Cyrock provides a private‑enterprise AI memory platform that replaces traditional monolithic server databases with a cell‑based, micro‑services architecture for vector search and GraphRAG workloads. By storing AI knowledge in RAM only when needed, the platform delivers petabyte‑scale similarity search and graph processing while cutting infrastructure compute and energy costs by up to 80%.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise AI applications require vector and graph databases to store massive, continuously growing knowledge bases, but traditional monolithic server architectures become inefficient and costly at scale, wasting up to 80% of compute and energy and failing to handle petabyte‑level data.
Solution
Cyrock.AI offers a private‑enterprise AI memory platform built on a cell‑based, micro‑services architecture that stores AI knowledge in RAM only when needed. By replacing always‑on monolithic servers with lightweight, serverless‑inspired storage cells, the platform reduces infrastructure costs and CPU usage by roughly 80% while supporting petabyte‑scale vector search and GraphRAG workloads. The solution integrates five core engines—including a neural vector database and a native Java‑embedded vector store—to deliver high‑performance, low‑latency retrieval for complex knowledge graphs. It is delivered as a fully managed, serverless cloud service, enabling organizations to scale AI workloads without over‑provisioning hardware.
Target Audience
Primary customers are large enterprises and cloud providers that run high‑throughput AI applications requiring scalable vector search and knowledge‑graph capabilities.
Features
- Cell‑based storage architecture that activates RAM only for active data, eliminating idle compute overhead
- Micro‑services design that allows independent scaling of vector search and graph processing engines
- Integrated neural vector database optimized for petabyte‑scale similarity search and GraphRAG queries
- Native embedded Java vector database for seamless integration with AI developer toolchains
- Serverless‑style provisioning that reduces energy consumption and operational costs by up to 80%
- Compatibility with Eclipse open‑source projects (Eclipse Serializer, EclipseStore, Eclipse Data Grid) for robust persistence and data grid capabilities