At Intervall, we create Digital Experiences. We are both Design Lab and Data Science Office. Our approach combines methods from both fields. We call it: Design and Computation.
We support clients across four different areas and their combinations – reaching from the creation of early concepts to the implementation of tailored solutions.
Building upon many years of experience, we advise clients on a strategic level and create innovative solutions and product visions for internal and external projects. On an operational level, we create real value added by applying advanced analytics to business processes as well as promoting small and mid-size product developments through a strong partner network.
We have learned that methods from design and computational thinking complement each other: the classical design process benefits from the stringency of data-driven computational thinking and can be used to improve networked and complex systems. And it is design that adds a human dimension to the process of computational thinking. The interplay of both – Design and Computational Thinking – constitutes our approach to shape a digital world.
We are experts of the digital domain and help our clients benefit from the Digital Transformation. Our motivation: to shape our future digital society.
The design process guides us by creativity and mutual inspiration. In order to understand problems and questions, we use methods from user–centered design. We talk to people, observe human behavior and evaluate findings based on our experience. Through computational thinking we gain new insights. Quantitative research, data analysis and structured deep dives into the respective domains help us understand fundamental coherence and complexity within systems. We verify our hypotheses with the help of algorithms, models and simulations. Lastly, we give a face to these new ideas and concepts. Every solution is different: Presentations, prototypes, apps, ... as well as podcasts and events. Through clear communication and great storytelling visions become tangible.
Digitalization enables connecting objects across different domains. In doing so products and services are transferred from the analog world to the digital one. They obtain a digital representation and thereby loose their physical limitations: In the digital space, objects can be freely multiplied, moved, changed and connected. New creative freedom arises which would have been conceptually unthinkable or technically impossible before.
In the context of digitalization, products should be considered in a systemic way – meaning, in the context of their already existing and potential connections. As a result, the advantages of the digital space can be fully exploited and added value created. The digitalization is therefore essentially a systematization. It is about bringing together previously independent components (things, properties, processes, rules, ...) from different domains and letting them interact within new systems – to create novel solutions to old and new challenges.
The increasing level of interconnection between analog and digital objects also results in an increasing level of complexity of the associated systems. Users of digital products and services can no longer understand the underlying mechanisms. They have to trust in the respective product and service providers, which surprisingly many people are already doing today. In the future, it will be about maintaining that level of customer confidence through transparent and secure product and service design.
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Design |
Computational |
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[1] Collect & Understand |
User Research Empathy |
Collect data Analyze data |
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[2] Conceptualize & Design |
Creativity Brainstorming Human-Centered |
Stringency & Structure Algorithms & Machine Learning Models & Simulations |
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[3] Shape & Develop |
Final design Storytelling |
Implementation Prototyping |
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Emotion Simplicity Aesthetics |
Ratio Speed Automation |
Art and science provide two different perspectives on the »Conditio humana« - on what it means to be human. The tension between them is constructive and reflects an open dialogue between rational thought and emotion, between head and gut. Similarly, Design and Computational Thinking offer different perspectives and tools.
The fascination of creating digital products is strongly connected to the computational power of today's computers. With their immense power to process large amounts of data in a short time, we have powerful tools and ways to design digital products.
Algorithms are like a recipe for the computer to follow in order to achieve certain goals. These goals can be manyfold, like detecting a fire from gas data or using vital data for monitoring personal well-being and consumer health application. To develop these algorithms, there are two opposing approaches...
Breaking down complex problems into their most basic and fundamental aspects – very often down to the physics – helps us understand the core principles of how a systems works (first principle thinking). Based on this understanding, we can explain how the signals in the data are formed and exactly how individual features in the data are to be interpreted (e.g. the specific aspects in the shape of an EV charging curve can tell you a lot about the battery health). Data analytics and algorithms can benefit from such fundamental understanding. Mathematical tools help us to extract useful information, that is hidden within the data.
However, sometimes the mechanisms of cause and effect are very complex and it can be difficult to gain a basic understanding. A complementary approach, that is based on a large amount of labelled data, uses generic machine learning architectures (often called AI) that can be trained with such data. The system will then learn to mimic a certain intended behavior. Surprisingly even very complex behavior can be achieved with such an approach. The key to successful development of AI algorithms is to carefully train those algorithms in a way that they generalize very well, so their application in a product is robust and reliable.
In the case of our algorithm development for the Bosch Sensortec BME688 gas sensor we use both bottom-up and top-down approaches. We develop conventional algorithms for air monitoring and more complex machine learning approaches for specific fire detection.
Handling reliability was key to the development of these security related use cases:
Together with Bosch Sensortec we are investigating the possibilities of AI-driven gas sensors since 2019. And it turned out, there are simply too many interesting use-cases that can be explored. That's why we transferred our process and knowledge on how to explore gas sensor use cases into a software application, that lets customers build their own algorithms in a simple to use and easily accessible way. A challenge from both design and computational perspectives – harmonizing multiple years of gas sensor experience and various data science approaches into a coherent hardware/software ecosystem: BME AI-Studio.
We stripped down our complex machine learning pipeline in a way that even non data scientists can make use of it. Therefore, we needed to ...
Providing widely available access for healthcare services through remote monitoring and automated data analytics is key for tomorrow's health care systems. The aging global population and rising healthcare costs necessitate efficient, accessible, and cost-effective healthcare solutions, which digital technologies can provide. Consumer health products are an important step, and in close collaboration with Bosch Sensortec, Universitätsklinikum Freiburg (Medical Center Freiburg), Saarland University and others, this is a constant effort at Intervall. In addition, the collected data and knowledge can later lead into medical grade products.
For example, monitoring and analyzing indoor air quality can have multiple benefits:
Sleep tracking is another field, where we have been working for years now to detect and optimize parameters for a healthy night. Therefore we like to collaborate internationally in public funded projects.
Enriching people's lives through digital products is now enabled by the power of ubiquitous computing. At Intervall, we use algorithms based on first-principle understanding and AI-powered technology in a variety of areas, such as charging infrastructure, smart home, personal wellbeing and many more.
We live in a complex world. Technology is progressing at a breathtaking rate, business structures are becoming increasingly tangled, user needs are more individual than ever. At the same time, digital interfaces already form the main access point to large parts of our professional and private lives, and will do so even more in the coming decades. At Intervall, we believe that designing these digital interfaces to our complex world is critical.
Everyone talks about machine learning, but only few understand it. The underlying mechanics are complex. With BME AI-Studio, we have developed a software ecosystem (including documentation and video tutorials) for Bosch Sensortec, that makes machine learning accessible and manageable for all users.
Both advanced experts and absolute beginners in the field are guided through all relevant steps and can train and test their own neural networks for the BME688 gas sensor.
How we approached the overall concept for BME AI-Studio:
The result was an amazingly clear software structure. Users are guided through the entire process from data acquisition, data collection and curation to the creation and configuration of machine learning algorithms.
Complexity not only arises due to technical sophistication or mathematical abstraction, also terminology and processes that have been optimized for many years can be very complex for newcomers. Together with Aurubis and their Digital Innovation Lab, we focus on making business processes in the domain of metal recycling more transparent and easy to understand. With a fresh outside perspective and rigor thinking we can carefully restructure what has evolved for more than 150 years.
Within the Aurubis Business Partner Portal we do not only cope with complex and very specific terminology of business-relevant processes, but also with a wide variety of users and requirements.
While designing the digital user interface for MAHLE chargeBIG we wanted to communicate MAHLE's expertise in constructing technical solutions, while at the same time making the user experience practical, efficient and fun to use.
We also put special attention into designing the charging experience itself. Since users can not really experience what's going on, we wanted to visualize the process from a engineering perspective. What is really happening when charging a car? Current is flowing along the cable, electrons are moving through the copper.
From pioneering machine learning interfaces to streamlining century-old business processes, Intervall consistently transforms complexity into clarity and thus enriching user experiences across the digital landscape.
At first there is an idea – similar to a fragile little plant – it takes a lot of protection and courage to let it blossom by following and developing that idea into a product concept for something new. It is part of the creative process to be very careful and imaginative in the beginning, so new ideas can grow. Only if they do this will they have the strength to defend themselves against justified and important objections. These objections are also necessary to prevent us from going down all possible paths and focusing on the most important and promising ideas.
Patience and thoroughness are essential, as is understanding matters in depth, feeling your way forward, iterating and then courageously breaking new ground. We are thankful that our customers share our view and have trust in us. The basic groundwork will pay off later, and we can move forward much faster at a later stage if we have laid the foundations properly.
When we developed a new trade show exhibition for Bosch Sensortec for the CES 2022, we had only very little time to do so. Nevertheless we spent the time in developing a thorough concept, capturing all aspects that are necessary for the visitors to understand, before we went on to develop the concept and design. The result is a very modular showcase that is easily adaptable and extendable.
The fact that the foundations are so important is true for projects that need to be completed within a couple of weeks, as well as projects that turn out to be multiple year long runners. Like the project where we developed a novel digital ecosystem for EV charging.
Together with MAHLE chargeBIG, we developed and designed a complete digital charging experience.
Sometimes, we also work on inventions under the hood, which does not mean that they are less important. When we developed the BME AI-Studio ecosystem it was a green field everywhere — from the technology to user interfaces, but also to the file formats that are the backbone of the entire ecosystem.
Together with Bosch Sensortec we developed multiple file formats that capture meta data, sensor data and relevant algorithm information in a comprehensive and meaningful way. This is a good example that green field innovations in software development have to be carried out carefully in order to progress fast later.
The new file formats enabled:
At Intervall, we are used to developing completely new things. That's why we often get involved in projects at a very early stage, whether it's a small trade fair stand or an entire EV charging ecosystem. Together with our customers, we love to create something new and unique.
At Intervall, we develop digital products for challenging problems which often require complex solutions. So it is only logical that we also take care of the task of software development. That involves the concept and design, but also the programming and testing of various software applications. With our team of internal and external developers we have created many successful software projects on multiple platforms and with a variety of different technologies. Modern Toolchains must be fast, flexible, reliable and secure — but each project needs its unique tech stack.
When it comes to ensuring quality in software development, automated processes are key to success. The moment you automate something you have to design the process carefully in order to make sure that it will work under all circumstances.
This approach shifts our focus to edge cases, addressing potential challenges proactively. It also makes use of the computational power, so automated tests can be run on every iteration of the software with almost no effort. Design and Computational Thinking come together for ensuring software quality.
With BME AI-Studio we have fully automated the CI/CD pipeline on Github:
This is crucial when designing and implementing a whole software ecosystem.
This is also true for complex data analytics and especially for machine learning pipelines. It is less important to be 100% bug free or security compliant, when exploring new data science approaches, but a systematic approach and reproducibility is key for automated experiments. Unit tests can be used to assert correctness, although the algorithms are in a constant flow of change.
We always like to think about the possibilities of data analytics when developing software systems. We did so when we developed the frontend and backend components for MAHLE chargeBIG.
Compared to the usual approach of parameterizing the system, event-driven architectures and the datasets that evolve from such systems allow you to change system parameters you haven’t thought of in the beginning. One example is the influence of intelligent load management that affects nearly all user touch points. Because we store all user interactions (and also hardware events) in an Event Store we can basically simulate every solution in the space of possible backend logics.
Finding the right technical solution for a product concept is an exciting and responsible task that we at Intervall enjoy tackling together with our customers. In software development, it is important to be innovative and thoughtful at the same time - both playful and serious - we like to work with wit and rigor at the same time.
Together with our customers we have a vision of what it means to be truly innovative. To create something new and substantial, to go where no one has gone before, to be courageous and try out new things also involves the possibility of failure. We are thankful that in the past our customers had the trust and faith in us to be bold, to dare something and in the end to be the first one, to pioneer new ways, to truly invent something.
The vision of pioneering innovation is clearly exemplified in our journey with Bosch. In this collaboration, we embraced the challenge of developing the world's first gas sensor with AI. It was due to the excellent hardware development of the Bosch Sensortec BME688 that we were able to work on a truly innovative gas sensor.
Similarly, together with MAHLE chargeBIG, we ventured into uncharted territory by challenging the existing EV charging solutions. We wanted to be the first ones who are allowed to bill for EV charging in Germany in accordance with calibration law.
This part of the software was encapsulated and equipped with appropriate interfaces in such a way that it can be operated for many years without changes after evaluation by the calibration authorities. This means that new and expensive reevaluations can be avoided during the continuous further development of the overall software. Together with MAHLE we developed the first fully digital EV charging solution compliant with calibration law, pioneering a path for all other charging infrastructure providers.
Our pioneering spirit is not confined to technical innovations alone. In an artistic endeavor with Finnova we once again broke new ground by being the first to develop a modular synthesizer that has a specific programming reflecting the interdependencies of the Finnova Product Portfolio as a switching matrix.
In every venture, whether it's developing groundbreaking AI-supported gas sensors with Bosch, redefining EV charging standards with MAHLE chargeBIG, or merging technology with art in our collaboration with Finnova, we consistently push the boundaries of what's possible. These examples underline our ongoing commitment to not only participate in the technological landscape, but to lead and redefine it.
We are a team of interdisciplinary experts including designers, physicists, data scientists, software developers, photographers and artists. Our focus is on the actual work, and we maintain flexibility in the type of employment relationship. In addition to our core team, we have a network of specialized freelancers who support us with their expertise in individual projects.
To understand the world includes to understand its limits. Planet Earth has been a good home for mankind ever since. We are living in the most interesting times since we as humans realized the finite nature of our planet's resources and the limits of the protective atmosphere surrounding us.
At Intervall, we try to contribute to the emerging global sustainability effort by first doing the obvious. Intervall is a paperless office with energy-saving devices and LED lighting. Additionally, our choices of technologies reflects our commitment to energy-conscious computing.
Through our strategic project selection and commitment to energy efficient technologies, Intervall is not only committed to sustainable practices in our operations, but also proudly contributes to the global mission of creating a more sustainable and resource efficient future.
The scientific approach is deeply intertwined with our concept of Design and Computational Thinking. Our approach to observing the world and collecting empirical information is manifested in our practices of user research and data collection. Systematic analysis of the findings and the formulation of hypotheses are at the heart of our work. We like to experiment, to verify or falsify different theses – building on existing scientific knowledge means standing on the shoulders of giants.
The world can be understood in a mechanistic manner, as a sequence of cause-effect chains, as a result of constant execution of a routine. In this image, the human body is a chemical-biological machine, and the mind is an information processing engine emerged from it. In accordance with that, modern psychology argues that neuronal computations make up human perception and thus the state of consciousness. Consequently, cognition and consciousness can be interpreted as a kind of »computation« (computational theory of mind [1]).
In general, computation can be discussed detached from the respective physical implementation. One speaks of Turing-complete machines which are computationally universal [2]. CPUs are the common example. However, the implementation of human consciousness is linked to exactly one »biological computer«, namely the associated human brain and its neurons (biological computing [3]). Humans and their minds are certainly far from understanding this »biological computer«, if that's even possible at all. Emerson M. Pugh put it in a quote this way: “If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.” This fact is also reflected in the structure of our sciences. Psychology, sociology and ethnology can not be reduced to biology, chemistry and physics in a reductionist sense. The natural sciences are separated from the social sciences by multiple boundaries of emergence [4].
The resulting gap between the scientific disciplines invites us to consider diverse questions from their respective peripheries. Coming from one side, we can take the human perspective, conduct behavioral research, analyze needs, or do ethnological studies. Coming from the other side, we can explore fundamental scientific contexts, set up experiments and conduct series of measurements. These are two different perspectives on the world.
Here we see parallels to our idea of design and computational thinking described above, which also represents two perspectives for approaching various challenges. Design advocates the human side and puts the user at the center, while computational thinking is thinking like a machine and puts data at its heart. Two complementary approaches merge: intuition, creativity, emotionality, empathy and art on the one hand – stringency, analytics, rationality, precision, science on the other.
This comparison has limits and by no means intends to deny the stringency of the social sciences, just as it does not want to neglect the importance of intuition in the natural sciences. Rather, it is meant as a metaphor and inspiration for the reader, and may be understood – especially in case of disagreement – as an invitation to engage in conversation.
[1] Computational theory of mind
[2] Turing completeness
[3] Biological computing
[4] Emergence
Intervall was founded in 2019, 1000 days ago. Every year, we like to look back in time and take inspiration from the history of Design and Computation in a broader sense, which we then turn into a poster.
Through openness, one can learn new things,
though imperfections, one can achieve beauty,
through flaws, one can gain new understanding,
through reflection, one can gain insight from the past.
Through openness,
one can learn new things,
though imperfections,
one can achieve beauty,
through flaws,
one can gain new understanding,
through reflection,
one can gain insight from the past.
Taking and giving inspiration is the engine of human culture and drives us in our work every day. We draw inspiration from many sources, some of which may also inspire you ...
Striving for excellence within an ongoing learning process is at the heart of Intervall. We question the status quo to re-think how things might be in the future. Interdisciplinary exchange is the basis for an individual, personal development and the constant reconsideration of our own approaches.
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Software Engineering Lead
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Rust Developer
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Python Developer
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Backend Developer
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Frontend Developer
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Data Scientist
For any inquiries please contact Jonas Heuer () or Dr. Daniel Kärcher ().
Intervall Beratung GmbH
Rotebühlplatz 5
70178 Stuttgart
Germany
