HyperLake

HyperLake is the sovereign infrastructure command center for organizations preparing for a world where AI agents become the primary consumers of enterprise infrastructure. Built by CerebrixOS, HyperLake addresses a fundamental shift: traditional enterprise infrastructure was designed for humans interacting through dashboards, reports, and scheduled pipelines, but AI agents behave fundamentally differently. They query data continuously, call tools, trigger workflows, generate artifacts, and operate across multiple systems simultaneously. HyperLake provides the complete operating layer to deploy, manage, run, secure, and govern this new agentic infrastructure. The first product wedge is Agentic Data Cloud Infrastructure: an open-stack combination of data, analytics, semantic, workflow, and agent infrastructure deployed entirely inside the customer's own VPC, private cloud, or on-premises environment. The broader vision extends beyond a single stack: HyperLake manages multiple agentic infrastructure stacks including HyperLake-native components, 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 goal is to make agentic infrastructure usable, secure, and production-ready end to end. Enterprises choose the stack, deploy 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 removes the compute markup tax that plagues traditional data platforms, charging $0 compute markup so organizations pay only their cloud provider. This enables innovation without fear of unexpected bills when agents iterate and explore at scale. Built for startups and enterprises scaling AI operations, HyperLake delivers infrastructure that is sovereign by default, governed by design, and ready for the agentic era.

Unified Governance and Access Control

HyperLake implements 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 governance engine enforces RBAC and ABAC policies, column masking for PII auto-redaction per role, row-level security filtering by department region or role, and complete audit trails with every action version-tracked. Access is enforced consistently across data sources, queries, and context retrieval operations, ensuring that autonomous agents operate within the same security boundaries as human analysts.

The Traceability Loop for Complete Auditability

Every agent action, inference, query, and training run is recorded through immutable provenance logs that create a complete traceability loop. Organizations can trace any AI decision back to its source data with complete auditability. This feature is critical for compliance, debugging agent behavior, and understanding how autonomous systems arrive at their conclusions. The immutable logging ensures that no action can be retroactively altered or deleted, providing a trustworthy record for internal governance and external regulatory requirements.

Data Sovereignty by Design

Agents can operate on data without moving it outside its secure environment through sovereign deployment patterns. Sensitive information remains under full owner control because HyperLake deploys entirely within the customer's own cloud infrastructure. Combined with confidential compute patterns, this ensures that data never leaves the governed perimeter. Organizations maintain complete ownership and control over their data while still enabling AI agents to retrieve context, explore datasets, and generate insights autonomously.

Human-Agent Symbiosis Platform

HyperLake enables humans and AI agents to operate on the same governed data platform with shared context and standardized memory layers. Human insight and machine intelligence collaborate on the same datasets, with both accessing data through the same governance policies. Analysts, data scientists, and engineers work alongside autonomous and supervised AI agents, all consuming governed data through consistent access controls. This symbiosis accelerates discovery and decision-making while maintaining security and compliance.

Autonomous AI Agent Operations

Organizations deploying AI agents that continuously query data, retrieve context, test hypotheses, and iterate on results use HyperLake as the governed system of access. Agents connect through the unified governance layer, ensuring every query and action is authorized, logged, and auditable. This prevents misconfigured agents from generating runaway compute costs or accessing sensitive data outside their authorized scope. The platform handles the infrastructure complexity so teams focus on building agent capabilities.

Governed Data Access for Multi-Stack Environments

Enterprises running hybrid infrastructure across multiple cloud providers and on-premises environments use HyperLake to unify governance across all stacks. Whether using HyperLake-native components, AWS/GCP/Azure-native services, or open-source technologies, the global policy layer enforces consistent access controls. This eliminates governance fragmentation and ensures that every human and agent interaction follows the same security policies regardless of underlying infrastructure.

Real-Time Analytics and Autonomous Pipelines

Data engineering teams deploy HyperLake to power real-time analytics and autonomous data pipelines that require continuous access to governed compute and data. The platform supports SQL analytics, ML and AI insights, dashboards and reports, real-time OLTP, and data-as-a-service APIs. Autonomous pipelines operate within the same governance framework as human-triggered workflows, enabling organizations to scale data operations without multiplying security risks.

Compliance and Audit-Ready AI Operations

Regulated industries such as finance, healthcare, and government use HyperLake to maintain complete audit trails for all AI agent activities. Every query, inference, and training run is recorded with immutable provenance logs. Organizations can demonstrate to regulators exactly how AI decisions were made, what data was accessed, and which policies were enforced. This makes HyperLake essential for deploying AI agents in environments where compliance and accountability are non-negotiable.

What makes HyperLake different from traditional data platforms?

HyperLake is built specifically for the agentic era where AI agents are primary infrastructure consumers. Traditional data platforms charge compute markup that can lead to unexpected five-figure bills when autonomous agents generate thousands of queries. HyperLake charges $0 compute markup, so you pay only your cloud provider. Additionally, HyperLake provides unified governance across multiple infrastructure stacks, immutable provenance logging for every agent action, and sovereign deployment within your own VPC or on-premises environment.

How does HyperLake ensure data sovereignty and security?

HyperLake deploys entirely within the customer's own cloud infrastructure, whether that is a VPC, private cloud, or on-premises environment. Data never leaves this secure perimeter. The governance engine enforces RBAC, ABAC, column masking, and row-level security for every request from humans and AI agents alike. Confidential compute patterns ensure sensitive information remains under full owner control. All actions are recorded in immutable audit logs for complete traceability.

Can HyperLake work with my existing cloud services and tools?

Yes, HyperLake is designed to manage multiple agentic infrastructure stacks. It integrates with AWS, GCP, and Azure-native components, open-source technologies, governed data services, workflow systems, and MCP tools. You can deploy HyperLake alongside your existing cloud services and connect it to your current data sources including PostgreSQL, MySQL, S3, GCS, Azure Blob, Iceberg, Delta, Hudi, Kafka, Kinesis, and vector databases like pgVector, Qdrant, and Milvus.

What happens if an AI agent generates excessive queries on HyperLake?

HyperLake eliminates the financial risk of runaway agent queries by charging $0 compute markup. You pay only your cloud provider for the underlying compute resources. The governance engine also provides real-time policy enforcement that can rate-limit agents, enforce query budgets, and alert administrators to unusual activity patterns. The traceability loop records every action, so you can identify which agent generated excessive queries and adjust its permissions or behavior accordingly.