Natural language processing (NLP) is a branch of AI that enables machines to process and interpret human language. In general, by implementing NLP, companies can leverage human language to interact with computers and data. Areas of NLP include semantic search, question answering (QA), conversational AI (chatbots), text summarization, document similarity, question generation, text generation, machine translation, text mining, speech recognition — to name a few use cases.

Modern enterprises have been rapidly integrating NLP into their internal products, processes and workflows. It is not only Amazon AWS or Microsoft who implemented NLP, but also enterprises such as Airbus, Infineon, Alcatel-Lucent, government agencies across the globe, as well as startup tech companies who are utilizing NLP to create amazing new products and services. NLP can be used in the financial industry, legal field, science, manufacturing, and many other verticals. Practical applications of NLP usually involve implementing semantic search and question answering to automate data analysis, decision-making process, fraud monitoring, claim management, regulatory actions, to reduce costs and improve customer satisfaction.

At deepset we believe in open NLP. This includes open language models, open source tools to build neural search and question answering, open communication and discussion, sharing experiences, as well as educating the developers and the users of NLP-enabled solutions. We believe that only through transparency and openness it is possible to apply natural language processing to various problems that enterprises and governments are facing.

The ever increasing volume of unstructured data in the enterprise, e.g., corporate documents, financial reports, research papers, legal contracts presents a difficult problem to solve for the enterprise product teams. This kind of data is usually hard for the enterprise software to process. NLP allows the developers to apply latest research to industry relevant, real-world use cases, such as semantic search and question answering. Able to streamline 80% of the mundane processing tasks, the NLP technology helps to ensure better efficiency in data processing, data analysis, reporting, as well as better customer experience, reduced costs of operation, and improved customer satisfaction. By leveraging natural language processing companies can create smart solutions to common business problems.

Natural language processing solutions (NLP) offer a variety of benefits of using artificial intelligence and machine learning tools to solve a range of common business and technology problems related to processing, sorting and making sense of data. We have helped the largest European companies and public sector organizations to instrument semantic search and question answering (QA) to automate data processing, legal analysis, regulatory compliance, and decision making. We've also built an enterprise SaaS product to complement our open source NLP framework (Haystack).