A lot of scientific knowledge still lives in documents
Papers, clinical trial reports, patents, internal research notes, regulatory documents. The volume grows faster than any team can read, and most of it is unstructured. Natural language processing can turn that text into information people can search and use.
Entity extraction
The foundational task is recognizing what a document is talking about. Named entity recognition identifies genes, proteins, drugs, diseases, biomarkers, and other domain entities in text, then normalizes them so that a gene mentioned under three different names in three papers is recognized as the same gene.
Relation extraction goes further, capturing that a paper reports drug X inhibiting target Y in disease Z.
Once that information is structured, researchers can search it by meaning as well as by keyword.
Retrieval augmented generation
Large language models on their own answer from what they learned in training, which is out of date, incomplete for specialized domains, and prone to producing plausible fiction. RAG helps by grounding the model in a specific set of documents: a question comes in, the system retrieves the most relevant passages from the corpus, and the model answers from those passages, citing them.
The quality of the answer depends heavily on what the system retrieves — which is why hybrid ranking, combining semantic similarity with keyword matching, and understanding query intent matter more than the model itself.
Keeping the data private
Research text is frequently confidential, proprietary, or regulated. Sending it to a public model API is often not an option.
For sensitive data, a common approach is to run the model inside the organization's own cloud environment so the raw data stays there. That also affects the system architecture.
Retrieval augmented generation is the pattern we use for questions asked over a document corpus. The retrieval layer finds the relevant passages and hands only those to the model, so the raw corpus never sits in a prompt, and the deployment can stay inside the environment the data is required to remain in.
Cloud first, headless architecture.
Query intent detection with automatic hybrid ranking.
Support for multiple data sources.
A knowledge graph layer.
A security design under which raw data is never shared with the AI models themselves.