HyperLake

HyperLake is a sovereign infrastructure platform purpose-built for organizations preparing for a world where AI agents become primary consumers of enterprise infrastructure. Unlike traditional data platforms designed for humans running dashboards, reports, and scheduled queries, HyperLake provides the command center to deploy, manage, run, secure, and govern agentic infrastructure. It is built for engineering and data teams who need to enable autonomous AI agents to query data, call tools, trigger workflows, generate artifacts, and operate across systems without compromising security or governance. The core value proposition is delivering an Agentic Data Cloud Infrastructure that is open-stack, deployed inside the customer’s own VPC, private cloud, or on-prem environment. HyperLake eliminates the compute markup problem that plagues modern data platforms, allowing organizations to pay only their cloud provider for compute while enabling unlimited agent experimentation. With unified governance, immutable audit trails, and data sovereignty by design, HyperLake creates a collaborative environment where humans and AI agents operate together on the same governed data platform. The broader vision extends beyond a single stack to manage multiple agentic infrastructures, including customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, and future production-ready agentic use cases. The goal is to make agentic infrastructure usable, secure, and production-ready end to end, enabling enterprises to choose their stack, deploy where data lives, govern every interaction, and scale new AI use cases without rebuilding the operating layer each time. HyperLake is backed by industry leaders and built on extensive client work, making it a trusted partner for the agentic shift.

Unified Governance and Access Control

HyperLake provides a global policy layer that evaluates every request, whether from a human or an AI agent, against dynamic governance rules in real time. This feature enforces role-based access control (RBAC), attribute-based access control (ABAC), column masking for automatic PII redaction per role, and row-level security that filters data by department, region, or role. Access is enforced consistently across all data sources, queries, and context retrieval operations, ensuring that every interaction adheres to organizational policies. This unified approach eliminates the complexity of managing separate governance systems for humans and agents, creating a single source of truth for access management. Teams can define policies once and have them apply universally, reducing administrative overhead while maintaining rigorous security standards. The real-time evaluation engine ensures that even the most dynamic agent behaviors are constrained within approved boundaries, preventing unauthorized data exposure or misuse.

The Traceability Loop

Every agent action, inference, query, and training run is recorded through immutable provenance logs, creating a complete audit trail for all operations. This feature allows organizations to trace any AI decision back to its source data with full auditability, providing transparency into how agents interact with infrastructure. The traceability loop is essential for compliance, debugging, and understanding agent behavior at scale. When hundreds of agents operate simultaneously, having a detailed record of every action enables teams to identify patterns, optimize performance, and investigate anomalies. The immutable nature of these logs ensures that records cannot be tampered with, supporting regulatory requirements and internal governance standards. This feature also facilitates collaboration between human analysts and AI agents by providing a shared understanding of past actions and decisions, enabling teams to refine agent behavior based on historical data.

Data Sovereignty by Design

HyperLake enables AI agents to operate on data without moving it outside its secure environment, ensuring sensitive information remains under full owner control. Through sovereign deployment and confidential compute patterns, organizations can deploy the entire stack within their own VPC, private cloud, or on-premises infrastructure. This design principle addresses the critical concern of data leakage and regulatory compliance, particularly for industries like healthcare, finance, and government where data residency is mandatory. Agents can access and process data locally without requiring data to be transferred to external services, reducing exposure risks. The sovereignty feature also supports hybrid and multi-cloud strategies, allowing organizations to maintain control over their most sensitive assets while still benefiting from AI agent capabilities. Teams can collaborate across departments and regions with confidence, knowing that data never leaves approved boundaries.

Human-Agent Symbiosis

HyperLake creates a collaborative platform where humans and AI agents operate on the same governed data, sharing context and standardized memory layers. This feature enables human insight and machine intelligence to work together on the same datasets, fostering a symbiotic relationship rather than a siloed one. Analysts, data scientists, and engineers can interact with the same data that agents access, using familiar tools like SQL analytics, dashboards, and reports. The shared context ensures that human decisions are informed by the same information that agents use, while agent actions are visible and understandable to human operators. This symbiosis accelerates insight generation, as agents can handle repetitive exploration and hypothesis testing while humans focus on strategic interpretation and decision-making. The feature also supports collaborative workflows where humans can review, approve, or modify agent actions before they execute, maintaining human oversight without bottlenecking agent productivity.

Autonomous Agent Data Access

Organizations deploying AI agents that need continuous access to governed data can use HyperLake as the system of access. Agents can query databases, retrieve context from vector stores, and explore datasets autonomously, all while being governed by the same policies that apply to human users. This use case is critical for customer service agents that need real-time order information, research agents that explore scientific literature, or operational agents that monitor system health. The unified governance layer ensures that agents only access data they are authorized to see, while the traceability loop records every query for audit purposes. Teams can deploy multiple agents simultaneously without fear of runaway costs or unauthorized data access, enabling rapid scaling of autonomous operations.

AI-Powered Analytics and Insights

Data science and analytics teams can leverage HyperLake to enable AI agents to perform continuous exploration, hypothesis testing, and insight generation across large datasets. Agents can autonomously run complex queries, identify patterns, and surface anomalies without human intervention, freeing analysts to focus on high-value interpretation and strategic recommendations. The platform supports SQL analytics, ML and AI insights, and real-time data processing, making it suitable for both batch and streaming use cases. Teams can collaborate with agents by sharing dashboards and reports, ensuring that human and machine insights are aligned. The $0 compute markup model allows teams to experiment freely without worrying about unexpected costs from agent-driven query volumes.

Governed Data-as-a-Service APIs

Enterprises that need to expose data to internal applications, external partners, or AI agents can use HyperLake to create governed Data-as-a-Service APIs. The platform provides a unified data layer that federates data from multiple sources, including OLTP databases, cloud storage, open formats like Iceberg and Delta, streaming systems, and SaaS APIs. Organizations can define access policies that apply consistently across all data sources, ensuring that API consumers receive only the data they are authorized to see. This use case is ideal for building data marketplaces, powering internal tools, or enabling third-party integrations. The audit trail provides complete visibility into who or what accessed which data and when, supporting compliance and billing requirements.

Secure Multi-Agent Workflows

Organizations running complex workflows that involve multiple AI agents collaborating on tasks can use HyperLake to ensure secure and governed interactions. Agents can call tools, trigger workflows, generate artifacts, and pass context between each other, all within a controlled environment. The platform’s real-time governance engine evaluates every inter-agent communication, preventing unauthorized data sharing or action execution. This use case is particularly relevant for enterprises building autonomous supply chain management systems, automated compliance monitoring, or intelligent customer journey orchestration. Teams can design workflows where agents handle different aspects of a process, with human oversight at critical decision points. The traceability loop provides a complete record of all agent interactions, enabling debugging, optimization, and compliance reporting.

How does HyperLake ensure data security when AI agents access sensitive information?

HyperLake enforces a global policy layer that evaluates every request from both humans and AI agents in real time. This includes role-based and attribute-based access control, column masking for automatic PII redaction, and row-level security that filters data based on organizational roles. All data remains within the customer’s own VPC, private cloud, or on-premises environment, ensuring sensitive information never leaves secure boundaries. Every action is recorded through immutable provenance logs, providing complete auditability for compliance and security review.

What does $0 compute markup mean and how does it benefit my organization?

HyperLake charges no markup on compute usage, meaning you only pay your cloud provider for the underlying compute resources. This model is critical for the age of autonomous AI, where a single misconfigured agent can generate thousands of queries in minutes. Traditional markup-based platforms can lead to unexpected five-figure bills overnight when agents iterate and explore at scale. With HyperLake, your team can experiment freely without fear of the invoice, enabling innovation and rapid scaling of AI use cases.

Can HyperLake work with my existing cloud infrastructure and tools?

Yes, HyperLake is designed to manage multiple agentic infrastructure stacks, including your existing cloud services from AWS, GCP, or Azure, as well as open-source technologies like PostgreSQL, MySQL, Kafka, and vector databases. The platform supports open formats like Iceberg, Delta, and Hudi, and provides 100+ connectors for SaaS and APIs. You can deploy HyperLake within your existing VPC or private cloud, integrating with your current data sources and tools without requiring a complete rebuild of your infrastructure.

How does HyperLake support collaboration between human analysts and AI agents?

HyperLake provides a shared platform where humans and AI agents operate on the same governed data with consistent policies and access controls. Analysts can use familiar tools like SQL analytics, dashboards, and reports, while agents perform autonomous exploration and hypothesis testing. Shared context and standardized memory layers allow human insight and machine intelligence to collaborate on the same datasets. The traceability loop ensures that all agent actions are visible to humans, enabling review, approval, and refinement of agent behavior.