The most advanced AI decision engine for steering customer interactions
Award-winning Artificial Intelligence solutions based on adaptive Deep Reinforcement Learning to solve key decision problems along the customer journey.
Our partners
Product
Today, optimizing the customer journey is done using simplistic statistical methods like A/B testing.
We provide a solution that handles complex data over the whole customer lifecycle.
Conventional approaches
Conventional approaches are iterative by design. Therefore, experiments can only adapt to environmental situations that lie in the past, and become outdated very fast.
Campaigns 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 campaigns, our algorithm enables a sustainable optimization of the customer lifecycle. We implement custom target definitions in tight collaboration with you.
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.
We deliver a tightly integrated and secure real-time REST API to access the Machine Learning functionality, and a graphical frontend for monitoring and reporting.
Use Cases
A general framework to increase long-term customer happiness and revenue.
B2C subscription businesses
Acting between data store and marketing tools, the Free Machines engine maximises the customer lifetime value by triggering the right E-Mails, SMS etc. at the right time.
Web shops
To dramatically reduce the amount of returned goods, Free Machines learns to identify customers with high-return behavior. Existing systems are then used to encourage multiple sales etc.
Online video services
In video ads, the Free Machines engine learns to strike the right balance between ad density and long-term customer satisfaction, depending on each individual situation of each customer in real-time.
Vision
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Problem
Based on manual data analysis or basic ML methods, today companies drive decisions heuristically.
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AlphaGo
AI technology has matured so significantly that we now can build fully automated, self-improving decision engines.
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Markets
AI decision-making for industries whose data is suited best, and where the potential uplift is the largest.
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Growth
free machines goes down market as further AI development makes data preparation cheaper.
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Goal
free machines transforms enterprise decision-making into an entirely rational and automated process.
Onboarding that minimizes risk
A gradual method to roll out a system that can grow in scale and scope.
1. Simulation
Based on existing data and current decision regimes, we build a simulation that provides our algorithm with a precise map of how your customer relations work.
2. Trial implementation
With the simulation results as a starting point, we implement the decision engine on a growing portion of your user base.
3. Full scale implementation
After the initial installation is done and fine-tuning has led to proven equilibra, we scale the coverage of the system, targeting full-scale uplift.
Team
A solid academic and professional background.
Dr. Hannes Lüling
Science
After completing his physics PhD in the field of computational neuroscience at the Technische Universität München, Hannes has acquired experience in Data Science since 2012. Recently he has been working as a Senior Data Scientist for ProSiebenSat.1 Digital and the BMW AG.
Dr. Olav Stetter
Technology
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 agile environments.
Dr. Uwe Stoll
Marketing
Uwe has worked as a Senior Machine Learning Expert for several startups and enterprise clients. He can build on a master degree in Marketing and Innovation Management, and a PhD in Semantic Web and Machine Learning.
A radically simple pricing, ensuring positive ROI
We succeed if you succeed.
1. Simulation
- Onboarding
- Data Science
2. Trial implementation
- Data handling fees based on complexity
- Full daily rates
3. Full implementation
- Data handling fees based on complexity
- Full daily rates
Career
Work with us on the future of Artificial Intelligence.
Where we are
We currently reside in the IBM Watson IOT Center, Munich.
Contact Us
Feel free to leave your contact details, and we will get back to you as soon as possible.
