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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Many companies need and want to gain experience with generative AI. The introduction of generative AI is therefore on the roadmap of many companies. This blog article is about how to successfully combine in-house expertise with generative AI – within a secure and GDPR-compliant framework – and what to look out for. The introduction of generative AI is not as complex as it first appears.
Learn all the technical basics you need now if you want to use generative AI in your company.
The techniques used to search for internal company information have developed continuously in recent years. The main techniques are federated search, keyword-based search and intelligent search based on large language models. This article explains and compares the various techniques.
If you want a basic understanding of the various technologies, this is the right place for you.
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
Discover how modern information management with “amberSearch” revolutionises efficiency for IT managers through the intelligent combination of enterprise search and AI. Gain access to decentralised systems and improve information discoverability to navigate the dynamic business world with confidence. Increase productivity and control information chaos thanks to this powerful solution.
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.
Thanks to amberSearch’s Enterprise Search, the international foodtech company Zentis works together even more efficiently. The R&D team benefits from centralised access to versatile knowledge in different systems and languages. The CREOS project enables the R&D staff to access information across countries. In phase 1, amberSearch was integrated for German and selected English-language systems, with a group of key users using the new R&D portal in Zentis’ SharePoint. In Phase 2, the collaboration expanded coverage and added translation capabilities. This allows users to search and get results in their preferred language. Both Zentis and amberSearch built know-how and overcame technical challenges such as maintaining access rights and networking with network drives without an additional VPN connection.
The introduction of an enterprise search requirement no longer has to be a large and comprehensive project as it used to be, but can be much leaner nowadays. In order not to have unintentionally complex implementations or technologically outdated solutions afterwards, a careful selection of the right providers is required. In order to clearly outline the requirements, we have compiled a short list to help with the selection of an enterprise search.
Over the years, we have built up expertise in the field of enterprise search and have identified recurring patterns over the course of various projects. In order to share our best practices with companies implementing enterprise search, we have written this blog article. Our learnings are mostly from onboarding with SMEs and can of course vary per company/industry. While our best practices are not universal, they are a good guide to what things to consider.
What technical steps have to be done to implement an enterprise search? You can find it here.