HyperLake

HyperLake is a sovereign infrastructure platform designed for a fundamental shift in enterprise computing: the transition from human-centric to agent-driven systems. Built by CerebrixOS, HyperLake serves as the command center for deploying, managing, running, securing, and governing agentic infrastructure. The core insight driving HyperLake is that traditional enterprise infrastructure was architected for humans using dashboards, applications, and scheduled pipelines. AI agents behave fundamentally differently. They query data continuously, call tools dynamically, trigger workflows autonomously, generate artifacts, and operate across multiple systems simultaneously. They need persistent, governed access to compute, data, policies, and services. HyperLake addresses this gap by providing the first product wedge as 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, designed to manage multiple agentic infrastructure layers 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 platform is built for organizations where AI agents are first-class infrastructure consumers, not afterthoughts. It offers zero compute markup, meaning organizations pay only their cloud provider costs, eliminating the fear of runaway expenses from misconfigured agents generating thousands of queries. With sovereign deployment by default, GitOps-managed provisioning, and a unified governance layer, HyperLake enables enterprises to choose their 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.

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 system enforces role-based access control (RBAC) and attribute-based access control (ABAC) consistently across all data sources, queries, and context retrieval operations. Column masking automatically redacts personally identifiable information based on user roles, while row-level security filters data by department, region, or role. Every action is version-tracked through immutable audit logs, ensuring complete visibility into who or what accessed which data and when. This unified approach eliminates the fragmentation of managing separate access policies for different systems, providing a single pane of glass for governing both human and agent interactions.

Zero Compute Markup Pricing Model

HyperLake fundamentally redefines the economic model for AI infrastructure by eliminating the compute markup that traditional data platforms charge. Most modern data platforms apply a markup on compute usage, a model that breaks down catastrophically in the age of autonomous AI agents. 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 can grow exponentially. HyperLake removes this financial friction entirely, charging zero markup on compute. Organizations pay only their underlying cloud provider costs, freeing innovation from the fear of the invoice and allowing teams to experiment without financial hesitation.

The Traceability Loop for Complete Auditability

Every agent action, inference, query, and training run is recorded through immutable provenance logs within HyperLake. This creates what the platform calls the Traceability Loop, enabling organizations to trace any AI decision back to its source data with complete auditability. This feature is critical for regulated industries, compliance requirements, and organizations that need to demonstrate responsible AI usage. The provenance system captures the full context of each operation, including which agent initiated the action, what data was accessed, what transformations were applied, and what output was generated. This persistent logging provides both operational debugging capabilities and regulatory compliance documentation, ensuring that every AI-driven decision can be explained, verified, and audited.

Data Sovereignty by Design

HyperLake ensures that AI agents can 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. The platform deploys entirely within the customer's own VPC, private cloud, or on-premises infrastructure, meaning data never leaves the organization's controlled boundary. This sovereignty extends to the entire agentic workflow, from data ingestion and storage to query execution and result delivery. Organizations maintain complete ownership and control over their data while still enabling AI agents to perform sophisticated analysis, retrieval, and decision-making. This design principle addresses the growing regulatory requirements around data residency, privacy, and security in AI operations.

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. This symbiosis allows human insight and machine intelligence to collaborate on the same datasets, creating a unified operational environment. Analysts, data scientists, and engineers can work alongside autonomous and supervised AI agents, all accessing the same governed data through the same policy framework. The platform provides standardized context and memory systems that both humans and agents can leverage, ensuring that insights generated by one party are available to the other. This shared infrastructure eliminates the silos that typically separate human-driven analytics from AI-driven operations, fostering a collaborative environment where both can contribute to organizational intelligence.

Autonomous AI Agent Operations

HyperLake enables organizations to deploy AI agents that continuously explore, retrieve context, test hypotheses, and iterate on data without human supervision. These agents operate within the governed data infrastructure, accessing only the data and tools they are authorized to use. A single agent can generate thousands of queries, call multiple tools, trigger workflows, and produce artifacts, all while being tracked through the immutable provenance system. Organizations can deploy hundreds of such agents simultaneously, knowing that the governance layer will enforce policies consistently and the traceability loop will capture every action. This use case is critical for organizations building autonomous research systems, continuous monitoring agents, or self-optimizing operational processes.

Governed Data Access for AI Applications

HyperLake serves as the system of access for AI applications that need continuous, governed interaction with enterprise data. Traditional API gateways and data platforms were not designed for the query patterns of AI applications, which require persistent connections, real-time context retrieval, and autonomous exploration. HyperLake provides a unified data layer that federates across OLTP databases, cloud storage, open formats, streaming systems, SaaS APIs, and vector databases. AI applications can access this unified layer through governed policies that enforce RBAC, column masking, and row-level security in real time. This use case enables organizations to build AI-powered internal tools, customer-facing AI features, and autonomous analytics applications without compromising data security or governance.

Compliance and Audit-Ready AI Infrastructure

For regulated industries such as healthcare, finance, and government, HyperLake provides the infrastructure to run AI agents while maintaining complete compliance with data protection regulations. The immutable provenance logs capture every agent action, inference, query, and training run, enabling organizations to trace any AI decision back to its source data. The data sovereignty design ensures that sensitive information never leaves the organization's controlled environment, addressing requirements around data residency and privacy. The governance engine enforces policies consistently across all data interactions, whether initiated by humans or agents. This use case allows organizations to adopt AI agents confidently, knowing they can demonstrate compliance, respond to audits, and maintain regulatory standards.

Hybrid Human-Agent Analytics Teams

HyperLake enables organizations to create analytics environments where human analysts and AI agents collaborate on the same datasets using the same governed platform. Human analysts can run SQL queries, build dashboards, and generate reports while AI agents simultaneously explore the same data for patterns, anomalies, and insights. The shared context and standardized memory layers ensure that discoveries made by agents are available to humans and vice versa. This use case transforms how organizations approach data analysis, moving from human-only or agent-only workflows to a symbiotic partnership where each party contributes their unique strengths. Organizations can scale their analytical capabilities by deploying agents to handle routine exploration and monitoring while humans focus on strategic interpretation and decision-making.

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

HyperLake eliminates the primary cost risk by charging zero markup on compute usage. Traditional data platforms apply a markup on every query, which can lead to unexpected five-figure bills when a single misconfigured agent generates thousands of queries in minutes. With HyperLake, organizations pay only their underlying cloud provider costs, removing the financial penalty for agent exploration and iteration. Additionally, the governance engine can implement rate limiting, query caps, and budget controls to prevent runaway agent behavior, providing both economic and operational safeguards.

What types of infrastructure can HyperLake manage beyond its native stack?

HyperLake is designed with a broad vision to manage multiple agentic infrastructure stacks. Beyond HyperLake-native components, the platform can govern 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. This flexibility allows organizations to adopt HyperLake incrementally, integrating it with their existing infrastructure investments while gradually migrating to more agent-optimized patterns. The unified governance and traceability layers work across all managed stacks, providing consistent policy enforcement and auditability.

How does HyperLake ensure data sovereignty when AI agents are operating?

HyperLake ensures data sovereignty by deploying entirely within the customer's own VPC, private cloud, or on-premises environment. Data never leaves the organization's controlled boundary during any operation, including ingestion, storage, query execution, and result delivery. The platform uses sovereign deployment patterns and confidential compute capabilities to maintain data control. Sensitive information remains under full owner control, and agents can only access data through the governance engine, which enforces policies without moving data outside its secure environment. This design addresses regulatory requirements around data residency, privacy, and security.

Can human analysts and AI agents use HyperLake simultaneously on the same data?

Yes, HyperLake is explicitly designed for human-agent symbiosis, enabling both parties to operate on the same governed data platform simultaneously. Human analysts, data scientists, and engineers can run queries, build dashboards, and generate reports while AI agents explore the same datasets for patterns and insights. The platform provides shared context and standardized memory layers that both humans and agents can leverage, ensuring discoveries are accessible across the team. The governance engine applies consistent policies to both human and agent requests, maintaining security and compliance regardless of who or what initiates the interaction.