We are a trust broker for collaborative AI, enabling federated learning across a consortium while keeping sensitive data on-premise.
German SMEs and "Hidden Champions" possess world-class domain expertise and high-quality proprietary data. However, they face a critical barrier: they lack the massive data volume required to train enterprise-grade AI models individually, yet they cannot pool data centrally due to strict GDPR regulations, trade secret concerns, and a lack of trust in competitors or cloud providers.
Datapool resolves this standoff. We 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—such as municipal utilities or regional manufacturers—to build collective intelligence that outperforms any individual effort, without ever exposing their sensitive assets.
Centralizing training data creates a "honeypot" for cyberattacks and massive legal liability.
Furthermore, moving petabytes of raw sensor data to the cloud is cost-prohibitive and slow. Federated Learning eliminates these bandwidth costs and security risks by design.
Federated Learning enables multiple entities to collaboratively train a shared model without sharing their raw data.
We observe your SOPs, document your WMS workflows, carrier portals, and exception handling.
Configure Datapool to handle claims processing, merchant inquiries, billing reconciliation, and more.
Datapool goes live in your communication channels - Slack, Teams, email, Jira, Salesforce.
Expand from one workflow to your entire back office. Claims, billing, onboarding, and beyond.
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.
Equipment manufacturers can collaboratively train anomaly detection models on sensor data from thousands of machines across different client sites.
By learning from rare failure patterns globally without sharing proprietary operational data, the collective model predicts breakdowns earlier and more accurately than any single manufacturer could achieve alone.
Regional utility providers (Stadtwerke) can unite to train load forecasting models on smart meter data.
By pooling insights on consumption patterns without exposing customer-specific usage details, the consortium optimizes grid stability and purchasing efficiency, outperforming isolated forecasts while strictly adhering to consumer privacy laws.