About M33
Five thousand satellites will launch this decade. Petabytes of Earth observation data accumulate daily. But the gap between raw imagery and usable insight remains a manual, fragile, project-by-project effort. M33 is building the platform that closes it.
The Opportunity
The Earth observation (EO) market is projected to exceed $8 billion by 2033, with value-added services growing from $3.1 billion to $4.9 billion.[1]Novaspace: EO Data & Services Marketnova.space ↗ Over 5,400 EO satellites are forecast to launch this decade alone, nearly triple the prior ten years.[2]Novaspace: Earth Observation Satellite Systemsnova.space ↗
The data exists. The bottleneck moved years ago from access to preparation. GIS analysts spend 45 to 80 percent of their project time not on analysis but on downloading tiles, resolving coordinate systems, aligning pixel grids, normalizing missing values, and handling cross-sensor differences.[3]CrowdFlower 2016 / Anaconda 2020forbes.com ↗ Every new project starts from scratch. Analytical knowledge stays trapped in individual notebooks and tool-specific pipelines.
The economic evidence is unambiguous. When USGS made Landsat data free in 2008, an archive that generated $5 million per year in sales began producing an estimated $25.6 billion per year in economic value.[4]USGS: Landsat Economic Valueusgs.gov ↗ The Copernicus Programme is projected to return 10 to 20 times its investment.[5]European Commission via GEOearthobservations.org ↗ Open data works. The constraint is the preparation layer between raw observations and usable outputs.
That preparation layer is what M33 builds.
By the Numbers
What We've Built
Fabric is a platform for building, running, and sharing geospatial processing workflows. It handles the entire pipeline from satellite data acquisition through harmonization to analysis-ready output delivery, with full provenance at every step.
The platform comprises four products at different stages of maturity. Engine and Studio are live and processing data today. Iris is in active development. Delta is planned for organizations requiring private deployment.
The processing library includes 36 containerized geospatial functions across six logical layers: area of interest definition, data acquisition, preprocessing, analysis, post-processing, and output delivery. Data sources include Sentinel-2, Sentinel-1 SAR, Landsat, SRTM, Copernicus DEM, OpenStreetMap, HydroSHEDS, and any STAC-compatible collection.
The Platform
Containerized, declarative pipeline engine. Ingests multi-source EO data, resolves cross-sensor differences, and produces harmonized outputs with W3C PROV-compliant provenance manifests. Runs reproducibly what takes days to assemble by hand.
LiveWeb application for building processing workflows without writing code. Three-column layout: task rail, configuration panel, live execution graph. Per-user isolated compute with automatic provisioning and suspension.
LiveNatural language interface that translates analyst intent into validated Patterns. Selects sensors, recommends bands and parameters, and generates complete pipelines. Deterministic and constrained, not generative guesswork.
In developmentFull platform running inside your security boundary. Designed for government, defense, and enterprise requiring data sovereignty, air-gap capability, and compliance with FedRAMP, IRAP, or NATO STANAG frameworks.
PlannedWhy Now
The data deluge is accelerating. 13,000 active satellites in orbit today, up 23% year-over-year. Copernicus alone serves 287 terabytes of downloads per day. The volume is growing faster than the workforce that knows how to prepare it.
Standards have finally converged. STAC became an OGC Community Standard in October 2025. Cloud-Optimized GeoTIFF became an OGC Standard in July 2023. CEOS Analysis Ready Data frameworks now have multi-sensor compliance specifications. For the first time, there is a coherent standards stack to build a platform on top of.
The preparation gap is widening. As data volume grows, the manual effort required to make it usable grows with it. New satellites mean new spectral bands, new resolutions, new revisit cadences, and new harmonization requirements. The preparation burden compounds while the tools to address it have not fundamentally changed. The gap between what the data could enable and what practitioners can realistically extract is growing every year.
The Moat
Iris is not a general-purpose model applied to satellite imagery. It is a constrained translation engine backed by the full Fabric task catalog, sensor specifications, and band-level metadata. Every Pattern it generates is validated against known physics. This domain depth is not replicable by wrapping a foundation model.
Every Pattern executed through Fabric contributes to a growing understanding of how sensors observe reality: which transformations work, what parameters produce valid results, and how cross-sensor connections manifest. This compound intelligence gets better with every workflow. Competitors starting later start with an empty graph.
Fabric does not assert what happened to data; it proves it. W3C PROV-compliant manifests document every input, transformation, and parameter. In markets where satellite-derived evidence informs policy, insurance claims, and carbon credit verification, verifiable provenance is not a feature. It is a requirement.
Market Segments
Wildfire monitoring, flood mapping, deforestation tracking, land use compliance. Reproducible workflows with audit-grade provenance for regulatory reporting.
Air-gapped deployment, data sovereignty, chain of custody documentation. Delta provides the full platform inside your own security boundary.
Crop health monitoring, climate risk assessment, infrastructure asset tracking, resource exploration. Standardized outputs that integrate with existing GIS toolchains.
Multi-temporal analysis, cross-sensor fusion, long-term environmental change detection. Reproducible pipelines that satisfy peer review and grant reporting requirements.
Glacier retreat tracking, coral reef monitoring, habitat loss assessment. Accessible workflows that don't require a dedicated GIS team to maintain.
Rapid flood extent mapping, post-disaster damage assessment, displacement monitoring. Fast time-to-insight when hours matter and infrastructure is compromised.
Principles
Outputs go to storage you control. Results are delivered as standard formats (Cloud-Optimized GeoTIFF, GeoJSON, STAC catalogs) that work with every major GIS tool. We do not build walls around your data or your workflows. Data does not accumulate on M33 infrastructure.
Every output includes a provenance manifest: which inputs were used, what parameters were applied, what processing steps ran, in what order. In a field where wildfire response, agricultural policy, and carbon credit verification depend on satellite-derived evidence, "trust us" is not good enough.
We are not replacing ArcGIS, QGIS, Google Earth Engine, or any data provider. We fill the gap between raw data and analysis-ready data. If your existing tools work for the analysis step, Fabric makes sure the data is ready for them.
The Vision
Fabric addresses the preparation layer because it is what everything else requires. But preparation is the beginning, not the destination.
The next layer is intelligence. Iris learns the dialect each sensor speaks, builds a compounding knowledge graph of transformation decisions, and surfaces cross-sensor connections that no individual analyst would accumulate alone. Not a general-purpose model applied to satellite imagery, but domain-specific intelligence that understands what a SAR moisture reading means in the context of an optical vegetation index and a historical precipitation record.
The layer beneath both is trust: structured provenance that moves data from claims about what happened to verifiable records of what happened. Processing receipts that travel with the data. Auditable chains where every transformation is documented, not asserted.
Each layer depends on the one below it. Intelligence over unharmonized data produces noise. Trust without reproducible processing is theater. The architecture is sequential by necessity, and Fabric is the foundation we are building everything else on.
The Team
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Our advisory council includes GIS domain experts who validate our task implementations, test our workflows against real-world projects, and keep us honest about what matters in the field.
We are building the team. If you work in geospatial processing, remote sensing, or Earth observation infrastructure and what you have read here resonates, .
Let's talk