Private Enterprise AI Memory Platform. Your Infrastructure. Your Data.

One Platform. Five Core Engines. The Unified AI Memory for Vector Search, GraphRAG, AI Caching, and Real-Time Vector Sync — Purpose-Built for High-Performance Private-Enterprise AI.

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Today‘s Vector Databases:

The Hidden Waste of Compute, Energy & Infrastructure Costs for GenAI.

All today‘s databases are monolithic, always-on servers that guzzle energy running massive servers 24/7, whether data is accessed or not.
Traditional Database Clusters are Highly Inefficient:

Traditional database clusters consist of monolithic database server clones. Most databases clusters are static and always oversized for peak loads. Scaling-out takes minutes and scaling-in involves computtationally intense reorganization of sharded data. Sharding often doubles or even triples the number of nodes.

The Serverless Billing-Trick:
Monolithic Database Server
Cyrock.AI Neural Vector Database – Inspired by Serverless Functions.

Cyrock.AI: Revolutionary Data Storage Cell Architecture.

01
From Monoliths to Microservices: A Proven Success.
For over a decade, software vendors and leading enterprises have invested heavily to abandon 24/7 monoliths in favor of serverless microservices that can scale to zero. This architectural shift aims to align infrastructure costs perfectly with actual demand - eliminating the massive financial waste of idle infrastructure. Continuing to run monolithic systems in the AI era is choosing to burn compute, energy, and capital.
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02
Transforming the Monolithic 24/7 Database Server to a Network of Micro Data Storage Cells.
We have replaced the monolithic database server with an elastic, scalable network of tiny, isolated micro data storage cells.

When the app requests data, a new cell is invoked in milliseconds, retrieves data from the storage layer, returns it to the app, and shuts down. Hot data is cached, thus cells can be both stateless or stateful. CPU power and RAM are only allocated to active cells. No compute, no costs.
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03
Distributing & Highly Scaling Complex Graphs on Kubernetes.
Cyrock.AI is designed to span even the most complex graphs across Kubernetes clusters and scale elastically with high efficiency through the Cyrock.AI Cell architecture. Each subgraph is associated with a serverless data cell that loads the subgraph only on demand.

This enables traversing through gigantic graphs while minimizing RAM consumption. High-efficiency Graph RAG at scale.
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04
Highly Scalable through Kubernetes.
The Cyrock.AI cell architecture runs on Kubernetes. The system treats the underlying infrastructure as a fluid resource rather than a rigid box. It turns data storage into a utility that grows exactly as your business grows, ensuring you never hit a performance or cost wall.
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05
Cyrock.AI: Stateful AI Memory Platform
Cyrock.AI is a unified AI memory layer providing vector search, GraphRAG and caching on a truly serverless cell infrastrucutre highly scalable on Kubernetes.
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06
80% TCO Reduction of Compute, Energy, Carbon Emissions & Infrastructure Costs.
The Cyrock.AI cell architecture runs on Kubernetes. The system treats the underlying infrastructure as a fluid resource rather than a rigid box. It turns data storage into a utility that grows exactly as your business grows, ensuring you never hit a performance or cost wall.

Want to find out how much you wil save?
Let's start with a POC.
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cell-architecture

From Monoliths to Microservices: A Proven Success.

For over a decade, leading enterprises and middleware providers have invested millions to abandon bloated 24/7 monoliths in favor of serverless microservices to eliminate the massive financial waste of idle infrastructure. By replacing always-on servers with event-driven execution that scales to zero, this architectural shift ensures you stop paying for unutilized capacity and align infrastructure costs perfectly with actual demand. Continuing to run monolithic systems in a cloud-native and AI era is choosing to burn compute, energy, and capital.
cell-architecture

Transforming the Monolithic DB Server to a Network of Micro Data Storage Cells.

We have replaced the monolithic database server with a network of tiny, isolated micro data storage cells that can elastically scale up depending on the workload and scale down to zero when the load decreases.
Only when the app requests data, a new cell is available in microseconds, retrieves data from disk, returns it to the app, and shuts down. Cells can also cache data and become stateful. CPU power and RAM is only allocated to active cells. No CPU power, no costs.
cell-architecture-on-kubernetes

Highly Scalable through Kubernetes.

Cyrock.AI's cell architecture runs on Kubernetes. Thus, Cyrock.AI treats the underlying infrastructure as a fluid resource rather than a rigid box. It turns data storage into a utility that grows exactly as your business grows, ensuring you never hit a performance wall.
80percent

80% TCO Reduction of Compute, Energy, Carbon Emissions & Infrastructure Costs.

Cyrock.AI's Cell Architecture ensures that the system only consumes computing power for data that are actually in use, while unused system regions are automatically shut down. This enables savings of up to 80% in CPU power, energy, carbon emissions, and infrastructure costs.
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Distributing & Highly Scaling Complex Graphs on Kubernetes.

Cyrock.AI is built to highly efficiently store, distribute, and scale any complex graph structure through Kubernetes, and to reload and restore completely or partially in RAM on-demand. Based on native Java object graphs, Cyrock.AI can handle any data types, structured and unstructured data, collections, vectors, and metadata.
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Cyrock.AI: Long-Term Memory for GenAI

A data storage cell is tiny, starts in milliseconds, fetches data from the storage, returns data to your app and shuts down. No CPU resources, no costs.
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Choose Your Cyrock.AI Version:

The Cyrock.AI
Technology Stack

For the highest demands on large enterprise GenAI workloads:

Cyrock.AI Ultimate AI Memory Cluster.

The world’s first cell-based, petabyte-scale AI memory layer designed for the most demanding enterprise AI workloads.
Cell Architecture
On-Demand cells scale dynamically with your workload, slashing idle compute costs to near-zero.
Multi-Model Support
Seamlessly unify high-speed Vector Search, complex GraphRAG, and structured business logic within a single
Scale to Near-Zero
Unlimited horizontal scalability and near-zero latency for massive datasets, ensuring you never hit a performance ceiling.
Sovereign by Design
Built for total isolation. Your data and your LLM interactions never leave your infrastructure, ensuring 100% compliance and IP protection.
Enterprise Resilience
Designed for mission-critical AI workloads with built-in high availability and seamless integration into the Kubernetes ecosystem.
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Embedded Java Vector Search Engine:

Cyrock.AI Java Embedded Vector Database.

The foundational, open-source engine of the Cyrock.AI ecosystem, designed for developers who need a high-performance vector database running directly within your JVM for single-node GenAI applications. Pure-Java. No more external APIs or vector databases.
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Mid-Tier Cluster:

Cyrock.AI Vector Grid.

Distributed in-memory vector search that turns your GenAI app into a high-performance search engine. High availability and seamless data replication for mid-sized vector datasets with zero infrastructure complexity. Built-in high-performance persistence for the highest RAM efficiency.
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Vector Data Synchronization:

Cyrock.AI VectraLink –Synchronization Platform.

A data integration platform that monitors your traditional databases and automatically updates your vector stores, ensuring your AI models always have access to the most current enterprise data. Database-independent.
Cyrock-AI-VectraLink
Next Generation Caching & In-Memory Data Processing:

Cyrock.AI Cache.

A high-performance distributed AI caching platform that allows caching and searching of massive datasets. Up to 60-90% RAM and infrastructure cost reduction compared to traditional cache solutions through built-in persistence, disk-swapping, and intelligent lazy-loading without sacrificing millisecond-level access.
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