EMS@C-LEVEL · 6 Nov 2024 · 26m
Priyansha Bagaria, founder and CEO of Loopr.ai, explains how her company uses AI-powered computer vision to automate quality inspection in aerospace, automotive, and general manufacturing. She details the deployment of their software applications, IQ Inspect for defect identification and IQ Verify for assembly verification, which are hardware-agnostic and integrate with existing camera infrastructure. Bagaria highlights key industry pain points, including the high cost of quality, labor shortages, and the loss of institutional knowledge as experienced workers retire, arguing that AI provides the consistency and scalability that human inspection cannot. She also discusses Loopr's strategic partnership with Microsoft and the importance of starting with high-value pilot projects to demonstrate clear ROI before scaling across factory floors.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Quality & testLoopr.ai (IQ Inspect and IQ Verify)5% reduction in defect escape
The AI system identifies defects in manufactured parts and verifies assembly, allowing customers to reduce defect escape rates and match or exceed manual inspection accuracy within two months of use.
“with one of the customers they were able to reduce 5% of the defect escape , which they didn't even know existed . So they were really missing on that…”
Priyansha Bagaria · Hear it at 4:33
Quality & testLoopr.ai (IQ Inspect and IQ Verify)better results than manual inspection after two months
The AI model, trained on data from existing or new cameras, performs continuous learning to identify welding, painting, and assembly defects, achieving better results than manual inspection after two months of experience.
“With another customer . They feel that the system , you know , with two months of experiences , is doing much better results than their manual inspection today…”
Priyansha Bagaria · Hear it at 4:33
early-stage companies
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