About M33

We remove the plumbing between
satellite data and the answer

M33 builds Fabric — the tools that take raw Earth observation data and make it analysis-ready, so the people who work with it spend their time on the analysis, not the preparation.

The data is ready.
Getting it ready isn't.

The bottleneck in Earth observation moved years ago — from getting the data to preparing it. Before a GIS analyst can answer a single question about vegetation, flooding, or land-use change, they download tiles, reconcile coordinate systems, align pixel grids, normalize missing values, and resolve the differences between sensors that were never meant to be compared. Analysts routinely spend 45 to 80 percent of a project not analyzing data, but preparing it.[1]CrowdFlower 2016 / Anaconda 2020CrowdFlower (Forbes, March 2016): 80% on preparation. Anaconda State of Data Science 2020: 45% on preparation.forbes.com ↗

And the knowledge they build doing it — which bands to use, what order to run things in, how to handle the edge cases — stays trapped in one-off scripts and notebooks. Every new project starts from scratch. When an analyst leaves, the workflow leaves with them.

That preparation layer is what M33 builds.

Three surfaces. One engine.

Engine

Processing core

The runtime that turns multi-source Earth observation data into harmonized, analysis-ready outputs — reproducibly, the same way every time. Every result carries a complete provenance record of what ran, on what data, in what order.

Live
Studio

Visual interface

Build processing workflows without writing code, and watch them run. Compose a workflow once and it becomes something you can reuse, hand off to a teammate, and audit later.

Live
Iris

Intelligence layer

Describe the analysis you need in plain language, and Iris turns it into a ready-to-run workflow — so you can go from question to pipeline without building it by hand.

In development

How we build. What we protect.

01

Your data stays yours

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.

02

Transparency is a feature

Every output includes a provenance record: 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.

03

The ecosystem already works

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.

Built by people
who do the work

M33 was founded by Michael Keys.

Our advisory council includes GIS domain experts who 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

Whether you want to use Fabric or partner with us.