Compensation of errors in signaling data by making the relevance evaluable with alterative measurement (Google waiting times). The approach to realize a data acquisition for Common Data /Frequent Data usage with simple means was born from the necessity of data acquisition.

In this article we present an Artificial Intelligence application on information extraction under the paradigm of less training data. In the specific case, data (offers, documents, photos, emails) from the real estate industry were processed. However, the presented solution can also be trained and used for any other information from completely different industries with little effort.

We use Transfer Learning with ULMFiT especially for our information extraction solutions. The main advantage is the small amount of data required to train the Deep Learning Model. Transfer Learning is one of the most important techniques used in the field of Deep Learning applications today. It is about the transfer of learned knowledge from a general to a specialized AI model.

Filing a document. With this project we show that it is possible to learn documents of different origin and type from a filing system in a secretary's office (default to filing) and to apply it to new documents.

Coupling between SAP and BIM system. We create the accuracy in the DIN booking key in the construction information system that it needs, starting from a general contractor invoice.