On our blog, you will find all the information you need on AI and its use in the company. From technical explanations and references to strategic topics, our blog highlights everything that is relevant to the use of AI in the company and contributes to decision-making.
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In the future, intelligent AI assistants will make our everyday work easier in certain situations. However, it is important to have the right co-pilot in use, namely one that works across all systems. The problem with most assistance system providers is that the assistance is limited exclusively to their own systems. This blog article shows how it can be done better.
Retrieval Augmented Generation (RAG) is an approach that enables companies to use generative AI sustainably. In this blog article, we explain what Retrieval Augmented Generation is and how we use this technique at amberSearch to get the most out of it for our customers.
Retrieval Augmented Generation is an NLP technique for enterprise search applications that solves many of the challenges faced by traditional generative Large Language Models (LLMs).
What are the use cases with generative AI in companies? Any software is only as good as the use case that needs to be solved. More and more companies are asking themselves how you can use generative AI in the company and how you can combine the information with internal know-how. This article serves as inspiration to develop your own use cases in the company
Our Microsoft Copilot comparison clearly shows that amberAI offers some distinct advantages over Microsoft Copilot in several key areas. With a competitive price point, amberAI enables broad accessibility to generative AI, while Copilot is burdened with higher costs. The increased data protection and clear compliance with the GDPR by amberAI underlines its responsibility and commitment to protecting customer data, in contrast to Microsoft’s less transparent approach. The cross-vendor functionality of amberAI allows for equivalent treatment of results regardless of systems, while Copilot is limited in terms of Microsoft platforms.
Learn how private AI is changing the way we use AI models. Discover how the practice of private AI makes it possible to train algorithms using only your own data to create customised solutions for your business. Learn more about the benefits of this approach and how it helps preserve a proprietary knowledge base that your competitors cannot benefit from. Read on to understand the concept of private AI in detail and how it differs from public AI models.
You want to know, how to combine your internal information with generative AI without having to upload everything? Than read more about it in this article.
ChatGPT is the hypetheme of the first half of 2023. We, too, have been wondering how OpenAI’s technology will influence Enterprise Search, as there are certainly intersections due to the processing of large amounts of unstructured data.
Briefly, do we see ChatGPT as a competitor? No, we see the technology as a possible enhancement & enablement to create – properly applied – further added value for our users. However, let’s briefly start with the basics:
Enterprise software can be installed either on-premise or in the cloud. This article explains what consequences this has. In order to understand the added value of the respective solution, you first need to know what exactly is hidden behind the two options.
According to a Statista survey, 82% of all companies surveyed in 2020 said they were already using either private or public cloud solutions. As the current trend is for more and more software solutions to be offered in the cloud, this article takes a closer look at the advantages and disadvantages of cloud and on-premise solutions.
Deep Learning and Machine Learning are two important technologies in the world of Artificial Intelligence (AI) and thus part of the umbrella term Data Science. Although they are sometimes used as synonyms, there are important differences between the two to understand.
Natural language processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through the use of natural language. It involves the development of algorithms and models that can understand, interpret, and generate human language.
Data is an essential component of modern society, especially for companies and its employees, as it allows us to make informed decisions, understand complex systems, and learn from the past. There are many different forms of data, each with their own unique characteristics and uses.
Learn how an enterprise search engine can help you too.
An enterprise search is an internal search engine used by companies to provide their employees with quick access to important information and data. It searches various data sources within a company, such as document management systems, customer and employee databases and project management tools. In today’s digital world, where companies are collecting and storing more and more data, it is becoming increasingly difficult to find the relevant information quickly.