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).
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
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?
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.
How can generative AI be combined with in-house data?
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.
Integrating modern AI systems into grown IT landscapes
Integrating modern AI systems into evolved IT landscapesThe integration of modern AI systems into evolved IT landscapes is a challenge that many companies are dealing with today. The development of artificial intelligence is progressing ever faster. German SMEs in particular have to make sure they don’t miss the boat when it comes to digitalisation. There […]
amberAI – ChatGPT for corporate use cases
Few technologies have generated as much hype in recent years as ChatGPT from OpenAI. We have also noticed this and know that many companies are asking themselves how they can also profit from GPT technology. But what many realise relatively quickly: ChatGPT has its limits and is only trained with public data until 2021. But: […]
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.