![]() ![]() ![]() ![]() We also wanted to be able to improve the product in an organic way, by iterating fast on new features without worrying about breaking anything in the document processing pipeline. We also knew that processing workloads would be spiky and therefore it would be hard to predict the level of computational resources needed at any given time. We knew that Parsel was going to be processing potentially sensitive documents (financial documents, initially, then expanding from there). ![]() In this post, we hear from the head of Parsel's data science team, Daniel Vliegenthart, about the technical architecture under the hood of Parsel and what that means for handling large document processing workloads without any performance degradation.įrom the moment we first started thinking about Parsel as a product, we had three adjectives in mind for its technical architecture: secure, scalable and supple. ![]()
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