Offering relevant content to your customers makes it easier for them to make a purchase decision. By simplifying the navigation through your catalog, customers have the chance to easily find what they are searching for and discover products they were not aware of. This gives you as a provider higher conversion rates and cross sales and your customers an overall better experience.
By combining classical approaches with recent advances in deep learning, modern recommendation systems are much more nuanced than their predecessors from a few years ago. This makes it possible to handle very large databases and provide your customers with novel and diverse recommendations, which are relevant at the same time.
By using pre-implemented solutions like amazon Personalize, you can quickly take advantage of the same technology that amazon is using to successfully provide high quality recommendations to its customers. We guide you through the process of setting up the tool and connecting your data streams. We are also more than happy to build a solution which does not rely on amazon for you - dida ❤ customization!
We are a software company specialized in Algorithms and Machine Learning (ML) and most of our 30+ employees have a scientific background in Mathematics and Physics. We follow the developments in ML research and have multiple years of experience bringing this knowledge to customers and into production.
No Machine Learning task is alike. The data that is available and the requirements always differ and might not be captured by off-the-shelf software. From us, you get an individual solution. You own the code, the data and we integrate it into your current software environment.
We offer initial proof of concepts and the complete development of production software. Due to the experience and background of our team, we can as well offer larger research projects.
In our Tech Lunch, we will give you 45 minutes of information on recommendation systems.
In our ML Expert Talk, we assess your existing solution and point out possibilities for improvement.
I want to learn more
I have a running recommendation system