We are a seed-stage company headquartered in Cleveland, OH. We build AI inspection software for mid-size discrete industrial manufacturers who run multi-variant production lines and cannot afford a dedicated vision engineering team to manage every changeover.
Aaron Zielinski managed quality operations at a mid-size Cleveland industrial fabrication shop for four years and watched the facility spend $280,000 over 18 months on three separate vision system revalidation projects. Each was triggered by a tooling change or material supplier switch that caused the rule-based Keyence inspection logic to generate a spike in false rejects that shut down the line until engineering could recalibrate the thresholds.
The fundamental problem was not that rule-based vision was bad at steady-state inspection. It was that every time the production environment changed, the rules had to be manually renegotiated by an engineer who understood both the tooling and the vision system. There was no mechanism for the inspection system to learn incrementally from the quality engineer's own judgment during changeovers.
Eolvision was built to solve exactly that problem: an edge AI inspection platform designed specifically for mid-size manufacturers with multi-variant production and limited vision engineering resources, built around active learning that lets quality engineers update models incrementally rather than requiring full engineering revalidation cycles on every changeover.
Give mid-size manufacturers AI-powered end-of-line inspection that adapts to product changeovers without requiring engineering revalidation. Most manufacturers running multi-variant production lines do not lack inspection capability — they lack inspection capability that holds up across product changeovers without consuming engineering resources to maintain it. Every tooling change, material supplier switch, or new product variant introduction should be an operational event, not an engineering project. Our mission is to make that true by building an inspection platform where the quality engineer — not a vision engineer — is the person who updates the model when production conditions change. We build for the facility where quality outcomes matter and engineering capacity is not unlimited.
These values are not aspirational statements — they are engineering constraints we treat as requirements. Active learning is the core mechanism the platform is built around, not a feature layered on top of rules. The quality engineer is the person whose judgment trains the model, because that person is present at the line and understands what a defect means for their product. We build for the hardware facilities already own, price for the cost transparency manufacturers need when evaluating changeover ROI, and include traceability in the base platform because warranty investigations should not require a premium support tier.
Eolvision is at the early revenue stage. We are actively deploying with mid-size discrete industrial manufacturers across the US Midwest who run 2–15 production lines and manage 8 or more product variants per year. Our team combines direct manufacturing operations experience, applied machine learning deployment at industrial facilities, and hands-on vision system commissioning across Ohio and Michigan manufacturing accounts. The platform runs on existing camera infrastructure — Cognex, Keyence, Basler, and Zebra — without requiring capital investment in new hardware.
Eolvision is headquartered in Cleveland, OH. We are a seed-stage company serving mid-size discrete industrial manufacturers across the US Midwest.
We work directly with quality engineers and operations managers at mid-size manufacturers. If you are managing inspection across multiple product variants and the changeover cost is measurable, we want to hear about your line.