Applied Machine Learning & Artificial Intelligence

Transfer of cutting-edge research to individual industry problems


image/svg+xml Cloud Computing ML & AI A P I

WUI Wildfire risk modeling with Artificial Intelligence

Dynamic risk modeling using remote sensing data

  • Our solution closes the information gap for risk modeling of wildland–urban interface (WUI) areas. WUI areas are a fast expanding risk pool for insurer.

  • In 2017 alone, US wildfire damage amounted to $16.2 billion. In comparison, in the 10 year period before (2007-2016) combined losses totaled roughly $15.8 billion.

  • Risk models based on claim history have limited predictive power for WUI areas. We provide additional data sources to improve risk model accuracy.

Our Solution

Our AI-models automatically detect wildfires on satellite images and digitize & categorize this information combined with additional data sources.

Our dynamically updated risk database contains data with a very high temporal & spacial resolution worldwide.

We provide a high throughput and low latency API for accurate and continuous risk assessment.

Digitalization & Automation

Sustainable digitalization and process automation using Artificial Intelligence (AI) and Machine Learning (ML) methods.

Data Digitization

  • Our machine learning models intelligently extract & categorize information from satellite images, health records, PDFs, location data, identity cards and more.
  • We use state-of-the art techniques in image recognition, natural language processing and signal processing to extract & categorize information.

Process Automation

  • We automate processes with ML that otherwise require human supervision.
  • We work on applications ranging from predictive maintenance in manufacturing to underwriting insurance risks.
  • We transfer existing processes into formal specifications that can be solved by ML-methods.


  • We believe that ML-solutions are best developed with iterative end-to-end prototypes.
  • This allows us to ensure that data quality & availability and model complexity reflect the business objective.
  • We develop prototypes that fulfill all critical requirements of the production system.
Our experienced team members worked in digitalization & research projects with:
Bayer Technology Services - Google Research - Victorinox - Swiss Re - IFH Cologne

Custom Machine Learning Solutions

Transferring academic & industry machine learning research into powerful solutions requires a combination of deep technical understanding, state-of-the-art tools, and the right development approach.

Artificial Intelligence & Machine Learning

We transfer cutting-edge academic & industrial research results in ML to industry applications.
  • ML research experience with industry leaders (Bayer AG, Swiss Re, Google Research)
  • Developer of popular ML library (fastFM)
  • Winner of high profile ML-competitions
  • Publications in top tier ML journals & speaker on conferences (WWW, ECML, PAKDD, JMLR)

✔ Profit from our ML-experience & expertise

Cloud Computing & Data Engineering

We build on powerful cloud computing technologies allowing us to quickly create individual & complex ML solutions.
  • Build on proven open source libraries (Nomad, Kubernetes, Docker, etc.)
  • Handles > 7 TB data per day
  • Secure & quickly scalable
  • Cross-cloud deployments
  • Funded by Google Cloud for Startups
  • N.A

✔ Save time & costs with our advanced Tech Stack

Agile Data Science & Software Engineering

We transfer proven software development practices to data science projects to iterate at agile speed.
  • Early prototype & continuous improvements
  • Continuous integration & delivery
  • Peer-reviewed code & easily deployable solutions (docker container)
  • N.A
  • N.A
  • N.A

✔ Work with maximum efficiency at agile speed


Sonja Strothmann

Sonja holds a Master of Science in Strategic Marketing from the Maastricht University. She has worked for +9 years on diverse Digitalization Projects as IT-Manager and Consultant for known Companies. At Victorinox in Switzerland she was responsable for the Online Operations Management.

Dr. Immanuel Bayer

Immanuel holds a Diploma in Computational Engineering Sciences from the RWTH Aachen University and a PhD degree in Machine Learning & Computer Sciences from the University of Konstanz. He worked at Bayer Technology Services, Google Research in Mountain View and Swiss Re in Zurich.

Drop us a line or two

We'd love to hear from you