TopK provides an AI-native, true hybrid search engine that unifies vector, keyword, and filtering capabilities in a single platform. Built for enterprises and AI teams, it enables billion-scale retrieval with low latency and deep control over relevance, deployed on object storage for cost efficiency.
Funding
$5.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
EIEFounders
Product
Problem
Legacy search systems and vector databases struggle to handle the scale and complexity of modern AI-driven applications, often requiring teams to stitch together multiple databases, APIs, and ranking layers. These fragmented infrastructures suffer from performance bottlenecks and rigid ranking mechanisms that fail to deliver low-latency hybrid results at scale, limiting the ability to tailor results to domain-specific needs.
Solution
TopK provides a unified, AI-native hybrid search engine that combines multi-vector dense embeddings, keyword matching, and advanced filtering and ranking in a single platform. Built on object storage, it supports billion-scale document collections per partition with predictable latency and cost, while maintaining sub-second data freshness and high write throughput. The engine is optimized for accuracy across real-world domains, using multi-vector models and late-interaction retrieval to improve recall. TopK also offers enterprise-grade security with encryption, fine-grained access controls, audit logging, and optional VPC or on-premises deployment, making it suitable for production workloads.
Target Audience
Primary customers are AI engineering teams and enterprises in e-commerce, finance, healthcare, and law that need scalable, high-performance search for AI agents, RAG pipelines, and production workloads requiring hybrid retrieval.
Features
- Multi-vector dense embeddings (e.g., Qwen3 VLE 8B) with late-interaction retrieval for improved accuracy over traditional dense-only approaches
- Unified hybrid search combining vector, keyword, and regex filtering in a single query engine
- Built on object storage, supporting 1B+ documents per partition with 70 MB/s writes and sub-second data freshness
- Postgres wire protocol compatibility, allowing any Postgres client to run semantic search and SQL queries
- Semantic index annotation for state-of-the-art multi-vector retrieval without manual embedding pipeline management
- Enterprise security features including encryption at rest and in transit, role-based access control, and audit logging