Factory Computer Vision and Predictive Maintenance

The mandate

I lead the industrial AI work at Zettamine aimed at mid-sized manufacturing: factory computer vision, predictive maintenance and cognitive digital twins. The largest manufacturers can fund bespoke industrial AI. The mid-market cannot, and that is exactly where the systems are worth the most and least available.

The problem

A mid-sized factory runs on equipment that fails expensively and quality processes that catch defects late. The data to predict the failure and see the defect is being generated continuously, on sensors and cameras that are already installed, and almost none of it is used. The barrier is not the technology, it is that industrial AI has been priced and scoped for the few, not the many.

The system

• Factory-floor vision. Computer-vision models for defect detection and process monitoring, running on the cameras already on the line.
• Predictive maintenance. Time-series models over machine telemetry that forecast failure before it stops the line, turning unplanned downtime into scheduled work.
• Cognitive digital twins. A live model of the plant that fuses vision and telemetry, so operators can see state, anticipate failure and reason about change in one place.

What it proves

Bringing industrial AI down to the mid-market is an architecture and economics problem, not a research one. The work is making systems that earn their keep on modest data and existing hardware, which is a harder constraint than an unlimited lab.