Natural Language Processing (NLP) and overlaying knowledge management

But NLP has much wider, diversified applications and can be used to carry out much more complex tasks. As of 2022, so-called Large Language Models (LLMs) are available for this, which can learn the general structure of a language from a huge amount of text documents numbering billions of pages, and interpret it semantically, or - so to speak - learn it. On top of these general models, task-specific sub-models can be built, which provide automated solutions to a particular problem using machine learning.

Today, the most popular applications of NLP are search engines, chatbots, document management systems, or even intelligent text-based email processing solutions. Search engines are not just limited to Google's search service. Applying the above solutions, companies can also create custom applications for searching within their internal, closed, and isolated document management systems. It is also possible to use machine learning-based solutions to automatically populate documents received or created by the company with assigned content-based metadata, such as relevant search terms or named entities like proper names, addresses, customer numbers. These data are automatically recognised in the text by an entity extraction model and stored with other metadata of the document.

Possible uses

Imagine a system that processes incoming emails, extracts names, customer numbers, contract numbers, amounts, dates, etc., and then associates them with the appropriate customer in the CRM system based on the information and identifiers extracted.

With this solution, both historical documents stored earlier by the company and newly received files can be managed in a unified way, searched, and easily accessed later. The implementation of such software can speed up the workflow of the administration staff and also make better use of the knowledge accumulated in the company.

In addition, such a solution can automate a whole lot of monotonous administrative steps by identifying the language of incoming documents, automatically collecting messages in unknown languages separately, and even immediately notifying the sender that processing may require more time.

Or, consider sorting incoming documents by priority, so administrators can process documents in order of importance, and if the CEO's signature is required somewhere, the system automatically detects it and signals this to the manager, saving time and effort.

What is needed for implementation?

Some components of the software outlined above can be used individually or even as part of a complete ecosystem to help the completion of the administrative tasks of the company. All that is required to implement the solution is the company assessing which software components are most needed to facilitate the workflow, and then collect its existing documents in a single location where the software can process them, whether they are emails, Word, PDF documents, or scanned images. Once implemented, administrators can monitor and control the efficiency of the software, and fine-tune the solution based on feedback.