Blog.

Context is a search problem. Without the right context, even the best models fail. This post describes why dense embedding based RAG is broken for agents and how multi-vector (late interaction) retrieval fixes it.

TopK now implements the Postgres wire protocol, so any Postgres client can run semantic search, hybrid search, and filtered retrieval as ordinary SQL.

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.

TopK introduces SMVE, a fast and scalable approach to late-interaction retrieval based on sparse random projections.

This post evaluates how managed vector databases handle large-scale, production-like workloads—including millions of vectors, concurrent querying, real-world filtering, and continuous ingestion—using reproducible benchmarks across several leading providers.

Listen to our founder Marek Galovic discuss the challenges and solutions in building a search database from the ground up, covering everything from vector search to hybrid retrieval and the architectural decisions that shaped TopK.

A case study on how TopK hybrid retrieval outperforms traditional RRF-based methods, improving nDCG@10 by an average of 4.5% (and up to 7.8% on some datasets) by leveraging score-aware ranking, dynamic weighting, and efficient result merging.

TopK enables billion-scale hybrid search with <100ms latency, fast indexing, and high-quality results.

Optimizing binary vector search using ARM NEON instructions to achieve 350GB/s throughput.

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.

At Data Council 2025, we introduced TopK, a unified cloud-native query engine that combines vector search, keyword matching, and flexible scoring in a single system. We demonstrated how TopK overcomes the limitations of traditional vector databases by supporting hybrid queries with custom ranking logic while maintaining high performance.

We’ve spent the last three years building the most popular vector database on the market. In that time we realized that a database built around vectors as a primary key is simply the wrong abstraction, creating an unnecessary obstacle for users in production.