Practical, deployed AI in manufacturing — not a strategy deck.
Peer-reviewed research on neural networks and expert systems for quality inspection was published in the early 1990s — decades before AI in manufacturing was a mainstream conversation. That early-research lens now applies directly to AI-driven production scheduling, computer-vision-based quality inspection, and the data infrastructure that makes any of it possible.
Deployed across a multi-plant footprint, generating well over $10M in savings since inception — optimization that actually runs the line, not a dashboard someone checks once a week.
Vision-based defect detection deployed to eliminate manual inspection across multiple production use cases, catching what human inspection misses at line speed.
There's a natural gap between enterprise AI platforms and the equipment integrators who actually put automation on the floor. Closing that gap turns AI from a dashboard exercise into closed-loop, on-the-floor execution.
Acceptable-use policy, human-in-the-loop guardrails, and clear risk ownership — especially important anywhere AI touches safety-critical or compliance-critical decisions on the plant floor.
None of the above works without clean data underneath it. Unifying disparate MES data sources into a single governed data asset is usually the unglamorous first step that makes every AI initiative afterward actually possible — skipping it is the most common reason AI pilots stall out before they reach the floor.