HyperLake

HyperLake is the vanguard infrastructure for organizations preparing for a world where AI agents are the primary consumers of compute, data, and services. Built by CerebrixOS, it is the first command center designed to deploy, manage, run, secure, and govern agentic infrastructure at scale. The product addresses a fundamental shift: traditional enterprise infrastructure was architected for humans using dashboards, reports, and scheduled pipelines. AI agents behave fundamentally differently. They query data continuously, call tools dynamically, trigger workflows autonomously, generate artifacts in real time, and operate across disparate systems. They require persistent, governed access to compute, data, policies, and services. HyperLake provides the sovereign, agentic data cloud infrastructure to meet this demand. The first product wedge is Agentic Data Cloud Infrastructure: an open-stack data, analytics, semantic, workflow, and agent infrastructure deployed entirely inside the customer's own VPC, private cloud, or on-premises environment. However, the vision extends far beyond a single stack. HyperLake is designed to manage multiple agentic infrastructure stacks, including HyperLake-native stacks, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data services, workflow systems, MCP tools, and future production-ready agentic use cases. The core value proposition is making agentic infrastructure usable, secure, and production-ready end to end. Enterprises can choose their preferred stack, deploy it where their data lives, govern every human and agent interaction, audit every action, and scale new AI use cases without rebuilding the operating layer each time. With a zero-compute markup model, HyperLake ensures that innovation is driven by experimentation, not fear of unexpected bills. It is built for early adopters who recognize that the future of data infrastructure is agent-driven, sovereign, and governed by design.

Unified Governance and Access Control

HyperLake provides a global policy layer that evaluates every request, whether from a human or an AI agent, against dynamic governance rules in real time. This unified system enforces role-based access control (RBAC), attribute-based access control (ABAC), column-level masking for PII auto-redaction, and row-level security filters based on department, region, or role. Access is enforced consistently across all data sources, queries, and context retrieval operations, ensuring that agents never operate outside governed boundaries.

The Traceability Loop

Every agent action, inference, query, and training run is recorded through immutable provenance logs. HyperLake creates a complete audit trail that traces any AI decision back to its source data. This feature is essential for compliance, debugging, and building trust in autonomous systems. Organizations can version-track every interaction, providing full visibility into how data is consumed and transformed by agents.

Data Sovereignty by Design

Agents can operate on data without ever moving it outside its secure environment. HyperLake enables sovereign deployment and confidential compute patterns, ensuring sensitive information remains under full owner control. Data never leaves the customer's VPC, private cloud, or on-premises environment. This architecture is critical for regulated industries where data residency and sovereignty are non-negotiable.

Human-Agent Symbiosis

HyperLake creates a shared data platform where humans and AI agents collaborate on the same datasets. Standardized memory layers and shared context allow human analysts, data scientists, and engineers to work alongside autonomous agents seamlessly. This feature transforms data infrastructure from a human-only tool into a collaborative environment where machine intelligence and human insight amplify each other.

Autonomous AI Agent Operations

Deploy and govern AI agents that continuously query data, retrieve context, test hypotheses, and iterate without human intervention. HyperLake provides the governed compute, data, and policy layer these agents need to operate autonomously at scale, with full auditability and security.

Governed Data-as-a-Service APIs

Expose governed data access to internal applications and external partners through secure APIs. HyperLake ensures every API call, whether from a human or an agent, is evaluated against the same global policy layer, with column masking and row-level security enforced automatically.

Real-Time Context Retrieval for AI Systems

Enable AI agents to retrieve real-time context from multiple data sources, including OLTP databases, cloud storage, streaming platforms, and vector databases. HyperLake federates these sources into a unified, governed data layer that agents can query continuously without moving data.

Multi-Stack Agentic Infrastructure Management

Manage multiple agentic infrastructure stacks from a single command center. HyperLake supports HyperLake-native stacks, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, and MCP tools. This allows enterprises to choose the best stack for each use case without rebuilding the operating layer.

How does HyperLake handle compute costs for AI agents?

HyperLake operates on a zero-compute markup model. You pay only your cloud provider for the compute resources consumed. This eliminates the risk of unexpected five-figure bills from misconfigured agents that generate thousands of queries in minutes. At scale, when hundreds of agents iterate and explore simultaneously, costs remain predictable and tied directly to your cloud usage.

Can HyperLake be deployed in my own cloud environment?

Yes. HyperLake is designed for sovereign deployment inside your own VPC, private cloud, or on-premises environment. Data never leaves your secure perimeter. You maintain full control over where your data lives and how it is accessed. This architecture is ideal for regulated industries and organizations with strict data residency requirements.

What types of data sources does HyperLake support?

HyperLake supports a wide range of data sources including OLTP and RDBMS systems like PostgreSQL and MySQL, cloud storage services like S3, GCS, Azure, and R2, open formats like Iceberg, Delta, and Hudi, streaming platforms like Kafka and Kinesis, vector databases like pgVector, Qdrant, and Milvus, and over 100 SaaS and API connectors. All sources are federated into a unified, governed data layer.

How does HyperLake ensure auditability for AI agent actions?

HyperLake records every agent action, inference, query, and training run through immutable provenance logs. This creates a complete traceability loop that can trace any AI decision back to its source data. Every interaction is version-tracked, providing full auditability for compliance, debugging, and building trust in autonomous systems.