From our development portfolio

Selected systems for demanding technical workflows.

Our development portfolio comprises expert software and AI systems for a range of technical workflows. This page presents a selection: SpatialAgents, the Geospatial API for AI agents, Climate Risk and Vulnerability Assessment (CRVA) built on that framework, and Düngewende.

01 / QGIS integration

SpatialAgents

A technical connection between coding agents and a QGIS workstation.

QGIS project from SpatialAgents Benchmark 08 with risk layers and evacuation routes — SpatialAgents / QGISThe open QGIS project with analysis and map artefacts generated through SpatialAgents.

Codex shell in the SpatialAgents workspace with QGIS and geospatial tools — SpatialAgents / CodexCodex and the agents it launches discover the available QGIS and geospatial tools through the SpatialAgents bridge.

Starting point: Coding agents require access to QGIS project state and specialist geospatial tools.
Our development: We develop the QGIS plugin, token-authenticated local bridge, agent workspaces and domain skills as one integrated system.
System: QGIS Desktop, local WebSocket bridge, agent CLI and the Geospatial API used directly in the workspace.
Application: Agents can complete tasks in the open QGIS project and generate map, data and project artefacts.
Fundamentals: Thirteen terms from the coding agent to measured evidence, each with a graphic, reading text and narration; the same sequence is available as a narrated presentation.
Projects and support: We research and develop. Projects, setup, training and support are provided by Geoinformatikbüro Dassau GmbH (GBD).

Explore SpatialAgents in detail View the complete Benchmark 08 Fundamentals and presentation

02 / Geospatial engineering

Geospatial API for AI agents

We develop the Geospatial API specifically for AI agents and continuously improve it through a recursive cycle of agent use, feedback and implementation. It also provides the computational foundation for Climate Risk and Vulnerability Assessment.

System architecture of the Geospatial API designed for AI agents: data from OpenStreetMap, climate, satellite, terrain, statistics and web sources is represented as raster, vector and time-series objects, processed and exported as geodata, maps, tables and reports — Geospatial API for AI agents / System architectureTyped geospatial objects and chained operations form the agent-oriented domain interface of SpatialAgents and the computational foundation of CRVA.

Recursive improvement cycle: AI agents exercise the API in practical geospatial analyses. Their feedback on interfaces, documentation, data types and errors flows back into the API, tests, benchmarks and agent skills.
Direct code use: Coding agents import the framework directly into Python workflows. This applies their code-generation and verification abilities with low token overhead; no MCP server is required.
Self-describing API: Descriptive method names, chainable operations and structured result objects support autonomous use of the interface.
Fault tolerance: Common input errors are caught early. Messages identify the cause, valid values, similar methods and concrete recovery options.

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03 / Climate-risk analysis

CRVA

Climate Risk and Vulnerability Assessment uses the Geospatial API as its computational foundation and connects geodata, scenarios, analyses, maps and draft reports in one integrated application.

Climate Risk and Vulnerability Assessment workflow: locations and portfolios are combined with spatial context, climate hazards, scenarios and time horizons, assessed for materiality and documented in maps, tables and draft reports — CRVA / Assessment workflowThe data path connects assets, spatial context, hazard fields and climate scenarios with materiality assessment and traceable result artefacts.

Starting point: Site and portfolio assessments bring hazard, terrain and climate data together while documenting uncertainty.
Our development: We develop project management, data preparation, computation, analysis, visualisation and report generation using the Geospatial API.
System: CRVA CLI and pipeline within the Geospatial API, geodata sources, scenario evaluation, maps, tables and draft reports.
Application: Structured draft reports connect data sources, scenarios, analyses, maps and tables for subsequent expert review.

Professional context

CRVA development draws, among other sources, on the steps described in the EU Taxonomy for screening, assessing materiality and identifying adaptation solutions. Professional and legal assessment is carried out for the intended use.

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04 / Energy & space

Düngewende

A geodata-based application for site analysis of decentralised green ammonia production.

Düngewende layer selection with solar parks, wind clusters and wind turbines — Domain layer tree for energy and administrative data.

Düngewende equipment catalogue showing ammonia systems and capacity values — Technical equipment catalogue with capacity comparison.

Starting point: Decentralised green ammonia production connects available wind and solar energy, suitable sites and technical equipment options.
Our development: We develop the specialist application on SpatialApp and bring spatial, technical and economic information into a shared interface.
System: GIS map, wind, solar and cluster layers, site views, equipment catalogue, comparison, reference data and economic calculations.
Application: Maps, tables and domain views support spatial exploration and comparison of possible system configurations.

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