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Digitalization in public administration means dealing with analog documents in practically all fields of activity. Processes such as e-file or ERV offer great advantages in increasing efficiency in the administration and processing of large volumes of text.

Numerous systems have been established for public administration. These products often have a high level of complexity and require a high level of resources, often the manual processing or interpretation of the texts still remains.

This is where our Word Processing solution creates the real added value in the form of decision support and process optimization.

Essentially, there are two different types of texts:

  • Texts are well structured or have a lot of metadata and are available in sufficient quantity.
  • Texts are unstructured, have different content types, and a high degree of editorial freedom.

In addition, there are two technological approaches:

  • Categorization and processing are realized by Machine Learning. As a result, a previously defined model is trained.
  • We use semantic extraction if Machine Learning is not possible, whereby the meaning of the text is interpreted. The basis is the creation of semantic models, which are often located in the department since the individual cases are well-known there.