The Platform for
Material Intelligence.
Clarity AI combines patented spectral unmixing with provenance, review, and deployment controls—turning complex analysis into decisions teams can trust.
Start with the data and context that define the problem.
Bring spectral and geospatial data together with the reference material, scientific assumptions, and operating constraints needed to analyze it correctly.
Sensor and image data
Work with satellite, airborne, drone, industrial, and customer-provided spectral data.
Reference material
Add spectral signatures, labels, samples, calibration data, and other evidence that grounds the analysis.
Scientific context
Define the methods, assumptions, evaluation criteria, and claim boundaries the workflow must respect.
Operational requirements
Capture the target, area, thresholds, timing, and delivery constraints that make the result useful.
Focus on the analysis, not the infrastructure.
Agent-orchestrated research campaigns
Clarity AI agents coordinate a research campaign across bounded analysis steps: gather the relevant data, run candidate methods, compare results against evaluation criteria, and use the evidence from each run to narrow the next question. Your team can inspect and redirect the campaign throughout.
Compute that adapts to your environment
Run on Metaspectral-managed compute or connect your own cloud and on-premises infrastructure. Clarity AI agents schedule and parallelize analyses, choose appropriate execution resources, and optimize the workflow around the available hardware, memory, and location of the data.
Choose the right method for the question.
Classical spectral analysis
Use established tools for calibration and correction, band math and spectral indices, dimensionality reduction, classification, target detection, and spectral-library analysis. Clarity AI agents can combine these methods into a documented workflow instead of leaving each step as a separate desktop task.
Spectral Unmixing for Operational Analysis
Apply Metaspectral's patented deep learning to unmixing, classification, and target detection when classical methods cannot represent complex spectral and spatial relationships. Synthetic training data can support targeting models from limited examples, including a single spectral signature when labelled field data is scarce.
Outputs built for action
Deliver target maps, material classifications, change layers, detections, and analyst-ready summaries in the form the operational workflow requires.
Know how every result was produced.
Clarity AI connects source data, methods, models, validation, review, and outputs in one inspectable record.
Data lineage
Trace each output to its acquisition, metadata, calibration, preprocessing, and transformation history.
Versioned methods
Record the workflow, model and method versions, generated artifacts, and final deliverables behind an answer.
Scientific review
Preserve acceptance criteria, review status, reviewer, and decision with the result.
Claim boundaries
Keep warnings, missing evidence, known limitations, and the defensible scope of the result visible.
Metaspectral can help design and validate scientifically sound workflows, or your own science team can define and approve the methods and evaluation criteria.
Move a validated workflow into operation.
Run Clarity AI on premises for real-time operations, or in the cloud for fast, scalable analysis once data is available.
On premises, in real time
Deploy analysis close to the sensor or operating line when a decision must be made immediately. Hardware acceleration and efficient pipelines support continuous, real-time inference without sending every data cube off site.
In the cloud, at scale
Use public or private cloud deployment for rapid analysis, collaboration, and larger-area workflows. Turnaround depends on when compatible data can be acquired, transferred, and ingested; it is not presented as real-time detection.
Give technical teams a clear path to implementation.
Review platform guides and deployment details, then work with Metaspectral on the architecture and workflow requirements for your environment.