The privacy-first AI layer for e-commerce
The award-winning Artificial Intelligence solution based on Deep Reinforcement Learning.
Today, optimizing conversions is done using simplistic methods like A/B testing.
We handle the complete package of personalization with an easy-to-integrate API HTTP layer.
Example use case: Personalized UX for mobile store
Conventional approaches are iterative by design. Therefore, experiments can only adapt to environmental situations that lie in the past, and become outdated very fast.
Features of stores are usually optimized in isolation, without regards to the bigger impact on the customer lifecycle. While this simplifies the algorithmic challenge, it also severly limits the total achievable uplift.
Conventional methods have a significant need for manual intervention. They usually demand a constant creation of hypotheses, checking of segments for consistency and interfacing with IT for implementation.
Explored uplifts suffer from a significant delay between findings and their exploitation. In dynamic environments, that can even lead to vanishing uplifts.
Conventional Machine Learning is heavily dependent on the quality of a process called feature engineering. This approach is very expensive, error prone and not robust to changes in the environment.
Many classical Machines Learning algorithms are not easily scalable. That leads to severe problems as the data set grows.
By fundamental design, our algorithmic core is able to continuously adapt to new situations. Designed as an end-to-end decision system, it is built to be deeply integrated into production systems and therefore can instantly react to changes in the data.
Instead of focusing on single features, our algorithm enables a sustainable optimization of the whole customer lifecycle. This can include the long-term goals that actually matter to your business.
After the simulation period, where the system learns behavioural structures of your business and your customers, the systems constantly moves towards a very high degree of automation. With extensive monitoring capabilities, we allow you to focus on the big picture.
The system continuously learns and is able to exploit these findings in real-time. You'll never again will have to wait for the next optimization iteration.
free machines is built upon state-of-the-art Deep Learning approaches. Manual feature engineering is replaced by deep network architectures that learn the optimal feature set automatically.
Our algorithmic core is built on highly scalable tensor arithmetic, it is extremely scalable on GPUs. Data access is handled by a redundant, high-availablility web interface. Therefore, we are capable to handle very large datasets.
That sounds great, we hear you say, but how does it drive conversions?
Nobody likes waiting for a shop to load, that's why we're focussed on speed & stability. Enjoy lightning speed with the Free Machines API.
We are the only personalization solution on the market that was designed from the very start to be GDPR compliant. Thanks to special encryption, the data we work on is fully anonymous.
Our technology is based on Deep Reinforcement Learning to ensure that your users find the content that is relevant to them in the shortest amount of time.
What our customers say
Thanks to the contextual personalization of Free Machines, we were able to generate a conversation rate of 11.9% already in the first 4 weeks.
Founder / CEO
Very impressive technology! Using it, we were able to raise the conversion rate for new customers by almost 69%.
Founder / CEO
We think Free Machines is the way to get rid of cumbersome A/B Testing.
A radically simple pricing, ensuring positive ROI
We succeed if you succeed.
- Activate add-on
- Setup variants
- 50% control group
- Real-time uplift measurement
- Reporting & Monitoring available
Profound market knowledge.
Dr. Hannes Lüling
Hannes holds a PhD in computational neuroscience from the Technical University of Munich. After several years as Senior Data Scientist at ProSiebenSat.1 Digital and BMW, he is now heading AI research at Free Machines.
Dr. Olav Stetter
Olav is a physicist by training, with a PhD on the dynamics of complex systems. He also has extensive experience building technologies, teams and products in lean startup environments.
Dr. Uwe Stoll
Uwe has worked as a Senior Machine Learning Expert for several startups and enterprise clients. He can build on a PhD in Semantic Web and Machine Learning, during which he did extensive research in the field of e-commerce.