Add ExeQut organization card

README.md

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 title: README
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+emoji: 🧬
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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).