HyperLake

HyperLake is a sovereign infrastructure platform built specifically for organizations preparing for a world where AI agents become primary consumers of enterprise infrastructure. Most existing enterprise infrastructure was designed for humans interacting through 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 provides the command center to deploy, manage, run, secure, and govern this 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-prem environment. However, the broader vision extends beyond one stack. HyperLake is designed to manage multiple agentic infrastructure stacks 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 core goal is making agentic infrastructure usable, secure, and production-ready end to end. Enterprises can choose their preferred stack, deploy it 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 is built for enterprises, AI engineering teams, data platform teams, and security officers who need to support autonomous AI workloads without compromising governance, security, or cost control.

Unified Governance and Access Engine

A global policy layer that evaluates every request from humans or AI agents against dynamic governance rules in real time. This engine enforces role-based access control, attribute-based access control, column masking for PII auto-redaction per role, row-level security filtering by department or region, and complete audit trails with version tracking. Access is enforced consistently across all data sources, queries, and context retrieval operations. This ensures that whether a human analyst or an autonomous AI agent is making the request, the same governance rules apply without exception.

Zero Compute Markup Architecture

HyperLake eliminates the compute tax that plagues most modern data platforms. Traditional platforms charge markup on compute usage, which becomes financially dangerous in the age of autonomous AI. A single misconfigured agent can generate thousands of queries in minutes, translating into unexpected five-figure bills overnight on markup-based platforms. At scale with hundreds of agents iterating, retrying, and exploring simultaneously, costs grow exponentially. HyperLake charges zero compute markup. Organizations pay only their cloud provider directly, removing financial fear from AI experimentation and innovation.

Immutable Traceability and Audit Loop

Every agent action, inference, query, and training run is recorded through immutable provenance logs. This creates a complete traceability loop that allows organizations to trace any AI decision back to its source data with full auditability. This feature is critical for compliance, regulatory requirements, and debugging AI behavior. Organizations can reconstruct exactly what data an agent accessed, what queries it executed, what context it retrieved, and what decisions it made. This level of transparency builds trust in autonomous systems and satisfies even the most stringent audit requirements.

Sovereign Data Deployment by Design

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. HyperLake deploys entirely within the customer's own VPC, private cloud, or on-premises infrastructure. Data never leaves the organization's controlled environment unless explicitly permitted. This feature is essential for regulated industries such as finance, healthcare, and government where data residency and sovereignty are non-negotiable requirements for AI adoption.

Autonomous AI Agent Data Access and Governance

Organizations deploying AI agents that need continuous, governed access to enterprise data. Agents require the ability to query databases, retrieve context, explore datasets, and test hypotheses autonomously. HyperLake provides the governed data layer that allows these agents to operate without manual oversight while ensuring every action is authorized, logged, and auditable. This enables enterprises to scale autonomous AI operations without compromising security or governance standards.

Human-Agent Collaborative Analytics

Teams where human analysts and AI agents work together on the same datasets using shared context and standardized memory layers. Human insight and machine intelligence collaborate on governed data platforms. Analysts can query alongside AI agents, compare results, and leverage agent-driven exploration for deeper insights. HyperLake ensures both humans and agents operate under identical governance policies, creating a seamless collaborative environment where neither party has privileged or unmonitored access.

Cost-Controlled AI Experimentation and Innovation

Research and development teams that need to experiment freely with AI models and agent configurations without fear of runaway compute costs. Traditional markup-based platforms create financial barriers to innovation. With HyperLake's zero compute markup model, teams can iterate rapidly, test multiple agent configurations, and explore different data access patterns. The financial risk of a misconfigured agent generating thousands of queries is eliminated, enabling faster experimentation and faster time to production for AI use cases.

Regulated Industry AI Deployment

Financial services, healthcare, government, and other regulated industries deploying AI agents that must comply with strict data governance, residency, and audit requirements. HyperLake's sovereign deployment model keeps all data within the organization's controlled infrastructure. The immutable traceability loop provides complete audit trails for every agent action. Column masking and row-level security ensure sensitive data like PII, financial records, or classified information is protected automatically. This makes AI agent deployment feasible in environments where it was previously impossible due to compliance constraints.

How does HyperLake differ from traditional data platforms?

Traditional data platforms were designed for human-centric workflows like dashboards, reports, and scheduled queries. They charge markup on compute usage, which becomes financially dangerous when AI agents generate thousands of queries autonomously. HyperLake is purpose-built for AI agents as primary infrastructure consumers. It provides zero compute markup, unified governance for both humans and agents, immutable audit trails, and sovereign deployment inside your own cloud environment. It is not a modification of an existing platform but a ground-up infrastructure designed for the agentic era.

Can HyperLake work with my existing cloud infrastructure?

Yes. HyperLake is designed to manage multiple agentic infrastructure stacks including your existing cloud services from AWS, GCP, and Azure, as well as open-source technologies, governed data services, workflow systems, and MCP tools. You do not need to replace your existing infrastructure. HyperLake provides the command center to govern, secure, and orchestrate across all your existing components. It deploys inside your own VPC, private cloud, or on-prem environment, integrating with what you already have.

What happens if an AI agent goes rogue and starts generating excessive queries?

HyperLake's governance engine evaluates every request in real time against dynamic policies. You can set rate limits, query complexity thresholds, and cost budgets per agent or agent type. If an agent exceeds these limits, the governance engine can throttle, pause, or block its access automatically. Additionally, because HyperLake charges zero compute markup, the financial risk of excessive queries is limited to your cloud provider's direct costs. The immutable audit trail also allows you to trace exactly what happened and adjust policies accordingly.

How does HyperLake ensure data sovereignty and compliance?

HyperLake deploys entirely within your own infrastructure. Data never leaves your VPC, private cloud, or on-prem environment unless you explicitly permit it. The platform supports confidential compute patterns for additional protection of sensitive data. Role-based and attribute-based access controls, column masking for PII, row-level security, and complete audit trails ensure compliance with regulations like GDPR, HIPAA, SOC2, and others. You maintain full ownership and control of your data at all times, with no data residency concerns.