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Our Technological Focus


Let's get to the basics.


We work on some of the most challenging and interesting areas in Machine Learning

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{ Challenges that we are solving }



When you have terabytes of unstructured data, it is important to think about how content can be consumed more efficiently. Our comprehension model can help us understand what users are looking for when they search for information.



When humans read, our experiences tell us which words and sentences we should focus on. We teach our model to do the same by using self-attention mechanisms to find out where the A.I. should pay more attention to.



Our machines read documents on the pixel level, allowing us to reconstruct tables and map data across an ocean of content. This unlocks limitless possibilities in transforming unstructured information into structured data like no other.



Our data annotators read and label a wide spectrum of information. Our annotation tool is used by esteemed researchers, radically changing the way industry practitioners transfer their knowledge to machines.

We actively work with university faculty members and researchers to advance our technology.

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