We came together because we'd all seen the same broken system from different angles. One of us spent years in manufacturing operations watching quality engineers drown in manual inspection. Another built AI systems that required six months to deploy and broke with lighting changes. The third designed electronics only to see structural defects—reversed capacitors, rotated ICs—escape visual inspection and cost $50K per return.

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Different perspectives. Same problem. Quality tools weren't solving quality problems.

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Here's what brought us together: A quality engineer walks into a new factory and is productive from day one—they understand quality, process failures, root causes. AI systems? Start from zero every time. No transfer of learning. No institutional knowledge.

We asked: Why can't quality systems learn the way quality engineers do?

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Foundation models changed the equation. Pre-training on manufacturing fundamentals means systems understand normalcy, not just memorized defects. Edge compute enables real-time deployment. LLMs connect visual defects to process causes to supplier batches—reasoning across the entire quality chain.

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MetaFloor exists to build integrated quality intelligence. Visual detection catches defects. Structural validation verifies design intent. Process intelligence connects quality to upstream causes. Traceability maps components through production. Four layers that build on each other, compounding knowledge across every deployment.

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Quality systems should scale like quality expertise does—transfer learning, adapt to reality, and improve with experience. We're building that infrastructure now.