Nature intelligence platform for sourcing, sustainability, and design teams.
Supply chains depend on living systems. The landscapes where materials are grown, harvested, and processed are subject to deforestation pressure, water stress, biodiversity loss, and ecological degradation — conditions that create both compliance risk and supply disruption. Most sourcing decisions are made without visibility into any of this.
Archaster closes that gap. It's an environmental intelligence platform that combines satellite imagery, deforestation monitoring, biodiversity assessment, and surface water analysis with AI-powered interpretation, giving sourcing and procurement teams, sustainability leads, and product designers ecological intelligence on any location in ~30 seconds. No GIS expertise required.
The platform synthesizes data from multiple research-grade sources into a single spatial analysis, then uses a domain-specific AI to generate cited, role-adapted assessments with data provenance.
Users identify a location — by drawing a polygon on the map, uploading supplier geometries, or selecting a region — and Archaster retrieves and analyzes environmental data for that area. The platform then presents both quantitative metrics (vegetation indices, deforestation alert counts, biodiversity records, water persistence data) and a qualitative AI-generated analysis that interprets those metrics for the user's specific professional context.
The AI analysis cites every data point it references, distinguishing between spatial data derived from the map (labeled as "Spatial" citations) and domain knowledge from research literature (labeled as "Reference" citations). This provenance system allows users to verify every claim the AI makes. Archaster's AI has deep domain knowledge in textile and building material science, including fiber composition, chemical finishes, recyclability, and emerging regulatory requirements such as ESPR and the Digital Product Passport. When combined with spatial analysis of sourcing regions, the platform connects material decisions to their landscape-level impacts — from the cotton field to the compliance report.
Six spectral vegetation indices computed from Copernicus Sentinel-2 imagery at 10-meter resolution: NDVI (plant health), EVI (dense canopy accuracy), NDWI (water detection), NDMI (vegetation moisture), SAVI (sparse vegetation), NBR (burn severity), and NDRE (crop nitrogen stress). Each index is computed for a user-selected date range and displayed as a color-mapped overlay on the map with per-pixel statistical summaries (mean, standard deviation, percentiles) for any drawn area of interest.
Near-real-time deforestation alerts from Global Forest Watch, displayed on the map with confidence levels (low, nominal, high). The platform performs automated buffer zone analysis at 500m and 2km radii around any drawn area of interest, counting alerts inside the site boundary and in each surrounding ring. This reveals encroachment patterns — when buffer zone alerts significantly exceed site alerts, the area is classified as intact but under approaching threat. Additional map layers include tree cover loss (2001–2024), tree cover gain, and tree cover density, sourced from Hansen/UMD/Google/USGS/NASA.
We use species occurrence data from the Global Biodiversity Information Facility (GBIF), which aggregates over 2.4 billion records from research institutions and citizen science programs worldwide. Archaster filters for research-grade observations and accepted taxonomic names only, excluding unverified sightings and synonym duplicates, to ensure data quality.
The platform scans the area of interest and surrounding buffers (2km and 10km) and categorizes observed species into nine functional ecological groups: apex predators and scavengers, large herbivores and seed dispersers, primary pollinators, secondary and specialist pollinators, soil engineers and decomposers, aquatic and semi-aquatic species, ecosystem engineers (plants and corals), ecosystem engineers (animals), and keystone and indicator species. Functional gaps — groups with zero records — may indicate missing ecological roles, though absence of records does not necessarily mean absence of species.
GBIF coverage varies significantly by region. Well-surveyed areas such as Western Europe, North America, and parts of Australia have dense, reliable records. Many tropical regions, particularly in Central Africa, Southeast Asia, and parts of South America, have sparse coverage — meaning ecologically rich landscapes may appear data-poor simply because fewer surveys have been conducted there. Archaster flags when record counts are low relative to the ecoregion's expected biodiversity, helping users distinguish between genuine functional gaps and data coverage limitations. Despite these limitations, GBIF remains the most comprehensive open-access biodiversity dataset available globally and provides a meaningful starting point for ecological assessment at any location.
For locations in Germany, Archaster provides two additional biodiversity layers sourced from the German Federal Agency for Nature Conservation (Bundesamt für Naturschutz, BfN):
Protected Species Alerts — Species listed under the EU Habitats Directive (FFH Directive, Annexes II, IV, and V) with confirmed presence in distribution grid cells overlapping the area of interest and buffer zones. Annex II species may trigger Special Area of Conservation obligations. Annex IV species carry strict protection requirements throughout their natural range, regardless of whether the location falls within a designated protected area. Annex V species are subject to management measures.
Invasive Species Alerts — Species listed under the EU Invasive Alien Species Regulation (EU 1143/2014) with confirmed presence in overlapping grid cells. On-site invasive species indicate active management obligations and potential ecosystem degradation. Nearby invasive species indicate colonization risk.
Both layers operate at approximately 10×10km grid cell resolution. Confirmed presence within a grid cell does not guarantee presence within the specific area of interest. Field surveys are recommended for site-level confirmation.
Coverage is currently limited to Germany. Additional countries will be added as national datasets become available.
Three surface water layers from the EC JRC Global Surface Water dataset, covering 1984 to 2021 at 30-meter resolution. Occurrence shows percentage of time water was present. Seasonality shows months per year with water. Transitions categorize change states: permanent, new permanent, lost permanent, seasonal, new seasonal, lost seasonal. Lost categories signal hydrological degradation; new permanent may indicate dam construction or land subsidence.
For current-state water detection, Archaster also computes NDWI (Normalized Difference Water Index) from Sentinel-2 imagery — detecting surface water bodies and soil moisture at 10m resolution with 5-day revisit frequency. NDMI (Normalized Difference Moisture Index) provides complementary vegetation moisture stress data, reflecting subsurface and soil water availability. Together the JRC historical dataset and the Sentinel-2 water indices provide both a 40-year baseline and near-real-time water monitoring for any location.
A domain-specialized AI that receives the full geospatial context and generates cited analysis adapted to seven professional personas: Product Designer, Interior Designer, Architect, Researcher, Sustainability Lead, Procurement Lead, and General Business Analyst. Grounded in circular economy, regenerative design, and nature-positive frameworks. Every claim is cited to its source.
Compare any two time periods side by side. The platform computes statistical differences in vegetation health, deforestation pressure, and water presence, allowing users to track environmental changes over any timeframe covered by Sentinel-2 (2015 to present).
Upload hundreds of supplier polygons. Analyze them all. Export the results. Designed for supply chain monitoring at scale with portfolio-level dashboards and site-specific alerts.
Archaster uses exclusively peer-reviewed, openly licensed, and institutionally maintained datasets:
Archaster is built for organizations that source materials from the living world and need to understand the environmental condition and risk profile of their sourcing regions:
Procurement teams evaluating supplier locations for environmental risk, assessing deforestation, biodiversity loss, or water stress, and generating evidence for supplier qualification decisions.
Sustainability leads preparing environmental disclosures under CSRD, TNFD, and GRI who need site-level and portfolio-level nature data with traceable provenance.
Product designers and material specifiers who need to understand how material provenance affects true environmental footprint beyond what supplier datasheets reveal.
Researchers and consultants conducting landscape-level ecological assessments who need a fast way to combine satellite data, biodiversity records, deforestation monitoring, and water analysis.
Archaster doesn't certify compliance — but it provides the ecological evidence base that compliance workflows require.
Archaster is not a GIS application. It does not replace QGIS, ArcGIS, or Google Earth Engine for users who need custom geospatial computations. It is designed for professionals who need environmental insights without geospatial expertise.
Archaster is not a prediction system. It reports on current and historical environmental conditions using live satellite data and research-grade datasets.
Archaster is developed by Archaster Labs, a founder-led, independent technology company. The platform's mission is to make environmental intelligence accessible to the teams whose decisions shape how materials are sourced, products are designed, and supply chains are managed.
Last updated: February 2026. See FAQ for common questions.