How It Works

From legacy workflow to cloud-native pipeline

Our dual-agent system understands your existing GIS logic and translates it to modern, maintainable Python code. No black boxes. Full transparency.

01

SCAN

Point our agent at your workflow folder

The scanning agent recursively discovers all scripts, data files, and dependencies in your workflow directory.

  • Detects ArcPy, QGIS, FME, and Excel macro files
  • Maps data flow between scripts and outputs
  • Identifies external dependencies and APIs
  • Creates a dependency graph of your entire workflow

Runs locally on your machine. Only metadata is analysed.

$ axis scan ./workflows

Scanning directory...

Found: 12 .py files, 3 .qgz projects

Mapping dependencies...

Dependency graph complete

02

ANALYSE

Dual agents understand your logic

Two specialised agents work together: one understands structure, the other translates logic. They cross-validate to prevent errors.

  • Agent 1: Structure mapping and data flow analysis
  • Agent 2: Logic translation to cloud-native Python
  • Cross-validation catches hallucinations and errors
  • Human-readable explanation of each transformation

98.7% accuracy on first run. Self-correcting on edge cases.

Agent 1: Structure Analysis

├── Input: raster_tiles/*.tif

├── Process: mosaic → reproject

└── Output: merged_cog.tif

Agent 2: Logic Translation

Translating arcpy.Mosaic_management...

03

MIGRATE

Get cloud-native Python pipeline

Your legacy workflow becomes a modern, maintainable Python pipeline with cloud-native data formats.

  • Output: GeoPandas, Rasterio, GDAL-based code
  • Data formats: STAC, COG, GeoParquet, PMTiles
  • Automated validation tests generated alongside
  • Full documentation and inline comments

You own the code. No vendor lock-in.

Generated: pipeline_v1.py

def process_rasters(input_dir: Path) -> COG:

"""Mosaic and reproject to COG"""

tiles = list(input_dir.glob("*.tif"))

mosaic = merge_tiles(tiles)

Tests: 12 passing

04

DEPLOY

Push to your cloud platform

One-click deployment to your existing infrastructure with monitoring, alerts, and scheduled execution.

  • Databricks, AWS, Azure, or Google Cloud
  • Automated scheduling and orchestration
  • Monitoring dashboards and alerting
  • Rollback and version control built-in

Integrates with your existing CI/CD pipeline.

$ axis deploy --target databricks

Packaging pipeline...

Uploading to workspace...

Configuring schedule: daily 02:00 UTC

Deployment complete

Dashboard: app.axisspatial.com/pipelines/xyz

Why It Works

Built for enterprise trust

Privacy First

Your files never leave your machine. Agents run in a sandboxed local environment. Only metadata is used for analysis.

Dual-Agent Validation

Two agents cross-validate each other. One maps structure, one translates logic. This prevents hallucinations and ensures accuracy.

Self-Testing Pipelines

Every generated pipeline includes automated tests. Outputs are validated against expected results before deployment.

Human-Readable Explanations

Each transformation comes with clear documentation. You understand exactly what changed and why.

Compatibility

What we support

Input Formats

  • ArcPy scripts (.py)
  • QGIS projects (.qgz, .qgs)
  • Excel with macros (.xlsm)
  • FME workflows (.fmw)
  • ModelBuilder exports
  • Manual click-by-click docs

Output Formats

  • Cloud Optimized GeoTIFF (COG)
  • GeoParquet
  • STAC Catalogs
  • PMTiles
  • FlatGeobuf
  • Zarr (for time series)

Ready to modernise your workflows?

Join the beta waitlist. We'll reach out when spots open.