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Natural Language Processing (NLP) is about machines understanding language. To do this, both syntax and semantics must be analyzed. Modern NLP models learn these two analyses from millions of sample texts.

Humans need years to learn how to use language. Nuances and countless irregularities make it difficult to recognise and distinguish. This makes it difficult for NLP solutions to convert unstructured language into structured data for use. Why do they still manage to do it?

Data is the foundation of any modern IT solution. Where the focus used to be on mapping processes, today it is often a matter of outsourcing entire heuristics to algorithms. The good news is that the ways in which data can be obtained and processed are becoming increasingly simple.

As soon as documents are digitized and intelligently evaluated, a wide range of possibilities for optimizing knowledge-based processes opens up. For critical processes, a case-based quality loop ensures optimal results.