Product Overview

Interpretable AI develops proprietary machine learning algorithms that achieve state-of-the-art performance while remaining completely transparent and understandable

Our Philosophy

  • Peer-reviewed research

    Our algorithms are based on years of research at MIT and published in the top peer-reviewed academic journals

  • Industry-ready: practical and scalable

    Each algorithm has a proven track record of success in large-scale experiments and industry applications.

Classical models revisited with modern optimization

Classical interpretable models such as regression and decision trees are interpretable but have limited predictive performance.

We take a fresh perspective on these problems and leverage modern optimization techniques to lift the performance to the level of black-box models without sacrificing interpretability.

These algorithms form the core of the recent graduate-level textbook Machine Learning Under A Modern Optimization Lens by co-founders Bertsimas and Dunn. This book details the transformative effect modern optimization is bringing to the fields of machine learning and artificial intelligence, and is guiding teaching at leading universities like MIT.

Interpretable AI Algorithms

Software modules spanning the entire data science lifecycle

Our technologies apply to all phases of the data science lifecycle, from data cleaning and exploration, through predictive modelling, to the end goal of data-driven decision making and driving business value.

More flexible than deep learning

  • Works with the data you actually have right now

    Our algorithms can work with datasets of any size or quality, whereas significant volumes of data are required just to get started with deep learning

  • No specialized hardware requirements

    All model training runs on standard CPUs without required specialized GPU hardware

  • Instant predictions and simple deployment at the edge

    Our models are lightweight with near-instant prediction even on embedded IoT devices, as opposed to deep learning where specialized hardware is required for edge inference

Accessible and Intuitive

  • Support for major languages

    Our algorithms are accessible from Julia, Python and R

  • Familiar and intuitive API

    Our API is well-documented and very similar to Scikit-Learn, so there is very little learning curve for data scientists

  • Flexible installation

    Our software can run on-premises or in private cloud systems as desired

Want to try Interpretable AI software?
We provide free academic licenses and evaluation licenses for commercial use.
We also offer consulting services to develop interpretable solutions to your key problems.

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