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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Enterprise Search
AI introduction checklist – your guide to successfully implementing generative AI in your company

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!

7 tips for defining and implementing an AI strategy for companies

7 tips for defining and implementing an AI strategy for companies

This blog post uses our best practices to explain to companies which aspects a sustainable AI strategy requires.

What is generative Multi-Hop Q&A?

What is generative Multi-Hop Q&A?

Multi-hop question answering (multi-hop Q&A) is a method for generating better quality answers in the field of generative AI applications. As a further expansion stage of a Retrieval Augmented Generation (RAG) system, the aim is to consider all aspects of the prompt as far as possible before an answer is generated. This blog article explains this technique.

How to Ensure Successful Generative AI Integration in Your Company

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

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.

Keyword-based vs Federated vs Intelligent Search – different enterprise search technologies explained

Keyword-based vs Federated vs Intelligent Search – different enterprise search technologies explained

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.

One copilot to rule them all

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.

What is Retrieval Augmented Generation and how does amberSearch use this technology?

What is Retrieval Augmented Generation and how does amberSearch use this technology?

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).

Use cases with generative AI in companies

Use cases with generative AI in companies

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

Efficient information management: A shortcut to effective information management for IT decision-makers based on our learnings from a wide range of customer projects

Efficient information management: A shortcut to effective information management for IT decision-makers based on our learnings from a wide range of customer projects

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.

amberAI vs Microsoft Copilot: A comparison

amberAI vs Microsoft Copilot: A comparison

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.

Private AI – Should you train Large Language Models with your own data?

Private AI – Should you train Large Language Models with your own data?

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.

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