AI Technologies
Agentic AI
Individual model calls become controllable systems based on a division of labour: specialised agents handle subtasks, use tools, and work together in verified workflows.
AI Technologies
Document & Knowledge Intelligence
Making large, heterogeneous bodies of information accessible and turning unstructured information into knowledge models that are usable for the business domain.
Methods and techniques
- Data extraction, OCR, and document analysis
- Semantic search and vector databases
- Retrieval-augmented generation (RAG): the model is given precisely the knowledge it needs for a sound answer
- Entity and relationship recognition
- Similarity analysis and cross-document linking
- Knowledge structuring and matching against reference data
Application examples
- Transcribing and structuring historical documents and making them queryable
- Making technical project documentation accessible as a knowledge architecture
- Knowledge assistants that back up their answers with source references
What matters
The added value lies not in the individual model but in the architecture that brings together models, databases, context, and quality requirements.
Classic text recognition asks: What is written on the page? We also ask: What function does this page have? What structure does it have? Which information is important, and how does it relate to other pages, persons, places, and titles?
Integrating knowledge instead of guessing
For us, retrieval-augmented generation (RAG) means more than fetching text passages from a database. An AI system is supplied precisely with the additional knowledge it needs for a sound decision: from vector databases, reference data, and reference data. Results carry their sources with them so that they remain traceable.
Further topics
AI Technologies
Multimodal AI
Every data source has its strengths and limits. The result becomes reliable when text, images, audio, sensor data, and geodata are interpreted together.
Solutions
Document and Knowledge Intelligence
Understanding documents instead of merely reading them: archives, files, and technical documentation become structured knowledge you can query.
Solutions
Engineering Intelligence
From existing knowledge to a usable engineering system: linking requirements, tests, failure analysis, and evidence with AI.
Projects & References
Document Intelligence for Mechanical Engineering
From document archive to knowledge architecture: historical project documents become the basis for faster proposals and secured experience-based knowledge.
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