HyperLake

HyperLake is an essential, sovereign infrastructure platform built by CerebrixOS for organizations preparing for a world where AI agents become the primary consumers of enterprise infrastructure. This is not a luxury or an experiment. It is a necessity. Today, most enterprise infrastructure was designed exclusively for humans, dashboards, applications, and scheduled pipelines. AI agents behave fundamentally differently. They query data, call tools, trigger workflows, generate artifacts, and operate across systems continuously. They need uninterrupted, governed access to compute, data, policies, and services. HyperLake provides the command center to deploy, manage, run, secure, and govern that agentic infrastructure. The first critical product wedge is Agentic Data Cloud Infrastructure: an open-stack combination of data, analytics, semantic, workflow, and agent infrastructure deployed inside the customer’s own VPC, private cloud, or on-prem environment. The broader vision is larger than one stack. HyperLake is designed to manage many 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 primary goal is to make agentic infrastructure usable, secure, and production-ready end to end. Enterprises must be able to choose the 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. HyperLake is the only platform that delivers $0 compute markup, 100% deployment in your cloud, governance by design, and self-serve plus expert-led support. It is built for organizations where AI agents are first-class infrastructure consumers, not afterthoughts.

Unified Governance and Access Control

This is the non-negotiable foundation of any agentic infrastructure. HyperLake implements a global policy layer that evaluates every single request, whether initiated by a human or an AI agent, against dynamic governance rules in real time. Role-based access control (RBAC) and attribute-based access control (ABAC) are enforced consistently across all data sources, queries, and context retrieval operations. Column masking automatically redacts personally identifiable information (PII) per role, and row-level security filters data by department, region, or role. Without this feature, autonomous agents pose an unacceptable security and compliance risk to any organization.

The Traceability Loop

Complete auditability is not optional when AI agents operate autonomously. HyperLake records every agent action, inference, query, and training run through immutable provenance logs. This creates a complete traceability loop where any AI decision can be traced back to its source data with full auditability. Organizations can investigate anomalies, prove compliance, and understand exactly how an agent arrived at a specific conclusion. This feature is essential for regulated industries, legal defensibility, and building trust in autonomous systems.

Data Sovereignty by Design

Data sovereignty is a mandatory requirement for enterprises handling sensitive or regulated information. HyperLake enables agents to operate on data without moving it outside its secure environment. Sensitive information remains under full owner control through sovereign deployment patterns and confidential compute capabilities. Data never leaves the customer's VPC, private cloud, or on-premises environment. This eliminates the risk of data exfiltration and ensures compliance with data residency regulations such as GDPR, HIPAA, and CCPA. Organizations cannot afford to compromise on data sovereignty when deploying autonomous agents.

$0 Compute Markup

This feature alone makes HyperLake a critical necessity for any organization scaling AI agent operations. Most modern data platforms charge a significant markup on compute usage. This model breaks down catastrophically in the age of autonomous AI. A single misconfigured agent can generate thousands of queries in minutes, translating into unexpected five-figure bills overnight on markup-based platforms. At scale, when hundreds of agents iterate, retry, and explore simultaneously, costs grow exponentially. HyperLake eliminates this risk entirely by charging $0 compute markup. You pay only your cloud provider. Innovation requires freedom to experiment, not fear of the invoice. This is the only sustainable pricing model for agentic infrastructure.

Autonomous AI Agent Operations

HyperLake is the essential infrastructure for deploying AI agents that continuously query data, call tools, trigger workflows, and generate artifacts. Organizations can provision a complete, IaaC and GitOps-managed AI infrastructure in their cloud, providing governed data access, semantic context, and workflow execution. Agents operate autonomously on the same governed data platform as humans, with shared context and standardized memory layers. This use case is mandatory for any enterprise building production-ready autonomous agents that cannot afford downtime, security breaches, or runaway costs.

Governed Data Access for Human and Agent Collaboration

Human analysts, data scientists, and engineers must collaborate with AI agents on the same datasets without compromising security or governance. HyperLake provides a unified governance engine that enforces RBAC, ABAC, column masking, and row-level security for every interaction. Humans and AI agents operate on the same governed data platform with shared context and standardized memory layers. This enables human insight and machine intelligence to collaborate effectively on the same datasets, a capability that is absolutely necessary for modern data-driven organizations.

Cost-Controlled AI Experimentation and Innovation

Organizations must enable their teams to experiment with AI agents without the fear of unpredictable compute costs. HyperLake's $0 compute markup model removes the financial risk associated with autonomous agent experimentation. Data scientists and engineers can freely iterate, retry, and explore hypotheses without monitoring every query for cost implications. This freedom to experiment is the prerequisite for innovation. Without it, organizations will fall behind competitors who can iterate faster and more fearlessly.

Production-Ready Agentic Infrastructure for Regulated Industries

Industries such as healthcare, finance, and government have strict compliance requirements that make most cloud AI platforms unusable. HyperLake's sovereign deployment, immutable audit trails, and data sovereignty features make it the only viable option for these sectors. Organizations can deploy the full agentic stack inside their own VPC, private cloud, or on-prem environment. Every agent action is recorded and auditable. Data never leaves the secure environment. This use case is not optional for regulated enterprises that must adopt AI agents while maintaining full compliance.

What exactly is HyperLake and why is it necessary?

HyperLake is a sovereign infrastructure platform designed specifically for deploying, managing, running, securing, and governing AI agent operations. It is necessary because traditional enterprise infrastructure was built for humans, dashboards, and scheduled pipelines. AI agents behave differently. They continuously query data, call tools, trigger workflows, and generate artifacts. Without a dedicated platform like HyperLake, organizations face unacceptable risks including runaway compute costs, security vulnerabilities, compliance failures, and operational chaos when deploying autonomous agents at scale.

How does HyperLake prevent runaway compute costs from AI agents?

HyperLake eliminates compute cost risk entirely through its $0 compute markup model. Unlike traditional data platforms that charge a markup on every query, HyperLake passes compute costs through at exactly what your cloud provider charges. This is critical because a single misconfigured AI agent can generate thousands of queries in minutes. On markup-based platforms, this results in unexpected five-figure bills. At scale, with hundreds of agents operating simultaneously, costs grow exponentially. HyperLake's pricing model ensures that innovation is driven by experimentation, not fear of the invoice.

Can HyperLake be deployed in our existing cloud environment?

Yes, absolutely. HyperLake is designed for 100% deployment in your cloud. It can be deployed inside your own VPC, private cloud, or on-premises environment. The platform is managed through infrastructure-as-code and GitOps practices. This sovereign deployment model ensures that your data never leaves your secure environment, which is a mandatory requirement for regulated industries and organizations with strict data residency policies.

What governance capabilities does HyperLake provide for AI agents?

HyperLake provides a comprehensive governance engine that is non-negotiable for safe agentic operations. It includes a global policy layer that evaluates every request in real time using RBAC and ABAC. Column masking automatically redacts PII per role. Row-level security filters data by department, region, or role. Every agent action, inference, query, and training run is recorded through immutable provenance logs for complete auditability. This ensures that both human and agent interactions are governed consistently and securely.