Recover materials. Map minerals. Detect wildfire and emissions. Monitor change.
Powered by material intelligence, Clarity AI turns complex sensor data into specific, reviewable answers.
Clarity AI, applied.
Recover valuable materials
Identify hard-to-sort polymers and validate recovery potential on your material stream.
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Map minerals from orbit
Turn hyperspectral imagery into reviewable mineral and alteration maps.
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Understand wildfire risk
Map fuel conditions and screen imagery for emerging fire signals across large territories.
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Monitor atmospheric plumes
Detect, quantify, and report supported plume activity across large areas.
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Industrial sorting that recovers more.
Earth observation that reveals change.
The platform powering
Material Intelligence.
Clarity AI turns complex spectral data into specific, reviewable outputs for industrial and Earth-observation teams.
Explore the TechnologyProvenance
Keep source data, processing history, models, and outputs connected so every result can be traced and reviewed.
Collaborative review
Give researchers, analysts, and operators one workspace to compare findings, share context, and move decisions forward.
Workflow orchestration
Let researchers focus on research while Clarity AI orchestrates repeatable analysis and the infrastructure behind it.
Built to move you from evaluation into production.
Clarity AI supports real-time systems and deploys on premises, in a private cloud, or in the public cloud.
Real-time operation
Run low-latency inference where decisions happen, with stable processing for continuous industrial and geospatial workflows.
Process incoming data fast enough to support real-time operational decisions.
Keep repeatable workflows running continuously in the environment your operation depends on.
On premises
Deploy inside your own environment when data residency, connectivity, or operational control require it.
Private cloud
Run Clarity AI within your organization's isolated cloud environment and security model.
Public cloud
Scale analysis using managed cloud infrastructure without asking researchers to provision or operate it.
Resources
Surface proof material that helps teams evaluate the category, the workflow fit, and the platform.
Metaspectral Partners with Planet to Deliver Trusted...
Metaspectral announced its partnership with Planet Labs PBC to deliver trusted, evidence-based spectral intelligence from Planet Tanager™ hyperspectral data through Metaspectral Clarity.
Seeing beyond the bands
Where hyperspectral analysis diverges from multispectral — and what that divergence reveals about a crop. Measured on real scenes.
Evaluating Deep Learning Spectral Unmixing From Pure...
A deep learning model trained only on synthetic mixtures — generated from pure reference spectra — outperforms classical solvers on four- and five-material mixtures across nine sensors. The benchmark: 325 real clay powder mixtures measured by lab spectrometers, pushbroom cameras, snapshot cameras, MWIR, and RGB.
Test your material. Scope your project.
Start with a scoped evaluation on your data, your environment, and the decisions you need to make.