Reduce variability,
increase productivity,
& improve quality.

Our scalable platform combines data science, technical know-how and deep industry expertise to drive continuous process improvement. We partner with forward-thinking leaders of the next industrial revolution in manufacturing.


  • PREDICTS

    problems before they occur

  • DELIVERS

    solutions based on a dynamic environment

  • PROVIDES

    low cost, quick onboarding that's scalable

  • OFFERS

    recommendations for improving product quality

  • OPTIMIZES

    outcomes and gets better over time

  • FEATURES

    simple, easy-to-read interface for greater adoption on plant floor

we dig deeper

ProcessMiner delivers SaaS-based predictive analytics, optimization, and recommendations to improve outcomes in complex manufacturing process.

There has never been a more opportune time for manufacturers to use data collection. Manufacturing plants that once worked autonomously can now send data steams and “talk” to plant operators about their speed, temperature, usage and other items. In turn, businesses can use these data streams to improve product quality, reduce waste and continuously optimize the manufacturing process.
But, before manufacturers can begin using this data, they’ll need to do one of two things. Either design a machine learning program that knows how to read the data and optimize the manufacturing process, or try to use an existing machine-learning program that can be adopted to your industry. The ProcessMiner analytical platform is a unique product that combines elements of both options.
It’s built by a team possessing decades of manufacturing experience along with the deep scientific knowledge of data analysis that’s required to reduce variability, increase productivity and improve quality at any complex continuous process manufacturing facility.
As a result, the ProcessMiner platform makes it easy for manufacturers to not only assess and structure their data but also use real-time actionable recommendations to improve operational efficiency and quality.

A.I. Manufacturing 2019 Conference Panel

Director of Science, Chitta Ranjan, discusses real-time AI manufacturing and the need for it’s real-time insights, the Data Science challenges behind it, and developed solutions at the AI Manufacturing Conference in Chicago.