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Global-Supply

Built on Biology

Biotechnology is increasingly the engine that drives therapies for our deadliest diseases, nutrition to feed a growing global population, and energy & materials for a sustainable future.

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Biological data are almost entirely unstructured

This makes biological data especially challenging to analyze, and most of the unstructured data is discarded.

Tools for Understanding and Predicting Biology

Immense quantities of routinely collected omic data are discarded simply because their unstructured content is so challenging to interpret

Mass Spectrometer

Mass Spectrometry

79 petabytes/day

Sequencer

Sequencing

43 petabytes/day

These discarded data contain untapped insights for data-backed decision making critical to:

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Drug Discovery

Cell Factory

Synthetic Biology

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Microbiome

Viral Vector

Drug Development

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Health & Longevity

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Biomarker Identification

Matterworks has built the first Large Spectral Model

An LLM-like semantic foundation for directly interpreting raw instrument data that offers an AI breakthrough for biology
Group 1724
  • Whole metabolome reading

    Solving the mass spec holy-grail of absolute quantitative metabolomics

  • Global biological context

    Search and sample comparisons using total-ome embeddings

  • API for fine-tuned prediction

    Low code or no-code solutions for discovery, clinical, diagnostics, and process models

...because biology lacked the semantic foundation for exploiting unstructured data

Contact us to learn more about using AI-based
tools for the future of biological engineering

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