The machine learning agency

Object detection, image classification, semantic segmentation, and related technologies have a variety of use cases ranging from autonomous surveillance to clever functions in smartphone apps and real-time text recognition. Information extracted from images or videos be used for a range of downstream tasks, whether it is to keep track of the position and velocity of an object, identify anomalies or find patterns.

Data in the form of text is abundant. Companies have access to thousands, if not millions of internal documents. The value in these and other accessible bodies of text can be leveraged to create value through a multitude of downstream tasks, such as automatic question answering, sentiment analysis, intelligent search, and automated communication.   

Machine learning on structured data from relational databases and spreadsheets to data produced by sensors is a mature subfield of AI. Downstream tasks include everything from logistics optimization to economic forecasting. Models range from lightweight and linear to complex algorithms taking into account the values in hundreds of variables.

Aside from creating personalized recommendations, there is a range of opportunities for making retail companies more profitable. The increasing bottom line comes from increasing revenue as well as streamlining operations, both can be addressed with ML. We have worked with a long-range of B2B and B2C retail companies and helped them transform their business with the use of AI. Further, personalization increase the user experience in a variety of other sectors, such as content and streaming platforms, ad networks, and social networking platforms.

The Medtech space is close to our hearts, and we’re proud to have been part of many successful AI applications in the space. Using object detection, image classification, time series classification, AI explainability, and more we have built applications for diabetes, rheumatoid arthritis, cardiology, skincare as well as dental health. Medtech applications range from decision support for medical professionals to instant feedback for patients.  

In this space, we have developed models for personal credit risk assessment, fraud detection, company default risk as well as macroeconomic forecasting. A wide range of different machine learning techniques can be used on different data sources to predict economic performance, assess various sources of risks, and for targeted marketing. We know that to be successful in applying AI in the fintech space you have to know the business as well as ML – knowledge we know very few others can match.