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Most advanced manufacturers of appliances have their own set of production tools that they use as standard – including standard work in their factories that outline work steps, expectations, etc. They try to build quality into the process (through things like poke-yoke) and have some pretty good systems for this, but there are places where this is hard. Specifically, adjusting mistake-proofing and quality inspection systems to different configurations coming down the same line – as many of their products/lines have to customize to customer specific orders.

A key challenge for automated quality control in mass-customized production is the need for 1000s of "training images" for every product variation coming down the line. We eliminate this need by defining compositional product models where machine learning algorithms are trained on sub-assemblies and combined together for individual build orders extracted from the Manufacturing Execution System (MES).

We work with manufacturers to integrate our solution into existing MES systems, or provide a custom barcode solution where our platform identifies each individual build order.

Our Pathfinder platform builds compositional models of a variable physical assembly task, given only a handful of recorded demonstrations of the task. Once such a model is built, Pathfinder can finely track the job status from live video, to guide a worker through the task, provide independent training, and perform analytics. Instead of requiring a team of specialized computer vision engineers to build custom interactive experiences that can track the steps a worker successfully performed which takes months, we enable such AI-driven feedback to be set up in hours by ordinary manufacturing engineers.
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