Spatial platform core
Project workspaces, MapLibre mapping, PostGIS data handling, metadata intelligence, user access and cloud deployment foundations.
Development roadmap
Building the core before scaling the ecosystem.
The roadmap communicates intended direction rather than fixed release commitments. Priorities will be refined through technical validation, pilot feedback and funding.
AI assistant and Model Builder
Metadata-aware planning, explicit data selection, tool orchestration, workflow previews and approval-led execution.
Secure execution and reusable applications
Sandboxed Python, asynchronous compute, private-model integration, usage controls, reusable analytical steps and application publishing.
Marketplace and organisational governance
Shared components, templates, enterprise controls, auditability, deployment choices and a broader contributor ecosystem.
Current priorities
What we are validating now.
- Reliable AI-readable geospatial metadata
- Fast vector and raster visualisation
- Transparent planning before execution
- Custom Python workflow steps
- Credit and resource usage tracking
- Secure, multi-project collaboration
- Practical workflows with pilot users
Roadmap dates are intentionally not published until validation and financing milestones are confirmed.
Security roadmap
Private deployment is part of the core product direction.
Planned validation includes tenant isolation, role-based access, encryption, auditability, configurable retention, private model endpoints, local model execution and deployment inside customer-controlled environments.