Fuse planetary and onsite data.
Hyperspectral imagery, assays, field observations, and experiment outcomes become one shared representation of a physical system.
Coactive Science
AI systems for strategic resources.
Coactive builds foundation models and active-learning workflows that help scientists, operators, and national partners decide what to measure next.
2,000+
critical-mineral sites screened
14
priority critical-mineral targets
3
national-lab workstreams
12
patents across AI and optimization
What we build
Models that turn scarce physical data into decisions.
Physical-world AI has a different bottleneck than internet AI. The valuable data is sparse, expensive, and tied to real operations. Coactive builds the loop that chooses the next measurement and learns from the result.
Select the next best measurement.
Active learning guides which satellite tile, field sample, or laboratory experiment should happen next to reduce uncertainty fastest.
Move from model output to field action.
Recommendations are built for scientists and operators working in mines, labs, coastlines, and other physical environments.
The thesis
Measure what matters next.
There is no Common Crawl for atoms. Satellite time, field sampling, wet-lab chemistry, and drilling assays all cost money and time. The hard problem is deciding which next measurement creates the most learning.
Coactive builds the model and the decision loop together: sense, score, sample, test, update, and deploy.
Shared model
Coactive Foundation Model
Choose
Next satellite tile
Choose
Next field assay
Choose
Next lab experiment
Return
Updated priors and decisions
Critical minerals
A sharper way to find and characterize strategic resources.
Coactive combines hyperspectral imagery, geological context, legacy assays, and active-learning field plans to prioritize critical-mineral sites across the western United States.
Orbit to onsite
The model becomes more valuable when it touches the ground.
A permitted Colorado mine partnership turns the AI story into an operating story: satellite screening, drone context, field sampling, assay results, and updated site priors in one feedback loop.
Request the minerals briefPermitted mine partnership / Colorado
Spectral substrate
Measured biochemistry at planetary scale.
Hyperspectral imagery gives Coactive a richer signal than RGB imagery alone: mineral signatures, vegetation stress, surface disturbance, and environmental context in a single sensing layer.
Orbital collection
AI for science
Lab workstream integration makes the loop concrete.
Coactive works where every experiment matters. Active learning selects the next chemistry, biology, or characterization experiment and turns each result into a better model.
CICERO autonomous separations
Agentic workflows for critical-materials recovery and separations chemistry.
MIME microbial recovery
Residual learning over biological datasets to predict metal-ion recovery.
Active sampling and ATLAS integration
Sampling pipelines for in-situ characterization of permitted sites.
Applications
One learning loop across physical-world domains.
Critical minerals is the first operating wedge. The same active-learning primitive applies wherever measurement is expensive and decisions compound.
Oceans and aquaculture
Early warning and operational intelligence for coastal chemistry and biological risk.
Industrial science
Experiment selection for domains where every measurement is expensive and decisions compound.
Join the mission
Build models that operate in the material world.
We are assembling a small team across machine learning, geospatial data, experimental science, and field operations. The work is technical, urgent, and tied to real-world outcomes.
Strategic partnerships
The material world needs models that know what to measure next.
Or email us directly at info@coactive.science