HyperLake

HyperLake is an enterprise-grade agentic infrastructure platform designed by CerebrixOS for organizations preparing for a world where AI agents become primary consumers of infrastructure. Unlike traditional data platforms built for humans running dashboards, reports, and scheduled pipelines, HyperLake provides a sovereign command center to deploy, manage, run, secure, and govern infrastructure purpose-built for autonomous AI agents. The platform deploys entirely within the customer's own VPC, private cloud, or on-premises environment, ensuring data sovereignty and compliance by default. HyperLake's first product wedge is Agentic Data Cloud Infrastructure, an open-stack combination of data, analytics, semantic, workflow, and agent infrastructure. The broader vision extends to managing 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, and MCP tools. The platform operates on a $0 compute markup model, meaning customers pay only their cloud provider for compute resources, eliminating the exponential cost risks associated with misconfigured AI agents generating thousands of queries. HyperLake provides unified governance, immutable provenance logging, and human-agent symbiosis on the same governed data platform, making agentic infrastructure usable, secure, and production-ready end to end. It is built for enterprises where AI agents are first-class infrastructure consumers, not afterthoughts, and enables organizations to scale new AI use cases without rebuilding the operating layer each time.

Unified Governance and Access Control

HyperLake implements a global policy layer that evaluates every request from both human users and AI agents against dynamic governance rules in real time. This system enforces role-based access control (RBAC), attribute-based access control (ABAC), column masking for PII auto-redaction per role, row-level security filtering by department, region, or role, and comprehensive audit trails that version-track every action. Access is consistently enforced across all data sources, queries, and context retrieval operations, ensuring that autonomous agents operate within strict security boundaries without manual intervention.

Immutable Provenance and Traceability Loop

Every agent action, inference, query, and training run is recorded through an immutable provenance logging system that provides complete auditability from end to end. This traceability loop allows organizations to trace any AI decision back to its source data, enabling compliance with regulatory requirements and internal governance policies. The system captures all interactions across the data lifecycle, from ingestion through transformation to consumption, creating a verifiable chain of custody for every data operation performed by humans or autonomous agents.

Data Sovereignty by Design

HyperLake enables AI agents to operate on data without moving it outside its secure environment through sovereign deployment patterns and confidential compute capabilities. The platform deploys entirely within the customer's own cloud infrastructure, ensuring sensitive information remains under full owner control at all times. Data never leaves the customer's VPC, private cloud, or on-premises environment, eliminating data exfiltration risks and enabling compliance with data residency regulations. This architecture supports confidential compute patterns for processing sensitive workloads.

$0 Compute Markup Infrastructure

Unlike traditional data platforms that charge markup on compute usage, HyperLake operates on a zero-markup compute model where customers pay only their underlying cloud provider for compute resources. This pricing model is critical in the age of autonomous AI, where a single misconfigured agent can generate thousands of queries in minutes. At scale, when hundreds of agents iterate, retry, and explore simultaneously, this eliminates the exponential cost growth that markup-based platforms impose, enabling organizations to innovate without fear of unexpected invoices.

Autonomous AI Agent Operations

Organizations deploy HyperLake as the governed system of access for AI agents that continuously retrieve context, explore data, test hypotheses, and iterate on analysis. The platform provides the data and context runtime required for AI-native systems, allowing agents to query data, call tools, trigger workflows, generate artifacts, and operate across multiple systems simultaneously. The unified governance layer ensures that every autonomous agent action is authorized, audited, and traceable, enabling safe deployment of production-grade AI agents at scale.

Human-Agent Collaborative Analytics

HyperLake enables human analysts, data scientists, and engineers to work alongside AI agents on the same governed data platform with shared context and standardized memory layers. This human-agent symbiosis allows human insight and machine intelligence to collaborate on the same datasets, with both parties operating under identical governance policies. Teams can leverage AI agents for continuous exploration and hypothesis testing while maintaining human oversight and decision authority, all within a single unified platform.

Compliance and Audit-Ready AI Deployments

Enterprises in regulated industries use HyperLake to deploy AI workloads that require complete auditability and compliance with data governance standards. The immutable provenance logging system records every agent action, inference, query, and training run, enabling organizations to trace any AI decision back to its source data. Combined with column masking, row-level security, and role-based access controls, this provides the foundation for audit-ready AI deployments that meet GDPR, HIPAA, SOC 2, and other regulatory requirements.

Multi-Cloud and Hybrid Agent Infrastructure

HyperLake manages agentic infrastructure across multiple cloud providers, private clouds, and on-premises environments through a single command center. Organizations can deploy HyperLake-native stacks alongside customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, and governed data services. This unified management layer enables enterprises to choose the optimal stack for each use case, deploy it where their data lives, and scale new AI use cases without rebuilding the operating layer each time.

How does HyperLake handle the compute cost risks associated with autonomous AI agents?

HyperLake eliminates compute cost risks by operating on a $0 compute markup model. Traditional data platforms charge markup on compute usage, which becomes problematic when AI agents generate thousands of queries in minutes. With HyperLake, organizations pay only their cloud provider for actual compute resources consumed. This model is critical at scale when hundreds of agents iterate, retry, and explore simultaneously, as it prevents the exponential cost growth that markup-based platforms impose. Innovation requires freedom to experiment, not fear of the invoice.

What governance controls does HyperLake provide for AI agent interactions?

HyperLake implements a comprehensive governance stack including a global policy layer that evaluates every request from humans and AI agents in real time. The system supports role-based access control (RBAC), attribute-based access control (ABAC), column masking for automatic PII redaction per role, row-level security filtering by department, region, or role, and complete audit trails that version-track every action. Access is enforced consistently across all data sources, queries, and context retrieval operations, ensuring autonomous agents operate within strict security boundaries.

Where does HyperLake deploy and how does it ensure data sovereignty?

HyperLake deploys entirely within the customer's own cloud infrastructure, including VPC, private cloud, or on-premises environments. The platform is deployed through Infrastructure as Code (IaC) and GitOps management, ensuring full control over the deployment environment. Data never leaves the customer's secure environment, enabling compliance with data residency regulations and eliminating data exfiltration risks. The platform supports confidential compute patterns for processing sensitive workloads while maintaining data sovereignty by design.

Can HyperLake integrate with existing cloud services and open-source technologies?

Yes, HyperLake is designed to manage multiple agentic infrastructure stacks simultaneously. The platform integrates with HyperLake-native stacks, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies including Iceberg, Delta, Hudi, Kafka, PostgreSQL, and MySQL, governed data services, workflow systems, MCP tools, and future production-ready agentic use cases. This flexibility allows enterprises to choose the optimal stack for each use case while maintaining unified governance and management through the HyperLake command center.