Flexible foundation for search over any data. Built on object storage for 10x lower cost and unlimited scale.
TopK vertically integrates retrieval, inference, and document processing to support search over structured and unstructured data in one platform.
Hybrid search, multi-vector retrieval, and custom scoring in one query. All the tools you need to ship state-of-the art search.
Sub-100ms latency at billion scale enabled by our storage format and query engine optimized for search.
All data is stored on object storage with scalable compute layer serving your requests. Use only what you need.
Pay only what you use.
Built from the ground up with tools to deliver high-quality results in any domain.
Up to80%
higher recall
1B+ docs
per partition
17ms p99
query latency on 10M
70 MB/s
writes per partition
Combine dense & sparse vectors, late interaction, keywords, filters, and custom scoring in a single query to optimize relevance for your use case.
Learn about architectureHigh-recall vector search across dense and sparse vector representations.
Keyword-based filtering with BM25 scoring.
Native late interaction retrieval over multi-vector embeddings.
Combine multiple ranking signals in a single query to optimize relevance.
Native SDKs for Python, JavaScript, Rust, and a SQL compatibility layer for the tools you already use.
Get started with:
SELECT _id, title,-- Semantic similaritysemantic_similarity(content, 'NVDA data center revenue in Q4 2025') AS semantic_score,-- Multi-vector retrievalmulti_vector_distance(page_embedding, '[[0.97, 0.17, ...], [0.14, 0.99, ...]]'::f32_matrix) AS visual_scoreFROM earnings_reports-- Keyword filterWHERE (match('nvidia') OR match('nvda'))-- Metadata filteringAND fiscal_year = 2025-- Custom scoringORDER BY (semantic_score * 0.7 + visual_score * 0.3) * source_quality DESCLIMIT 10;
Scale to billions of documents per partition with predictable latency and cost.
p99 hot query latency, 1M documents, 8 concurrent clients.
Turn your private documents into grounded knowledge for agents.
File Search ingests complex unstructured documents and provides grounded answers with precise citations.
Read the docs▋
NVIDIA grew 265% YoY to $22.1B in Q4 FY2024, far outpacing AMD's 24% growth to $7.7B:
Built on our multi-vector retrieval stack, File Search delivers the most accurate answers across multiple correctness-sensitive domains.
See full benchmarksAnswer accuracy judged by GPT-5 on ViDoRe V3
Make your agents more accurate and reliable by giving them access to your private documents with precise, citation-backed answers.
Build multi-modal semantic search with built-in embeddings and hybrid retrieval.
Build recommendation systems with efficient filtering and online updates.
Give agents persistent, searchable memory so they can recall past interactions, facts, and user preferences. Use custom scoring to prioritize recent memories.
TopK is built from the ground up with enterprise security in mind. Data is encrypted in transit and at rest, access is scoped by role, and our infrastructure is audited continuously. When you need full control, we can deploy to your VPC or on-prem.
Deep dives into search, retrieval, and what we're building.
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.
Start building for free. Move to production with usage-based pricing or private deployment in your VPC.