Qsee

www.qsee.io

Qsee’s ML/AI powered Quality predictive analytics ensures that process manufacturers will achieve optimized productivity by addressing excess expenses on waste, rework, recalls, energy, and pollution. Qsee is disrupting the traditional approach to process manufacturing quality control by both projecting and preventing defective products early in the manufacturing process capturing benefits from Zer0-Defect® production runs. We are an Israeli start-up developing cutting edge AI technologies that utilize a state-of-the-art predictive/prescriptive analytics approach for the process of manufacturing quality-control space. With the use of machine learning software Qsee builds unique AI models for each production machine/line by correlating data with quality outcomes (pass/fail or multiple-failure-states). The AI engine generates an array of hypotheses based on machine variables and then further tests these hypotheses in order to determine which factors are causative of quality failure. After producing the model, the software begins to predict real time product quality outcomes before they begin.

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Qsee’s ML/AI powered Quality predictive analytics ensures that process manufacturers will achieve optimized productivity by addressing excess expenses on waste, rework, recalls, energy, and pollution. Qsee is disrupting the traditional approach to process manufacturing quality control by both projecting and preventing defective products early in the manufacturing process capturing benefits from Zer0-Defect® production runs. We are an Israeli start-up developing cutting edge AI technologies that utilize a state-of-the-art predictive/prescriptive analytics approach for the process of manufacturing quality-control space. With the use of machine learning software Qsee builds unique AI models for each production machine/line by correlating data with quality outcomes (pass/fail or multiple-failure-states). The AI engine generates an array of hypotheses based on machine variables and then further tests these hypotheses in order to determine which factors are causative of quality failure. After producing the model, the software begins to predict real time product quality outcomes before they begin.

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City (Headquarters)

Ra'anana

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Employees

1-10

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Founded

2018

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