Federated data science platform for privacy-preserving analytics

Devtron, founded in 2020, provides technology for performing collaborative machine learning across decentralized datasets. Its platform enables organizations to train AI models

without moving raw data from its original storage location, supporting environments where privacy regulations, security policies, or operational constraints prevent centralization. The system facilitates federated analytics across distributed cloud infrastructures, business units, or partner organizations by allowing each party to contribute to model training while retaining local control of sensitive data.

Devtron’s approach applies cryptographic techniques to preserve confidentiality during model training and to generate insights from siloed datasets without exposing underlying records. This architecture is used for applications such as threat detection, operational analytics, and hybrid infrastructure monitoring. By reducing the need for data aggregation, Devtron helps organizations minimize attack surface exposure and maintain compliance with data handling requirements while still enabling multi-party AI development.

Total Funding is reportedly in excess of $28 million with a $12 million Series A led by Tiger Global with participation from FinTech Collective, Afore Capital, and Essence Venture Capital.