AI & Automation

We deliver strategic AI implementation and automation for geospatial and enterprise workflows.

AI Consulting

Strategic AI decisions still come from vendor slides rather than from measured evidence. We help engineering, data and operations teams cut through the noise and validate AI options against their own data and constraints. This covers modern language models, agentic systems, computer vision and EU AI Act risk classification, and includes objective comparisons between proprietary services and open-source alternatives. Where data sovereignty matters, we deploy open-source models directly into your infrastructure so that you retain full control over your data. We follow Eric Ries's Lean Startup approach – build, measure, learn – so that investment scales with proven outcomes rather than with promises.

Development of AI Tools

We build computer vision systems for satellite, aerial and ground-based imagery, covering segmentation, object detection, classification and geometry post-processing. Reference work includes an end-to-end CNN tool for construction-trench segmentation, with downstream centre-line derivation and LightGBM-based geometry snapping, delivered to GEO DATA GmbH and deployed in production on Microsoft Azure with dynamically scaled NVIDIA GPU clusters. Earlier work includes a CNN for alpine land-surface segmentation at over 95％ accuracy on Sentinel-2 and orthophoto data, using ResNet50 and U-Net via fastai. Our stack covers PyTorch, fastai, scikit-learn, LightGBM, Geopandas, Rasterio, Shapely, NumPy, Azure Machine Learning and Azure Container Apps.

AI Agents & Automation

We design and deploy agentic AI systems for research automation, geospatial workflow orchestration and structured information extraction. Pipelines are built on PydanticAI, LangChain and CrewAI with strongly typed outputs, evaluation harnesses and per-call cost tracking, and integrate with models from Anthropic, Mistral, OpenAI and Hugging Face depending on task and compliance requirements. Our open-source multi-agent speaker-finder demonstrates the architecture on GitHub. Vector databases and explicit schema validation ensure controllable, auditable behaviour, and every system is mapped to EU AI Act risk classes from the outset.

Data Science & Analytics

Our work spans comprehensive data engineering and geospatial intelligence.

Data Analytics

We build ETL pipelines, validation tooling and analytics dashboards for structured and geospatial datasets across energy, geoinformatics and research. Reference work includes recursive-CTE SQL analytics and a NetworkX-based Python dashboard for power-grid topology validation at a distribution system operator, together with custom Python and SQL tooling for the anonymisation of sensitive mapping data. We also design and curate machine-learning train and test datasets with geographic content. Our analytics stack covers Python, Pandas, NumPy, NetworkX, SQL, PostgreSQL, PostGIS, ArangoDB, MongoDB, Bokeh, Plotly, MLflow, AutoKeras and PyCaret.

Geoanalytics

We turn satellite imagery, aerial photography and spatial databases into operational intelligence through segmentation, classification, change detection and topology analysis. We process satellite data, orthophotos and proprietary imagery with PyTorch, fastai, Geopandas, Rasterio and SNAP, and run training and inference locally or in the cloud on GPU clusters with dynamic scaling. On the database side, we work natively with PostgreSQL and PostGIS for vector and raster analytics, and with ArangoDB for graph-based spatial topology. Our reference work spans three projects: alpine land-surface classification at over 95％ accuracy on Sentinel-2 and orthophoto data; construction-trench segmentation for GEO DATA GmbH; and Holocene landscape-factor datasets curated through web scraping and data wrangling for a joint Max Planck and Helmholtz Institute research project.

Earth Sciences

Our work is anchored in peer-reviewed earth-science research. Five research expeditions in the Himalayas, the Karakoram and the Andes inform how we model natural hazards, surface change and human–nature interaction. Publications include the Journal of Mountain Science, the E&G Quaternary Science Journal and the UIS 2024 Springer chapter on machine-learning infrastructure for environmental administration. We apply this depth to flood dynamics, glacial and proglacial systems and geomorphology.

Software Development

We build production-ready software, data engineering and cloud infrastructure for geospatial and AI workloads.

Data Engineering & Web Development

We deliver end-to-end data engineering, including web scraping, ETL pipelines, validation tooling and web-based interfaces for data collection, review and visualisation. Reference work includes a Flask-based survey application with a PostgreSQL backend and interactive selection menus for a geoinformation software vendor, and ETL pipelines that transform PostgreSQL and PostGIS data into ArangoDB for a distribution system operator. We have also delivered an end-to-end Streamlit machine-learning application for the Max Planck Institute and Helmholtz Institute. The application allows researchers to ingest their own datasets, select an analysis environment, choose between machine-learning methods with configurable hyperparameters, and visualise the results to support scientific hypothesis-building. Since 2023 it has continued as the open-source automatedscientificdiscovery project on GitHub. Our web stack covers Python, JavaScript, HTML, Flask, Django, FastAPI, Streamlit, Bootstrap, SQLAlchemy, BeautifulSoup4 and Requests.

Cloud Computing Solutions

We architect, deploy and document production-grade cloud pipelines on Microsoft Azure, AWS and Hetzner. Reference work includes the GEO DATA construction-trench prediction pipeline, delivered end-to-end on Azure with dynamically scaled NVIDIA GPU clusters. The system incorporates MLOps practice and governance aligned with the EU AI Act, with all artefacts versioned and documented in Azure DevOps repositories. We also support AWS-hosted machine-learning workloads, including the Max Planck and Helmholtz Streamlit application. Earlier cloud work includes Azure deep-learning training on Sentinel-2 and orthophoto data. Our cloud stack covers Microsoft Azure, AWS, Hetzner, Docker, Azure Machine Learning, Azure Data Factory, Azure Container Apps, Azure DevOps, GitLab and GitHub.

Custom Software Solutions

For specialised needs that go beyond off-the-shelf tools, we design and build custom applications, ranging from agentic AI tools and internal dashboards to survey platforms, geometry-aware machine-learning utilities and inference tools. Reference work includes the speaker-finder multi-agent system on GitHub, the open-source automatedscientificdiscovery machine-learning application, a LightGBM-based snapping tool for geometry-aware line output and a NetworkX-based Python dashboard for power-grid topology visualisation and validation. Our stack covers Python, JavaScript, Django, Flask, FastAPI, Streamlit, Bokeh and Plotly, with code, infrastructure and documentation versioned for clean handover.

Environmental Analysis

Our analysis is grounded in earth science and covers natural hazards, climate impacts and environmental change.

Natural Hazard Analysis

We provide geomorphological and remote-sensing analysis of natural hazards, with particular depth in flood dynamics. Our research on flood events and the morphological evolution of river systems has been published in the Journal of Mountain Science and in the E&G Quaternary Science Journal. We combine satellite imagery, fluvial-geomorphological analysis and machine-learning segmentation to support risk assessment for asset owners, infrastructure operators, insurers and governments, with deliverables ranging from one-off assessments to repeatable production pipelines.

Climate System Analysis

We deliver data-driven analysis of climate signals and their physical impacts, using satellite time series, derived indices and machine-learning models. Our earth-science foundation, including research on Holocene glacial and proglacial systems in the Himalayas and a curated Holocene landscape-factor dataset developed for a joint Max Planck and Helmholtz Institute project, informs how we approach climate adaptation, infrastructure exposure and resilience planning over multi-decadal horizons. We integrate satellite, model and observational datasets into client-specific decision pipelines.

Environmental Monitoring

Using satellite and aerial imagery, we build repeatable, automated assessment pipelines for continuous monitoring of land-surface change, vegetation, water bodies and infrastructure. Pipelines combine segmentation models, change-detection routines and operations dashboards, and are delivered with documentation suitable for both technical and non-technical audiences. Reference work includes alpine land-surface monitoring at over 95％ classification accuracy on Sentinel-2 and orthophoto data.

Our Services