Scientific insights are often locked in free-text documents and semi-structured data sets — yet reproducible insights demand structured, machine-readable data. Our solutions combine natural language processing, semantic enrichment, and automation to convert scientific content into data that is consistent, traceable, and ready for analysis as well as training and refinement of AI models.
With the “FAIRify Data® Tool” we have developed a highly modular application which allows to ingest a wide range of scientific input from databases, tables, and documents and to enrich, standardize, FAIRify and analyze them in any possible way. The workflows are designed for flexibility - supporting everything from expert-in-the-loop configurations to full automation (including recording & sharing options).
Whether processing literature, clinical documentation, patents, or internal reports, our approach integrates syntactic analysis, domain-aware entity recognition, identifier normalization, format transformation and much more. The tool contains more than 70 highly configurable modules which can be used independently, step-by-step or concatenated to transform raw input into insights.
Our deliverables are not just “parsed text”: they are semantically annotated data sets that fit directly into your own analytics pipelines and workflows. You can directly generate insights with our solutions, but our structured data is also ideally suited as input for training, fine-tuning, or evaluating AI/ML models.
Unstructured Science Needs Structure – Now More Than Ever:
Life science data is inherently complex, fragmented, and diverse. While large language models (LLMs) have opened new horizons in biomedical NLP and knowledge discovery, they face critical limitations when dealing with highly specific scientific content:
Very heterogeneous sources (e.g., articles, patents, internal reports) containing solid facts as well as unproven hypotheses
Sparse datasets in niche domains limit the reliability of model outputs
Terminological ambiguity across disciplines and languages
Missing or imprecise entity linking to authoritative identifiers (e.g., for genes, drugs, diseases, institutions)
Without structured, semantically enriched data, even the best LLMs produce suboptimal results.
There are hundreds of free and commercial solutions for structuring and semantically analyzing scientific data.
What makes our application special is that it has been built and optimized over 20 years in more than 10,000 actual analyses in pharmaceutical R&D. Every single module and function in the FAIRify Data Tool was built because it was needed for a real, business-critical analysis and not because it was technically feasible. Furthermore, all optimizations were steered by domain experts which made sure the results are correct and meaningful.
