HyperLake

HyperLake is a sovereign infrastructure platform purpose-built for the emerging era where AI agents are primary consumers of enterprise infrastructure, not afterthoughts. It provides a comprehensive command center to deploy, manage, run, secure, and govern agentic infrastructure across any environment. The core product 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. HyperLake is designed for organizations that need to support AI agents that behave fundamentally differently from human users: they query data continuously, call tools, trigger workflows, generate artifacts, operate across multiple systems, and require constant access to governed compute, data, policies, and services. Beyond a single stack, HyperLake manages many 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 platform eliminates the traditional compute markup model that breaks down under autonomous AI workloads, offering $0 compute markup so organizations only pay their cloud provider. HyperLake provides unified governance, immutable provenance logging, data sovereignty by design, and human-agent symbiosis on a single governed platform. It is backed by significant industry partnerships and built on extensive work with enterprise clients across multiple sectors.

Unified Governance and Access Control

HyperLake implements a global policy layer that evaluates every request from both humans and AI agents against dynamic governance rules in real time. This system enforces RBAC and ABAC policies consistently across all data sources, queries, and context retrieval operations. Column masking automatically redacts PII based on user roles, while row-level security filters data by department, region, or role. Every action is version-tracked through comprehensive audit trails, ensuring complete visibility into who or what accessed which data and when. This unified approach prevents governance gaps that typically emerge when different systems apply different policies to human versus agent interactions.

The Traceability Loop

Every agent action, inference, query, and training run is recorded through immutable provenance logs that provide complete auditability. HyperLake creates a permanent record of each step in the AI decision-making process, enabling organizations to trace any AI decision back to its source data with precision. This feature is essential for regulatory compliance, debugging agent behavior, and maintaining trust in autonomous systems. When hundreds of agents operate simultaneously, the traceability loop ensures that every action can be reconstructed and reviewed, providing the transparency required for production deployments in regulated industries.

Data Sovereignty by Design

Agents can operate on data without ever moving it outside its secure environment, keeping sensitive information under full owner control. HyperLake achieves this through sovereign deployment patterns that run entirely within the customer’s own VPC, private cloud, or on-premises infrastructure. Confidential compute patterns further protect data during processing. This architectural approach eliminates the data exfiltration risks associated with sending sensitive information to external AI services, while still enabling powerful autonomous agent capabilities. Organizations maintain complete ownership and control over their data at all times.

Human-Agent Symbiosis

Humans and AI agents operate on the same governed data platform with shared context and standardized memory layers. This design allows human insight and machine intelligence to collaborate effectively on the same datasets without friction. Analysts, data scientists, and engineers work alongside autonomous and supervised agents, all governed by the same policies and accessing the same trusted data foundations. The platform provides consistent access patterns for both human-driven dashboards and agent-driven continuous queries, enabling organizations to transition smoothly from human-centric to hybrid operational models.

Autonomous AI Agent Operations

Enterprises deploying multiple AI agents for continuous data exploration, hypothesis testing, and iterative decision-making can use HyperLake as the governed system of access. The platform handles the massive query volumes generated by autonomous agents without unexpected cost spikes, as it charges $0 compute markup. Agents can continuously retrieve context, test hypotheses, and iterate on analyses while being governed by the same policies that apply to human users. This use case is critical for organizations scaling from a few experimental agents to hundreds of production agents operating simultaneously across different business functions.

Regulated Data Analytics with AI

Financial services, healthcare, and government organizations can deploy AI agents to analyze sensitive data while maintaining strict regulatory compliance. HyperLake’s unified governance layer enforces column masking, row-level security, and comprehensive audit trails across all agent interactions. The immutable provenance logs provide regulators with complete visibility into how AI systems reached their conclusions. Data sovereignty features ensure that sensitive information never leaves the organization’s controlled environment, meeting the most stringent data residency and privacy requirements.

Multi-Cloud Agentic Infrastructure Management

Organizations using multiple cloud providers can deploy HyperLake as a unified command center for all their agentic infrastructure. The platform manages HyperLake-native stacks alongside AWS, GCP, and Azure-native components, as well as open-source technologies and third-party services. This centralized management eliminates the operational complexity of maintaining separate governance, security, and monitoring systems for each cloud environment. Teams can deploy new agentic use cases across any cloud without rebuilding the operating layer each time.

Human-AI Collaborative Data Platforms

Enterprises transitioning from traditional human-centric data platforms to hybrid human-agent systems can use HyperLake to maintain continuity. The platform supports both human-driven dashboards and reports alongside agent-driven continuous retrieval and autonomous exploration. Shared context and standardized memory layers allow human analysts to collaborate with AI agents on the same datasets, each contributing their unique strengths. This use case enables organizations to augment their existing data teams with AI capabilities without disrupting established workflows or requiring complete infrastructure overhauls.

What makes HyperLake different from traditional data platforms?

HyperLake is specifically designed for the agentic era where AI agents are primary infrastructure consumers. Unlike traditional platforms built for humans running occasional dashboards and reports, HyperLake handles the massive query volumes generated by autonomous agents without unexpected cost spikes. It charges $0 compute markup, meaning organizations only pay their cloud provider. The platform also provides unified governance that applies consistently to both human and agent interactions, immutable provenance logging for complete auditability, and data sovereignty features that keep sensitive information under full owner control.

How does HyperLake handle data governance for AI agents?

HyperLake implements a global policy layer that evaluates every request from humans and AI agents against dynamic governance rules in real time. This system enforces role-based and attribute-based access control, column masking for PII auto-redaction, row-level security filters, and comprehensive audit trails for all actions. The governance engine applies consistently across all data sources, queries, and context retrieval operations, ensuring that AI agents cannot bypass security policies that apply to human users. This unified approach prevents governance gaps that typically emerge when different systems apply different policies.

Can HyperLake be deployed in our existing cloud environment?

Yes, HyperLake is designed to be deployed 100% in your cloud, whether that is AWS, GCP, Azure, a private cloud, or on-premises infrastructure. The platform runs entirely within your own VPC, ensuring data sovereignty and compliance with data residency requirements. HyperLake manages many agentic infrastructure stacks including your existing cloud-native components, open-source technologies, and third-party services. This deployment model eliminates the need to move data to external platforms while still providing powerful agentic infrastructure capabilities.

What happens when an AI agent generates thousands of queries?

HyperLake is built to handle the massive query volumes that autonomous agents generate without unexpected cost spikes or performance degradation. Unlike traditional platforms that charge compute markups and can generate five-figure bills overnight from a single misconfigured agent, HyperLake charges $0 compute markup. At scale, when hundreds of agents iterate, retry, and explore simultaneously, costs remain predictable because you only pay your cloud provider’s actual compute costs. The platform also provides governance controls to prevent runaway agent behavior and maintain operational stability.