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. In this blog post, we have explained how to combine both enterprise search and GPT technology in a meaningful way.However, let’s briefly start with the basics:

What are Large Language Models?

Large Language Models (LLMs) are complex AI models that are trained to understand and generate human-like texts in different languages. They are based on extensive databases and can recognise patterns and relationships in text-based documents.

ChatGPT: A brief introduction

GPT stands for “Generative Pre-trained Transformer”. It is an AI-powered language modelling technology that represents a breakthrough in natural language processing (NLP). ChatGPT is the latest development in this field and is being developed by OpenAI. It is designed to perform real-time human-like conversations with high accuracy. ChatGPT works contextually and uses complex algorithms to accurately understand user queries and needs.

The recent popularity of ChatGPT can be attributed to its amazing ability to deliver human-like responses and interactions. It has numerous use cases in marketing, texting and various “secretary-like” tasks.

What are the weaknesses of ChatGPT?

The big (so far) weakness of ChatGPT is that it is only trained on data up to 2021 and only has know-how up to that point. If set up correctly, it can also process content from the World Wide Web, but everything that happens behind a firewall, i.e. on the systems of a company, remains (thank God) a black box for ChatGPT. Moreover, it is currently hosted exclusively in America at Azure.

In this article, however, we do not want to talk specifically about the provider OpenAI, but rather evaluate the GPT technology with its potential in connection with amberSearch. There are in fact various providers (including Google, but also Aleph Alpha from Germany) who are working on comparable models and in some cases already make them publicly available.

How can GPT and Enterprise Search cross-fertilise each other?

The combination of GPT technology & enterprise search engines such as amberSearch, for example, enable internal know-how to be treated with a similar intelligence as ChatGPT does with information from the internet. The challenge in the enterprise, however, is that there are access rights and information comes in many different formats and data sources.

In our case, we use the existing AI models of amberSearch (also based on Large Language Models, moreover self-trained by us) to retrieve the most relevant documents considering access rights. These documents are then passed by amberSearch to amberAI – which creates relevant text based on the user’s query and the search results found. More information about amberAI can be found in this blogpost.

The big advantage of amberAI?

It takes all access rights into account and does not need to be specially trained.

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Advantages of combining GPT technology and the Enterprise Search use case

  1. Advanced information processing: By merging GPT technology and Enterprise Search, the AI can answer queries more effectively and package speckled information extracted from Enterprise Search into meaningful answers rather than just replaying it.
  2. Accurate answers: GPT technology enables precise and contextual answers by accurately understanding the content delivered by the search engine and enriching use cases known from ChatGPT (e.g. formulating a sales email) with the company’s know-how.
  3. Time saving: The combination of GPT technology and enterprise search speeds up the retrieval of information even more, as the GPT technology can summarise documents and the user does not have to jump through all systems. Nevertheless, it is important that the user checks afterwards whether ChatGPT has quoted correctly from the documents.

Some use cases:


So do we see GPT technology & Enterprise Search as a match made in heaven? Yes and no. GPT technology offers various use cases that go far beyond the combination with Enterprise Search. However, the combination with Enterprise Search offers added values that GPT technology alone will never be able to realise. It is therefore an extremely sensible and value-added match. The combination of the two technologies ensures that users get accurate, effective and relevant information when they need it most.

By the way, we explained how to calculate the business case of an enterprise search in this article.