To maximize uptime and reduce scrap, factory operators must know in real time when and where errors occur. However, industries reliant on visual inspection and legacy controllers remain underserved by IIoT platforms. Existing industrial data platforms cannot integrate the latest advances in machine vision and streaming data analytics, and most analysis tools are designed for use by specialists like data scientists.
The IndustrialML founding team all come from career backgrounds in manufacturing and experienced these problems firsthand in operating and engineering roles. Each of us struggled with trying to merge data from fragmented, siloed sources and multiple systems. Moreover, in our roles at large corporate factories, we could not generate statistics fast enough to be useful to production lines, and even when we could, we found that the results of advanced analytics were difficult for most engineering teams, not classically trained in statistics or machine learning, to duplicate or pick up. We realized that for the vast majority of manufacturers who need to maintain legacy equipment, a new kind of platform was needed for production data. When our team met in late 2018, we resolved to tackle this problem together.
The IndustrialML platform bridges data from multiple disparate sources, enabling deeper context into production status. We integrate real time monitoring, machine vision, and advanced analytics applications to deliver actionable insights to operators and engineers. We make factories smarter with machine learning.