to ensure your models are functioning accurately with quality inputs. When environments do change outside a reasonable range of outcomes, our models are retrained and redeployed.