Johannes Kunz
Core Competences
With 30 years of experience in management roles on C-Level and management consulting (as a partner in global firms and a freelancer), Johannes has the necessary background to fully understand your business. Additionally, information technology for him was always a key business enabler, so his work always included the questions: “How can IT be part of making our business better?” and: “How does IT offer us new business opportunities?“
It is no surprise that he has been at the forefront of helping or creating IT driven businesses during those years, ranging from SaaS services to business models that were only possible with IT at the core. And it is equally no surprise that he was using elements of Artificial Intelligence way before the mainstream was getting involved.
Johannes loves technology, but not for technology’s sake. The combination of business background and IT curiosity gives him the unique ability to help you answer the above two questions.
But it doesn’t stop there. Having been in startup situations regularly over the course of his career, he has never shied away from doing the hard work of actually creating solutions, from defining the architecture and developing and testing code himself in most common languages. He is also familiar with most key frameworks currently used in machine learning and with most large language models.
Johannes holds Master’s degrees in Business IT and Law from the University of Zurich, and a PhD in Economics from the University of St. Gallen. He has worked on four continents and is fluently trilingual.
Featured Projects
RAG-driven Legal Chatbot
A small-to-medium language model reliably answering German legal questions based on a strong RAG pipeline.
read more…Generative AI Audit Framework
Development of a structured Generative AI audit framework. This included establishing a methodology for Level 1 and Level 2 GenAI audits
read more…Electrical Switch Monitor
Public transportation: using AI-driven vision to monitor old-fashioned electrical relays and also to evaluate potential failures for predictive maintenance.
read more…Hydropower Plant Operations
Create a control and monitoring solution for all plant operations, including predictive maintenance logic and intrusion monitoring.
read more…Customer Interaction Analysis
SaaS project platform: the objective was to evaluate dialogue quality using an AI model to ensure timely intervention and customer care.
read more…Motion-sensitive wearables
Wearable fabric-based devices with embedded microcontrollers and sensitivity for motion, heartbeat, body temperature and sweat detection.
read more…IT Operations Automation
Financial Services: develop strategy and business cases to introduce automation and AI into the IT operations environment.
read more…An AI-supported RAG-Chatbot developed for legal and administrative workflows. Designed to support caseworkers in navigating complex regulations - currently focused on German Social Welfare - the system provides fast, contextual access to relevant legal information and assists in decision-making for applications and case management. The modular architecture allows seamless expansion into additional legal domains and regulatory frameworks.
Development of a multidimensional Blackbox Chatbot Audit framework for evaluating chatbot user experience and business value in customer service environments. The audit methodology combines structured use-case testing with qualitative and quantitative evaluation dimensions, including answer quality, response speed, dialogue quality, and user interface assessment.
The framework also incorporates hallucination testing and edge-case analysis to assess robustness and real-world usability. The project included extensive market and user-frustration research, methodology development, pilot implementation, and iterative testing and retesting phases. The resulting audit framework is used to evaluate chatbot performance, identify optimization potential, and assess user retention likelihood and overall business impact.
For a public transportation network, the objective was to optimize the monitoring of their legacy electrical switchboards. These decade-old items that are often located in very remote areas are prone to failures and tracking of errors was not possible. The objective was to enable real-time tracking and the recognition of upcoming failures from changed switching behavior. The key elements were:
- Development of specific hardware configuration with custom housings (3D printed) to mount instead of regular switchboard covers;
- Camera control and initial image generation on Raspberry Pi integrated in housing;
- Initial scan of switch layout and labels;
- Identification of switching operations and registration of new positions;
- Identification of irregular switching patterns (delays, other irregularities) to indicate upcoming failures for predictive maintenance;
- Update of central database and cloud solution with last state and observed switching patterns;
The solution was implemented using Python on Raspberry Pi devices, backbone and cloud processing were done using a LAMP stack, with PyTorch, TensorFlow and OpenCV.
Create an integrated monitoring and surveillance solution for small-scale hydropower plants in remote locations. The solution included a full range of required settings:
- real-time monitoring and logging of operations
- failure detection and automated
- predictive maintenance logic to identify early failure
- camera-based intrusion and irregularity detection
The solution was a hybrid solution using Siemens PLCs, combined with small edge computing elements (Raspberry Pi and Arduino). All primary logic (particularly shutdown and load adjustment) was local, but key decisions and aggregations were executed online based on regular data transmission to a cloud-based management and operations suite.
For a project management SaaS solution where customers were matched with freelancers, a custom AI solution was established with the purpose to improve experiences for all parties. Key purposes were to create an early warning system to help customer service intervene in case of issues:
- Identification of unusual patterns (delays indicating inaction, intense exchanges);
- Flagging of language transgressions on both sides (use of inappropriate language, aggression);
- Matching of final ratings with evaluation of flow and dialogue quality to foster a more honest rating culture;
- Language style matching to improve future matching of freelancers to clients;
The solution was implemented using Python on a LAMP stack, with self-developed machine learning libraries.
Arduino-based, with edge ml functionality and periodic link via BLE to connected phone. Machine learning algorithms on cloud detecting various health states and delivering alerts to phone or web app. All logic woven into a cotton fabric wristband.
The objective of this project was to identify approaches on integration automation approaches and AI to more stably and reliably document and manage a highly complex IT environment with systems ranging from legacy mainframes to local server farms to cloud services. The objective was a massive simplification of maintaining up-to-date system views, status views and predictive error management.