Organizations using embedding-retrieval for semantic search, vector embeddings, and retrieval-augmented generation (RAG)
Explore organizations tagged with embedding-retrieval and discover how they implement vector embeddings, dense passage retrieval (DPR), semantic search, and retrieval-augmented generation (RAG) to power search, recommendations, and knowledge retrieval. This curated list highlights production deployments across embedding models (SBERT, OpenAI, Cohere), vector databases (FAISS, Milvus, Pinecone), and scalable similarity-search architectures, with actionable insights on latency, recall, and integration patterns. Use the filtering UI to narrow results by tech stack, embedding model, vector DB, industry, or deployment scale; compare case studies, request demos, or export findings to inform vendor evaluation and architecture decisions. Start exploring these organizations to find best practices, implementation templates, and partners for building robust semantic search and RAG solutions.
