About Spectral

We give machines a sense for matter. Wherever light interacts with the world, it leaves behind a spectral trace — a hidden language describing the composition, structure, and state of things. From molecules to minerals, from cells to distant planets, these signals carry the signatures of reality. At Spectral, we build models that learn to interpret them.

Inspired by the breakthroughs in foundation models, we develop machine learning systems that can process spectral data across domains. We treat spectroscopic signals not as isolated measurements, but as structured information — a grammar of matter. By learning this grammar, our models gain the ability to perceive, classify, and compare the physical world in ways that mimic — and sometimes exceed — human sensing.

Our Work

We're developing a domain-agnostic foundation model for spectral data: a model that can interpret the fingerprints of materials whether they come from a laboratory instrument, a hyperspectral satellite, or a medical scanner. Our goal is to make machine perception of matter as flexible and powerful as machine perception of images or text — and to unlock new capabilities in science, industry, and beyond.