IssunDB – a new embedded graph database with vector and text search
Sparse matrix graph operations with MCP server integration for AI agents.
open source version xtrace sdk
Homomorphic encryption on vector search when Pinecone and Qdrant require plaintext on server.
Enterprises doing RAG on sensitive data (medical, legal, financial)
Pinecone · Weaviate · Qdrant
We built a private vector database from the ground up that performs similarity search on encrypted vectors. The server never sees your plaintext embeddings or documents.
The problem we’re trying to solve: every vector DB today requires plaintext on the server. If you're doing RAG over sensitive data (medical, legal, financial), your embeddings — which researchers have shown can be inverted to recover original text — sit exposed on someone else's infrastructure.
XTrace encrypts everything on your machine first. Vectors get Paillier homomorphic encryption, text gets AES-256. The server stores and searches only ciphertexts. Your keys never leave your environment.
We just open-sourced the SDK (Apache 2.0). You can run the encryption verification tests offline without even creating an account.
Trade-offs we're upfront about: there's latency overhead from the encryption operations. We're actively optimizing this. The free tier is rate-limited but fully functional.
Happy to answer questions about the crypto approach, architecture decisions, or anything else.
Sparse matrix graph operations with MCP server integration for AI agents.
Encrypted semantic search via modular arithmetic—98% quality, 8x faster than homomorphic encryption.
Git-style version control for vectors when Chroma and Qdrant can't time-travel.
Graph-vector-FTS in one database, but Weaviate and Neo4j already offer hybrid search.
Encrypted widget queue solves the UX vs security tension for offline-first health apps.
Using a single-file .pardus format with CREATE/INSERT/SELECT + SIMILARITY queries gives a very familiar developer UX for embedding storage. The combination of graph-based ANN, full transactions, thread-safety, and zero external dependencies is an uncommon and useful engineering combo for local-first AI work; it would win more attention with benchmark comparisons and richer ecosystem integrations (connectors/clients).