HyperLake is built for organizations preparing for a world where AI agents become primary users of infrastructure.

Today, most enterprise infrastructure was designed for humans, dashboards, applications, and scheduled pipelines. AI agents behave differently. They query data, call tools, trigger workflows, generate artifacts, operate across systems, and require continuous access to governed compute, data, policies, and services.

HyperLake provides the command center to deploy, manage, secure, govern, and operate agentic infrastructure at enterprise scale. The first product wedge is Agentic Data Cloud Infrastructure: open-stack data, analytics, semantic, workflow, and agent infrastructure deployed directly inside the customer’s own VPC, private cloud, or on-prem environment.

The broader vision extends beyond a single stack. HyperLake is designed to manage multiple agentic infrastructure stacks simultaneously, including HyperLake-native deployments, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data systems, workflow platforms, MCP tools, and future production-ready agentic applications.

HyperLake combines sovereign AI infrastructure, federated data systems, governance frameworks, and autonomous workflow infrastructure into a unified operational layer for enterprise AI. The platform supports open technologies such as Apache Iceberg, Trino, PostgreSQL, Kubernetes, vector databases, streaming systems, and cloud-native object storage, enabling organizations to avoid vendor lock-in while maintaining deployment flexibility across environments.

Governance is embedded directly into the runtime through RBAC, ABAC, row-level security, policy enforcement, provenance logging, immutable audit trails, and secure access controls for both humans and AI agents. HyperLake also supports governed retrieval pipelines, agentic analytics, real-time streaming, AI-ready APIs, semantic services, and secure data access layers that allow agents to safely reason over enterprise systems without uncontrolled infrastructure access.

The platform is designed to support the next generation of enterprise AI workloads, including autonomous agents, AI copilots, retrieval systems, multi-agent orchestration, governed workflow automation, and real-time operational intelligence. Organizations can unify structured data, vector retrieval, APIs, semantic layers, and agent runtimes into a single governed environment while scaling new AI use cases without rebuilding the operating layer each time.

HyperLake is built for organizations preparing for a world where AI agents become primary users of infrastructure.

Today, most enterprise infrastructure was designed for humans, dashboards, applications, and scheduled pipelines. AI agents behave differently. They query data, call tools, trigger workflows, generate artifacts, operate across systems, and require continuous access to governed compute, data, policies, and services.

HyperLake provides the command center to deploy, manage, secure, govern, and operate agentic infrastructure at enterprise scale. The first product wedge is Agentic Data Cloud Infrastructure: open-stack data, analytics, semantic, workflow, and agent infrastructure deployed directly inside the customer’s own VPC, private cloud, or on-prem environment.

The broader vision extends beyond a single stack. HyperLake is designed to manage multiple agentic infrastructure stacks simultaneously, including HyperLake-native deployments, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data systems, workflow platforms, MCP tools, and future production-ready agentic applications.

HyperLake combines sovereign AI infrastructure, federated data systems, governance frameworks, and autonomous workflow infrastructure into a unified operational layer for enterprise AI. The platform supports open technologies such as Apache Iceberg, Trino, PostgreSQL, Kubernetes, vector databases, streaming systems, and cloud-native object storage, enabling organizations to avoid vendor lock-in while maintaining deployment flexibility across environments.

Governance is embedded directly into the runtime through RBAC, ABAC, row-level security, policy enforcement, provenance logging, immutable audit trails, and secure access controls for both humans and AI agents. HyperLake also supports governed retrieval pipelines, agentic analytics, real-time streaming, AI-ready APIs, semantic services, and secure data access layers that allow agents to safely reason over enterprise systems without uncontrolled infrastructure access.

The platform is designed to support the next generation of enterprise AI workloads, including autonomous agents, AI copilots, retrieval systems, multi-agent orchestration, governed workflow automation, and real-time operational intelligence. Organizations can unify structured data, vector retrieval, APIs, semantic layers, and agent runtimes into a single governed environment while scaling new AI use cases without rebuilding the operating layer each time.

HyperLake also enables enterprises to deploy applications and services closer to where data resides, reducing operational friction, latency, and unnecessary data movement across environments. The platform is designed for enterprises that require secure AI adoption without sacrificing governance, compliance, infrastructure portability, or operational control. It also supports hybrid infrastructure strategies, allowing enterprises to integrate existing systems, cloud-native services, and future AI infrastructure layers into one governed operational environment. By operating entirely inside customer-controlled infrastructure, HyperLake enables organizations to build sovereign, auditable, secure, and production-ready AI systems with full observability, compliance, operational control, infrastructure portability, and long-term architectural flexibility for rapidly evolving AI ecosystems.