Realise the potential of your data: AI-based chat systems for accurate information in real time
In a world where information is constantly growing, it is becoming increasingly difficult to find the relevant data quickly. Companies and customers invest a lot of time searching for the right answers in huge amounts of data. Valuable information such as sales documents, manuals or technical specifications are often hidden in documents and various formats such as PDF, XSLX, HTML or DOCX.
Simple search functions are not enough to provide accurate answers, so accessing this content is often time-consuming and frustrating. Traditional index-based systems only provide references to documents that need to be manually searched for the relevant information - causing delays in service, sales and communication. This leads to inefficient processes and can have a negative impact on customer satisfaction.
Solution: Fast, targeted answers through AI-based technology.
Our solution uses modern AI technology to access and process various data sources in a targeted manner. In this way, it
directly delivers precise and relevant information for search queries that is required for well-founded decisions - whether
in customer service, business intelligence or content creation.
Our technology combines text generation with targeted information retrieval without the need for time-consuming fine-tuning
of an LLM. This enables significantly improved reliability and utilisation of internal knowledge stored in documents and data
sources. The approach of a RAG architecture combines a modern search with the possibilities of an LLM (Large Language Models)
and thus enables customised chat systems with their own files.
How RAG ("Retrieval Augmented Generation") works:
Large language models (LLMs) are able to answer many questions well, but only with the knowledge they have learnt during
training. They do not have the ability to retrieve specific information. While general knowledge questions can be answered
well without additional sources, they quickly reach their limits when it comes to company-specific or current information.
Without access to in-house or more recent data sources, errors or ‘hallucinations’ often occur, where incorrect
or fabricated facts are presented.
Our solution solves this problem by incorporating current and specific information from multiple sources, resulting in reliable
and always up-to-date answers.
Pain points in information procurement that can be resolved:
- Inefficient search processes (e.g. internal documentation).
- Answers that are currently only available as a list of links, whose documents still need to be searched in the second step
- Unstructured data that is difficult to access
Two major advantages of RAG systems in information retrieval:
- Answers are provided as ‘summarised text’ (generated by an LLM), not as a list of links
- UI + UX like an LLM, but the answers are based on your own data / documents