(Translated with MistralAI without further editing)
In modern industrial research and development (R&D), success is increasingly determined by the intelligent integration of data, processes, and AI. How LabV addresses this as an integrative solution—bringing together fragmented data sources, creating structured knowledge, and optimizing R&D processes—is explained by Mr. Jouanique in this interview.
Dear Mr. Jouanique!
LabV is a platform for R&D. What specific functions does the platform offer, and how does it differ from other solutions on the market?
Our focus is on consolidating all relevant data from the R&D process into a single system: from material data and formulations to process parameters and measurement and test results. This is complemented by features for structured documentation of experiments and projects, as well as an integrated AI assistant.
The key point is that we do not see AI in isolation, but always in connection with a clean data foundation. Many companies work with fragmented data, such as Excel files or PDFs. As a result, a lot of time is lost in day-to-day work because information, while available, cannot be used efficiently. Our approach is therefore to first bring data, knowledge, and processes into a meaningful context. Intelligent functions can then be built on this foundation.
These include, for example:
We differentiate ourselves from other platforms in three main ways:
First, we consistently combine the data platform with AI. We do not believe in AI based on Excel spreadsheets, but in Material Intelligence built on structured data.
Second, our approach is practical and industry-oriented. We come from an industrial laboratory environment ourselves and develop the solution in close collaboration with our customers.
Third, data sovereignty, the protection of intellectual property, and a trustworthy infrastructure are central principles for us. That is why we place great emphasis on ensuring that our customers’ data is processed in accordance with European data protection standards and stored in an infrastructure that meets these requirements.
Many industrial companies work with established IT and data structures. How does LabV succeed in integrating into existing systems? And what experiences have you had so far in implementing the platform in companies?
Integration is a key success factor for us. Companies do not start from scratch; they already have existing testing equipment, data sources, ERP structures, specialized software, or historically grown data repositories. Our goal is not to ignore this reality, but to integrate existing systems and data in a way that makes them usable together.
Technically, we work with an open, universal interface logic. This means we connect data from different sources—such as laboratory equipment, existing databases, ERP systems, or document-based repositories. The aim is to turn distributed information into a usable, structured knowledge base.
It is also important how we approach the implementation of the platform: We do not introduce LabV as a “pure IT project,” but in cooperation with the R&D department. This means we understand the actual workflows and design the implementation so that it works in everyday laboratory and development environments.
Implementation is particularly successful where companies achieve practical benefits early on: for example, through significantly better accessibility of historical data, fewer duplicate tests, or more efficient project work. From many conversations with customers, we know that the greatest added value initially lies not in futuristic AI scenarios, but in making existing knowledge usable across projects.
The use of AI raises questions about trust, transparency, and data security. How do you address these concerns?
We take these concerns very seriously. In the industrial environment, the use of artificial intelligence should be critically examined. After all, it involves sensitive data, intellectual property, product quality, and, in many cases, safety-critical decisions. We therefore do not believe in AI as an end in itself, but as a tool for better decision-making.
For us, AI is an assistance system: it supports experts but does not replace them. We see AI as a kind of co-engineer or co-chemist. The system makes information more accessible, helps identify patterns, and prepares decisions. However, the responsibility remains with the developer.
What role do strategic partnerships play in the further development of LabV, and what opportunities do you see for collaborations with industry, research, or investors?
We are open to partnerships if they make a clear contribution to advancing Material Intelligence in industrial R&D. Partnerships can arise for us at different levels. On the technological side, for example, in infrastructure, data integration, or individual AI components. Industrial collaborations are also interesting, where new applications for our software platform are developed together. Another area is research projects with universities, for example in Explainable AI.
How do you see the role of data and AI in industrial research and development changing in the coming years, and what should companies pay particular attention to today?
From our perspective, the discussion will shift in the coming years. Away from the question of whether AI is relevant in industrial R&D, toward how it can be used meaningfully, safely, and economically.
50% of AI pilot projects fail. That is why the first and most important step is often less spectacular than many expect: Companies must first organize their knowledge—their data and processes—into a structured form that enables meaningful use of AI in the first place. Without this foundation, AI remains piecemeal. If these prerequisites are met, however, AI can be a very effective tool for increasing efficiency and securing long-term competitiveness.
We are working to further develop AI functions, such as our AI assistant, from pure assistance to optimization. New technologies enable companies of all sizes to benefit from AI-supported predictions. This new way of working in R&D is already a reality and is currently spreading across all industrial sectors in Germany.
About Charles Jouanique
Charles Jouanique has over 15 years of experience in the industrial sector. In addition to developing new digital business models, he is also an entrepreneur and a multiple-time business angel for startups. He is a co-founder of LabV, a software company within the NETZSCH Group, where he heads the Sales & Marketing division.
More About LabV
At LabV, the team is currently working on several projects, including the implementation of a multi-agent architecture for their platform. The goal is to provide even stronger support for developers in research and development by enhancing data analysis and experiment planning, ultimately shortening development cycles. A core principle guides their approach: Decisions always remain with the human expert, while AI serves solely as a supportive tool in the development process.
Additionally, LabV regularly hosts webinars where they discuss current challenges in industrial R&D and present potential solutions. They also offer live demos, giving interested parties insights into how the platform works and showcasing the latest developments.
