Protegrity specializes in data protection and privacy-preserving AI infrastructure, offering tokenization and encryption capabilities that enable secure AI model training on sensitive data without exposing raw information.
Core
1 full, 2 partial of 4
Vector Search
Similarity search over high-dimensional embeddings. ANN algorithms (HNSW, IVF, DiskANN). Query latency, recall accuracy,...
Partial
Hybrid Search
Combine vector similarity with keyword/BM25 search in a single query. Fusion algorithms for optimal retrieval.
None
Metadata Filtering
Filter vector search results by structured metadata (tags, dates, categories). Pre-filtering vs post-filtering approache...
Partial
Multi-tenancy
Isolate data between tenants/users/orgs within a single deployment. Namespace, collection, or partition-based isolation.
Full
Operations
3 full, 1 partial of 4
Scale & Performance
Handle billions of vectors. Horizontal scaling, sharding, replication. Benchmark performance at production volumes.
Full
Real-time Ingestion
Stream new vectors in real-time without rebuilding indexes. Support for upserts, deletes, and incremental updates.
Partial
Managed Cloud
Fully managed SaaS offering with auto-scaling, backups, and zero-ops. Multi-region and cloud provider support.
Full
Self-hosted / OSS
Deploy on your own infrastructure. Open-source availability, Docker/K8s deployment, data residency compliance.
Full
Ecosystem
0 full, 3 partial of 3
RAG Framework Integration
Native integrations with LangChain, LlamaIndex, Haystack, and other RAG orchestration frameworks. Connectors and plugins...
Partial
Embedding Management
Built-in embedding generation, model management, and automatic re-embedding when models change. Embedding versioning.
Partial
Multimodal Support
Store and search across text, image, audio, and video embeddings. Cross-modal retrieval capabilities.