HyperLake

HyperLake is a sovereign infrastructure platform purpose-built for organizations that are preparing for a world where AI agents become the primary consumers of enterprise infrastructure. Unlike traditional data platforms designed for human-centric workflows such as dashboards, reports, and scheduled pipelines, HyperLake addresses the fundamentally different behavior patterns of AI agents. These agents continuously query data, call tools, trigger workflows, generate artifacts, operate across disparate systems, and require uninterrupted access to governed compute, data, policies, and services. HyperLake provides the command center to deploy, manage, run, secure, and govern this new class of agentic infrastructure. The product is delivered as an open-stack data, analytics, semantic, workflow, and agent infrastructure deployed entirely inside the customer’s own VPC, private cloud, or on-premises environment. This ensures data sovereignty by design, as agents operate on data without moving it outside its secure environment. The broader vision extends beyond a single stack; HyperLake is designed to manage multiple agentic infrastructure stacks including its native stack, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data services, workflow systems, and MCP tools. The platform eliminates the compute tax problem common in modern data platforms by charging zero markup on compute usage, allowing enterprises to innovate and experiment without fear of unexpected costs. HyperLake is built for enterprises where humans and AI agents operate together on data at scale, providing unified governance, immutable audit trails, and a global policy layer that evaluates every request in real time.

Unified Governance and Access Control

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 access control, attribute-based access control, column masking for automatic PII redaction per role, and row-level security that filters data by department, region, or role. Every action is version-tracked through an immutable audit trail, ensuring complete visibility into who or what accessed which data and when.

Zero Compute Markup Architecture

HyperLake eliminates the traditional compute tax that plagues modern data platforms. While most platforms charge a markup on compute usage, which becomes exponentially costly when AI agents generate thousands of queries in minutes, HyperLake passes through compute costs at exactly what the cloud provider charges. This zero markup model enables enterprises to let hundreds of agents iterate, retry, and explore simultaneously without fear of unexpected five-figure bills.

Sovereign Deployment and Data Control

HyperLake is deployed entirely within the customer’s own VPC, private cloud, or on-premises environment using Infrastructure as Code and GitOps management. This sovereign deployment ensures that sensitive data never leaves the customer’s secure environment. Agents can operate on data without moving it, and confidential compute patterns keep sensitive information under full owner control. This design is critical for regulated industries and enterprises with strict data residency requirements.

Immutable Provenance and Traceability

Every agent action, inference, query, and training run is recorded through immutable provenance logs. HyperLake creates a complete traceability loop that allows organizations to trace any AI decision back to its source data with full auditability. This feature is essential for compliance, debugging, and maintaining trust in AI-driven operations. The system records every interaction between humans and agents on the same governed data platform.

Autonomous AI Agent Operations

Enterprises can deploy autonomous AI agents that continuously retrieve context, explore data, test hypotheses, and iterate without human intervention. HyperLake provides the governed data access and compute infrastructure these agents need to operate at scale. The platform handles thousands of concurrent query requests from multiple agents while maintaining consistent policy enforcement and cost predictability through zero compute markup.

Human-Agent Collaborative Analytics

HyperLake enables a new paradigm where human analysts, data scientists, and AI agents operate on the same governed data platform. Shared context and standardized memory layers allow human insight and machine intelligence to collaborate on identical datasets. Humans can run exploratory analyses while agents simultaneously perform automated pattern detection, creating a symbiotic relationship that accelerates insights and decision-making.

Regulated Data Environments for AI

Organizations in highly regulated industries such as healthcare, finance, and government can use HyperLake to deploy AI agents while maintaining strict compliance. The platform ensures data sovereignty, enforces column-level masking for PII, provides row-level security filters, and creates complete audit trails for every agent action. This allows organizations to leverage AI without compromising regulatory requirements or exposing sensitive data.

Multi-Stack Agentic Infrastructure Management

Large enterprises with complex existing infrastructure can use HyperLake to manage multiple agentic stacks simultaneously. The platform integrates with AWS, GCP, and Azure-native components, open-source technologies, governed data services, workflow systems, and MCP tools. This unified management layer allows enterprises to choose the best stack for each use case while maintaining consistent governance, security, and auditability across all environments.

How does HyperLake ensure data sovereignty for AI agents?

HyperLake is deployed entirely within the customer’s own VPC, private cloud, or on-premises environment. Data never moves outside this secure boundary. Agents operate on data in place, and the platform uses confidential compute patterns to keep sensitive information under full owner control. This sovereign deployment model ensures that organizations maintain complete ownership and control over their data while enabling AI agent operations.

What is the compute markup model and how does it affect costs?

Traditional data platforms charge a markup on compute usage, which can lead to exponential cost increases when AI agents generate thousands of queries. HyperLake charges zero markup on compute usage. You pay only what your cloud provider charges for compute resources. This model allows enterprises to let hundreds of agents iterate, retry, and explore simultaneously without unexpected five-figure bills, removing the financial barrier to AI experimentation.

Can HyperLake integrate with existing cloud services and tools?

Yes, HyperLake is designed to manage multiple agentic infrastructure stacks including customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data services, workflow systems, and MCP tools. The platform provides a unified governance and management layer across all these components, allowing enterprises to leverage existing investments while adding AI agent capabilities.

How does HyperLake handle governance for both humans and 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 includes role-based and attribute-based access control, column masking for automatic PII redaction, row-level security filters, and complete audit trail tracking. The same policies apply consistently whether the request comes from a human analyst or an autonomous agent, ensuring uniform security and compliance across all interactions.