- Introduces a novel architecture to keep confidential inference data secure through zero-trust design.
- Keeps vector embeddings end-to-end encrypted throughout their lifecycle (including at search time).
- Works with DBs such as Postgres and Redis, transforming traditional DBs into Confidential Vector DBs.
- Exposes a familiar API, making it easy to integrate with existing AI workflows.
- Provides high-performance indexing and retrieval which can be GPU-accelerated with CUDA.
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