From Hours to Seconds: How AI Makes Decades of Knowledge Accessible at Stadler Rail
Genow, Stadler Rail and Data Migration International show how a specialized AI agent connects historical and current data in the right business context.
Key takeaways
At Digital Lounge 2026 in Kreuzlingen, Genow, Data Migration International and Stadler Rail presented a specialized AI agent that brings historical and current company data together in the right context.
- The use case: answering service requests about trains that have been in operation for decades, without spending hours searching across systems.
- The challenge: the relevant knowledge is spread across systems, documents and historical data.
- The solution: a Genow agent that links structured and unstructured knowledge and understands how it relates.
- The data foundation: the JiVS platform by Data Migration International keeps decades-old data accessible and preserves its business context.
- The result: knowledge that used to require hours of manual work is available in seconds, with traceable sources.
- The stage: the moderated panel “Turning Your Decades of Data into Immediate Operational Value Powered by AI” at Digital Lounge @Lakeside 2026, 7 and 8 August 2026, Castle Seeburg and Fischerhaus in Kreuzlingen.
Trains are highly complex systems. They are made up of countless parts, materials and technical components, and they often stay in service for several decades. The knowledge Stadler Rail has built up around customer projects, suppliers, products and their long-term operation is correspondingly broad and valuable.
“The value of AI agents does not come from finding information. It comes from understanding its context.”
Dr. Sara Jourdan, Co-Founder & CEO of Genow
Making that knowledge usable for a specific service request is the hard part: the relevant information sits in different systems, documents and historical records. A single request therefore often means searching numerous sources and connecting them to each other. A process that can take several hours.
Finding information is not enough
Together with Stadler Rail and Data Migration International (DMI), Genow developed a specialized AI agent that intelligently links structured and unstructured knowledge.
This is about more than locating the right documents or individual facts. The agent also understands how they relate: which customer project does a part belong to? Which suppliers, materials or technical specifications are relevant? And which historical information has to be taken into account to answer a service request reliably?
Only that context turns a conventional search into a dependable basis for daily work. This is exactly what the Genow Context Engine is built for: it connects structured data from ERP and CRM systems with unstructured sources such as data sheets, manuals and project documentation, and interprets them in the right business context.
Historical data becomes part of active knowledge
The JiVS platform by Data Migration International plays a decisive role here. It keeps even decades of historical data accessible and preserves its business context.
The Genow agent navigates this data landscape, brings relevant information from different sources together and presents the results in a structured form. The underlying sources stay traceable, so users can verify the answers and reuse them directly.
Knowledge that used to be available only through significant manual effort is now accessible within seconds.
A joint presentation at Digital Lounge 2026
The outcome of the collaboration was presented at Digital Lounge @Lakeside 2026, the fifth edition of the industry gathering hosted by Data Migration International. It took place on 7 and 8 August 2026 at Castle Seeburg and Fischerhaus in Kreuzlingen, Switzerland, in the year of DMI’s 30th anniversary and with more than 300 CIOs and executives on site.
The joint use case was the subject of the moderated panel “Turning Your Decades of Data into Immediate Operational Value Powered by AI”. On stage, Dr. Sara Jourdan, Co-Founder and CEO of Genow, presented together with Thomas Failer, CEO and Founder of Data Migration International, Michael Hutab, CIO of Stadler Rail, and Stefanie Egenhofer from Group AI Strategy & Governance at Stadler Rail.
The project shows what becomes possible when industrial companies, technology partners and startups combine their respective strengths: deep domain expertise, a solid data foundation, and specialized AI that does not just find information but understands it in its business context.
Our thanks go to everyone involved at Stadler Rail and Data Migration International, and to the entire DMI team, for a successful collaboration and a great Digital Lounge 2026.
See the same use case on your own data
If service requests in your company also run through several systems and decades of history, we are happy to show how a specialized agent resolves that. In a 30-minute demo we start from one of your real requests.
For the framework industrial companies expect, see our page on security and compliance. The integrations overview shows which systems we connect to.
FAQ about the project with Stadler Rail and DMI
When and where did Digital Lounge 2026 take place?
Digital Lounge @Lakeside 2026 took place on 7 and 8 August 2026 at Castle Seeburg and Fischerhaus in Kreuzlingen, Switzerland. It is hosted by Data Migration International. This was the fifth edition, in the year of the company's 30th anniversary, with more than 300 CIOs and executives attending. More on the Digital Lounge website.
What was presented at Digital Lounge 2026?
A specialized AI agent that Genow developed together with Stadler Rail and Data Migration International. It brings historical and current company data together to answer service requests about trains, some of which have been in operation for decades. It was presented in the moderated panel "Turning Your Decades of Data into Immediate Operational Value Powered by AI".
Which problem does the AI agent solve at Stadler Rail?
The knowledge needed for a service request is spread across different systems, documents and historical records. Compiling it manually can take several hours. The agent connects those sources and delivers the result within seconds.
How is the agent different from a conventional search?
It does not just find documents, it understands how they relate: which customer project a part belongs to, which suppliers, materials and specifications are relevant, and which historical information has to be taken into account. That business context comes from the Genow Context Engine.
What role does DMI's JiVS platform play?
JiVS keeps decades-old historical data accessible while preserving its business context. It provides the data foundation the Genow agent navigates, instead of leaving historical records as an archive with no connection to day-to-day work.
Are the agent's answers verifiable?
Yes. The underlying sources stay traceable. Users can see where a statement comes from, check it, and reuse the result directly.
Can the use case be transferred to other industrial companies?
Yes. Wherever products stay in operation for a long time and the related knowledge has accumulated across systems over the years, the starting point is the same. In a demo we walk through the process using one of your own requests.
