Iris
Tell Iris what you need in plain language and it generates a complete Fabric Pattern in seconds — no hallucinated steps, just a pipeline that's ready to run. The intelligence layer between your intent and the pipeline.
The gap
A GIS analyst knows they need vegetation change detection over the Amazon basin for the last 90 days. They know which sensors matter, roughly which spectral indices to use, and what the output should look like. What they do not know (or do not want to rebuild every time) is the exact sequence of download, preprocessing, and analysis steps required to get from intent to result.
That wiring is where projects stall. Selecting the right data sources, configuring band combinations, setting up the correct preprocessing chain, ensuring spatial alignment across sensors. It is not the analysis itself that takes days; it is the preparation that precedes it.[1]USGS: The Value of Data Managementusgs.gov ↗
Iris is designed to eliminate the gap between knowing what you want and having a pipeline to produce it. Describe the goal. Iris turns it into a complete Pattern that Engine can execute — recommending the sensors, bands, and preprocessing steps that fit, and checking the result for validity before it reaches you.
How it works
State what you need in plain language: "fire scar analysis for Northern California, September 2025" or "download and harmonize Sentinel-2 and Landsat for my study area." Iris accepts freeform input or guided selections, whatever gets your intent across fastest.
Iris identifies the analysis type (vegetation, fire, flood, terrain, urban, infrastructure) and turns your goal into a complete Pattern — recommending the sensors, bands, preprocessing, and analysis steps that fit, assembled in the right order.
Every Pattern Iris generates is checked for validity before it's returned — well-formed, internally consistent, and actually runnable on Fabric. If something doesn't add up, Iris asks for clarification rather than guessing.
Iris flags how confident it is in each result, so you know what's ready to run and what needs a second look. A high-confidence Pattern is ready to execute through Fabric Engine immediately. A lower one tells you exactly what to review before running.
What Iris handles
NDVI time series, vegetation change detection, and fire scar mapping via dNBR. Iris selects appropriate optical sensors and preprocessing based on your study area and time range.
NDWI and MNDWI water body mapping, flood extent analysis, coastal change detection. SAR-optical fusion workflows when cloud cover limits optical data availability.
Pre/post fire comparison with NBR and dNBR, wildfire risk mapping, landslide susceptibility, multi-source disaster response workflows combining SAR, optical, and elevation data.
NDBI built-up area detection, construction monitoring, impervious surface mapping, road network change detection. Urban analysis workflows that combine optical imagery with OSM vector data.
SRTM elevation download, terrain preprocessing, slope and aspect derivation. Iris configures the correct resolution, reprojection, and grid alignment for your area of interest automatically.
The Patterns where Iris adds the most value: multi-step workflows that combine Sentinel-2, Landsat, SAR, elevation, and vector data with the correct preprocessing chain to make them spatially and radiometrically comparable.
Why it doesn't hallucinate
Iris is not a chatbot with a geospatial skin. It only proposes steps Fabric can actually run — so instead of inventing plausible-looking operations that fail at execution, it stays inside the boundary of what the platform can really do.
That constraint is the point. A system that can generate any arbitrary processing step is a system that can hallucinate one. Iris works within what Fabric can execute, and within that boundary, it is precise.
The result: plain-language input, a pipeline you can trust to run, and your judgment in control of what actually executes.
The name
Iris is not an acronym. It is the iris: the part of the eye that adapts to conditions, controlling how much light enters. The part of the visual system that perceives.
Fabric is the connective tissue that harmonizes raw sensor data into composable, analysis-ready formats. Iris is the intelligence that perceives through it, translating human intent into the language Fabric speaks.
In the context of Observational Grammar, M33's foundational framework for how sensors form a language of evidence about reality, Fabric handles syntax and semantics. Iris handles pragmatics: context, confidence, and learning. The layer where independent sensor readings stop being claims and start becoming evidence.
Integrated
Iris is designed as the conversational entry point to Studio, the visual interface where you build, configure, and run Fabric Patterns. Generate a Pattern from plain language with Iris, then refine it visually in Studio's workspace. Or build from scratch without Iris. Both paths produce the same Pattern that Engine executes.
Explore Fabric Studio →Also in the platform
Fabric Engine The processing core that executes what Iris generates