Cloud platform

Multi-cloud
AI platform.

Architecture, engineering, and operations for AI workloads across public cloud, private infrastructure, and hybrid enterprise environments.

Futuristic Prognity AI cloud command center
Operating layer

One operating model,
multiple clouds.

We help teams reduce console sprawl, standardize controls, and make AI infrastructure easier to deploy, monitor, and govern across environments.

  • Public and private cloud architecture
  • Cost and capacity visibility
  • Unified identity and access patterns
  • Monitoring, alerting, and runbooks

Enterprise cloud capabilities

Built for teams that need control, reliability, security, and a practical path to AI workload delivery.

Cloud Architecture

Landing zones, account structure, network topology, and workload placement designed around enterprise constraints.

Platform Engineering

Reusable infrastructure patterns, deployment paths, and guardrails for teams shipping AI workloads.

Cost Visibility

Tagging, allocation, forecasting, and workload reviews to make cloud spend easier to understand and govern.

Security Controls

Identity, policy, secrets, segmentation, and audit trails wired into the platform from the start.

Reliability Engineering

Monitoring, alerting, incident paths, and recovery patterns matched to business criticality.

AI Workload Enablement

GPU, data, model serving, and orchestration patterns for training and inference environments.

Workflow Automation

Infrastructure automation and repeatable delivery flows for changes that should not depend on manual tickets.

Operating Model

Team interfaces, ownership boundaries, documentation, and support paths that keep the platform usable.

Cloud solutions

Hybrid Cloud Foundation

A secure base for workloads spanning public cloud, private infrastructure, and existing enterprise networks.

  • Landing zones
  • Network design
  • Identity integration
  • Policy guardrails

AI Platform Cloud

Cloud patterns for GPU workloads, model serving, data movement, and observability across AI systems.

  • GPU orchestration
  • Model serving
  • Data connectivity
  • Telemetry

Cloud Modernization

Migration and modernization work that balances application risk, cost, reliability, and operational readiness.

  • Migration waves
  • Dependency mapping
  • Resilience reviews
  • Runbooks

Managed Cloud Operations

Operational support for cloud platforms that need governance, incident handling, and continuous improvement.

  • Monitoring
  • Change control
  • Cost reviews
  • Platform support

Cloud provider focus

Amazon Web Services (AWS)
Compute, data, networking, and managed AI services
Microsoft Azure
Enterprise identity, data platforms, AI services, and hybrid operations
Google Cloud Platform
Data, analytics, Kubernetes, and AI workload patterns
Private Cloud / On-Prem
Secure infrastructure, GPU environments, and hybrid connectivity

Simplify your cloud operating model

Bring cloud architecture, AI infrastructure, security, and operations into one coherent delivery path.