About us
Who we are
01 / Management
Daniel Tremer and Simon Deussen
We founded Control-F because data from the plant has to add up. We build on it ourselves — from the first measurement to the key figure in running operation.
02 / The team
And the whole team
Our experts in data, software and plant engineering bring experience from industry and from international research institutes into the work they do for you every day. We are not a hype shop but a small team of experts who love engineering and work together as equals.
-
We are a data boutique from Konstanz working for the whole DACH region. Small enough to be agile, and experienced enough to know what works.
Our experts in data, software and plant engineering bring experience from respected industrial companies and international research institutes into the work they do for you every day.
We are not a hype shop but a small team of experts who love engineering and work together as equals.
-
We manage solutions, not only projects.
We build code instead of PowerPoints.
We have respect for every person and no fear of complex problems.
-
We work remote-first and believe in flexibility and ownership rather than micromanagement. We shape our processes around what the project asks for, and we work with our heads instead of our elbows. Everyone on the team can genuinely move something. Every good idea counts.
Our team of data engineers and AI specialists commands the architecture of complex data systems. We believe in the beauty of numbers and of efficient processes, and we make sure that standstill is no longer an option for you.
Our values
6-
Clarity and a focus on results
We deliver clarity and measurable results, without detours, without distractions. Success for us means impact in the business, not effort in the process.
-
Ownership and adaptability
We rely on ownership and flexibility. Instead of rigid rules we trust the initiative and adaptability of our team.
-
Technical command and critical AI
We command our tools, and we question them. We use AI where it creates real value: critically examined, rather than blindly trusted.
-
Pragmatism and speed
We aim for elegant, fast solutions and avoid unnecessary complexity. What counts is the effect, not the show.
-
Transparency and open communication
We communicate openly and honestly, internally and with our clients. Clear words build trust and prevent misunderstandings.
-
Learning and development
We are convinced that learning is never finished. We keep developing, we keep training, and we actively look for new perspectives, as a team and as individuals.
The team
-
Simon Deussen
Founder & Managing Partner
Simon founded control-f and runs it as Managing Partner and Lead Data Architect. A B.Sc. in media informatics at the Hochschule der Medien Stuttgart, with a bachelor thesis written at Daimler TSS on converting screen designs into HTML and CSS with deep learning, was followed by three years at inovex, first as a Machine Learning Engineer and then, alongside a master's in autonomous systems at the Hochschule Bonn-Rhein-Sieg, in data management and analytics. That is the ground control-f is built on: turning disordered company data into decisions that can be trusted, Predictive Maintenance, which catches plant and machine failures before they happen, and data pipelines, which still run two years after handover. Pragmatic solutions rather than AI that stays in the slide deck.
-
Daniel Tremer
Managing Partner
Daniel joined control-f as Managing Partner, bringing over a decade of experience in data science and machine learning to the company's work with industrial enterprise clients. His background includes several years in the automotive industry across research and development, IT, and infrastructure roles, as well as running his own AI and data analytics practice before joining control-f. His interests span the full spectrum of applied AI, from large language models and generative systems to production data engineering, and he leads control-f's work building practical, cutting-edge AI solutions for enterprise clients.
-
Piet Brömmel
Data Engineer
Piet has a background in machine learning and data engineering. He holds a master's in computer science from TU Dortmund and previously worked as a data scientist at Fraunhofer, working with robots and computer vision. His interests span software development, LLMs and everything around them, algorithmic art, and hiking.
-
Henry Beiker
Data Engineer
Henry brings software development and AI testing to control-f's data engineering work. A B.Sc. and then a master's in computer science at the Humboldt-Universität zu Berlin ran alongside three and a half years of development work. At Howto Health that has meant leading software and data engineering projects, APIs for data processing and integration and an in-house task planning tool built against requirements taken straight from clients. In a research team at Fraunhofer FOKUS it has meant a simulation environment in Unreal Engine for testing models for object detection, a test framework for autonomous robots on train tracks, and the evaluation workflows around both. That is the same question the master's thesis takes up, improving object detection by simulating the scenarios that provoke false negatives. The stack is Python, SQL and JavaScript with Django, FastAPI and Flask, React in front and PostgreSQL underneath.
-
Robin Marzucca
Data Engineer
Robin brings a background in theoretical particle physics to control-f's data engineering work. He completed his PhD in quantum field theory and spent several years as a postdoctoral researcher at the University of Zurich's Physik-Institut, working on scattering amplitudes and precision calculations in quantum field theory, alongside earlier research stints at UCLouvain and the Institute for Particle Physics Phenomenology at Durham University.
-
Marie Ernø-Møller
Data Engineer
Marie brings a background in theoretical physics to control-f's data engineering work. An MSc in quantum physics from the University of Copenhagen, with a thesis on double-copy amplitudes and their applications in general relativity, led to gravitational-wave research at Humboldt-Universität zu Berlin, where quantum field theory and effective field theory methods were applied to black hole mergers. That research training now sits alongside a deliberate move into machine learning and full-stack engineering: an MLOps bootcamp, containerised data pipelines built with Airflow, MLflow, Grafana and Prometheus, backend work in FastAPI and PostgreSQL, and a self-built mobile application whose emphasis is relational schema design and time-series metrics.
-
Birk Burghardt
Data Engineer
Birk brings a computer science background and several years of applied data science to control-f's data engineering work. After image classification of heraldic material for the Digital History Department at Humboldt University, the path led to a Data Science Expert role at Lyreco Deutschland. There the work covered data models and reporting with Power BI, ETL processes and workflow automation in Python.
