Bloomreach Discovery

A search architecture that combines dense vector embeddings (semantic search) with sparse representations like BM25 (lexical search) to achieve better overall search quality. The industry standard approach for production RAG systems in 2026.

A re-ranking tool provided by Cohere, which can be integrated into LLM applications via frameworks like LangChain to improve the relevance and order of retrieved documents from search systems, including those utilizing vector databases.

Qdrant is a production-ready vector similarity search engine with advanced filtering, payload storage, and Rust-based performance. It uses custom HNSW for fast queries, supports dynamic collections, and offers both self-hosted and cloud options. Great for real-time apps like semantic search and RAG; faster than Weaviate in pure vector ops but less feature-rich in ML modules.

Spotify's lightweight C++ library for approximate nearest neighbor search using random projection trees, known for its simplicity, read-only file-based indexes, and use in recommendation systems.

Enterprise AI search and discovery platform combining keyword, semantic, and personalized retrieval with analytics, permissions, and governance. Abstracts vector database complexity while adding enterprise-ready features for large knowledge bases and commerce catalogs.

Experience optimization suite (Mastercard) providing recommendations, targeting, and personalization without requiring vector database infrastructure. Operates at the application level with turnkey personalization and experimentation capabilities for marketing and growth teams.