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Our Vision
We turn powerful ideas into intelligent, high-performance software.We build high-performance, secure software across web, mobile, and desktop. From concept to code, we blend user-centric design with scalable technology to deliver real value.
Our Services
Learn more(Our Services) Our expertise spans a wide range of cutting-edge technologies and platforms to deliver tailored solutions that meet the specific requirements of our clients.A Look at Our Work
Learn more(A Look at Our Work)Scips
Scips is a lightweight learning platform built for German schools that focuses on simplicity, classroom-first workflows, and strong privacy: guided onboarding for teachers and students, class‑based organisation that mirrors real school structures, built‑in tools like a document scanner and stylus correction for fast homework submission and feedback, and end‑to‑end encryption for sensitive student data with secure key transfer between devices.
Features- 🧭 Intuitive user interface
- 🚀 Guided onboarding for a fast start
- 📚 Easy homework collection
- 🖌️ Stylus-based correction for teachers
- 📷 Built-in document scanner
- 🔐 End-to-end encryption
- 👩🏫 Class and subject management
- 📣 Announcements for parents & students
- 📈 100% scalable for schools of any size
Research
This thesis presents the development of a voice assistant that enables intuitive, hands-free control of car seats through natural language. Developed in collaboration with Tratter S.R.L. for BMW, the system utilizes advanced AI models - including OpenAI's Whisper and GPT - to interpret complex voice commands and convert them into precise seat adjustments. The assistant enhances driver safety and comfort by minimizing distractions and is designed with a modular architecture that ensures flexibility and scalability. The innovations in speech recognition and language understanding also offer potential for applications beyond the automotive industry.
Read more(Development of a Voice Assistant to Control Car Seats using advanced AI models)This work presents a fully automated framework for software performance testing that utilizes agentic AI and large language models. A desktop application was developed that autonomously generates, executes, and analyzes performance tests by employing LangGraph agents, MCP servers, and the PPTAM framework. The system integrates with existing project artifacts such as codebases, GitHub issues, and API documentation to design realistic test scenarios and deliver actionable performance insights. Tested on real microservice applications, the tool demonstrated high accuracy, scalability, and minimal need for human intervention, offering a novel approach to performance testing in modern software development.
Read more(Automating Software Performance Tests using Agentic AI)