More efficient research through AI-based document analysis
Engineers, researchers and quality experts lose valuable time every day searching for material characteristics, test reports and standard documents. AI-based document analysis unlocks this hidden knowledge, makes information immediately accessible, reduces search costs and enables data-based decisions. Discover how material intelligence platforms turn scattered documents into valuable insights.
The problem with hidden knowledge in companies
Decades-old test reports, technical specifications and standard documents contain valuable insights — yet they often lie dormant unused in chaotic folder structures. Engineers, researchers and quality experts lose valuable time every day looking for relevant information in this “dead knowledge.”
According to studies employees spend up to 2.5 hours per day with research in unstructured data. An enormous loss of time — especially for companies that need to make quick, data-based decisions.
The reason:
- Unstructured data in messy folder structures
- Inconsistent file names that make it difficult to find
- PDFs and scanned documents that are difficult to search
Particularly in material development and quality assurance, valuable findings lie dormant in decades-old test reports, standard documents and technical specifications — often forgotten and unused. But with AI-supported document analysis, these sources of knowledge can be opened up.
AI as a key to material intelligence
Material intelligence platforms use artificial intelligence (AI) not only to find information in documents, but also to put it in a meaningful context.
But how does AI-based document search work?
- PDF documents are analyzed, broken down into text modules and semantically linked
- The AI searches relevant documents based on specific questions
- Important information is summarized and provided with references to the original documents
This allows engineers and researchers to search specifically for material characteristics, test reports or standards — without having to manually search through documents for hours.
From material data to standards: AI as a research assistant
The possible applications for AI in materials research and quality control are diverse — just a few examples:
- Quick material research
Which material characteristics are relevant for a particular product? The AI delivers the answer in seconds, without the hassle of searching through material data sheets. - Comparison with previous tests
Old test reports can be searched specifically to find out which tests have already been carried out and what results are available. - Direct access to norms & standards
Anyone who wants to know which equations apply to the crystallization kinetics of polymers according to a DIN standard will receive a precise answer with a direct link to the original document. - Efficient training & knowledge management
Engineers working with new devices can simply ask the AI instead of digging through entire manuals. - Multi-language document search
AI-assisted translation helps global teams, making technical documents searchable in various languages.
How to efficiently use documents
Structured document management is crucial for using information in a targeted manner and accessing it quickly. The process is divided into four central steps:
- Collect documents
As a first step, all relevant documents are systematically compiled. This includes frequently used reports, manuals, and technical documentation. The aim is to make all important information available at a central location so that it can be found quickly and is not scattered across different systems or storage locations. - Create structure
In order for documents to be used efficiently, they must be divided into useful categories. Grouping by projects, topics or departments ensures clear organization and makes access easier. In addition, a well-thought-out structure improves the performance of AI-powered search functions, as it can deliver relevant answers more quickly. - Ensuring timeliness
In order to obtain accurate and reliable information, only current and relevant documents should be used. Duplicate or outdated files should be sorted out regularly to ensure the quality and efficiency of the database. This ensures that users can access accurate and up-to-date information at any time. - Regular care
Continuous maintenance of the document collection is essential to keep the database up to date and complete. New documents must be added and existing information reviewed. This is the only way the AI-supported search remains reliable and always delivers helpful results.
This structured procedure ensures that documents are not only stored but actively used to make well-founded decisions and to efficiently design work processes.
Top tip
Although AI can search unstructured data, a clear folder structure significantly improves the quality of search results!
Using knowledge instead of searching — with material intelligence
LabV combines AI-supported document analysis with structured material data management — and thus enables a completely new way of acquiring knowledge. Instead of spending valuable time manually researching test reports, standard documents and technical specifications, engineers and quality experts immediately receive the relevant information — precise, context-related and with direct access to the original data.
The LabV AI Assistant: Recognizes material characteristics, analyses previous test reports and answers specific questions about standards or specifications — without searching for hours.
Automatic document analysis: LabV searches unstructured data, places it in the right context and creates comprehensible summaries.
Integrated material data management: All important data is centrally organized, structured and always remains up-to-date — a basis for secure, data-driven decisions.
Companies benefit in several ways:
- Faster product development through immediate access to material and testing data
- More efficient quality assurance through automated standards research
- Data-based decisions for R&D and engineering without inefficient research processes
The decisive advantage: LabV is not an isolated solution, but can be seamlessly integrated into existing IT systems — without costly changes or lengthy implementation projects. The question is no longer whether AI is being used in materials research — but how quickly companies can benefit from LabV.