AI You Can Trust. Results You Can See.

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Our Industry Applications

Learn how DarwinAI’s pioneering Explainable AI (XAI) platform allows innovative manufacturers to increase profitability by leveraging data and advanced factory automation in use cases such as quality inspection.

Manufacturing

Our AI At Work

  • Defect Detection

    Improve quality control and inspection performance by leveraging visual (images, video) and auditory data to rapidly recognize deviations—reducing your costs for rework and scrap.

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  • Adaptive Factory Automation

    Increase output and efficiency by enabling sophisticated automation with predictive maintenance, adaptive robotic picking, federated equipment learning, and automated equipment work instruction.

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Our Technology

Developed by a team of world-class engineers and renowned scholars, DarwinAI’s pioneering Explainable AI technology applies to a range of automation and human-in-the-loop decision making use cases. The insights that we provide to operators originate from the same deep understanding of AI models that enables us to build superior enterprise solutions.

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Industry surveys reveal that 90% of AI models don’t make it into production: the development process is manual, iterative, and costly, and even gathering enough of the right data to build a reliable solution is a challenge—all but guaranteeing disappointing results.

However, unlike other AI products on the market or custom in-house solutions, our pioneering XAI platform overcomes these hurdles, completely changing the outcome of AI projects.

The Explainability Imperative

As enterprises, manufacturers, and other organizations adopt AI, “Explainability” features that reveal how and why AI makes its decisions will be especially important—for a few reasons.

  1. Effectiveness and Efficiency

    Explainability makes the development, maintenance, and extension of AI-powered solutions both more effective and more efficient—helping manufacturers to introduce better solutions, sooner, to maximize lifetime ROI.

  2. Assistance and Auditability

    Some manufacturing use cases leverage explainability as part of their operational implementation, revealing to an operator in real time the reasons why a particular decision—for example, an inspection outcome—has been made and logging that information in an auditable database.

  3. Regulatory Compliance

    Because of concerns about bias, privacy, security, and other factors, many regulators are introducing rules that govern the use of artificial intelligence. As a result, businesses subject to such legislation—which includes many manufacturers—may not be able to use AI solutions that lack explainability features.

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Trusted By Innovators

Our technology is trusted by some of the world’s leading enterprises.

Interested in leveraging AI within your enterprise?