HyperLake

HyperLake is a sovereign infrastructure platform purpose-built for the agentic era, where AI agents are becoming primary consumers of enterprise infrastructure. Developed by CerebrixOS, HyperLake addresses a critical gap in modern data architecture: traditional platforms were designed for human-centric workflows like dashboards, reports, and scheduled queries, but AI agents behave fundamentally differently. They continuously explore data, retrieve context in real time, call tools, trigger workflows, generate artifacts, and operate across multiple systems simultaneously. HyperLake provides the command center to deploy, manage, run, secure, and govern this new class of agentic infrastructure. The product's first wedge is Agentic Data Cloud Infrastructure, an open-stack 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 to manage multiple agentic infrastructure stacks, including HyperLake-native components, customer-owned cloud services, AWS/GCP/Azure-native resources, open-source technologies, governed data services, workflow systems, MCP tools, and future production-ready agentic use cases. HyperLake is built for organizations where AI agents are first-class infrastructure consumers, not afterthoughts, enabling enterprises to choose their stack, deploy where data lives, govern every human and agent interaction, audit every action, and scale new AI use cases without rebuilding the operating layer each time. The platform offers $0 compute markup, meaning customers only pay their cloud provider directly, eliminating the exponential cost risks associated with autonomous agent behavior.

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 supports role-based access control (RBAC) and attribute-based access control (ABAC), column masking for automatic PII redaction per role, row-level security to filter data by department, region, or role, and comprehensive audit trails that version-track every action. Access is enforced consistently across all data sources, queries, and context retrieval operations, ensuring that autonomous agents operate within the same security boundaries as human users.

The Traceability Loop

Every agent action, inference, query, and training run is recorded through immutable provenance logs, creating a complete audit trail that traces any AI decision back to its source data. This feature provides full auditability for compliance requirements, debugging, and performance optimization. Organizations can reconstruct exactly how an agent arrived at a particular conclusion, what data it accessed, and which transformations were applied, enabling transparency and accountability in AI-driven operations at scale.

Data Sovereignty by Design

HyperLake enables AI agents to operate on data without moving it outside its secure environment, keeping sensitive information under full owner control through sovereign deployment and confidential compute patterns. The platform deploys entirely within the customer's own VPC, private cloud, or on-premises environment, ensuring that data never leaves the organization's controlled infrastructure. This sovereign approach is critical for regulated industries, intellectual property protection, and compliance with data residency requirements.

Human-Agent Symbiosis

Humans and AI agents operate on the same governed data platform, sharing context and standardized memory layers that allow human insight and machine intelligence to collaborate on the same datasets. This feature enables analysts, scientists, and engineers to work alongside autonomous and supervised agents, all accessing governed data through a unified interface. Shared context means that insights generated by agents are immediately available to human collaborators, and vice versa, creating a continuous feedback loop that accelerates discovery and decision-making.

Autonomous AI Agent Operations

Enterprise AI agents require continuous access to governed compute, data, policies, and services to operate effectively. HyperLake provides the infrastructure foundation for autonomous agents that query data, call tools, trigger workflows, generate artifacts, and operate across multiple systems. Organizations can deploy supervised or fully autonomous agents that explore data, test hypotheses, iterate on analyses, and retrieve real-time context without incurring unexpected compute costs, thanks to the $0 compute markup model.

Governed Data Access for Machine Learning Pipelines

ML and AI teams need reliable, governed access to training data, feature stores, and inference endpoints. HyperLake enables data scientists to provision complete AI infrastructure using infrastructure-as-code and GitOps-managed deployments, with governed data access built into every layer. The platform supports continuous retrieval patterns required for modern ML workflows, including feature engineering, model training, and real-time inference, all while maintaining consistent governance across human and agent interactions.

Real-Time Analytics and Autonomous Reporting

Traditional analytics platforms break down when AI agents generate thousands of queries per minute. HyperLake provides a unified data layer that federates across OLTP databases, cloud storage, open formats like Iceberg and Delta, streaming platforms, vector databases, and SaaS APIs. This enables autonomous reporting systems that continuously explore data, detect anomalies, and generate insights without manual intervention, all governed by the same policy engine that controls human analyst access.

Compliance and Audit-Ready AI Operations

Regulated industries require complete auditability for every AI-driven action, from data access to model inference. HyperLake's traceability loop records every agent action, query, and training run through immutable provenance logs, enabling organizations to trace any AI decision back to its source data. This use case is critical for financial services, healthcare, government, and any enterprise where AI operations must be explainable, auditable, and compliant with regulatory requirements.

How does HyperLake handle the compute tax problem for AI agents?

HyperLake operates on a $0 compute markup model, meaning customers pay only their cloud provider directly with no additional markup from HyperLake. This eliminates the exponential cost risks associated with autonomous agent behavior, where a single misconfigured agent can generate thousands of queries in minutes. Traditional markup-based platforms can produce unexpected five-figure bills overnight when agents iterate, retry, and explore simultaneously. HyperLake ensures that innovation requires freedom to experiment, not fear of the invoice.

Can HyperLake integrate with existing cloud services and infrastructure?

Yes, HyperLake is designed to manage many agentic infrastructure stacks beyond its own native components. The platform supports 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. Enterprises can choose their preferred stack and deploy it where their data lives, whether in their own VPC, private cloud, or on-premises environment.

What governance capabilities does HyperLake provide for AI agents?

HyperLake implements a comprehensive governance engine with role-based and attribute-based access control, column masking for automatic PII redaction per role, row-level security to filter data by department, region, or role, and complete audit trails that version-track every action. Every request from humans or AI agents is evaluated against dynamic governance rules in real time, ensuring consistent enforcement across all data sources, queries, and context retrieval operations.

How does HyperLake ensure data sovereignty and security?

HyperLake deploys entirely inside the customer's own VPC, private cloud, or on-premises environment, ensuring that data never leaves the organization's controlled infrastructure. Agents can operate on data without moving it outside its secure environment, and sensitive information remains under full owner control through sovereign deployment and confidential compute patterns. This design is critical for regulated industries, intellectual property protection, and compliance with data residency requirements.