We are a trust broker for collaborative AI, enabling machine learning across a consortium while keeping sensitive data on-premise.
German SMEs possess world-class domain expertise and high-quality proprietary data. However, they face a critical barrier: The data needed to train competitive AI models is distributed across multiple organizations, yet sharing that data is neither legally permissible due to strict regulatory frameworks (e.g., GDPR) nor commercially desirable (e.g., risks of exposing trade secrets or lack of trust in external providers).
We resolve this standoff and operate as a neutral "Trust Broker," orchestrating Federated Learning networks where the model travels to the data, not the other way around. This allows non-competing cohorts to build collective intelligence that outperforms any individual effort, without ever exposing their sensitive assets.
Traditional ML requires data centralization. Moving raw data creates massive compliance friction and logistical bottlenecks as well as a high-risk honeypot for cyberattacks.
This concentration of sensitive info is often a non-starter that prevents the data scale necessary for competitive AI, leaving organizations structurally excluded from collaborative innovation.
Federated Learning enables multiple entities to collaboratively train a shared model without revealing their raw data. The training process repeats 4 steps after the initialization process.
The current model parameters are distributed to selected authorized clients in the consortium.
Each client trains the model locally within their secure infrastructure using their private data.
The updated parameters, representing what was learned locally, are sent back to the central server.
The server collects updates from participants and aggregates them into a new, smarter global model.
Raw data never crosses organizational boundaries. Training happens locally, ensuring sensitive information remains strictly on-premise.
Model updates are orders of magnitude smaller than raw datasets, minimizing bandwidth requirements and storage overhead.
Fully satisfy legal requirements (GDPR) by keeping full control and ownership of your data at all times.
Support continuous improvements and flexible consortium expansion. As new partners join, the model gets smarter.
Decentralization lowers the impact of potential security breaches. There is no central "honeypot" of data to attack.
Federated Learning is not limited to specific applications or sectors and creates value wherever data is sensitive, distributed, and operationally critical. It acts as an enabler for organizations previously excluded from AI adoption due to insufficient standalone data. The technology is particularly viable for scenarios where data sovereignty regulation or competitive secrecy prevents the centralization required for traditional AI.
Modern production environments generate large volumes of machine, process, and quality data across sites and suppliers. Federated Learning helps manufacturers use this distributed data to improve predictive maintenance, defect detection, process stability, and planning accuracy without exposing trade secrets or production know how. This leads to higher uptime, better product quality, lower waste, and faster continuous improvement across plants.
Energy systems depend on reliable forecasting, resilient operations, and strict data governance across grids, assets, and regional operators. Federated Learning enables utilities to improve demand forecasting, asset health monitoring, outage prevention, and grid optimization by learning from broader system patterns while keeping infrastructure and customer data protected. This supports higher reliability, lower operating costs, better resource efficiency, and more secure cross-organization collaboration.