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Use Cases



Handwritten documents can be read out and prefilled using machine learning algorithms. By further input, these documents can be edited again, which again benefits the accuracy of the algorithm.

Electronic patient records can be evaluated using machine learning algorithms. This automatically provides doctors with suggestions for diagnoses and therapy options.

Diagnostic suggestions can be made by analyzing X-ray or MRI images using an algorithm.

Companies working with thousands of customers and suppliers need to categorize their documents so that requests can be handled in time.

Authorities need up-to-date information on the use of their land to be able to plan public infrastructure.

Runways are controlled by air traffic controllers observing the runways from the tower. As this task requires constant attention and the detection of security sensitive objects on the runway or in the air, Computer Vision can support air traffic controllers.

Through standardized images of different leathers, a machine learning algorithm can classify leather and assign it to the right garments.

Photos of defective spare parts are analyzed by a machine learning algorithm and the damage is classified. This information is used in the development of new products and in the selection of suppliers.

The optimum price of a building insurance can be determined by the customer specifying the address of a building, for example, and any existing pictures of the customer.

Crop Classification is a highly relevant topic in agriculture for farmers, authorities and companies selling to farmers alike.

Defective components can cause major production losses. To prevent this, an algorithm can recognize patterns and distinguish faulty parts from faultless ones at an early stage.

Neural networks are used to detect and filter out patterns of fraud cases. Conspicuous damage reports are reported to claim handlers and checked manually.

Standard documents can be classified and automatically completed using voice input and machine learning algorithms. This document can be checked and edited afterwards.

Construction plans are complex and include a high amount of relevant data. This information, however, is not standardized and available in a structured format. Therefore, data cannot be analyzed accurately and construction projects cannot be compared on a profound basis.

Companies receive dozens of invoices every day. Handling invoices and ensuring payment deadlines are met is a central task at every accounting division.

Rental contracts are often very complex and have dozen of pages. However, for some tasks, like in a Due Diligence process, only some information is important.

A machine learning algorithm analyses the properties and historical and current data of a property. This results in a score by which the attractiveness of an object can be evaluated.

In high-growth cities (mainly in Asia and Africa), it becomes a major challenge for urban planners to keep track of settlements and infrastructure need.

Machine learning algorithms allow standard clauses and deviations from the standard to be defined and identified on the basis of historical contracts. This makes it possible to highlight deviations and identify and categorize contracts according to predefined criteria.

Customer service agents enter information on complaints or complaints in forms. Information from these clusters can flow into the optimization of business processes.

A photo of an object is taken to simplify the process of creating the advertisement. This picture gets analysed and the finished advertisement is displayed to the customer.

An algorithm can analyze thousands of influencer styles and outfits and predict next season's trends.

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