Scalytics Connect provides a federated data platform that enables secure, decentralized access to distributed data sources while ensuring compliance with regulations like GDPR and HIPAA. By minimizing data transfer costs and eliminating complex ETL processes, it allows organizations to achieve real-time insights and accelerate AI development across various environments.
Funding
$15K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations face challenges in leveraging distributed data sources for AI development due to data transfer costs, complex ETL processes, and the need to comply with data privacy regulations like GDPR and HIPAA. Integrating data from cloud, on-premises, and legacy systems often creates silos, hindering the development of transparent and scalable AI solutions.
Solution
Scalytics Connect provides a federated data platform that enables secure and decentralized access to distributed data sources, streamlining AI development while ensuring compliance. The platform minimizes data transfer by processing data locally, reducing cloud expenses and enabling real-time insights. It supports hybrid deployments across cloud, on-premises, and air-gapped environments, integrating with existing IT systems. Scalytics Connect facilitates the development of secure, explainable AI by unifying data access and providing tools for GPU orchestration and workload distribution.
Target Audience
The primary customers are enterprises in industries such as finance, government, healthcare, and legal that require secure, scalable, and compliant AI infrastructure.
Features
- Federated learning at its core, enabling secure, explainable, decentralized ML + AI
- Support for HIPAA, SOC 2, CJIS, and state-level privacy laws
- Deployment options on AWS, Azure, GCP (including GovCloud), on-premises, and air-gapped environments
- Full GPU orchestration for optimizing GPU resources across infrastructure
- Kafka-native MCP, Digital Twins, and private semantic search
- Integration with data warehouses and data lakes, eliminating ETL processes
- Built-in federation features to ensure compliance with data regulations