Two of the World's Largest Machine Vision Companies Couldn't Solve This Problem. Here's What We Did.
A Tier-1 metal components manufacturer runs production across 8,000+ product models. Each requires a distinct inspection profile. Each production run switches automatically via PLC. When they went to market for an automated weld seam inspection AI system, they did what any rational procurement team would do: they evaluated the two dominant names in hardware-bundled vision systems.
Neither could solve the problem.
That outcome is worth examining. Not as a competitive claim, but as a technical case study in AI vision for metal fabrication. The gap between what those vendors offered and what this manufacturer needed reveals something specific about where traditional machine vision architecture breaks down — and what software-native AI inspection actually makes possible.
The Requirement That Broke the Incumbents
The production environment is not unusual for precision metal fabrication. What made it difficult is the combination: 8,000+ product models, automated PLC-triggered changeovers between runs, and a weld seam inspection AI requirement that had to maintain detection rate across every variant without operator intervention between switches. The rule-based vision vendors evaluated both had mature hardware platforms, deep integrations, and long field histories. Their architecture assumes a manageable, bounded SKU environment — a human engineer configures each product recipe and the system holds that configuration. At 8,000+ models with automated PLC switchover, that model collapses. The configuration overhead alone makes it unworkable. Neither vendor could deliver a system that met the requirement. The technical evaluation ended there.
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