FROM THE TEAM
TopK Team
Writing on search, retrieval, and building TopK.
Introducing topk-embed-v1: Frontier retrieval at the lowest cost
Open-source multimodal multi-vector embedding models, with a production model offered inside TopK for $0.05/1M tokens. Frontier document retrieval quality at the lowest cost with compact multi-vector representations.
High-Quality Search, Out of the Box
TopK's semantic_index annotation brings state-of-the-art multi-vector retrieval to production — no embedding pipeline, no separate vector store, no reranking service.
Scaling Without Complexity: Billion-Scale Hybrid Search with TopK
TopK enables billion-scale hybrid search with <100ms latency, fast indexing, and high-quality results.
We Raised $5.5 Million to build an AI-Native Search Engine for Enterprises
TopK has raised $5.5 million in seed funding to accelerate the development of its unified, AI-native search platform, enabling organizations to seamlessly combine vector, keyword, and custom ranking in a single system. The round was led by top-tier investors, fueling our mission to redefine search for the AI era.