Who we are - what we do
About 9senses
9senses was founded by a group of AI veterans who want to provide solid services in an area where many offers are present, and where customers often lack the experience to decide about their validity. We are vendor-independent and able to clearly determine what AI can and cannot (or should not) do.
Our objective is to help businesses and society alike to adapt to Artificial Intelligence, with deep insights, and no dependency on any vendor.
Our Team
Meet a selection of our highly experienced team members. And: if you would like to become part of our growing practice, have a look at our job openings.
Juliette Schuster
- Human-Technology Interaction
- Governance
- Strategy
- Ethics
Alex Schmitz
More than 25 years of computer science, and 15+ years of Machine Learning make Alexander a solid systems architect and a ML wizard who is versed in almost all frameworks. He is equally experienced in Data Warehousing and Data Lakes, with solid data governance knowledge. read more…
Valentino Strebel
- Vision
- Human-Technology Interaction
- NLP
- AI Transformation
- Automation
Fatima Ahmedii
Fatima is a true expert in Machine Learning and image processing. She has deep knowledge in Python development and other programming languages (such as Java, JavaScript, C#). Her core competence is finding solid hybrid solutions between what AI can do and what requires solid coding. read more…
Alona Liuzniak
Alona is an expert in generative AI using natural language processing. With a strong machine learning and image processing background, she currently mostly focuses on delivering Chatbot solutions that follow Responsible and Explainable AI concepts. read more…
Johannes Kunz
With more than 20 years of experience in Artificial Intelligence topics, ranging from strategic to software and hardware design, Johannes brings more to the table than most experts. He has seen the complete development of AI over the past decades and knows what is possible, but also what isn’t. read more…
Sophia Wagner
AI Engineer with a research background in mathematics, specializing in LLMs, semantic search, and RAG systems. Passionate about turning complex information into practical AI solutions. read more…
Peter Gemeiner
Peter is an accomplished professional with a strong background in machine learning, data science, and software development. Holding a Ph.D. from the Vienna University of Technology, his expertise spans multiple domains, including artificial intelligence, cloud computing, and software development. read more…Our Offices
9senses has a global reach. We started in Switzerland and are slowly expanding our footprint. 2026 additions are: Germany, the UK, North America, and Oceania. And since offices are so 20th century, we have made our office map a bit more fun to look at. Play around, watch the planets move, check out the moon, or catch an office tile and click it.
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…Facility Management Process Digitalization
Transformation and standardization of international facility management processes through the introduction of scalable structures.
read more…ERP/CRM Product Development
Development and implementation of a modular ERP/CRM system to digitalize central business processes.
read more…Intelligent Data Retrieval Agent
Production-ready AI retrieval system using LLMs and semantic search to transform fragmented data into reliable, searchable knowledge.
read more…Visual Search Recommendations
eCommmerce plugin that enables searching for visually similar products, helping customers to find and compare multiple related items.
read more…Visual Assistance for Seniors
Visual Assistance App: enabling visual assistance for seniors by helping position determination using Computer Vision and Deep Learning.
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.
Supported the transformation and standardization of international facility management processes by designing scalable end-to-end process and service structures.
The project included gathering and harmonizing requirements across multiple country organizations, translating business needs into digital process and system solutions, and coordinating international rollouts including SIT, UAT, training, and change management.
Consistent process modeling and documentation using BPMN 2.0 and SAP Signavio helped establish sustainable governance, transparency, and operational efficiency.
SAP Signavio, BPMN 2.0, ERP Systems, Digital Workflow Platforms, Interface Integration, SIT/UAT, Requirements Management
- Computer Vision
- Automation
- Infrastructure Management
- Human-Technology Interaction
Led the development and implementation of a modular ERP/CRM system to digitalize central business processes.
The project included end-to-end product ownership, requirements analysis, prioritization, and scaling of the system, including mobile solutions and extensions.
Responsibilities also covered the management of cross-functional development teams, the establishment of testing, quality, and operations processes, and the introduction of ITIL-based change and incident structures. Governance, KPI, PMO, and documentation standards were developed to support sustainable product and project management, while product strategy and stakeholder alignment were managed at leadership level.
ERP/CRM Systems, Mobile Solutions, Agile Product Development, Requirements Management, UAT, ITIL, Change & Incident Management, KPI/PMO Structures, Stakeholder Management
- Computer Vision
- Automation
- Infrastructure Management
- Data Warehouse
- Human-Technology Interaction
Development of a production-ready retrieval agent for querying large, fragmented, and undocumented enterprise data repositories using large language models and semantic search. The solution transformed scattered legacy records into structured, searchable knowledge, enabling users to retrieve relevant information within seconds instead of manually searching across hundreds of documents.
The project combined retrieval-augmented generation (RAG), vector search, and modern LLM technologies to deliver reliable, context-aware information retrieval. Designed with a modular architecture, the system supports scalability, maintainability, and future extensions while ensuring robust retrieval quality across heterogeneous data sources.
AI Engineer
Python • LangChain • OpenAI • Qdrant • Semantic Search • RAG • Streamlit
For VisualSearch, the focus was on developing and deploying e-commerce plugins to enhance recommendation functionalities in web shops. The plugins were successfully launched in the store and happily adopted by customers, demonstrating their practical use in improving e-commerce experiences. Key elements included:
- computation of visual embeddings from appearances of e-commerce products
- building and maintaining a search index using these embeddings
- providing a cloud-based API for Shopware and Prestashop plugins
Peter acted as the project manager and key data scientist on this project, leveraging AWS services, Python, and deep learning frameworks like Keras. The project integrated cloud-based solutions using CloudFormation, Lambda, and Gateway for scalable and efficient deployment, using SQL and DynamoDB for data management.
For the Austrian Institute of Technology (as a part of the national research project LARAH), a prototype of an assistance system for visual indoor localization for disabled and elderly persons was developed. By leveraging Computer Vision and Deep Learning, innovative algorithms for position determination were implemented. The project resulted in a functional prototype, including two Android applications for real-time localization. Key elements included:
- visual recognition of persons using Deep-Learning models
- visual reconstruction and localization of indoor environments using Structure-from-Motion and Machine Learning algorithms
Peter acted as the project manager and key data scientist on this project, developed under Python using Deep Learning models like TensorFlow, custom-developed Structure-from-Motion software and custom camera calibration software. Additionally, as a part of the project, two Android apps were modified and integrated together onto the Robot Operating System on the mobile platform.
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.