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# 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](https://huggingface.co/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](https://exequt.com).