Dialog-Based Communication with AI: Ask Questions, Get Answers

More and more companies are recognizing the increased value of artificial intelligence (AI) and are turning to intelligent applications to help them master their business requirements. Intelligent enterprise search, so-called insight engines, create innovative possibilities for knowledge transfer through dialog-based search, increasing productivity and efficiency in the company.

Artificial intelligence is becoming more widespread and increasingly enabling intelligent business understanding and automation. Insight Engines combine classic enterprise search technologies with AI-based speech recognition methods and machine and deep learning.

The Intelligent Information Center

Increasing volumes of data mean that employees spend more and more of their working day searching for relevant information instead of being able to concentrate on their core work.

Insight Engines start where users enter, create and store data and documents. They index all data from the connected data sources, analyze it, and make it available to users in the appropriate format - regardless of whether it is structured (tables) or unstructured (content from documentation, articles, audio, video).

This revolutionizes how users search for and access information as part of their daily work. Combined with a dialog-like user interface, they are able to establish a human-like dialog and not only find relevant information and content but also provide it proactively and in the right context (conversational search).

Inconsistency of Human Language

Just as people communicate with each other in dialog, self-learning programs help understand natural language and enable this interaction with corporate data. One of the biggest challenges in this context is the ambiguity of human language.

For example, the words "golf/gulf" has several different meanings. On the one hand, you have the sport, and on another, you have a body of water such as the Gulf of Mexico. Humans deduce what is meant quite easily from the context. For machines, this is not always so simple. Similarly, colloquialisms, dialects, irony, or typos and misspellings make it difficult to understand. Machine learning, especially deep learning, as well as natural language processing (NLP) and natural language understanding (NLU) ensure that the various parts of a sentence, such as nouns, verbs, and adjectives, and their meaning, as well as the relationship to each other, are understood correctly.

NLP is used to process human natural language by machine. This technique helps the Insight Engine to understand the content and convert it into meaningful information. This requires not only the understanding of individual words and sentences but also the comprehension of complete text contexts and facts. With NLU - a sub-discipline of NLP - the solution derives the intention or intent of the user from the query. Despite the usual human "inaccuracies" (e.g., typos), words and content can be understood, and questions with "how," "where," "who," "why," and "when" can be answered adequately.

User Behavior and Relevant Personalization

Insight engines also analyze the behavior of their users. Based on their individual way of working, their search and click behavior, or even how often or in what context they call up certain information, Insight Engines classify the relevance of individual search queries and content. They store this knowledge for the future and prioritize relevant content for future search queries. As a result, search performance becomes more and more optimized the longer the technology is in use. AI-based technologies such as machine and deep learning work in the background to achieve this.

In addition to the way users work and behave, their individual access permissions also play an overriding role. Based on the personal authorizations, an Insight Engine adapts the results display. This not only means that users only receive the data for which they have the appropriate authorization but also that the results are perfectly tailored to the individual needs of the user. For example, when asked for a specific name, customer service employees receive data on contracts, orders, and products, while a user from purchasing receives quotations, and those responsible for accounting receive open orders, open invoices, reminders, or lists of balances. See other functional areas!

The graphical preparation of the results can be individually adapted to the requirements. Building blocks such as layouts, search fields, results, navigation elements, and filters can be individually combined and customized. The presentation can therefore be adapted as required. This gives employees a very individual and specific view of corporate knowledge.

Today, Insight Engines act as an intelligent interface between people and information, providing a significant competitive advantage.