Active information provision instead of Document Search
Turning unstructured documents into usable Knowledge
Companies and organizations have large amounts of valuable information in reports, PDFs, minutes, findings, and other document collections that have grown over time. The problem: this content is often unstructured, named differently, and distributed across many individual documents.
Our AI-based approach systematically unlocks this knowledge. Relevant information is identified in the documents, extracted, standardized, and linked with each other over a longer period of time. In this way, a heterogeneous collection of documents becomes a structured, semantically enriched knowledge base.
Users then no longer simply access individual documents, but the information and relationships contained within them – via a natural-language research interface tailored to the respective use case.
Example: The intelligent patient record
How AI structures and links medical information from many documents and makes it usable over Time
- Over the years, numerous medical reports, findings, laboratory reports, and other documents are created for a patient. In traditional information systems, this information is often stored as individual PDFs or other unstructured documents.
- For a physician, this means: Relevant information may have to be gathered from a long treatment history and many individual documents.
- AI therefore first identifies key medical information in the existing documents – for example, diagnoses, symptoms, laboratory and measurement values, as well as medications – and assigns it to the respective point in time in the treatment history.
- Different terms for the same medical matter can be semantically consolidated. In this way, the many individual documents become a structured long-term perspective on the patient.
- A physician can then ask a specific question in natural language, for example:
“How has the blood pressure developed over time?”
The system recognizes the intention of the question, accesses the previously structured information, and presents the relevant development clearly as a time series, table, or other suitable representation. At the same time, it remains traceable which documents the individual values originate from.
More complex questions can also be supported. In the case of a symptom such as headaches, for example, the system can identify relevant pre-existing conditions, measurement values, or medications that could be important for the physician’s research.
The key point: The AI is not intended to independently make a medical diagnosis. It acts as a research assistant and guides the physician specifically to the information and original sources needed for their own professional assessment.
Here, the physician does not use a traditional chatbot, but rather a UI that uses natural-language input similar to a chatbot while also directly presenting the aggregated data, for example in the form of a graphical visualization or time series, together with the aggregated metadata for a quick overview.
The particular added value for the doctor
Especially in everyday medical practice, time is a critical factor. A physician treats many patients and cannot remember every individual diagnosis, medication, or development over the course of several years.
The intelligent patient record therefore condenses an extensive treatment history into the information that is currently relevant to the specific question.
From a collection of documents to active information provision
The key difference:
A traditional search finds documents.
Generative AI formulates answers.
Our approach extracts and structures the knowledge contained in the documents – and makes it usable for specific applications.
How documents become an intelligent knowledge base – in 3 Steps
Identify and Structure
Relevant information is automatically extracted from unstructured sources and semantically standardized.
Aggregate and Link
Information from many individual documents is combined into a consistent overall picture – for example, across a patient’s entire history.
Provide Based on the Situation
A natural-language and application-specific research interface provides exactly the information the user needs for their current task
Unstructured data becomes usable knowledge
This creates much more than better document search: unstructured data becomes an intelligent knowledge base that provides professional users with relevant information quickly, specifically, and transparently.
Make the knowledge in documents specifically usable
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