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Matter Intelligence

Ultraspectral imaging satellites that map materials

Raised to date

$35.9M

of $49.1M offered across 1 Form D filing

Still open
$13.2M
Investors
5
Last filed
Apr 22, 2026

About

Matter Intelligence is a remote-sensing company building ultraspectral imaging sensors that capture thousands of spectral bands from ultraviolet through thermal infrared, revealing the molecular chemistry of materials that conventional cameras cannot see. It pairs those sensors with a physics-informed foundation model — which the company calls a Large World Model — to identify materials, temperature, and composition rather than just shapes. Its first satellite, EARTH-1, is intended to deliver sub-meter hyperspectral and thermal imaging and to map Earth's material composition in real time. The company was founded in 2023 by former NASA JPL engineers Vishnu Sridhar and Thomas Chrien with former Caltech scientist Nathan Stein, and emerged from stealth in October 2024 with $12M in seed funding led by Lowercarbon Capital.

Matter claims the world's first ultraspectral sensor — thousands of bands spanning UV to thermal infrared rather than the dozens of bands typical of hyperspectral instruments — and couples it with a physics-informed AI model, so the output is molecular chemistry and material identity rather than a pattern-matched image classification.

Funding

  • EquityFiled Apr 22, 2026Form D
    $35.9M/ $49.1M

Product

Ultraspectral sensor

Imaging sensor / remote sensing hardware

An imaging sensor capturing thousands of spectral bands from the ultraviolet through the thermal infrared, resolving the molecular composition, chemistry, and temperature of materials in a scene rather than only their visible appearance.

  • Thousands of spectral bands spanning ultraviolet to thermal infrared
  • Material identification at chemical precision
  • Predictive failure detection before problems become visible

Built for

Earth-observation and geospatial data buyers, plus autonomous systems, robotics, aerospace, and infrastructure-monitoring operators that need material-level rather than visual-level sensing

People on file