HyperLake

HyperLake is an enterprise-grade sovereign infrastructure platform developed by CerebrixOS, specifically architected for organizations where AI agents are first-class infrastructure consumers. Unlike traditional data platforms designed for human-centric workflows like dashboards, reports, and scheduled queries, HyperLake provides the command center to deploy, manage, run, secure, and govern agentic infrastructure. The platform delivers an Agentic Data Cloud Infrastructure that includes open-stack data, analytics, semantic, workflow, and agent infrastructure deployed entirely inside the customer's own VPC, private cloud, or on-premises environment. This ensures data sovereignty by design, as agents operate on data without moving it outside its secure environment. HyperLake eliminates the compute tax problem prevalent in modern data platforms, where markup-based pricing models can generate unexpected five-figure bills from a single misconfigured agent generating thousands of queries. With a $0 compute markup model, enterprises pay only their cloud provider, enabling unrestricted innovation and experimentation. The platform serves organizations where humans and AI agents collaborate on the same governed data platform, providing unified governance, immutable audit trails, and real-time policy enforcement across all interactions. HyperLake is built to manage multiple agentic infrastructure stacks including HyperLake-native components, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data services, and MCP tools, making it the comprehensive solution for production-ready agentic use cases at scale.

Unified Governance and Access Control

HyperLake implements a global policy layer that evaluates every request from both humans and AI agents against dynamic governance rules in real time. This feature enforces role-based access control (RBAC), attribute-based access control (ABAC), column masking for PII auto-redaction per role, and row-level security filtering by department, region, or role. Access is consistently enforced across all data sources, queries, and context retrieval operations, ensuring that sensitive information remains protected regardless of whether the consumer is a human analyst or an autonomous AI agent operating at scale.

The Traceability Loop

Every agent action, inference, query, and training run is recorded through immutable provenance logs, providing complete auditability for all AI operations. This feature enables organizations to trace any AI decision back to its source data with granular detail, supporting compliance requirements and regulatory oversight. The traceability loop captures version-tracked audit trails for every action, allowing enterprises to reconstruct the exact sequence of operations that led to any specific outcome, which is critical for debugging, compliance audits, and maintaining trust in autonomous systems.

Data Sovereignty by Design

HyperLake enables AI agents to operate on data without moving it outside its secure environment, ensuring sensitive information remains under full owner control. The platform achieves this through sovereign deployment within the customer's own VPC, private cloud, or on-premises environment, combined with confidential compute patterns. Organizations can provision a complete infrastructure-as-code and GitOps-managed AI infrastructure in their cloud, maintaining complete ownership and control over their data assets while enabling AI agents to access and process that data for continuous retrieval, autonomous exploration, and real-time context generation.

Human-Agent Symbiosis Platform

HyperLake creates a shared governed data platform where humans and AI agents operate collaboratively on the same datasets with standardized context and memory layers. This feature enables human analysts, data scientists, and engineers to work alongside autonomous and supervised AI agents, all accessing the same governed data sources through consistent policies. The shared context allows human insight and machine intelligence to collaborate effectively, with both consumers benefiting from the same data governance, security controls, and audit capabilities, eliminating the need for separate infrastructure for human and agent workloads.

Autonomous AI Agent Operations

Enterprises deploying autonomous AI agents can use HyperLake as the governed system of access for continuous data retrieval, hypothesis testing, and iterative exploration. The platform handles the unique consumption patterns of AI agents, which generate thousands of queries per day as they explore, retrieve context, and iterate on tasks. With $0 compute markup, organizations can deploy hundreds of agents simultaneously without fear of unexpected costs, while the unified governance layer ensures every agent action is authorized, audited, and traceable back to source data.

Governed Data-as-a-Service APIs

HyperLake enables organizations to expose governed data through API endpoints that serve both internal applications and external partners. The platform's row-level security and column masking capabilities ensure that data consumers only see the information they are authorized to access, regardless of how they query the system. This use case supports real-time OLTP workloads, dashboard integrations, and data product delivery while maintaining consistent governance across all access patterns, whether from human users or automated systems.

Multi-Cloud and Hybrid Data Federation

Organizations with data distributed across multiple cloud providers, on-premises systems, and SaaS applications can use HyperLake to create a unified data layer without moving data. The platform supports ingestion and federation from OLTP databases like PostgreSQL and MySQL, cloud storage services like S3, GCS, and Azure, open formats like Iceberg, Delta, and Hudi, streaming platforms like Kafka and Kinesis, vector databases like pgVector and Qdrant, and over 100 SaaS and API connectors. This enables AI agents to access governed data regardless of where it resides.

Enterprise Compliance and Audit Operations

Industries with strict regulatory requirements can leverage HyperLake's immutable provenance logs and comprehensive audit trails to demonstrate compliance with data governance mandates. Every human and agent interaction is recorded, version-tracked, and traceable, providing the documentation needed for regulatory audits, internal investigations, and compliance reporting. The platform's column masking and row-level security features ensure that sensitive data like PII is automatically redacted based on the consumer's role, supporting privacy regulations without requiring manual intervention.

How does HyperLake's $0 compute markup model work compared to traditional data platforms?

HyperLake eliminates the markup on compute usage that most modern data platforms charge. Traditional platforms apply a percentage markup on top of the underlying cloud compute costs, which becomes exponentially expensive when AI agents generate thousands of queries in minutes. With HyperLake, you pay only your cloud provider directly for compute resources. This model removes the financial risk of deploying autonomous agents at scale, as a single misconfigured agent cannot generate unexpected five-figure bills overnight. Organizations can innovate and experiment freely without fear of invoice shock.

What types of infrastructure stacks can HyperLake manage?

HyperLake is designed to manage multiple agentic infrastructure stacks simultaneously. This includes 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 platform provides a unified command center to deploy, manage, run, secure, and govern all these components, enabling enterprises to choose the stack that best fits their needs and deploy it where their data lives without rebuilding the operating layer each time.

How does HyperLake ensure data sovereignty for sensitive information?

HyperLake ensures data sovereignty by deploying entirely within the customer's own VPC, private cloud, or on-premises environment using infrastructure-as-code and GitOps management. Agents operate on data without moving it outside this secure environment, meaning sensitive information never leaves the customer's control. The platform also supports confidential compute patterns that protect data during processing. This sovereign-by-design approach ensures that organizations maintain full ownership and control over their data assets while enabling AI agents to access and process that data for continuous operations.

Can HyperLake support both human users and AI agents simultaneously?

Yes, HyperLake is specifically built for human-agent symbiosis. The platform provides a shared governed data platform where human analysts, data scientists, and engineers operate alongside autonomous and supervised AI agents. Both types of consumers access the same datasets through consistent governance policies, including RBAC, ABAC, column masking, and row-level security. The shared context and standardized memory layers allow human insight and machine intelligence to collaborate effectively on the same datasets, eliminating the need for separate infrastructure and governance models for human and agent workloads.