TopK
TopK provides an AI-native, true hybrid search engine that unifies multi-vector dense embeddings, keyword matching, and advanced filtering in a single platform built on object storage. It supports billion-scale document collections per partition with sub-second data freshness and Postgres wire protocol compatibility, enabling any Postgres client to run semantic and hybrid search as ordinary SQL.
- Artificial Intelligence
- Developer Tools
- Enterprise Software
- Software Only
Funding
Raised to date
$5.5MRaised 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.
across 1 round
IE$5.5M · Seed
Announced Jul 2025
Disclosed cumulative: $5.5M
Founders
Founder details are not available yet.
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