What we do

We tag and annotate objects and patterns in image files to create high-quality training data for machine learning.


Machine learning is already being used in many areas: identification of road users for autonomous driving, detection of diseases in health care, aerial image and earth observation data analysis, recognition of handwritten documents in business processes, and in cultural heritage archives. Humans are indispensable in the development of artificial intelligence. They must perform the necessary preliminary work by manually marking shapes, patterns, movements and objects in image data so that the software can then learn from this information. Manual preparatory work is very time-consuming and therefore expensive.

We can do this work for you at a reasonable price. Thanks to our impact sourcing partner Digital Divide Data, who runs operations in South-East-Asia and Africa based on a social mission, we are able to index your data quickly and reliably, with consistently high quality and at a fraction of European labour costs. We can flexibly adapt our capacities to your data volume, which will give you a lower entry barrier for feasibility studies and a faster time-to-market. Read the interview with founder Daniel Lanz.



We help you determine which data is important for your project, and identify the ideal methods for tagging, labeling, and data annotation.

Data annotation

We segment your image data and manually mark all objects and patterns that are important for your learning algorithm.


Our tagging service enables you to create an index of all identified objects, images and other content and media types.



The spaceVocs project uses an electronic nose and AI processes to recognise the condition of wheat based on its odour patterns. In this way, we are trying to determine data that can be used to optimise water and nutrient requirements and detect diseases as early as possible..


In the beeScreen project, we analyse satellite data and use artificial intelligence to evaluate honey bee locations for professional beekeepers. This involves data on changes in vegetation, air quality, drought stress and temperatures in relation to bee health and honey yield.


In the beeNose project, an electronic nose was combined with AI processes to detect the conditions of bee colonies and in the area of plant cultivation based on odour patterns or so-called volatile organic compounds (VOCs).


LANZ was founded in 2018 by Daniel Lanz, who has over 20 years of professional experience in digitization, data acquisition and software automation. Previously, Lanz was long-time CEO at Hamburg-based software and digitization company Content Conversion Specialists, Managing Director at communications agency Neonred and Director Business Development at data services provider Digital Divide Data.


European Space Agency
Paris, France
Bibliothèque nationale du Luxembourg
Luxembourg, Luxembourg
Fraunhofer IAIS
Fraunhofer Institute for Intelligent Analysis and Information Systems
St. Augustin, Germany


INNOspace® network initiative of the German Aerospace Center (DLR)
Bonn/Aachen, Germany
INNOspace® network initiative of the German Aerospace Center (DLR)
Bonn/Aachen, Germany
Digital Hub Chemistry and Digital Health
Initiative of the Federal Ministry of Economics and Energy, Germany
Mannheim/Ludwigshafen, Germany