Our Technology

DarwinAI’s Explainable AI (XAI) meets your most demanding use cases and performance requirements

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Now you can outperform your own in-house projects, manual processes, existing software solutions—and your competition—in a fraction of the development time.

Developed by a team of world-class engineers and renowned scholars, our 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 that have a smaller memory footprint, are more computationally efficient, and perform with extraordinarily high levels of precision.

Whether you’re building AI for the cloud or the edge, our Explainable AI technology can meet your most demanding use case requirements.

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, unlocking new applications and performance possibilities—and setting us apart from other AI technology companies.

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While alternative approaches often only work in ideal scenarios, solutions built on our platform perform in real-world enterprises, thanks to:

  • Lower data requirements: our solutions require only a fraction of the number of data samples needed by traditional AI systems, making it much easier to get projects started
  • Purpose-built models: XAI accelerates the creation and calibration of deep learning models to your specific use case by making it significantly easier to correct data errors and fine-tune sensitivity to reduce false positives and false negatives
  • Reduced computation needs: Smaller memory footprints and high computational efficiency allow you to run powerful deep learning on embedded microprocessors in edge devices
  • Ongoing learning loops: We enable a feedback loop that makes the system smarter over time by continually learning from real production data

Our Partners

We’re proud to work with technology leaders who are pushing the boundaries of AI.

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Harnessing Explainable AI You Can Trust

Explainable AI isn’t some meaningless buzzword—it’s a powerful tool that can deliver breakthrough results for your enterprise’s deep learning projects, in any industry, by:

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  1. Accelerating Development:

    Automatically generate new models—custom fit to your training data—which meet performance targets (e.g., accuracy) within operating restrictions (e.g., parameters, size, FLOPs)—dramatically accelerating project development timelines

  2. Providing Transparency:

    Reveal bottlenecks in models, to visualizing performance comparisons of different experiments, to identifying errors, biases, and issues in both models and data

  3. Increasing Trust and Understanding:

    Reveal critical factors which cause the model to make decisions—showing why decisions are made, unearthing hidden bias, and enabling more effective and efficient audits

  4. Delivering Production-Ready Models:

    Produce ready-to-use, high-performance, and trustworthy models, any of which can be employed in your application and deployment environment

It’s What Sets Us Apart

Our proprietary XAI technology is the byproduct of years of scholarship from our academic team at the University of Waterloo, Canada. It improves upon the shortcomings of existing explainability techniques by:

  • Capturing the inextricable link between data and models: in the words of our Chief Scientist, “There is no data understanding without model understanding and no model understanding without data understanding.”
  • Accurately explaining the critical factors that lead the model to make each decision: that is, in the absence of these critical factors, the prediction being made appreciably changes
  • Quantitatively explaining the way the model makes a decision: the algorithm produces meaningful and actionable outputs which developers can use to make their models better
  • Reflecting model intuition direction regardless of how it ‘reflects back’ to us: the process articulates model reasoning authentically as human intuition can differ significantly from model intuition

Taking a Closer Look

In essence, our technology garners an intricate understanding of a model’s inner workings, thus allowing it to better explain the model’s behavior. Put another way, it uses XAI technology to obtain and provide a direct and global understanding of the model’s decision-making process—this understanding is then harnessed to modify and optimize the models to maximize performance subject to the parametric restrictions of the particular use case and deployment scenario.

Interested in learning more about our unique technology and what it can do for you?