Geospatial Data Processing

Your geodata pipelines,
built by AI.

TerraFlow replaces manual FME workflows with AI agents that ingest, transform, and deliver spatial data autonomously. Describe what you need in plain language. The pipeline builds itself.

⚡ Try Format Detection — Free View API
Before TerraFlow
$3,500+/year for FME licenses
Hours building visual workflows by hand
Specialized ETL knowledge required
Single-threaded, slow on large datasets
Debug topology errors manually
With TerraFlow
Fraction of the cost, unlimited pipelines
Describe your pipeline in natural language
AI handles format detection and mapping
Parallel processing, cloud-native
Auto-healing for spatial data errors

What the AI handles

Six capabilities that replace weeks of manual pipeline work.

Auto-Format Detection

Drop in Shapefiles, GeoJSON, GeoPackage, KML, GeoTIFF, or any of 50+ spatial formats. TerraFlow identifies and parses them automatically.

Natural Language Pipelines

"Reproject to EPSG:4326, clip to Paris boundary, export as GeoJSON." Speak your transformation. The pipeline assembles itself.

Schema Auto-Mapping

AI maps fields between different schemas, resolving naming mismatches, type conflicts, and encoding issues without manual configuration.

Error Healing

Topology errors, CRS mismatches, invalid geometries. TerraFlow detects issues and applies fixes before they break your pipeline.

Parallel Processing

Cloud-native architecture splits workloads across cores. Large datasets that took hours in legacy tools finish in minutes.

Pipeline Orchestration

Chain transformations, schedule recurring jobs, route outputs to PostGIS, S3, or any downstream system. Fully automated delivery.

$142B

Geospatial analytics market by 2028

400+

Spatial data formats supported

12.6%

Market CAGR growth rate

0

Lines of code to write

Spatial data should flow, not fight.

TerraFlow is built by a GIS engineer who spent years wrestling FME workflows. Every pain point in spatial ETL, from format chaos to CRS nightmares, is a solved problem here.