Enterprise search is necessary to integrate AI into company processes in the long term. This article explains the role of enterprise search.
Generative AI and its use in the company
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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Data Security
GDPR and generative AI: how companies protect their data
This article explains how generative AI can be used in compliance with the GDPR!
AI introduction checklist – your guide to successfully implementing generative AI in your company
This AI introduction checklist reveals exactly what needs to be considered when introducing an AI solution!
How to Ensure Successful Generative AI Integration in Your Company
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.
Technical foundations for the introduction of generative AI in companies
Learn all the technical basics you need now if you want to use generative AI in your company.
One copilot to rule them all
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.
Understanding Retrieval Augmented Generation: A New Frontier in AI
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 should you choose an enterprise search by?
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.
Krämmel Group: Innovation through Intelligent Search Technology
As a third-generation family business, the Krämmel Unternehmensgruppe now has over 200 employees. In the core areas of project development, planning & construction and facility management, the construction company offers its customers the complete construction process from a single source. In this way, the Krämmel group of companies has been guaranteeing high quality for its customers for more than 75 years and thus lives up to its own motto “Building made easy”.
Cloud vs. on-premise solutions – advantages and disadvantages
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 vs. Machine Learning – Understanding the Differences
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.
Optimizing R-Biopharm’s Processes with Advanced Search
R-Biopharm AG is one of the leading biotechnology companies in Germany. The product portfolio of R-Biopharm AG ranges from analytical instruments in clinical diagnostics to nutrition care products and food & feed analysis. With technically sophisticated products, the former hidden champion from Darmstadt has developed into one of the most relevant players in this market with over 1,400 employees worldwide. However, in order to develop to this level, a lot of specific know-how is needed. R-Biopharm has not only been able to build this up successfully in recent years, but has also increasingly digitalised it.
Revolutionizing Information Retrieval through amberSearch
For years, Microsoft Teams has been developing into the central hub of the so-called knowledge workers. And not without success. Teams recently cracked 280 million monthly active users.
So we have exciting news for all amberSearch users:
With our new Teams app, we are picking up users where they spend the majority of their work. From now on, amberSearch users can also start a search from Teams.
Learning from the Internet — How AI Simplifies Corporate Searches
Search engines on the Internet have been around for a long time. In 1998, the concept behind Google was introduced by graduates of Stanford University. Today, in addition to the top dog Google, there are other providers such as Bing and DuckDuckGo. Without them, the Internet would probably be an impenetrable jungle for most people.
Engineering Solutions by Müller Maschinentechnik
The statistics show it: Accessing the right information in the company is increasingly difficult. For example, every third search in the company already takes between 5 and 25 minutes. What’s more, in 67.5% of all cases it takes at least 3 searches to find the right information. This trend is not only felt by large companies with thousands of employees, but also by medium-sized companies with dozens or hundreds of employees. The reasons for this are trivial: rapidly increasing data volumes, employee fluctuations and an increased number of options for storing information.