ExeQut

We build data and AI systems for research and government, with a focus on biomedical data infrastructure, search, and knowledge graphs.

Our work covers the full path from raw source data to something a researcher can actually find: ingestion and transformation pipelines, entity resolution, knowledge graphs, search platforms, and the domain models that make specialised data discoverable.

What we publish here

Models we build in house for biomedical text and metadata. We care about two things in particular, and both show up in how we evaluate:

• Honest measurement. We score against human-labelled ground truth, hold the evaluation set out of training, and report single-threshold results rather than tuned upper bounds.
• Current vocabulary. Biomedical language moves. A model trained on a frozen vocabulary quietly stops recognising the terms researchers are actually using.

Models

• ExeQut MeSH Tagger - biomedical MeSH tagging across 25,489 current MeSH descriptors, trained from scratch on roughly 3 million indexed MEDLINE records. It reaches 0.663 micro-F1 against expert human indexing, ahead of the widely used open model for this task.

Work with us

We take on data platform, search, and applied machine learning work in biomedical and public sector settings. Get in touch at exequt.com.