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How Document AI is Helping

In recent years, companies have been investing in machine learning and AI systems to increase the efficiency of their processes.

These efforts have tended to focus on improving repetitive tasks, such as scheduling appointments or automatically sorting e-mails. These applications are promising, but they do not address a major headache for data managers: how to make sense of vast amounts of unstructured information. This includes textual documents such as reports and receipts that people frequently want to search through when looking for specific pieces of information.


New types of AI algorithms can be applied in this context to find connections between unstructured text and allow users to ask questions about it. A well known example is Google’s Translate service which allows you to type something in one language, have it translated into another and then read in that language.

Other AI algorithms include IBM’s Watson which is designed to process literature on cancer diagnoses for example, while still others are focused on analysing relevant social media posts. Companies like Zanrans are using these new technologies to create text analytics platforms able to understand unstructured documents and answer queries about them.

NLP for document AI processing:

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