Full Geospatial Capability. No Technical Barrier.

Geospatial Analysis at the Speed of a Question

Run any geospatial analysis, from spectral indices and change detection to machine learning classification and time-series forecasting, by typing what you want to know. AI agents write the code, access the satellite data, execute the analysis, and deliver the complete output. Whether you are a GIS specialist looking to multiply your throughput or an analyst who has never opened QGIS, you get the same institutional-grade result.

All major satellite sources accessed automaticallyAny spectral index, change detection, or spatial model95%+ accuracy on change detection, statistically validatedAll code shown, all methodology documented

Built for your workflows

Most organizations need satellite insights but can't access them effectively

01

The Real Problem in Geospatial Teams Today

The bottleneck is not capability. It is execution time. GIS specialists are skilled, but most of their hours go toward data sourcing, script setup, pipeline management, and format conversion, not the analytical thinking that actually creates value. Non-specialist team members cannot contribute at all, so every request queues behind the one person who knows the tools.

02

The Capability That Exists but Stays Locked

Sentinel-1 SAR, Sentinel-2, Landsat, MODIS, NAIP, and NASA Earth data products cover almost every analytical use case a team could have. The satellite data is public, updated every 4 to 6 days, and comprehensive. The barrier is not the data. It is the technical overhead required to access and process it. Most of that overhead produces no analytical value.

03

Geospatial Analysis Without the Setup Tax

Klarety removes the setup tax entirely. GIS professionals describe what analysis they need in plain English and receive a fully executed result, including all code, all processed imagery, all statistical outputs, and complete methodology documentation. Non-GIS team members ask questions and receive the same quality of output. The analytical expertise is in the agents. You supply the domain knowledge and the question.

What Our GEOAgent Does

Transform complex spatial analysis into clear insights using natural language

Natural Language Processing

Ask questions in plain English without GIS training or technical expertise. AI processes relevant satellite imagery automatically based on your queries. Statistical analysis with confidence intervals included in every result. Interactive maps delivered instantly through your browser.

Professional Remote Sensing

Electro-optical imagery and SAR data for all-weather observations. NDVI for vegetation health and NDWI for water body analysis. Burn Area Index for fire damage detection and assessment. Global land cover classification for comprehensive terrain understanding.

Automated Change Detection

Proprietary algorithms with over 95% accuracy in change detection. Urban development and building change monitoring. Fire-affected region identification with statistical validation. Temporal imagery comparison with ground-truth data integration. OpenStreetMap integration for complete spatial context.

Technical Specifications

Professional-grade geospatial analysis with validated accuracy

Satellite Sources

Sentinel-1 SAR, Sentinel-2, Sentinel-3, Sentinel-5P, Landsat, NAIP, MODIS, Copernicus, NASA Earth data, ERA5-Land, Planetary Computer

Spatial Resolution

10 meter Sentinel-2, sub-meter NAIP, 30 meter Landsat, daily global MODIS, all-weather Sentinel-1 SAR

Temporal Coverage

4 to 6 days repeat globally, Landsat archive to 1972, daily MODIS, real-time SAR

Analysis Types

Spectral indices, change detection, object detection, image segmentation, land cover classification, time-series forecasting, ML pipelines, flood modeling, terrain analysis

Accuracy

95% or higher on change detection, validated through high-resolution case studies, confidence intervals included in every output

Output Formats

GeoTIFF, COG, GeoJSON, SHP, GPKG, KML, CSV, JSON, PNG, Python files, LaTeX formulas, statistical model files

Geospatial AI Capabilities

Advanced AI-powered spatial analysis made accessible

01
Artificial Intelligence

Every Spectral Index and Imagery Analysis, Executed Automatically

Agents compute any spectral index or imagery analysis a remote sensing expert would run: NDVI for vegetation health, NDWI for water body detection, NDMI for moisture stress, NDSI for snow cover, NBR for burn area severity, EVI, SAVI, and any other index relevant to your question. Beyond indices, agents run image segmentation, object detection and classification, building footprint detection, road network extraction, land cover classification, and any other pixel-level or feature-level analysis. You describe the question. Agents select the right index, write the processing code, execute it against the right satellite source, and return the output with statistical validation.

02
Features

Change Detection Across Any Time Period, Any Location

Agents compare satellite imagery across any time range, from days to decades, to detect, measure, and explain physical changes on the ground. What changed, how much, when, and what it looks like. Outputs include before and after imagery composites, difference maps, quantified change measurements, statistical summaries with confidence intervals, and narrative analysis explaining what the change means. Accuracy on change detection exceeds 95 percent, validated through high-resolution case studies. SAR imagery from Sentinel-1 extends change detection through cloud cover and at night, ensuring results regardless of weather conditions.

03
Productivity

Advanced Spatial Analysis Without Writing a Line of Code

Agents execute any spatial operation that a GIS professional would run from natural language: buffer and proximity analysis, overlay and intersection, density mapping, spatial clustering, network analysis, watershed delineation and flood simulation, terrain and elevation modeling, zoning and land use analysis, and custom spatial models. Upload your own vector or raster data in GeoJSON, SHP, GPKG, GeoTIFF, or KML format and reference it in any analysis using @layername. Your proprietary boundaries, asset locations, and field data combine with global satellite coverage and real-time context in a single analysis. OpenStreetMap vectors and building footprint data are integrated automatically where relevant.

04
Artificial Intelligence

Statistical Modeling, Machine Learning, and Forecasting Built In

Beyond basic spatial analysis, agents run the full quantitative toolkit: time-series decomposition and trend analysis, anomaly detection, standardized drought and vegetation stress indices, machine learning classification and regression pipelines, scenario modeling with probability distributions, yield forecasting, correlation analysis, and confidence interval validation. Every model is built from scratch for your specific question, every assumption is documented, and every result is reproducible. For teams that need to present findings to investment committees, regulatory bodies, or research peers, the complete derivation is always available.

ROI Metrics

Transform your spatial analysis workflow

Minutes vs weeks

for complete satellite imagery analysis with full documentation

Any team member can contribute

not just the one GIS specialist on your team

Full reproducibility

every result auditable, every calculation shown, every source cited

No installation or environment management

agents run in a fully managed sandboxed environment

Customer Success Stories

What professionals are saying about geospatial AI

  • "Being able to upload our custom datasets and combine them with satellite imagery through natural language queries has completely changed our workflow. I describe the spatial analysis and receive executed results with all code attached."
    Jennifer LeeGIS Analyst, Environmental Planning Solutions
  • "We get 95% accuracy on change detection with confidence intervals included in every output. The statistical rigor is equivalent to what our in-house team produces, but delivered in minutes instead of days."
    James ChenRemote Sensing Manager, Environmental Consulting Corp
  • "Our planning team can now run proximity analysis and land cover classification without waiting for our GIS specialist. The conversational interface produces the same quality output our specialist would produce, with full methodology documentation."
    Dr. Robert MartinezUrban Planning Director, City Analytics Corp
  • "The automated methodology documentation and statistical validation make our spatial analysis results defensible for regulatory compliance. Every number traces back to code and data we can show an auditor."
    David ThompsonSenior Researcher, Infrastructure Development Institute

Frequently Asked Questions

Everything you need to know about Klarety and our satellite analysis platform.

Geospatial AI analysis is the automated process of accessing satellite and Earth observation data, executing spatial and statistical analysis on that data, and producing decision-ready intelligence outputs, all driven by natural language questions rather than manual GIS workflows. In Klarety, AI agents handle every technical step: selecting the right satellite sources for your question, writing Python code to process the imagery, executing that code in a managed environment, computing spectral indices, running change detection or machine learning models, and returning the complete output with all code shown, all methodology documented, and all sources cited. You ask a question. The analysis runs automatically.
For most analytical use cases, yes. Klarety agents access the same public satellite data sources used by research institutions and GIS professionals, including Sentinel-1 SAR, Sentinel-2, Landsat, MODIS, and NASA Earth data products, and execute the same categories of spatial analysis that GIS software runs. The difference is that all setup, scripting, and execution is handled automatically. GIS professionals use Klarety to multiply their output, running multiple analyses in the time it previously took to configure one pipeline. Non-GIS team members use it to access geospatial intelligence they could never produce independently before. For highly specialized cartographic production or deep Esri ecosystem integration, dedicated GIS platforms remain the right tool.
Any analysis a qualified GIS analyst or remote sensing specialist would perform. This includes spectral index computation (NDVI, NDWI, NDMI, NDSI, NBR, EVI, SAVI, and any other index), image segmentation and object detection, building footprint and road network extraction, land cover classification, change detection across any time period, time-series trend analysis, buffer and proximity analysis, overlay and intersection, density mapping and spatial clustering, watershed delineation, flood simulation, terrain and elevation modeling, machine learning classification pipelines, statistical modeling with confidence intervals, anomaly detection, and scenario forecasting with probability distributions. Agents write the code from scratch for each specific question and execute it against the right satellite sources automatically.
Completely automatically. When you submit a question, agents determine which satellite sources are most appropriate. They access Sentinel-1 SAR for all-weather and night coverage, Sentinel-2 for optical analysis at 10 meter resolution, Landsat for historical data going back to 1972, MODIS for daily global monitoring, NAIP for high-resolution US coverage, NASA Earth data products including SMAP, GRACE, and GPM, ERA5-Land climate reanalysis, and other sources as relevant. Data is processed in a fully managed sandboxed environment with no setup or installation required from the user. All Python code written and executed by agents is returned as part of the output.
Change detection accuracy exceeds 95 percent, validated against high-resolution case studies. Every analysis includes statistical confidence intervals and, where applicable, cross-validation against multiple data sources. Agents use SAR imagery from Sentinel-1 to extend analysis through cloud cover and at night, ensuring results are not degraded by weather. All code is shown, all methodology is documented, and all calculations include full derivation logs. Results produced by Klarety meet the evidentiary standard for regulatory filings, investment committee presentations, and peer-reviewed research.
Klarety is useful to both audiences, for different reasons. For GIS specialists, the primary benefit is throughput. Instead of spending time on satellite data access, preprocessing, script setup, environment management, and boilerplate code, you describe what analysis you need and receive the executed result with all code visible. You can review the methodology, verify the logic, and extend or adapt the output immediately. Many GIS analysts use Klarety to run ten analyses in the time that would previously go toward setting up one. You bring the domain expertise and the analytical judgment. Klarety handles the execution.
Yes. Upload your own datasets in CSV, GeoJSON, GeoTIFF, SHP, GPKG, or KML format and reference them in any analysis using the @layername syntax. Agents incorporate your data alongside global satellite sources automatically. Your field survey boundaries, internal asset polygons, portfolio site locations, custom classification shapefiles, or any other proprietary spatial layer become part of the analysis without any data engineering overhead. Your data and global satellite data are processed together in a single executed output.

Run Your Next Geospatial Analysis in Minutes, Not Days

Any spectral index. Any change detection. Any spatial model. Describe what you need and receive a fully executed geospatial analysis with all code shown, all methodology documented, and all outputs ready to use.