From intent to execution
Orchestration, compliance, and operations. From cloud intent to GPU execution.
Dedicated infrastructure, operated through the Dapple control plane.
Above the infrastructure sits the Dapple control plane. It handles orchestration, compliance enforcement, security, and ongoing operations so enterprises don't need internal teams to build and maintain that capability.
Three layers, clear boundaries
Customer Environment
The customer's existing environment remains the system of record for identity, policy, observability, and machine learning workflows. Dapple integrates with these services directly.
Dapple Control Plane
Our proprietary layer that handles orchestration, compliance enforcement, security, and ongoing operations. It's what makes dedicated AI infrastructure usable for regulated enterprise without requiring internal teams to build and maintain that capability.
Private AI Infrastructure
The physical systems used to run AI workloads. Dapple models infrastructure explicitly rather than abstracting it away. Topology, failure boundaries, and capacity are first-class primitives.
Four integrated layers from cloud integration to developer tooling
Cloud Integration
Native integration with enterprise cloud services, identity, and observability.
GPU Orchestration
Topology-aware, deterministic scheduling across heterogeneous accelerator environments.
Compliance Layer
Policy-driven execution with audit trails, model versioning, and data residency enforcement.
Developer Experience
Native support for standard AI/ML frameworks and tooling.
Execution guarantees for enterprise AI
Topology-Aware Scheduling
Workload placement accounts for physical GPU arrangement, network fabric, and failure domains. Infrastructure topology is a first-class primitive.
Deterministic Execution
Workloads run only when full placement correctness is verified. Requirements checked before admission.
Compliance Enforcement
Policy, isolation, and residency constraints enforced at execution time. Recorded as auditable evidence.
Managed Operations
End-to-end operational responsibility. Structured onboarding, SLA-backed support, continuous monitoring.
Designed for production performance and regulatory requirements
Optimized execution environments for high-value AI workloads
AI Training
Private infrastructure for large-scale, regulated training runs with deterministic capacity.
Inference
Scalable inference with predictable latency, cost control, and execution guarantees.
Sensitive Workloads
Isolated, governed environments for workloads that require strict data residency, audit trails, and operational control.