As personal AI agents like OpenClaw become more powerful by leveraging intimate user data, privacy has emerged as a fundamental bottleneck.

We’re releasing HEVEC, a vector database built on homomorphic encryption, enabling end-to-end privacy with real-time search at scale.

HEVEC is designed as a drop-in alternative to plaintext vector databases and supports real-time encrypted search at scale (1M vectors in ~187 ms).

Key points: - A secure, drop-in alternative to plaintext vector databases - End-to-end homomorphic encryption for both data and queries - Real-time encrypted search at scale (1M vectors in 187 ms)

As personal AI agents become deeply personalized, data ownership must belong to users.

HEVEC enforces this through privacy-by-design architecture.

We’d appreciate feedback from the AI, systems, and privacy communities.